From 32513647cdb78e0b28398ad68fc7d35e332b545d Mon Sep 17 00:00:00 2001 From: Jaehyun Lim Date: Sat, 21 May 2016 22:58:56 +0900 Subject: [PATCH 1/5] update for inception v4 (esp. inception-resnet-v2) --- checkpoints.lua | 10 +- dataloader.lua | 6 +- datasets/create-imagenet-lmdb.lua | 189 + datasets/imagenet-lmdb.lua | 87 + datasets/imagenet.lua | 11 +- drawnet.lua | 104 + inceptionv4-cls-aux.dot | 1445 +++++++ inceptionv4-cls-aux.svg | 2137 ++++++++++ inceptionv4-cls.dot | 4311 +++++++++++++++++++ inceptionv4-cls.svg | 6383 +++++++++++++++++++++++++++++ main.lua | 2 +- main.sh | 9 + models/inceptionv4.lua | 320 ++ models/inceptionv4aux.lua | 399 ++ models/init.lua | 8 +- opts.lua | 10 +- train.lua | 43 +- 17 files changed, 15460 insertions(+), 14 deletions(-) create mode 100644 datasets/create-imagenet-lmdb.lua create mode 100644 datasets/imagenet-lmdb.lua create mode 100644 drawnet.lua create mode 100644 inceptionv4-cls-aux.dot create mode 100644 inceptionv4-cls-aux.svg create mode 100644 inceptionv4-cls.dot create mode 100644 inceptionv4-cls.svg create mode 100755 main.sh create mode 100644 models/inceptionv4.lua create mode 100644 models/inceptionv4aux.lua diff --git a/checkpoints.lua b/checkpoints.lua index 97fe54d98..60fb143b4 100644 --- a/checkpoints.lua +++ b/checkpoints.lua @@ -25,7 +25,7 @@ function checkpoint.latest(opt) return latest, optimState end -function checkpoint.save(epoch, model, optimState, bestModel) +function checkpoint.save(epoch, model, optimState, bestModel, opt) -- Don't save the DataParallelTable for easier loading on other machines if torch.type(model) == 'nn.DataParallelTable' then model = model:get(1) @@ -34,16 +34,16 @@ function checkpoint.save(epoch, model, optimState, bestModel) local modelFile = 'model_' .. epoch .. '.t7' local optimFile = 'optimState_' .. epoch .. '.t7' - torch.save(modelFile, model) - torch.save(optimFile, optimState) - torch.save('latest.t7', { + torch.save(paths.concat(opt.resume, modelFile), model) + torch.save(paths.concat(opt.resume, optimFile), optimState) + torch.save(paths.concat(opt.resume, 'latest.t7'), { epoch = epoch, modelFile = modelFile, optimFile = optimFile, }) if bestModel then - torch.save('model_best.t7', model) + torch.save(paths.concat(opt.resume, 'model_best.t7'), model) end end diff --git a/dataloader.lua b/dataloader.lua index a995bffad..fa49301b1 100644 --- a/dataloader.lua +++ b/dataloader.lua @@ -39,7 +39,11 @@ function DataLoader:__init(dataset, opt, split) end torch.setnumthreads(1) _G.dataset = dataset - _G.preprocess = dataset:preprocess() + if opt.netType == 'inceptionv4' or opt.netType == 'inceptionv4aux' then + _G.preprocess = dataset:preprocess(328, 299) + else + _G.preprocess = dataset:preprocess(256, 224) + end return dataset:size() end diff --git a/datasets/create-imagenet-lmdb.lua b/datasets/create-imagenet-lmdb.lua new file mode 100644 index 000000000..a6f6c6ea2 --- /dev/null +++ b/datasets/create-imagenet-lmdb.lua @@ -0,0 +1,189 @@ +--[[ + A script to conver images to lmdb dataset + + References + 1. https://github.com/facebook/fb.resnet.torch/blob/master/datasets/init.lua + 2. https://github.com/eladhoffer/ImageNet-Training/blob/master/CreateLMDBs.lua +]]-- + +local ffi = require 'ffi' +local image = require 'image' + +-- Define local functions +local function isvalid(opt, cachePath) + local imageInfo = torch.load(cachePath) + if imageInfo.basedir and imageInfo.basedir ~= opt.data then + return false + end + return true +end + +local function split(dataset) + local tokens = {} + for word in string.gmatch(dataset, '([^-]+)') do + table.insert(tokens, word) + end + assert(tokens[2] == 'lmdb', string.format('opt.dataset should be -lmdb form; opt.dataset = %s', dataset)) + return tokens[1] +end + +local function _loadImage(path) + local ok, input = pcall(function() + return image.load(path, 3, 'float') + end) + + -- Sometimes image.load fails because the file extension does not match the + -- image format. In that case, use image.decompress on a ByteTensor. + if not ok then + local f = io.open(path, 'r') + assert(f, 'Error reading: ' .. tostring(path)) + local data = f:read('*a') + f:close() + + local b = torch.ByteTensor(string.len(data)) + ffi.copy(b:data(), data, b:size(1)) + + input = image.decompress(b, 3, 'float') + end + + return input +end + +local function getItem(basedir, imageClass, imagePath, i) + local path = ffi.string(imagePath[i]:data()) + local image = _loadImage(paths.concat(basedir, path)) + local class = imageClass[i] + + return { + input = image, + target = class, + } +end + + + +-- Init opt +local opt = {} +opt.gen = 'gen' +opt.dataset = 'imagenet-lmdb' +opt.data = '/media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC' + +opt.data_lmdb = '/media/data1/image' +opt.shuffle = true +--print(opt) + +-- Load imageInfo +local cachePath = paths.concat(opt.gen, split(opt.dataset) .. '.t7') +if not paths.filep(cachePath) or not isvalid(opt, cachePath) then + paths.mkdir('gen') + + local script = paths.dofile(split(opt.dataset) .. '-gen.lua') + script.exec(opt, cachePath) +end +local imageInfo = torch.load(cachePath) +--print(imageInfo) + +-- Create LMDB +local lmdb = require 'lmdb' + +local train_env = lmdb.env{ + Path = paths.concat(opt.data_lmdb, 'train_lmdb'), + Name = 'train_lmdb' +} + +local val_env= lmdb.env{ + Path = paths.concat(opt.data_lmdb, 'val_lmdb'), + Name = 'val_lmdb' +} + +local path = ffi.string(imageInfo.train.imagePath[1]:data()) +--local image = self:_loadImage(paths.concat(self.dir, path)) +--local class = self.imageInfo.imageClass[i] +print(path) + +local n_images = (#imageInfo.train.imagePath)[1] +print(string.format("n_image: %d", n_images)) + +local idxs +if opt.shuffle then + idxs = torch.randperm(n_images) +else + idxs = torch.range(1, n_images) +end +print(string.format("opt.shuffle: %s, idxs[1]: %d", opt.shuffle, idxs[1])) + +local basedir = paths.concat(imageInfo.basedir, 'train') +--local item = getItem(basedir, imageInfo.train.imageClass, imageInfo.train.imagePath, idxs[1]) +--print(item.target) +----print(item.input) +--print(#item.input) +--print(item.input[1][1][1]) + +train_env:open() +local txn = train_env:txn() +local cursor = txn:cursor() +for i = 1, 1000 do --n_images do + local item = getItem(basedir, imageInfo.train.imageClass, imageInfo.train.imagePath, idxs[i]) + + cursor:put(string.format("%07d", i), item, lmdb.C.MDB_NODUPDATA) + if i % 100 == 0 then + txn:commit() + print(train_env:stat()) + collectgarbage() + txn = train_env:txn() + cursor = txn:cursor() + end + xlua.progress(i, n_images) +end +txn:commit() +train_env:close() + +--[[ +local sys = require 'sys' +local n_test = 5000 +sys.tic() +-------Read------- +train_env:open() +print(train_env:stat()) -- Current status +local reader = train_env:txn(true) --Read-only transaction +--local y = torch.Tensor(10,3,256,256) +local y = {} + +local idxs = torch.randperm(n_test) +for i=1,n_test do + local item = reader:get(string.format("%07d", idxs[i])) + if i % 1000 == 0 then + print(string.format('%d: %d', i, idxs[i])) + print(#item.input) + end + --print(item) + --print(#item.input) + --print(item.input[1][1][1]) +end +reader:abort() +train_env:close() +print(sys.toc()) +collectgarbage() + +sys.tic() +-------Read------- +train_env:open() +print(train_env:stat()) -- Current status +local reader = train_env:txn(true) --Read-only transaction +--local y = torch.Tensor(10,3,256,256) +local y = {} + +for i=1,n_test do + local item = reader:get(string.format("%07d", i)) + if i % 1000 == 0 then + print(string.format('%d: %d', i, i)) + print(#item.input) + end + --print(item) + --print(#item.input) + --print(item.input[1][1][1]) +end +reader:abort() +train_env:close() +print(sys.toc()) +]]-- diff --git a/datasets/imagenet-lmdb.lua b/datasets/imagenet-lmdb.lua new file mode 100644 index 000000000..70811bc99 --- /dev/null +++ b/datasets/imagenet-lmdb.lua @@ -0,0 +1,87 @@ +-- +-- Copyright (c) 2016, Facebook, Inc. +-- All rights reserved. +-- +-- This source code is licensed under the BSD-style license found in the +-- LICENSE file in the root directory of this source tree. An additional grant +-- of patent rights can be found in the PATENTS file in the same directory. +-- +-- ImageNet dataset loader +-- + +local image = require 'image' +local paths = require 'paths' +local t = require 'datasets/transforms' +local lmdb = require 'lmdb' + +local M = {} +local ImagenetLMDBDataset = torch.class('resnet.ImagenetLMDBDataset', M) + +function ImagenetLMDBDataset:__init(imageInfo, opt, split) + self.imageInfo = imageInfo[split] + self.opt = opt + self.split = split + --self.dir = paths.concat(opt.data, split) + --assert(paths.dirp(self.dir), 'directory does not exist: ' .. self.dir) + + self.env = lmdb.env{ + Path = paths.concat(opt.data, string.format('%s_lmdb', split)), + Name = string.format('%s_lmdb', split) + } + assert(env:open(), 'directory does not exist: ' .. string.format('%s_lmdb', split)) + self.stat = env:stat() -- Current status + + self.reader = env:txn(true) --Read-only transaction + self.idxs = torch.randperm(n_images) + assert(self.imageInfo.imageClass:size(1) == #self.idxs, string.format('Something wrong with lmdb. The lmdb db should have %d number of items, but it has %d', self.imageInfo.imageClass:size(1), #self.idxs)) +end + +function ImagenetLMDBDataset:get(i) + local item = reader:get(string.format("%07d", self.idxs[i])) + return item +end + +function ImagenetLMDBDataset:size() + return self.imageInfo.imageClass:size(1) +end + +-- Computed from random subset of ImageNet training images +local meanstd = { + mean = { 0.485, 0.456, 0.406 }, + std = { 0.229, 0.224, 0.225 }, +} +local pca = { + eigval = torch.Tensor{ 0.2175, 0.0188, 0.0045 }, + eigvec = torch.Tensor{ + { -0.5675, 0.7192, 0.4009 }, + { -0.5808, -0.0045, -0.8140 }, + { -0.5836, -0.6948, 0.4203 }, + }, +} + +function ImagenetLMDBDataset:preprocess() + if self.split == 'train' then + return t.Compose{ + t.RandomSizedCrop(224), + t.ColorJitter({ + brightness = 0.4, + contrast = 0.4, + saturation = 0.4, + }), + t.Lighting(0.1, pca.eigval, pca.eigvec), + t.ColorNormalize(meanstd), + t.HorizontalFlip(0.5), + } + elseif self.split == 'val' then + local Crop = self.opt.tenCrop and t.TenCrop or t.CenterCrop + return t.Compose{ + t.Scale(256), + t.ColorNormalize(meanstd), + Crop(224), + } + else + error('invalid split: ' .. self.split) + end +end + +return M.ImagenetLMDBDataset diff --git a/datasets/imagenet.lua b/datasets/imagenet.lua index a28294ee3..8f0581c7a 100644 --- a/datasets/imagenet.lua +++ b/datasets/imagenet.lua @@ -77,10 +77,13 @@ local pca = { }, } -function ImagenetDataset:preprocess() +function ImagenetDataset:preprocess(minSize, cropSize) + -- minSize : 256, cropSize : 224 for resnet + -- minSize : 328, cropSize : 299 for inceptionv4 + if self.split == 'train' then return t.Compose{ - t.RandomSizedCrop(224), + t.RandomSizedCrop(cropSize), t.ColorJitter({ brightness = 0.4, contrast = 0.4, @@ -93,9 +96,9 @@ function ImagenetDataset:preprocess() elseif self.split == 'val' then local Crop = self.opt.tenCrop and t.TenCrop or t.CenterCrop return t.Compose{ - t.Scale(256), + t.Scale(minSize), t.ColorNormalize(meanstd), - Crop(224), + Crop(cropSize), } else error('invalid split: ' .. self.split) diff --git a/drawnet.lua b/drawnet.lua new file mode 100644 index 000000000..d28427ece --- /dev/null +++ b/drawnet.lua @@ -0,0 +1,104 @@ +local generateGraph = require 'optnet.graphgen' + +-- visual properties of the generated graph +-- follows graphviz attributes +local graphOpts = { + displayProps = {shape='ellipse',fontsize=14, style='solid'}, + nodeData = function(oldData, tensor) + --return oldData .. '\n' .. 'Size: '.. tensor:numel() + local text_sz = '' + for i = 1,tensor:dim() do + if i == 1 then + text_sz = text_sz .. '' .. tensor:size(i) + else + text_sz = text_sz .. ', ' .. tensor:size(i) + end + end + return oldData .. '\n' .. 'Size: {'.. text_sz .. '}\n' .. 'Mem size: ' .. tensor:numel() + end +} + +local function copyInputs(sample) + -- Copies the input to a CUDA tensor, if using 1 GPU, or to pinned memory, + -- if using DataParallelTable. The target is always copied to a CUDA tensor + local input = torch.CudaTensor() + --print('type of input: ' .. torch.type(input)) + --print(sample:size()) + input:resize(sample:size()):copy(sample) + + return input +end + +local function drawModel(model, input, name) + -- model: A network architecture + -- input: The input for the given network architecture + -- name: The model name (string). + -- The files, '.dot' and '.svg' will be generated. + + local input_ + if torch.type(input) == 'table' then + input_ = {} + --print('table: ', #input) + for i = 1,#input do + input_[i] = copyInputs(input[i]) + --print(torch.type(input_[i])) + end + else + input_ = copyInputs(input) + --print(torch.type(input_)) + end + + g = generateGraph(model, input_, graphOpts) + graph.dot(g, name, name) + + --print(torch.type(g)) + --print(g) + --print(#g.nodes) + --print(g.nodes[#g.nodes]:label()) + --print(g:leaves()) + + return g +end + +--------------------------------------------------------------------------- +-- Sample input +local eps = 1e-12 +local batch_size = 1 +local n_rois = 10 +local height = 299 +local width = 299 + +local input_image = torch.floor(torch.rand(batch_size, 3, height, width) * 256) +local input_rois = torch.cat(torch.floor(torch.rand(n_rois, 1) * height/2), + torch.floor(torch.rand(n_rois, 1) * width/2), 2) +input_rois = torch.cat(input_rois, + torch.add(input_rois, + torch.cat(torch.floor(torch.rand(n_rois, 1) * height/2), + torch.floor(torch.rand(n_rois, 1) * width/2), 2))) +input_rois = torch.cat(torch.floor(torch.rand(n_rois,1) * batch_size) + 1, input_rois, 2) +--print(#input_rois) +--print(input_rois[1]) +--print(#input_image) + +--------------------------------------------------------------------------- +-- Create net w/ options +print('Create network architectures') + +--local createModel = paths.dofile('models/inceptionv4.lua') +local createModel = paths.dofile('models/inceptionv4aux.lua') + +local opt = {}; +opt.cudnn = 'fastest' + +local model = createModel(opt) + +--local g = drawModel(model, input_image, 'inceptionv4-cls') +local g = drawModel(model, input_image, 'inceptionv4-cls-aux') + +--local output = model:forward(input_image:cuda()) +--print(output) + +print('Done.') + + + diff --git a/inceptionv4-cls-aux.dot b/inceptionv4-cls-aux.dot new file mode 100644 index 000000000..c554d7a97 --- /dev/null +++ b/inceptionv4-cls-aux.dot @@ -0,0 +1,1445 @@ +digraph G { + graph [bb="0,0,2069,13164"]; + node [label="\N", + shape=oval + ]; + n1 [color=cyan, + fontsize=14, + height=1.3356, + label="Input\nStorage id: 1\nSize: {1, 3, 299, 299}\nMem size: 268203", + pos="654,13116", + shape=ellipse, + style=solid, + width=2.6788]; + n2 [color=chocolate4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(3 -> 32, 3x3, 2,2) without bias\nStorage id: 2\nSize: {1, 32, 149, 149}\nMem size: 710432", + pos="654,12984", + 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width=3.5652]; + n19 -> n20 [pos="e,473.14,10787 455.11,10824 459.51,10815 464.16,10806 468.68,10796"]; + n21 [color=black, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 15\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="532,10476", + shape=ellipse, + style=solid, + width=2.5643]; + n20 -> n21 [pos="e,525.51,10524 502.48,10692 508.55,10648 517.64,10581 524.13,10534"]; + n34 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 24\nSize: {1, 192, 71, 71}\nMem size: 967872", + pos="673,9684", + shape=ellipse, + style=solid, + width=2.6788]; + n21 -> n34 [pos="e,649.57,9731 549.35,10428 567.07,10377 592,10290 592,10213 592,10213 592,10213 592,9947 592,9873 621.82,9792.4 645.27,9740.4"]; + n23 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="925,11136", + shape=ellipse, + style=solid, + width=3.5652]; + n22 -> n23 [pos="e,923.9,11184 923.09,11220 923.28,11212 923.48,11203 923.67,11195"]; + n24 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="926,11004", + shape=ellipse, + style=solid, + width=2.5643]; + n23 -> n24 [pos="e,925.63,11052 925.36,11088 925.43,11080 925.49,11071 925.56,11063"]; + n25 [color=goldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 64, 7x1, 1,1, 3,0) without bias\nStorage id: 18\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="927,10872", + shape=ellipse, + style=solid, + width=7.0968]; + n24 -> n25 [pos="e,926.63,10920 926.36,10956 926.43,10948 926.49,10939 926.56,10931"]; + n26 [color=blue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="925,10740", + shape=ellipse, + style=solid, + width=3.5652]; + n25 -> n26 [pos="e,925.73,10788 926.27,10824 926.15,10816 926.02,10807 925.89,10799"]; + n27 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="909,10608", + shape=ellipse, + style=solid, + width=2.5643]; + n26 -> n27 [pos="e,914.79,10656 919.18,10692 918.15,10683 917.07,10675 916.02,10666"]; + n28 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 64, 1x7, 1,1, 0,3) without bias\nStorage id: 20\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="901,10476", + shape=ellipse, + style=solid, + width=7.0968]; + n27 -> n28 [pos="e,903.92,10524 906.09,10560 905.59,10552 905.06,10543 904.54,10535"]; + n29 [color=azure3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="893,10344", + shape=ellipse, + style=solid, + width=3.5652]; + n28 -> n29 [pos="e,895.92,10392 898.09,10428 897.59,10420 897.06,10411 896.54,10403"]; + n30 [color=azure3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", + pos="863,10212", + shape=ellipse, + style=solid, + width=2.5643]; + n29 -> n30 [pos="e,873.81,10260 882.17,10296 880.19,10288 878.11,10278 876.08,10270"]; + n31 [color=bisque3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(64 -> 96, 3x3) without bias\nStorage id: 22\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="848,10080", + shape=ellipse, + style=solid, + width=6.1434]; + n30 -> n31 [pos="e,853.48,10128 857.54,10164 856.6,10156 855.61,10147 854.65,10139"]; + n32 [color=floralwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="798,9948", + shape=ellipse, + style=solid, + width=3.5652]; + n31 -> n32 [pos="e,816.02,9995.9 829.95,10032 826.62,10023 823.11,10014 819.69,10005"]; + n33 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", + pos="755,9816", + shape=ellipse, + style=solid, + width=2.5643]; + n32 -> n33 [pos="e,770.36,9863.4 782.48,9900.1 779.58,9891.3 776.52,9882.1 773.55,9873.1"]; + n33 -> n34 [pos="e,701.4,9730 726.55,9769.9 720.19,9759.8 713.38,9749 706.83,9738.6"]; + n35 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 192, 3x3, 2,2) without bias\nStorage id: 25\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="535,9552", + shape=ellipse, + style=solid, + width=6.8916]; + n34 -> n35 [pos="e,584.12,9599.3 628.58,9641.2 616.83,9630.1 603.99,9618 591.69,9606.4"]; + n38 [color=cadetblue3, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 27\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="816,9420", + shape=ellipse, + style=solid, + width=3.7843]; + n34 -> n38 [pos="e,819.68,9468.2 740.84,9649.6 760.36,9636.8 779.67,9620.2 792,9600 814.16,9563.7 819.56,9515.5 819.76,9478.3"]; + n36 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="533,9420", + shape=ellipse, + style=solid, + width=3.5652]; + n35 -> n36 [pos="e,533.73,9468.5 534.27,9503.7 534.15,9495.5 534.02,9487 533.89,9478.6"]; + n37 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", + pos="612,9288", + shape=ellipse, + style=solid, + width=2.6788]; + n36 -> n37 [pos="e,584.55,9334.2 561.07,9372.8 566.97,9363.1 573.25,9352.8 579.3,9342.8"]; + n39 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="694,9156", + shape=ellipse, + style=solid, + width=2.6788]; + n37 -> n39 [pos="e,665.6,9202 640.45,9241.9 646.81,9231.8 653.62,9221 660.17,9210.6"]; + n38 -> n39 [pos="e,715.47,9203.1 794.33,9372.5 773.47,9327.7 741.9,9259.9 719.77,9212.3"]; + n40 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 29\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="226,9024", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n40 [pos="e,361.38,9062.6 610.6,9131.8 544.32,9113.4 450.01,9087.2 371.38,9065.4"]; + n43 [color=antiquewhite3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 31\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="526,8892", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n43 [pos="e,544.37,8940.1 648.93,9113.3 636.73,9100.7 624.12,9086.4 614,9072 587.16,9033.8 564.16,8986.1 548.42,8949.6"]; + n49 [color=goldenrod, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 35\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="849,9024", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n49 [pos="e,794.35,9070.8 742.62,9114.2 756.48,9102.6 771.77,9089.8 786.34,9077.6"]; + n62 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1030,7308", + shape=ellipse, + style=solid, + width=2.6788]; + n39 -> n62 [pos="e,1058.9,7354.3 788.88,9146.2 890.4,9134.9 1042.5,9111.8 1084,9072 1143.3,9015.1 1130,8975.1 1130,8893 1130,8893 1130,8893 1130,\ +7571 1130,7495.2 1093,7414.7 1064,7363.3"]; + n41 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="153,8892", + shape=ellipse, + style=solid, + width=3.5652]; + n40 -> n41 [pos="e,179.07,8939.4 199.65,8976.1 194.57,8967 189.21,8957.5 184,8948.2"]; + n42 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="400,8232", + shape=ellipse, + style=solid, + width=2.5643]; + n41 -> n42 [pos="e,335.78,8266.6 154.45,8843.7 155.92,8791.2 158,8704.1 158,8629 158,8629 158,8629 158,8495 158,8394.3 255.57,8315.9 326.8,8272"]; + n58 [color=cadetblue4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 41\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="676,7836", + shape=ellipse, + style=solid, + width=2.6788]; + n42 -> n58 [pos="e,619.49,7875.1 404.82,8183.8 410.34,8146 421.95,8092.8 446,8052 487.53,7981.6 559,7920.1 611.27,7881.1"]; + n44 [color=cornsilk1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="482,8760", + shape=ellipse, + style=solid, + width=3.5652]; + n43 -> n44 [pos="e,497.92,8808 509.99,8843.7 507.13,8835.3 504.13,8826.4 501.2,8817.7"]; + n45 [color=cornsilk1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="461,8628", + shape=ellipse, + style=solid, + width=2.5643]; + n44 -> n45 [pos="e,468.6,8676 474.36,8711.7 473.01,8703.4 471.59,8694.6 470.21,8686"]; + n46 [color=firebrick1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 33\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="452,8496", + shape=ellipse, + style=solid, + width=7.0968]; + n45 -> n46 [pos="e,455.29,8544.5 457.73,8579.7 457.16,8571.5 456.57,8563 455.99,8554.6"]; + n47 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="507,8364", + shape=ellipse, + style=solid, + width=3.5652]; + n46 -> n47 [pos="e,487.28,8411.6 472.01,8447.7 475.7,8439 479.59,8429.8 483.38,8420.8"]; + n48 [color=aquamarine, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="548,8100", + shape=ellipse, + style=solid, + width=2.5643]; + n47 -> n48 [pos="e,540.61,8148.2 514.38,8315.9 521.29,8271.7 531.65,8205.5 539.04,8158.3"]; + n48 -> n58 [pos="e,653.58,7882.9 570.44,8053.1 592.36,8008.2 625.72,7939.9 649.06,7892.2"]; + n50 [color=antiquewhite2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="899,8892", + shape=ellipse, + style=solid, + width=3.5652]; + n49 -> n50 [pos="e,880.98,8939.9 867.05,8976.1 870.38,8967.4 873.89,8958.3 877.31,8949.4"]; + n51 [color=antiquewhite2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="893,8760", + shape=ellipse, + style=solid, + width=2.5643]; + n50 -> n51 [pos="e,895.19,8808.5 896.82,8843.7 896.44,8835.5 896.05,8827 895.66,8818.6"]; + n52 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 37\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="846,8628", + shape=ellipse, + style=solid, + width=7.0968]; + n51 -> n52 [pos="e,863.03,8676.1 876.16,8712.4 873.03,8703.8 869.73,8694.6 866.5,8685.7"]; + n53 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="855,8496", + shape=ellipse, + style=solid, + width=3.5652]; + n52 -> n53 [pos="e,851.71,8544.5 849.27,8579.7 849.84,8571.5 850.43,8563 851.01,8554.6"]; + n54 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="836,8364", + shape=ellipse, + style=solid, + width=2.5643]; + n53 -> n54 [pos="e,842.88,8412 848.09,8447.7 846.87,8439.4 845.58,8430.6 844.33,8422"]; + n55 [color=forestgreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 39\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="832,8232", + shape=ellipse, + style=solid, + width=7.0968]; + n54 -> n55 [pos="e,833.46,8280.5 834.54,8315.7 834.29,8307.5 834.03,8299 833.77,8290.6"]; + n56 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="796,8100", + shape=ellipse, + style=solid, + width=3.5652]; + n55 -> n56 [pos="e,809.03,8148 818.9,8183.7 816.56,8175.3 814.11,8166.4 811.71,8157.7"]; + n57 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="764,7968", + shape=ellipse, + style=solid, + width=2.5643]; + n56 -> n57 [pos="e,775.54,8015.9 784.45,8052.1 782.34,8043.5 780.12,8034.5 777.95,8025.7"]; + n57 -> n58 [pos="e,706.34,7881.8 733.72,7922.3 726.73,7911.9 719.24,7900.9 712.04,7890.2"]; + n59 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 42\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="741,7704", + shape=ellipse, + style=solid, + width=6.3961]; + n58 -> n59 [pos="e,717.41,7752.2 698.92,7789.2 703.41,7780.2 708.18,7770.6 712.82,7761.4"]; + n60 [color=cadetblue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 43\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="857,7572", + shape=ellipse, + style=solid, + width=3.5652]; + n59 -> n60 [pos="e,816.9,7617.9 782.55,7656.4 791.46,7646.4 800.94,7635.8 810.04,7625.6"]; + n61 [color=cyan3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 44\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="931,7440", + shape=ellipse, + style=solid, + width=2.6788]; + n60 -> n61 [pos="e,905.04,7486.6 883.3,7524.8 888.7,7515.3 894.45,7505.2 900,7495.5"]; + n61 -> n62 [pos="e,996.36,7353.2 964.79,7394.6 973,7383.9 981.84,7372.2 990.29,7361.1"]; + n63 [color=darkgoldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1030,7176", + shape=ellipse, + style=solid, + width=2.6788]; + n62 -> n63 [pos="e,1030,7224.5 1030,7259.7 1030,7251.5 1030,7243 1030,7234.6"]; + n64 [color=darkseagreen, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 45\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="810,6648", + shape=ellipse, + style=solid, + width=3.7843]; + n63 -> n64 [pos="e,789.55,6695.7 962.95,7141.2 900.68,7105.7 811.91,7043.3 773,6960 734.47,6877.5 762.15,6769.9 785.93,6705.3"]; + n65 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias\nStorage id: 46\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1030,6912", + shape=ellipse, + style=solid, + width=6.8916]; + n63 -> n65 [pos="e,1030,6960.2 1030,7127.9 1030,7083.7 1030,7017.5 1030,6970.3"]; + n68 [color=darkorchid4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 256, 1x1) without bias\nStorage id: 48\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1357,7044", + shape=ellipse, + style=solid, + width=6.3961]; + n63 -> n68 [pos="e,1251.7,7086.9 1104.6,7145.3 1145.3,7129.1 1196.7,7108.7 1242.4,7090.6"]; + n77 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="1030,5856", + shape=ellipse, + style=solid, + width=2.817]; + n64 -> n77 [pos="e,980.71,5898.3 823.88,6600.1 838.06,6548.1 858,6461.3 858,6385 858,6385 858,6385 858,6119 858,6033.2 923.3,5953.6 973.44,5905.2"]; + n66 [color=goldenrod2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 47\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1030,6780", + shape=ellipse, + style=solid, + width=3.5652]; + n65 -> n66 [pos="e,1030,6828.5 1030,6863.7 1030,6855.5 1030,6847 1030,6838.6"]; + n67 [color=goldenrod2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 47\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1030,6516", + shape=ellipse, + style=solid, + width=2.6788]; + n66 -> n67 [pos="e,1030,6564.2 1030,6731.9 1030,6687.7 1030,6621.5 1030,6574.3"]; + n67 -> n77 [pos="e,1030,5904.3 1030,6467.7 1030,6415.2 1030,6328.1 1030,6253 1030,6253 1030,6253 1030,6119 1030,6048.6 1030,5967.6 1030,5914.6"]; + n69 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 49\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1425,6912", + shape=ellipse, + style=solid, + width=3.5652]; + n68 -> n69 [pos="e,1400.7,6959.4 1381.5,6996.1 1386.2,6987.1 1391.2,6977.7 1396,6968.5"]; + n70 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 49\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1399,6780", + shape=ellipse, + style=solid, + width=2.6788]; + n69 -> n70 [pos="e,1408.4,6828 1415.5,6863.7 1413.9,6855.4 1412.1,6846.6 1410.4,6838"]; + n71 [color=burlywood4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 50\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1349,6648", + shape=ellipse, + style=solid, + width=7.3732]; + n70 -> n71 [pos="e,1367.1,6696.1 1381.1,6732.4 1377.8,6723.8 1374.2,6714.6 1370.8,6705.7"]; + n72 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 51\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1343,6516", + shape=ellipse, + style=solid, + width=3.5652]; + n71 -> n72 [pos="e,1345.2,6564.5 1346.8,6599.7 1346.4,6591.5 1346,6583 1345.7,6574.6"]; + n73 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 51\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="1338,6384", + shape=ellipse, + style=solid, + width=2.6788]; + n72 -> n73 [pos="e,1339.8,6432.5 1341.2,6467.7 1340.9,6459.5 1340.5,6451 1340.2,6442.6"]; + n74 [color=bisque, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 52\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1317,6252", + shape=ellipse, + style=solid, + width=6.8916]; + n73 -> n74 [pos="e,1324.7,6300.5 1330.4,6335.7 1329,6327.5 1327.7,6319 1326.3,6310.6"]; + n75 [color=antiquewhite4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 53\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1252,6120", + shape=ellipse, + style=solid, + width=3.5652]; + n74 -> n75 [pos="e,1275.2,6167.4 1293.5,6204.1 1289.1,6195.1 1284.3,6185.7 1279.7,6176.5"]; + n76 [color=antiquewhite4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 53\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="1203,5988", + shape=ellipse, + style=solid, + width=2.6788]; + n75 -> n76 [pos="e,1220.5,6035.4 1234.3,6072.1 1231,6063.3 1227.5,6054.1 1224.1,6045.1"]; + n76 -> n77 [pos="e,1083.4,5897.1 1150.2,5947.3 1131.6,5933.4 1110.7,5917.6 1091.5,5903.2"]; + n78 [color=chocolate3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 55\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="410,5724", + shape=ellipse, + style=solid, + width=6.5343]; + n77 -> n78 [pos="e,571.96,5759 937.85,5835.7 844.27,5816.1 697.13,5785.2 581.95,5761.1"]; + n81 [color=beige, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 57\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="899,5724", + shape=ellipse, + style=solid, + width=6.5343]; + n77 -> n81 [pos="e,945.82,5771.5 986.74,5812.1 975.98,5801.4 964.31,5789.8 953.11,5778.7"]; + n94 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="1075,4008", + shape=ellipse, + style=solid, + width=2.817]; + n77 -> n94 [pos="e,1100.2,4054.8 1096.9,5819.7 1114.7,5807.1 1132,5791.1 1143,5772 1182.9,5702.6 1162,5673 1162,5593 1162,5593 1162,5593 1162,4271 \ +1162,4196.4 1129.8,4115.7 1104.6,4063.8"]; + n150 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialAveragePooling(5x5, 4,4)\nStorage id: 105\nSize: {1, 1152, 4, 4}\nMem size: 18432", + pos="1390,5724", + shape=ellipse, + style=solid, + width=4.5661]; + n77 -> n150 [pos="e,1288.1,5761.8 1109.9,5826.1 1159.4,5808.3 1223.8,5785 1278.3,5765.3"]; + n79 [color=azure3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 56\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="420,5592", + shape=ellipse, + style=solid, + width=3.5652]; + n78 -> n79 [pos="e,416.35,5640.5 413.64,5675.7 414.27,5667.5 414.92,5659 415.57,5650.6"]; + n80 [color=azure3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 56\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="468,5328", + shape=ellipse, + style=solid, + width=2.6788]; + n79 -> n80 [pos="e,459.35,5376.2 428.64,5543.9 436.73,5499.7 448.86,5433.5 457.51,5386.3"]; + n90 [color=dodgerblue4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 63\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="868,4536", + shape=ellipse, + style=solid, + width=2.6788]; + n80 -> n90 [pos="e,784,4560 483.33,5280.2 498.98,5228.3 521,5141.6 521,5065 521,5065 521,5065 521,4799 521,4671.8 671.44,4598.5 774.21,4563.3"]; + n82 [color=darkorange2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 58\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="891,5592", + shape=ellipse, + style=solid, + width=3.5652]; + n81 -> n82 [pos="e,893.92,5640.5 896.09,5675.7 895.59,5667.5 895.06,5659 894.54,5650.6"]; + n83 [color=darkorange2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 58\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="876,5460", + shape=ellipse, + style=solid, + width=2.6788]; + n82 -> n83 [pos="e,881.43,5508 885.54,5543.7 884.58,5535.4 883.57,5526.6 882.58,5518"]; + n84 [color=firebrick1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 59\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="868,5328", + shape=ellipse, + style=solid, + width=7.3732]; + n83 -> n84 [pos="e,870.92,5376.5 873.09,5411.7 872.59,5403.5 872.06,5395 871.54,5386.6"]; + n85 [color=cyan4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 60\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="868,5196", + shape=ellipse, + style=solid, + width=3.5652]; + n84 -> n85 [pos="e,868,5244.5 868,5279.7 868,5271.5 868,5263 868,5254.6"]; + n86 [color=cyan4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 60\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="868,5064", + shape=ellipse, + style=solid, + width=2.6788]; + n85 -> n86 [pos="e,868,5112.5 868,5147.7 868,5139.5 868,5131 868,5122.6"]; + n87 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 61\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="868,4932", + shape=ellipse, + style=solid, + width=7.3732]; + n86 -> n87 [pos="e,868,4980.5 868,5015.7 868,5007.5 868,4999 868,4990.6"]; + n88 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 62\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="868,4800", + shape=ellipse, + style=solid, + width=3.5652]; + n87 -> n88 [pos="e,868,4848.5 868,4883.7 868,4875.5 868,4867 868,4858.6"]; + n89 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 62\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="868,4668", + shape=ellipse, + style=solid, + width=2.6788]; + n88 -> n89 [pos="e,868,4716.5 868,4751.7 868,4743.5 868,4735 868,4726.6"]; + n89 -> n90 [pos="e,868,4584.5 868,4619.7 868,4611.5 868,4603 868,4594.6"]; + n91 [color=azure1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 64\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="884,4404", + shape=ellipse, + style=solid, + width=6.5343]; + n90 -> n91 [pos="e,878.16,4452.5 873.82,4487.7 874.83,4479.5 875.88,4471 876.91,4462.6"]; + n92 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 65\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="945,4272", + shape=ellipse, + style=solid, + width=3.5652]; + n91 -> n92 [pos="e,923.21,4319.4 906.02,4356.1 910.22,4347.1 914.65,4337.7 918.96,4328.5"]; + n93 [color=gold1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 66\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="988,4140", + shape=ellipse, + style=solid, + width=2.817]; + n92 -> n93 [pos="e,972.5,4187.9 960.52,4224.1 963.39,4215.4 966.4,4206.3 969.34,4197.4"]; + n93 -> n94 [pos="e,1044.9,4054 1018.2,4093.9 1024.9,4083.8 1032.2,4073 1039.1,4062.6"]; + n95 [color=aquamarine, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="1075,3876", + shape=ellipse, + style=solid, + width=2.817]; + n94 -> n95 [pos="e,1075,3924.5 1075,3959.7 1075,3951.5 1075,3943 1075,3934.6"]; + n96 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 67\nSize: {1, 1152, 8, 8}\nMem size: 73728", + pos="736,2952", + shape=ellipse, + style=solid, + width=3.7843]; + n95 -> n96 [pos="e,703.89,2998.9 977.79,3861.9 846.03,3838.1 627,3774.3 627,3613 627,3613 627,3613 627,3215 627,3138.6 666.97,3058.7 698.51,3007.5"]; + n97 [color=bisque3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 68\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="916,3612", + shape=ellipse, + style=solid, + width=6.5343]; + n95 -> n97 [pos="e,926.24,3660.1 1020.6,3835.1 1006.3,3822.6 991.78,3807.8 981,3792 955.82,3755 939.1,3707.1 928.93,3670.2"]; + n103 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 72\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1225,3744", + shape=ellipse, + style=solid, + width=6.5343]; + n95 -> n103 [pos="e,1171.9,3791.1 1122.9,3833.5 1136,3822.1 1150.5,3809.6 1164.3,3797.6"]; + n109 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 76\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1733,3744", + shape=ellipse, + style=solid, + width=6.5343]; + n95 -> n109 [pos="e,1566.3,3777.9 1167.9,3856.6 1268.4,3836.8 1431.1,3804.6 1556.2,3779.9"]; + n118 [color=firebrick4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 82\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1165,2556", + shape=ellipse, + style=solid, + width=2.5643]; + n96 -> n118 [pos="e,1120.3,2598.1 783.96,2907 865.14,2832.4 1029,2681.9 1112.9,2604.9"]; + n98 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 69\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="911,3480", + shape=ellipse, + style=solid, + width=3.5652]; + n97 -> n98 [pos="e,912.83,3528.5 914.18,3563.7 913.87,3555.5 913.54,3547 913.22,3538.6"]; + n99 [color=darkolivegreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 69\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="910,3348", + shape=ellipse, + style=solid, + width=2.6788]; + n98 -> n99 [pos="e,910.37,3396.5 910.64,3431.7 910.57,3423.5 910.51,3415 910.44,3406.6"]; + n100 [color=blue1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 70\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="903,3216", + shape=ellipse, + style=solid, + width=6.8916]; + n99 -> n100 [pos="e,905.56,3264.5 907.45,3299.7 907.01,3291.5 906.55,3283 906.1,3274.6"]; + n101 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 71\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="963,3084", + shape=ellipse, + style=solid, + width=3.5652]; + n100 -> n101 [pos="e,941.57,3131.4 924.66,3168.1 928.75,3159.2 933.06,3149.9 937.25,3140.8"]; + n102 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 71\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="1092,2820", + shape=ellipse, + style=solid, + width=2.4261]; + n101 -> n102 [pos="e,1069.5,2866.7 985.91,3036.5 1008,2991.5 1041.6,2923.5 1065,2875.9"]; + n102 -> n118 [pos="e,1148.7,2603.3 1102.7,2772 1111.3,2735.7 1123.9,2684.4 1137,2640 1139.6,2631.2 1142.5,2622 1145.4,2613"]; + n104 [color=azure4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 73\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1349,3480", + shape=ellipse, + style=solid, + width=3.5652]; + n103 -> n104 [pos="e,1326.9,3527.8 1247.3,3695.9 1268.4,3651.2 1300.1,3584.2 1322.5,3536.9"]; + n105 [color=azure4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 73\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1374,3348", + shape=ellipse, + style=solid, + width=2.6788]; + n104 -> n105 [pos="e,1365,3396 1358.1,3431.7 1359.7,3423.4 1361.4,3414.6 1363,3406"]; + n106 [color=chocolate, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 74\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1417,3216", + shape=ellipse, + style=solid, + width=6.8916]; + n105 -> n106 [pos="e,1401.4,3264.1 1389.4,3300.4 1392.3,3291.8 1395.3,3282.6 1398.2,3273.7"]; + n107 [color=deeppink1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 75\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1357,3084", + shape=ellipse, + style=solid, + width=3.5652]; + n106 -> n107 [pos="e,1378.4,3131.4 1395.3,3168.1 1391.3,3159.2 1386.9,3149.9 1382.7,3140.8"]; + n108 [color=deeppink1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 75\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1234,2688", + shape=ellipse, + style=solid, + width=2.4261]; + n107 -> n108 [pos="e,1248.5,2735.6 1342.3,3035.9 1319.5,2963 1275.9,2823.3 1251.6,2745.2"]; + n108 -> n118 [pos="e,1189.2,2602.7 1209.9,2641.5 1204.7,2631.9 1199.3,2621.6 1194,2611.7"]; + n110 [color=darkolivegreen1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 77\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1735,3612", + shape=ellipse, + style=solid, + width=3.5652]; + n109 -> n110 [pos="e,1734.3,3660.5 1733.7,3695.7 1733.9,3687.5 1734,3679 1734.1,3670.6"]; + n111 [color=darkolivegreen1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 77\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1736,3480", + shape=ellipse, + style=solid, + width=2.6788]; + n110 -> n111 [pos="e,1735.6,3528.5 1735.4,3563.7 1735.4,3555.5 1735.5,3547 1735.6,3538.6"]; + n112 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 78\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1804,3348", + shape=ellipse, + style=solid, + width=7.3732]; + n111 -> n112 [pos="e,1779.3,3396.2 1760,3433.2 1764.7,3424.1 1769.8,3414.4 1774.7,3405.1"]; + n113 [color=coral3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 79\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1812,3216", + shape=ellipse, + style=solid, + width=3.5652]; + n112 -> n113 [pos="e,1809.1,3264.5 1806.9,3299.7 1807.4,3291.5 1807.9,3283 1808.5,3274.6"]; + n114 [color=coral3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 79\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="1736,3084", + shape=ellipse, + style=solid, + width=2.6788]; + n113 -> n114 [pos="e,1762.7,3130.6 1785,3168.8 1779.4,3159.3 1773.5,3149.2 1767.8,3139.5"]; + n115 [color=coral4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 80\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1730,2952", + shape=ellipse, + style=solid, + width=6.8916]; + n114 -> n115 [pos="e,1732.2,3000.5 1733.8,3035.7 1733.4,3027.5 1733,3019 1732.7,3010.6"]; + n116 [color=firebrick1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 81\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1479,2820", + shape=ellipse, + style=solid, + width=3.5652]; + n115 -> n116 [pos="e,1553.2,2859.4 1644.7,2906.8 1618.1,2893 1588.8,2877.9 1562.4,2864.2"]; + n117 [color=firebrick1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 81\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1437,2688", + shape=ellipse, + style=solid, + width=2.4261]; + n116 -> n117 [pos="e,1452,2735.4 1463.8,2772.1 1461,2763.3 1458,2754.1 1455.1,2745.1"]; + n117 -> n118 [pos="e,1232.5,2589.2 1371.8,2655.9 1332.7,2637.1 1282.7,2613.2 1241.6,2593.6"]; + n119 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 83\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="545,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n118 -> n119 [pos="e,707,2459 1079.8,2537.1 986.63,2517.6 834.95,2485.8 716.87,2461"]; + n122 [color=blue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 85\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="1034,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n118 -> n122 [pos="e,1080.6,2471.3 1122.8,2513.2 1111.7,2502.1 1099.5,2490 1087.8,2478.4"]; + n135 [color=firebrick4, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 82\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1212,708", + shape=ellipse, + style=solid, + width=2.5643]; + n118 -> n135 [pos="e,1236.4,754.41 1229.5,2521.4 1248.1,2508.5 1266.5,2491.9 1278,2472 1317.9,2402.6 1297,2373 1297,2293 1297,2293 1297,2293 1297,971 \ +1297,896.34 1265.4,815.3 1240.7,763.43"]; + n141 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialAveragePooling(5x5, 3,3)\nStorage id: 98\nSize: {1, 2048, 2, 2}\nMem size: 8192", + pos="1525,2424", + shape=ellipse, + style=solid, + width=4.5661]; + n118 -> n141 [pos="e,1423.1,2461.8 1240,2527.9 1290.2,2509.8 1357.1,2485.6 1413.4,2465.3"]; + n120 [color=bisque1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 84\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="556,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n119 -> n120 [pos="e,551.98,2340.5 549,2375.7 549.69,2367.5 550.42,2359 551.13,2350.6"]; + n121 [color=bisque1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 84\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="588,2028", + shape=ellipse, + style=solid, + width=2.4261]; + n120 -> n121 [pos="e,582.23,2076.2 561.76,2243.9 567.16,2199.7 575.24,2133.5 581,2086.3"]; + n131 [color=gold, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 91\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="1003,1236", + shape=ellipse, + style=solid, + width=2.4261]; + n121 -> n131 [pos="e,924.44,1257.2 605.64,1980.4 623.66,1928.6 649,1842.1 649,1765 649,1765 649,1765 649,1499 649,1367 809.94,1293.9 914.79,1260.3"]; + n123 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 86\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="1026,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n122 -> n123 [pos="e,1028.9,2340.5 1031.1,2375.7 1030.6,2367.5 1030.1,2359 1029.5,2350.6"]; + n124 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 86\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="1011,2160", + shape=ellipse, + style=solid, + width=2.4261]; + n123 -> n124 [pos="e,1016.4,2208 1020.5,2243.7 1019.6,2235.4 1018.6,2226.6 1017.6,2218"]; + n125 [color=deeppink2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 87\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="1003,2028", + shape=ellipse, + style=solid, + width=7.3732]; + n124 -> n125 [pos="e,1005.9,2076.5 1008.1,2111.7 1007.6,2103.5 1007.1,2095 1006.5,2086.6"]; + n126 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 88\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="1003,1896", + shape=ellipse, + style=solid, + width=3.5652]; + n125 -> n126 [pos="e,1003,1944.5 1003,1979.7 1003,1971.5 1003,1963 1003,1954.6"]; + n127 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 88\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="1003,1764", + shape=ellipse, + style=solid, + width=2.4261]; + n126 -> n127 [pos="e,1003,1812.5 1003,1847.7 1003,1839.5 1003,1831 1003,1822.6"]; + n128 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 89\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1003,1632", + shape=ellipse, + style=solid, + width=7.3732]; + n127 -> n128 [pos="e,1003,1680.5 1003,1715.7 1003,1707.5 1003,1699 1003,1690.6"]; + n129 [color=darkslategray, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 90\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1003,1500", + shape=ellipse, + style=solid, + width=3.5652]; + n128 -> n129 [pos="e,1003,1548.5 1003,1583.7 1003,1575.5 1003,1567 1003,1558.6"]; + n130 [color=darkslategray, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 90\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="1003,1368", + shape=ellipse, + style=solid, + width=2.4261]; + n129 -> n130 [pos="e,1003,1416.5 1003,1451.7 1003,1443.5 1003,1435 1003,1426.6"]; + n130 -> n131 [pos="e,1003,1284.5 1003,1319.7 1003,1311.5 1003,1303 1003,1294.6"]; + n132 [color=darkseagreen1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 92\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1019,1104", + shape=ellipse, + style=solid, + width=6.5343]; + n131 -> n132 [pos="e,1013.2,1152.5 1008.8,1187.7 1009.8,1179.5 1010.9,1171 1011.9,1162.6"]; + n133 [color=cornsilk, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 93\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1080,972", + shape=ellipse, + style=solid, + width=3.5652]; + n132 -> n133 [pos="e,1058.2,1019.4 1041,1056.1 1045.2,1047.1 1049.7,1037.7 1054,1028.5"]; + n134 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 94\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1128,840", + shape=ellipse, + style=solid, + width=2.5643]; + n133 -> n134 [pos="e,1110.9,887.43 1097.3,924.07 1100.6,915.31 1104,906.07 1107.3,897.07"]; + n134 -> n135 [pos="e,1183,753.82 1156.9,794.26 1163.6,783.95 1170.7,772.87 1177.6,762.24"]; + n136 [color=firebrick4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 82\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="1212,576", + shape=ellipse, + style=solid, + width=2.5643]; + n135 -> n136 [pos="e,1212,624.48 1212,659.7 1212,651.54 1212,642.99 1212,634.6"]; + n137 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialAveragePooling(8x8, 1,1)\nStorage id: 95\nSize: {1, 2048, 1, 1}\nMem size: 2048", + pos="1212,444", + shape=ellipse, + style=solid, + width=4.5661]; + n136 -> n137 [pos="e,1212,492.48 1212,527.7 1212,519.54 1212,510.99 1212,502.6"]; + n138 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="nn.View(2048)\nStorage id: 95\nSize: {1, 2048}\nMem size: 2048", + pos="1212,312", + shape=ellipse, + style=solid, + width=2.0688]; + n137 -> n138 [pos="e,1212,360.48 1212,395.7 1212,387.54 1212,378.99 1212,370.6"]; + n139 [color=goldenrod2, + fontsize=14, + height=1.3356, + label="nn.Dropout(0.200000)\nStorage id: 96\nSize: {1, 2048}\nMem size: 2048", + pos="1212,180", + shape=ellipse, + style=solid, + width=2.7262]; + n138 -> n139 [pos="e,1212,228.48 1212,263.7 1212,255.54 1212,246.99 1212,238.6"]; + n140 [color=bisque4, + fontsize=14, + height=1.3356, + label="nn.Linear(2048 -> 1003)\nStorage id: 97\nSize: {1, 1003}\nMem size: 1003", + pos="1212,48", + shape=ellipse, + style=solid, + width=2.9789]; + n139 -> n140 [pos="e,1212,96.483 1212,131.7 1212,123.54 1212,114.99 1212,106.6"]; + n142 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 256, 1x1) without bias\nStorage id: 99\nSize: {1, 256, 2, 2}\nMem size: 1024", + pos="1560,2292", + shape=ellipse, + style=solid, + width=6.5343]; + n141 -> n142 [pos="e,1547.3,2340 1537.7,2375.7 1540,2367.3 1542.4,2358.4 1544.7,2349.7"]; + n143 [color=darkslategray, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 100\nSize: {1, 256, 2, 2}\nMem size: 1024", + pos="1560,2160", + shape=ellipse, + style=solid, + width=3.5652]; + n142 -> n143 [pos="e,1560,2208.5 1560,2243.7 1560,2235.5 1560,2227 1560,2218.6"]; + n144 [color=darkslategray, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 100\nSize: {1, 256, 2, 2}\nMem size: 1024", + pos="1560,2028", + shape=ellipse, + style=solid, + width=2.4261]; + n143 -> n144 [pos="e,1560,2076.5 1560,2111.7 1560,2103.5 1560,2095 1560,2086.6"]; + n145 [color=darkslategray, + fontsize=14, + height=1.3356, + label="nn.View(1024)\nStorage id: 100\nSize: {1, 1024}\nMem size: 1024", + pos="1560,1896", + shape=ellipse, + style=solid, + width=2.0688]; + n144 -> n145 [pos="e,1560,1944.5 1560,1979.7 1560,1971.5 1560,1963 1560,1954.6"]; + n146 [color=firebrick2, + fontsize=14, + height=1.3356, + label="nn.Linear(1024 -> 1024) without bias\nStorage id: 101\nSize: {1, 1024}\nMem size: 1024", + pos="1560,1764", + shape=ellipse, + style=solid, + width=4.3706]; + n145 -> n146 [pos="e,1560,1812.5 1560,1847.7 1560,1839.5 1560,1831 1560,1822.6"]; + n147 [color=deeppink3, + fontsize=14, + height=1.3356, + label="nn.BatchNormalization\nStorage id: 102\nSize: {1, 1024}\nMem size: 1024", + pos="1560,1632", + shape=ellipse, + style=solid, + width=2.817]; + n146 -> n147 [pos="e,1560,1680.5 1560,1715.7 1560,1707.5 1560,1699 1560,1690.6"]; + n148 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.ReLU\nStorage id: 103\nSize: {1, 1024}\nMem size: 1024", + pos="1560,1500", + shape=ellipse, + style=solid, + width=2.0688]; + n147 -> n148 [pos="e,1560,1548.5 1560,1583.7 1560,1575.5 1560,1567 1560,1558.6"]; + n149 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="nn.Linear(1024 -> 1002)\nStorage id: 104\nSize: {1, 1002}\nMem size: 1002", + pos="1560,1368", + shape=ellipse, + style=solid, + width=2.9789]; + n148 -> n149 [pos="e,1560,1416.5 1560,1451.7 1560,1443.5 1560,1435 1560,1426.6"]; + n151 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 106\nSize: {1, 128, 4, 4}\nMem size: 2048", + pos="1425,5592", + shape=ellipse, + style=solid, + width=6.5343]; + n150 -> n151 [pos="e,1412.3,5640 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style=solid, + width=6.2816]; + n39 -> n40 [pos="e,363.74,42722 621.84,42792 553.37,42774 455.09,42747 373.59,42725"]; + n43 [color=gold2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 31\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="526,42552", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n43 [pos="e,553.2,42600 668.58,42771 658.33,42759 647.45,42745 638,42732 609.32,42692 579.95,42645 558.48,42609"]; + n49 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 35\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="873,42684", + shape=ellipse, + style=solid, + width=6.2816]; + n39 -> n49 [pos="e,814.4,42731 757.47,42775 772.95,42763 790.17,42749 806.48,42737"]; + n62 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1050,40968", + shape=ellipse, + style=solid, + width=2.6788]; + n39 -> n62 [pos="e,1080.1,41014 800.96,42807 905.56,42796 1064.7,42773 1108,42732 1167.5,42675 1154,42635 1154,42553 1154,42553 1154,42553 1154,41231 \ +1154,41155 1115.4,41074 1085.2,41023"]; + n41 [color=deeppink, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="153,42552", + shape=ellipse, + style=solid, + width=3.5652]; + n40 -> n41 [pos="e,179.07,42599 199.65,42636 194.57,42627 189.21,42617 184,42608"]; + n42 [color=deeppink, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="293,41892", + shape=ellipse, + style=solid, + width=2.5643]; + n41 -> n42 [pos="e,265.49,41938 165.73,42504 178.72,42452 197,42365 197,42289 197,42289 197,42289 197,42155 197,42079 232.71,41999 260.57,41947"]; + n58 [color=cadetblue1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 41\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="653,41496", + shape=ellipse, + style=solid, + width=2.6788]; + n42 -> n58 [pos="e,607.29,41539 325.41,41847 353.07,41810 394.52,41756 434,41712 487.74,41652 554.33,41588 599.88,41546"]; + n44 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="507,42420", + shape=ellipse, + style=solid, + width=3.5652]; + n43 -> n44 [pos="e,513.88,42468 519.09,42504 517.87,42495 516.58,42487 515.33,42478"]; + n45 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="490,42288", + shape=ellipse, + style=solid, + width=2.5643]; + n44 -> n45 [pos="e,496.15,42336 500.81,42372 499.72,42363 498.58,42355 497.45,42346"]; + n46 [color=firebrick2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 33\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="484,42156", + shape=ellipse, + style=solid, + width=7.0968]; + n45 -> n46 [pos="e,486.19,42204 487.82,42240 487.44,42232 487.05,42223 486.66,42215"]; + n47 [color=darkgoldenrod, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="514,42024", + shape=ellipse, + style=solid, + width=3.5652]; + n46 -> n47 [pos="e,503.14,42072 494.92,42108 496.84,42099 498.87,42091 500.85,42082"]; + n48 [color=darkgoldenrod, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="536,41760", + shape=ellipse, + style=solid, + width=2.5643]; + n47 -> n48 [pos="e,532.04,41808 517.96,41976 521.67,41932 527.23,41865 531.19,41818"]; + n48 -> n58 [pos="e,632.31,41543 556.51,41713 576.48,41668 606.85,41600 628.18,41553"]; + n50 [color=brown3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="899,42552", + shape=ellipse, + style=solid, + width=3.5652]; + n49 -> n50 [pos="e,889.59,42600 882.46,42636 884.13,42627 885.88,42619 887.6,42610"]; + n51 [color=brown3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="895,42420", + shape=ellipse, + style=solid, + width=2.5643]; + n50 -> n51 [pos="e,896.46,42468 897.54,42504 897.29,42496 897.03,42487 896.77,42479"]; + n52 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 37\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="870,42288", + shape=ellipse, + style=solid, + width=7.0968]; + n51 -> n52 [pos="e,879.1,42336 885.97,42372 884.37,42364 882.67,42355 881.02,42346"]; + n53 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="887,42156", + shape=ellipse, + style=solid, + width=3.5652]; + n52 -> n53 [pos="e,880.85,42204 876.19,42240 877.28,42231 878.42,42223 879.55,42214"]; + n54 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="879,42024", + shape=ellipse, + style=solid, + width=2.5643]; + n53 -> n54 [pos="e,881.92,42072 884.09,42108 883.59,42100 883.06,42091 882.54,42083"]; + n55 [color=aquamarine2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 39\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="820,41892", + shape=ellipse, + style=solid, + width=7.0968]; + n54 -> n55 [pos="e,841.3,41940 858.03,41977 853.96,41968 849.65,41958 845.45,41949"]; + n56 [color=deeppink2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="790,41760", + shape=ellipse, + style=solid, + width=3.5652]; + n55 -> n56 [pos="e,800.86,41808 809.08,41844 807.16,41835 805.13,41827 803.15,41818"]; + n57 [color=deeppink2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="757,41628", + shape=ellipse, + style=solid, + width=2.5643]; + n56 -> n57 [pos="e,768.9,41676 778.09,41712 775.91,41704 773.62,41694 771.39,41686"]; + n57 -> n58 [pos="e,688.36,41541 722.08,41583 713.34,41572 703.88,41561 694.85,41549"]; + n59 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 42\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="729,41364", + shape=ellipse, + style=solid, + width=6.3961]; + n58 -> n59 [pos="e,701.53,41412 679.58,41450 685.02,41440 690.81,41430 696.43,41421"]; + n60 [color=cornflowerblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 43\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="863,41232", + shape=ellipse, + style=solid, + width=3.5652]; + n59 -> n60 [pos="e,817.33,41277 776.62,41317 787.48,41306 799.09,41295 810.15,41284"]; + n61 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 44\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="946,41100", + shape=ellipse, + style=solid, + width=2.6788]; + n60 -> n61 [pos="e,917.28,41146 892.26,41185 898.61,41175 905.37,41165 911.88,41154"]; + n61 -> n62 [pos="e,1014.8,41013 981.21,41055 989.93,41044 999.37,41032 1008.4,41021"]; + n63 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1050,40836", + shape=ellipse, + style=solid, + width=2.6788]; + n62 -> n63 [pos="e,1050,40884 1050,40920 1050,40912 1050,40903 1050,40895"]; + n64 [color=bisque2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 45\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="570,40704", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n64 [pos="e,707.74,40742 965.84,40812 897.37,40794 799.09,40767 717.59,40745"]; + n67 [color=darkkhaki, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 47\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="833,40572", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n67 [pos="e,860.69,40620 999.56,40794 984.81,40782 969.13,40767 956,40752 921.87,40713 889.15,40665 866.05,40628"]; + n73 [color=dodgerblue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 51\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1191,40704", + shape=ellipse, + style=solid, + width=6.2816]; + n63 -> n73 [pos="e,1140.8,40751 1095.4,40793 1107.5,40782 1120.8,40770 1133.4,40758"]; + n86 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1365,38988", + shape=ellipse, + style=solid, + width=2.6788]; + n63 -> n86 [pos="e,1391.9,39035 1144.7,40826 1243.1,40815 1387.9,40792 1426,40752 1482,40694 1458,40654 1458,40573 1458,40573 1458,40573 1458,39251 \ +1458,39176 1423.6,39095 1396.7,39044"]; + n65 [color=darkorange1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="460,40572", + shape=ellipse, + style=solid, + width=3.5652]; + n64 -> n65 [pos="e,498.02,40618 530.6,40656 522.15,40646 513.16,40636 504.54,40626"]; + n66 [color=darkorange1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="616,39912", + shape=ellipse, + style=solid, + width=2.5643]; + n65 -> n66 [pos="e,582.47,39957 471.28,40524 482.8,40472 499,40385 499,40309 499,40309 499,40309 499,40175 499,40097 542.7,40017 576.68,39966"]; + n82 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 57\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1045,39516", + shape=ellipse, + style=solid, + width=2.6788]; + n66 -> n82 [pos="e,986.5,39555 649.8,39867 679.23,39830 723.83,39775 767,39732 830.83,39668 849.94,39655 922,39600 939.79,39586 959.64,39573 978.12,\ +39560"]; + n68 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="812,40440", + shape=ellipse, + style=solid, + width=3.5652]; + n67 -> n68 [pos="e,819.6,40488 825.36,40524 824.01,40515 822.59,40507 821.21,40498"]; + n69 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="793,40308", + shape=ellipse, + style=solid, + width=2.5643]; + n68 -> n69 [pos="e,799.88,40356 805.09,40392 803.87,40383 802.58,40375 801.33,40366"]; + n70 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 49\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="786,40176", + shape=ellipse, + style=solid, + width=7.0968]; + n69 -> n70 [pos="e,788.56,40224 790.45,40260 790.01,40252 789.55,40243 789.1,40235"]; + n71 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="838,40044", + shape=ellipse, + style=solid, + width=3.5652]; + n70 -> n71 [pos="e,819.35,40092 804.92,40128 808.38,40119 812.01,40110 815.56,40101"]; + n72 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="869,39780", + shape=ellipse, + style=solid, + width=2.5643]; + n71 -> n72 [pos="e,863.41,39828 843.58,39996 848.81,39952 856.64,39885 862.22,39838"]; + n72 -> n82 [pos="e,1014.8,39562 899.06,39734 929.53,39689 976.68,39619 1009.1,39570"]; + n74 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1206,40572", + shape=ellipse, + style=solid, + width=3.5652]; + n73 -> n74 [pos="e,1200.6,40620 1196.5,40656 1197.4,40647 1198.4,40639 1199.4,40630"]; + n75 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1202,40440", + shape=ellipse, + style=solid, + width=2.5643]; + n74 -> n75 [pos="e,1203.5,40488 1204.5,40524 1204.3,40516 1204,40507 1203.8,40499"]; + n76 [color=chocolate, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 53\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1174,40308", + shape=ellipse, + style=solid, + width=7.0968]; + n75 -> n76 [pos="e,1184.2,40356 1191.9,40392 1190.1,40384 1188.2,40375 1186.3,40366"]; + n77 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1189,40176", + shape=ellipse, + style=solid, + width=3.5652]; + n76 -> n77 [pos="e,1183.6,40224 1179.5,40260 1180.4,40251 1181.4,40243 1182.4,40234"]; + n78 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1168,40044", + shape=ellipse, + style=solid, + width=2.5643]; + n77 -> n78 [pos="e,1175.6,40092 1181.4,40128 1180,40119 1178.6,40111 1177.2,40102"]; + n79 [color=deeppink1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 55\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1161,39912", + shape=ellipse, + style=solid, + width=7.0968]; + n78 -> n79 [pos="e,1163.6,39960 1165.5,39996 1165,39988 1164.6,39979 1164.1,39971"]; + n80 [color=darkgoldenrod1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1143,39780", + shape=ellipse, + style=solid, + width=3.5652]; + n79 -> n80 [pos="e,1149.5,39828 1154.5,39864 1153.3,39855 1152.1,39847 1150.9,39838"]; + n81 [color=darkgoldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1105,39648", + shape=ellipse, + style=solid, + width=2.5643]; + n80 -> n81 [pos="e,1118.7,39696 1129.3,39732 1126.8,39724 1124.1,39714 1121.6,39706"]; + n81 -> n82 [pos="e,1066.4,39563 1083.8,39601 1079.6,39592 1075,39582 1070.6,39572"]; + n83 [color=chocolate2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 58\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1122,39384", + shape=ellipse, + style=solid, + width=6.3961]; + n82 -> n83 [pos="e,1094.2,39432 1071.9,39470 1077.4,39460 1083.3,39450 1089,39441"]; + n84 [color=darkorchid4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 59\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1212,39252", + shape=ellipse, + style=solid, + width=3.5652]; + n83 -> n84 [pos="e,1180.2,39299 1154.5,39336 1161,39327 1167.8,39317 1174.4,39307"]; + n85 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 60\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1272,39120", + shape=ellipse, + style=solid, + width=2.6788]; + n84 -> n85 [pos="e,1250.7,39167 1233.5,39204 1237.7,39195 1242.1,39186 1246.4,39176"]; + n85 -> n86 [pos="e,1332.9,39034 1304,39074 1311.4,39064 1319.3,39053 1326.9,39042"]; + n87 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1365,38856", + shape=ellipse, + style=solid, + width=2.6788]; + n86 -> n87 [pos="e,1365,38904 1365,38940 1365,38932 1365,38923 1365,38915"]; + n88 [color=gold, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 61\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="894,38724", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n88 [pos="e,1029.9,38763 1281.3,38832 1214.5,38813 1119.3,38787 1040,38765"]; + n91 [color=burlywood3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 63\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1194,38592", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n91 [pos="e,1215,38640 1322.2,38812 1310.7,38800 1298.8,38786 1289,38772 1261.7,38733 1236.9,38686 1219.5,38650"]; + n97 [color=darkseagreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 67\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1524,38724", + shape=ellipse, + style=solid, + width=6.2816]; + n87 -> n97 [pos="e,1468.2,38771 1414.4,38815 1429,38803 1445,38790 1460.3,38777"]; + n110 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1708,37008", + shape=ellipse, + style=solid, + width=2.6788]; + n87 -> n110 [pos="e,1736.1,37054 1459.9,38846 1562.5,38835 1716.9,38812 1759,38772 1818.3,38715 1805,38675 1805,38593 1805,38593 1805,38593 1805,37271 \ +1805,37196 1769.1,37115 1741,37063"]; + n89 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="821,38592", + shape=ellipse, + style=solid, + width=3.5652]; + n88 -> n89 [pos="e,847.07,38639 867.65,38676 862.57,38667 857.21,38657 852,38648"]; + n90 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1075,37932", + shape=ellipse, + style=solid, + width=2.5643]; + n89 -> n90 [pos="e,1009.8,37966 823.02,38544 825.09,38491 828,38404 828,38329 828,38329 828,38329 828,38195 828,38093 928.28,38015 1001.1,37971"]; + n106 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 73\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1306,37536", + shape=ellipse, + style=solid, + width=2.6788]; + n90 -> n106 [pos="e,1259.6,37579 1081.1,37884 1087.3,37846 1099.3,37794 1121,37752 1154.5,37688 1210.6,37626 1252.3,37586"]; + n92 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1180,38460", + shape=ellipse, + style=solid, + width=3.5652]; + n91 -> n92 [pos="e,1185.1,38508 1188.9,38544 1188,38535 1187.1,38527 1186.1,38518"]; + n93 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1136,38328", + shape=ellipse, + style=solid, + width=2.5643]; + n92 -> n93 [pos="e,1151.7,38375 1164.1,38412 1161.2,38403 1158,38394 1155,38385"]; + n94 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 65\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1127,38196", + shape=ellipse, + style=solid, + width=7.0968]; + n93 -> n94 [pos="e,1130.3,38244 1132.7,38280 1132.2,38272 1131.6,38263 1131,38255"]; + n95 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1182,38064", + shape=ellipse, + style=solid, + width=3.5652]; + n94 -> n95 [pos="e,1162.3,38112 1147,38148 1150.7,38139 1154.6,38130 1158.4,38121"]; + n96 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1223,37800", + shape=ellipse, + style=solid, + width=2.5643]; + n95 -> n96 [pos="e,1215.6,37848 1189.4,38016 1196.3,37972 1206.6,37905 1214,37858"]; + n96 -> n106 [pos="e,1291.3,37584 1237.7,37752 1251.8,37708 1273.1,37641 1288.1,37593"]; + n98 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1567,38592", + shape=ellipse, + style=solid, + width=3.5652]; + n97 -> n98 [pos="e,1551.4,38640 1539.6,38676 1542.4,38667 1545.4,38658 1548.2,38650"]; + n99 [color=brown1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1559,38460", + shape=ellipse, + style=solid, + width=2.5643]; + n98 -> n99 [pos="e,1561.9,38508 1564.1,38544 1563.6,38536 1563.1,38527 1562.5,38519"]; + n100 [color=aquamarine4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 69\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1521,38328", + shape=ellipse, + style=solid, + width=7.0968]; + n99 -> n100 [pos="e,1534.8,38376 1545.4,38412 1542.9,38404 1540.2,38395 1537.7,38386"]; + n101 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1530,38196", + shape=ellipse, + style=solid, + width=3.5652]; + n100 -> n101 [pos="e,1526.7,38244 1524.3,38280 1524.8,38272 1525.4,38263 1526,38255"]; + n102 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1511,38064", + shape=ellipse, + style=solid, + width=2.5643]; + n101 -> n102 [pos="e,1517.9,38112 1523.1,38148 1521.9,38139 1520.6,38131 1519.3,38122"]; + n103 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 71\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1507,37932", + shape=ellipse, + style=solid, + width=7.0968]; + n102 -> n103 [pos="e,1508.5,37980 1509.5,38016 1509.3,38008 1509,37999 1508.8,37991"]; + n104 [color=darkorchid1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1471,37800", + shape=ellipse, + style=solid, + width=3.5652]; + n103 -> n104 [pos="e,1484,37848 1493.9,37884 1491.6,37875 1489.1,37866 1486.7,37858"]; + n105 [color=darkorchid1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1443,37668", + shape=ellipse, + style=solid, + width=2.5643]; + n104 -> n105 [pos="e,1453.1,37716 1460.8,37752 1459,37743 1457.1,37735 1455.3,37726"]; + n105 -> n106 [pos="e,1350.2,37579 1399.3,37626 1386.1,37613 1371.4,37599 1357.7,37586"]; + n107 [color=gold3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 74\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1440,37404", + shape=ellipse, + style=solid, + width=6.3961]; + n106 -> n107 [pos="e,1392.1,37451 1349.5,37493 1360.8,37482 1373.1,37470 1384.8,37459"]; + n108 [color=bisque1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 75\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1544,37272", + shape=ellipse, + style=solid, + width=3.5652]; + n107 -> n108 [pos="e,1507.7,37318 1477.3,37356 1485.1,37347 1493.4,37336 1501.4,37326"]; + n109 [color=black, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 76\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1612,37140", + shape=ellipse, + style=solid, + width=2.6788]; + n108 -> n109 [pos="e,1588,37187 1568.4,37224 1573.2,37215 1578.3,37205 1583.3,37196"]; + n109 -> n110 [pos="e,1675.1,37054 1644.8,37095 1652.6,37084 1660.9,37073 1669,37062"]; + n111 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1708,36876", + shape=ellipse, + style=solid, + width=2.6788]; + n110 -> n111 [pos="e,1708,36924 1708,36960 1708,36952 1708,36943 1708,36935"]; + n112 [color=chocolate1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 77\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1228,36744", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n112 [pos="e,1365.7,36782 1623.8,36852 1555.4,36834 1457.1,36807 1375.6,36785"]; + n115 [color=coral2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 79\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1512,36612", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n115 [pos="e,1533.5,36660 1656.8,36835 1642,36822 1626.5,36807 1614,36792 1583.3,36754 1556.5,36706 1538,36669"]; + n121 [color=burlywood1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 83\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1849,36744", + shape=ellipse, + style=solid, + width=6.2816]; + n111 -> n121 [pos="e,1798.8,36791 1753.4,36833 1765.5,36822 1778.8,36810 1791.4,36798"]; + n134 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2023,35028", + shape=ellipse, + style=solid, + width=2.6788]; + n111 -> n134 [pos="e,2049.9,35075 1802.7,36866 1901.1,36855 2045.9,36832 2084,36792 2140,36734 2116,36694 2116,36613 2116,36613 2116,36613 2116,35291 \ +2116,35216 2081.6,35135 2054.7,35084"]; + n113 [color=chartreuse2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1139,36612", + shape=ellipse, + style=solid, + width=3.5652]; + n112 -> n113 [pos="e,1170.5,36659 1195.9,36696 1189.5,36687 1182.7,36677 1176.2,36667"]; + n114 [color=chartreuse2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1265,35952", + shape=ellipse, + style=solid, + width=2.5643]; + n113 -> n114 [pos="e,1230.3,35997 1140.4,36564 1141.9,36511 1144,36424 1144,36349 1144,36349 1144,36349 1144,36215 1144,36136 1189.4,36056 1224.6,36005"]; + n130 [color=coral4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 89\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1702,35556", + shape=ellipse, + style=solid, + width=2.6788]; + n114 -> n130 [pos="e,1643.3,35594 1300.4,35908 1331.4,35870 1378.4,35816 1423,35772 1487.5,35709 1505.8,35695 1578,35640 1596,35626 1616.1,35612 1634.8,\ +35600"]; + n116 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1468,36480", + shape=ellipse, + style=solid, + width=3.5652]; + n115 -> n116 [pos="e,1483.9,36528 1496,36564 1493.1,36555 1490.1,36546 1487.2,36538"]; + n117 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1447,36348", + shape=ellipse, + style=solid, + width=2.5643]; + n116 -> n117 [pos="e,1454.6,36396 1460.4,36432 1459,36423 1457.6,36415 1456.2,36406"]; + n118 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 81\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1438,36216", + shape=ellipse, + style=solid, + width=7.0968]; + n117 -> n118 [pos="e,1441.3,36264 1443.7,36300 1443.2,36292 1442.6,36283 1442,36275"]; + n119 [color=darkviolet, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1493,36084", + shape=ellipse, + style=solid, + width=3.5652]; + n118 -> n119 [pos="e,1473.3,36132 1458,36168 1461.7,36159 1465.6,36150 1469.4,36141"]; + n120 [color=darkviolet, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1525,35820", + shape=ellipse, + style=solid, + width=2.5643]; + n119 -> n120 [pos="e,1519.2,35868 1498.8,36036 1504.2,35992 1512.2,35925 1518,35878"]; + n120 -> n130 [pos="e,1671.6,35602 1555.2,35774 1585.9,35729 1633.3,35659 1665.9,35610"]; + n122 [color=cyan, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1885,36612", + shape=ellipse, + style=solid, + width=3.5652]; + n121 -> n122 [pos="e,1872,36660 1862.1,36696 1864.4,36687 1866.9,36678 1869.3,36670"]; + n123 [color=cyan, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1879,36480", + shape=ellipse, + style=solid, + width=2.5643]; + n122 -> n123 [pos="e,1881.2,36528 1882.8,36564 1882.4,36556 1882,36547 1881.7,36539"]; + n124 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 85\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1832,36348", + shape=ellipse, + style=solid, + width=7.0968]; + n123 -> n124 [pos="e,1849,36396 1862.2,36432 1859,36424 1855.7,36415 1852.5,36406"]; + n125 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1841,36216", + shape=ellipse, + style=solid, + width=3.5652]; + n124 -> n125 [pos="e,1837.7,36264 1835.3,36300 1835.8,36292 1836.4,36283 1837,36275"]; + n126 [color=dodgerblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="1822,36084", + shape=ellipse, + style=solid, + width=2.5643]; + n125 -> n126 [pos="e,1828.9,36132 1834.1,36168 1832.9,36159 1831.6,36151 1830.3,36142"]; + n127 [color=cornflowerblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 87\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1818,35952", + shape=ellipse, + style=solid, + width=7.0968]; + n126 -> n127 [pos="e,1819.5,36000 1820.5,36036 1820.3,36028 1820,36019 1819.8,36011"]; + n128 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1800,35820", + shape=ellipse, + style=solid, + width=3.5652]; + n127 -> n128 [pos="e,1806.5,35868 1811.5,35904 1810.3,35895 1809.1,35887 1807.9,35878"]; + n129 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="1762,35688", + shape=ellipse, + style=solid, + width=2.5643]; + n128 -> n129 [pos="e,1775.7,35736 1786.3,35772 1783.8,35764 1781.1,35754 1778.6,35746"]; + n129 -> n130 [pos="e,1723.4,35603 1740.8,35641 1736.6,35632 1732,35622 1727.6,35612"]; + n131 [color=blueviolet, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 90\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1780,35424", + shape=ellipse, + style=solid, + width=6.3961]; + n130 -> n131 [pos="e,1751.8,35472 1729.3,35510 1734.9,35500 1740.8,35490 1746.6,35481"]; + n132 [color=aquamarine3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 91\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1870,35292", + shape=ellipse, + style=solid, + width=3.5652]; + n131 -> n132 [pos="e,1838.2,35339 1812.5,35376 1819,35367 1825.8,35357 1832.4,35347"]; + n133 [color=cadetblue1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 92\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="1931,35160", + shape=ellipse, + style=solid, + width=2.6788]; + n132 -> n133 [pos="e,1909.3,35207 1891.9,35244 1896.1,35235 1900.6,35226 1905,35216"]; + n133 -> n134 [pos="e,1991.3,35074 1962.7,35114 1970,35104 1977.8,35093 1985.3,35082"]; + n135 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2023,34896", + shape=ellipse, + style=solid, + width=2.6788]; + n134 -> n135 [pos="e,2023,34944 2023,34980 2023,34972 2023,34963 2023,34955"]; + n136 [color=dimgray, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 93\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1543,34764", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n136 [pos="e,1680.7,34802 1938.8,34872 1870.4,34854 1772.1,34827 1690.6,34805"]; + n139 [color=forestgreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 95\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1843,34632", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n139 [pos="e,1870.2,34680 1985.6,34851 1975.3,34839 1964.5,34825 1955,34812 1926.3,34772 1896.9,34725 1875.5,34689"]; + n145 [color=darkgreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 99\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2190,34764", + shape=ellipse, + style=solid, + width=6.2816]; + n135 -> n145 [pos="e,2131.4,34811 2074.5,34855 2090,34843 2107.2,34829 2123.5,34817"]; + n158 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2379,33048", + shape=ellipse, + style=solid, + width=2.6788]; + n135 -> n158 [pos="e,2405.6,33095 2118,34887 2222.6,34876 2381.7,34853 2425,34812 2484.5,34755 2471,34715 2471,34633 2471,34633 2471,34633 2471,33311 \ +2471,33236 2437,33155 2410.3,33104"]; + n137 [color=darkturquoise, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1470,34632", + shape=ellipse, + style=solid, + width=3.5652]; + n136 -> n137 [pos="e,1496.1,34679 1516.6,34716 1511.6,34707 1506.2,34697 1501,34688"]; + n138 [color=darkturquoise, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1646,33972", + shape=ellipse, + style=solid, + width=2.5643]; + n137 -> n138 [pos="e,1608.5,34016 1482.7,34584 1495.7,34532 1514,34445 1514,34369 1514,34369 1514,34369 1514,34235 1514,34155 1563.8,34074 1602.2,34024"]; + n154 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 105\nSize: {1, 128, 35, 35}\nMem size: 156800", + pos="1995,33576", + shape=ellipse, + style=solid, + width=2.6788]; + n138 -> n154 [pos="e,1950.2,33619 1676.9,33927 1703.2,33890 1742.9,33836 1781,33792 1833,33732 1898,33668 1942.6,33626"]; + n140 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1824,34500", + shape=ellipse, + style=solid, + width=3.5652]; + n139 -> n140 [pos="e,1830.9,34548 1836.1,34584 1834.9,34575 1833.6,34567 1832.3,34558"]; + n141 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1807,34368", + shape=ellipse, + style=solid, + width=2.5643]; + n140 -> n141 [pos="e,1813.2,34416 1817.8,34452 1816.7,34443 1815.6,34435 1814.5,34426"]; + n142 [color=burlywood2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 97\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1801,34236", + shape=ellipse, + style=solid, + width=7.0968]; + n141 -> n142 [pos="e,1803.2,34284 1804.8,34320 1804.4,34312 1804,34303 1803.7,34295"]; + n143 [color=aliceblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1852,34104", + shape=ellipse, + style=solid, + width=3.5652]; + n142 -> n143 [pos="e,1833.7,34152 1819.6,34188 1822.9,34179 1826.5,34170 1830,34161"]; + n144 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="1883,33840", + shape=ellipse, + style=solid, + width=2.5643]; + n143 -> n144 [pos="e,1877.4,33888 1857.6,34056 1862.8,34012 1870.6,33945 1876.2,33898"]; + n144 -> n154 [pos="e,1975.2,33623 1902.8,33793 1921.9,33748 1950.9,33680 1971.3,33632"]; + n146 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2216,34632", + shape=ellipse, + style=solid, + width=3.5652]; + n145 -> n146 [pos="e,2206.6,34680 2199.5,34716 2201.1,34707 2202.9,34699 2204.6,34690"]; + n147 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", + pos="2212,34500", + shape=ellipse, + style=solid, + width=2.5643]; + n146 -> n147 [pos="e,2213.5,34548 2214.5,34584 2214.3,34576 2214,34567 2213.8,34559"]; + n148 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 101\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2187,34368", + shape=ellipse, + style=solid, + width=7.0968]; + n147 -> n148 [pos="e,2196.1,34416 2203,34452 2201.4,34444 2199.7,34435 2198,34426"]; + n149 [color=gold2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2204,34236", + shape=ellipse, + style=solid, + width=3.5652]; + n148 -> n149 [pos="e,2197.8,34284 2193.2,34320 2194.3,34311 2195.4,34303 2196.5,34294"]; + n150 [color=gold2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", + pos="2182,34104", + shape=ellipse, + style=solid, + width=2.5643]; + n149 -> n150 [pos="e,2190,34152 2196,34188 2194.6,34179 2193.1,34171 2191.6,34162"]; + n151 [color=burlywood1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 103\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2174,33972", + shape=ellipse, + style=solid, + width=7.0968]; + n150 -> n151 [pos="e,2176.9,34020 2179.1,34056 2178.6,34048 2178.1,34039 2177.5,34031"]; + n152 [color=darkorchid, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2139,33840", + shape=ellipse, + style=solid, + width=3.5652]; + n151 -> n152 [pos="e,2151.7,33888 2161.3,33924 2159,33915 2156.6,33906 2154.3,33898"]; + n153 [color=darkorchid, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", + pos="2072,33708", + shape=ellipse, + style=solid, + width=2.5643]; + n152 -> n153 [pos="e,2095.6,33755 2115,33792 2110.2,33783 2105.2,33773 2100.3,33764"]; + n153 -> n154 [pos="e,2021.9,33622 2045.3,33662 2039.4,33652 2033.2,33641 2027.2,33631"]; + n155 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 106\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2142,33444", + shape=ellipse, + style=solid, + width=6.3961]; + n154 -> n155 [pos="e,2089.9,33491 2041.5,33534 2054.5,33522 2068.8,33510 2082.4,33498"]; + n156 [color=darkgoldenrod4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 107\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2228,33312", + shape=ellipse, + style=solid, + width=3.5652]; + n155 -> n156 [pos="e,2197.6,33359 2173,33396 2179.2,33387 2185.6,33377 2191.9,33368"]; + n157 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 108\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2287,33180", + shape=ellipse, + style=solid, + width=2.6788]; + n156 -> n157 [pos="e,2266,33227 2249.1,33264 2253.3,33255 2257.6,33246 2261.8,33236"]; + n157 -> n158 [pos="e,2347.3,33094 2318.7,33134 2326,33124 2333.8,33113 2341.3,33102"]; + n159 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", + pos="2379,32916", + shape=ellipse, + style=solid, + width=2.6788]; + n158 -> n159 [pos="e,2379,32964 2379,33000 2379,32992 2379,32983 2379,32975"]; + n160 [color=chartreuse3, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 109\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2159,32388", + shape=ellipse, + style=solid, + width=3.7843]; + n159 -> n160 [pos="e,2138.6,32436 2312,32881 2249.7,32846 2160.9,32783 2122,32700 2083.5,32618 2111.2,32510 2134.9,32445"]; + n161 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias\nStorage id: 110\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32652", + shape=ellipse, + style=solid, + width=6.8916]; + n159 -> n161 [pos="e,2379,32700 2379,32868 2379,32824 2379,32757 2379,32710"]; + n164 [color=blue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 256, 1x1) without bias\nStorage id: 112\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2706,32784", + shape=ellipse, + style=solid, + width=6.3961]; + n159 -> n164 [pos="e,2600.7,32827 2453.6,32885 2494.3,32869 2545.7,32849 2591.4,32831"]; + n173 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2379,31596", + shape=ellipse, + style=solid, + width=2.817]; + n160 -> n173 [pos="e,2329.7,31638 2172.9,32340 2187.1,32288 2207,32201 2207,32125 2207,32125 2207,32125 2207,31859 2207,31773 2272.3,31694 2322.4,31645"]; + n162 [color=blue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32520", + shape=ellipse, + style=solid, + width=3.5652]; + n161 -> n162 [pos="e,2379,32568 2379,32604 2379,32596 2379,32587 2379,32579"]; + n163 [color=blue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2379,32256", + shape=ellipse, + style=solid, + width=2.6788]; + n162 -> n163 [pos="e,2379,32304 2379,32472 2379,32428 2379,32361 2379,32314"]; + n163 -> n173 [pos="e,2379,31644 2379,32208 2379,32155 2379,32068 2379,31993 2379,31993 2379,31993 2379,31859 2379,31789 2379,31708 2379,31655"]; + n165 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2774,32652", + shape=ellipse, + style=solid, + width=3.5652]; + n164 -> n165 [pos="e,2749.7,32699 2730.5,32736 2735.2,32727 2740.2,32718 2745,32708"]; + n166 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2748,32520", + shape=ellipse, + style=solid, + width=2.6788]; + n165 -> n166 [pos="e,2757.4,32568 2764.5,32604 2762.9,32595 2761.1,32587 2759.4,32578"]; + n167 [color=darkorange2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 114\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2698,32388", + shape=ellipse, + style=solid, + width=7.3732]; + n166 -> n167 [pos="e,2716.1,32436 2730.1,32472 2726.8,32464 2723.2,32455 2719.8,32446"]; + n168 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2692,32256", + shape=ellipse, + style=solid, + width=3.5652]; + n167 -> n168 [pos="e,2694.2,32304 2695.8,32340 2695.4,32332 2695,32323 2694.7,32315"]; + n169 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", + pos="2687,32124", + shape=ellipse, + style=solid, + width=2.6788]; + n168 -> n169 [pos="e,2688.8,32172 2690.2,32208 2689.9,32200 2689.5,32191 2689.2,32183"]; + n170 [color=azure1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 116\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2666,31992", + shape=ellipse, + style=solid, + width=6.8916]; + n169 -> n170 [pos="e,2673.7,32040 2679.4,32076 2678,32068 2676.7,32059 2675.3,32051"]; + n171 [color=dimgray, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2601,31860", + shape=ellipse, + style=solid, + width=3.5652]; + n170 -> n171 [pos="e,2624.2,31907 2642.5,31944 2638.1,31935 2633.3,31926 2628.7,31916"]; + n172 [color=dimgray, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2552,31728", + shape=ellipse, + style=solid, + width=2.6788]; + n171 -> n172 [pos="e,2569.5,31775 2583.3,31812 2580,31803 2576.5,31794 2573.1,31785"]; + n172 -> n173 [pos="e,2432.4,31637 2499.2,31687 2480.6,31673 2459.7,31658 2440.5,31643"]; + n174 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 119\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1890,31464", + shape=ellipse, + style=solid, + width=6.5343]; + n173 -> n174 [pos="e,2031.1,31503 2291.3,31572 2221.8,31553 2123,31527 2040.8,31505"]; + n177 [color=deeppink3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 121\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2379,31464", + shape=ellipse, + style=solid, + width=6.5343]; + n173 -> n177 [pos="e,2379,31512 2379,31548 2379,31540 2379,31531 2379,31523"]; + n190 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2555,29748", + shape=ellipse, + style=solid, + width=2.817]; + n173 -> n190 [pos="e,2580.2,29795 2478.5,31586 2529.6,31576 2588.2,31556 2623,31512 2673,31450 2642,31413 2642,31333 2642,31333 2642,31333 2642,30011 \ +2642,29936 2609.8,29856 2584.6,29804"]; + n175 [color=coral, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1900,31332", + shape=ellipse, + style=solid, + width=3.5652]; + n174 -> n175 [pos="e,1896.3,31380 1893.6,31416 1894.3,31408 1894.9,31399 1895.6,31391"]; + n176 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="1957,31068", + shape=ellipse, + style=solid, + width=2.6788]; + n175 -> n176 [pos="e,1946.7,31116 1910.3,31284 1919.9,31240 1934.3,31173 1944.6,31126"]; + n186 [color=cadetblue3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 127\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2348,30276", + shape=ellipse, + style=solid, + width=2.6788]; + n176 -> n186 [pos="e,2264.8,30301 1971.2,31020 1985.6,30968 2006,30881 2006,30805 2006,30805 2006,30805 2006,30539 2006,30413 2153.8,30339 2255.1,30304"]; + n178 [color=darkorange2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2371,31332", + shape=ellipse, + style=solid, + width=3.5652]; + n177 -> n178 [pos="e,2373.9,31380 2376.1,31416 2375.6,31408 2375.1,31399 2374.5,31391"]; + n179 [color=darkorange2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2356,31200", + shape=ellipse, + style=solid, + width=2.6788]; + n178 -> n179 [pos="e,2361.4,31248 2365.5,31284 2364.6,31275 2363.6,31267 2362.6,31258"]; + n180 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 123\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,31068", + shape=ellipse, + style=solid, + width=7.3732]; + n179 -> n180 [pos="e,2350.9,31116 2353.1,31152 2352.6,31144 2352.1,31135 2351.5,31127"]; + n181 [color=antiquewhite1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,30936", + shape=ellipse, + style=solid, + width=3.5652]; + n180 -> n181 [pos="e,2348,30984 2348,31020 2348,31012 2348,31003 2348,30995"]; + n182 [color=antiquewhite1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2348,30804", + shape=ellipse, + style=solid, + width=2.6788]; + n181 -> n182 [pos="e,2348,30852 2348,30888 2348,30880 2348,30871 2348,30863"]; + n183 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 125\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30672", + shape=ellipse, + style=solid, + width=7.3732]; + n182 -> n183 [pos="e,2348,30720 2348,30756 2348,30748 2348,30739 2348,30731"]; + n184 [color=floralwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30540", + shape=ellipse, + style=solid, + width=3.5652]; + n183 -> n184 [pos="e,2348,30588 2348,30624 2348,30616 2348,30607 2348,30599"]; + n185 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2348,30408", + shape=ellipse, + style=solid, + width=2.6788]; + n184 -> n185 [pos="e,2348,30456 2348,30492 2348,30484 2348,30475 2348,30467"]; + n185 -> n186 [pos="e,2348,30324 2348,30360 2348,30352 2348,30343 2348,30335"]; + n187 [color=deeppink1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 128\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2364,30144", + shape=ellipse, + style=solid, + width=6.5343]; + n186 -> n187 [pos="e,2358.2,30192 2353.8,30228 2354.8,30220 2355.9,30211 2356.9,30203"]; + n188 [color=coral4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 129\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2425,30012", + shape=ellipse, + style=solid, + width=3.5652]; + n187 -> n188 [pos="e,2403.2,30059 2386,30096 2390.2,30087 2394.7,30078 2399,30068"]; + n189 [color=darkorange1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 130\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2469,29880", + shape=ellipse, + style=solid, + width=2.817]; + n188 -> n189 [pos="e,2453.1,29928 2440.9,29964 2443.8,29955 2446.9,29946 2449.9,29937"]; + n189 -> n190 [pos="e,2525.2,29794 2498.8,29834 2505.5,29824 2512.6,29813 2519.5,29803"]; + n191 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2555,29616", + shape=ellipse, + style=solid, + width=2.817]; + n190 -> n191 [pos="e,2555,29664 2555,29700 2555,29692 2555,29683 2555,29675"]; + n192 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 131\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2066,29484", + shape=ellipse, + style=solid, + width=6.5343]; + n191 -> n192 [pos="e,2207.1,29523 2467.3,29592 2397.8,29573 2299,29547 2216.8,29525"]; + n195 [color=blue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 133\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2555,29484", + shape=ellipse, + style=solid, + width=6.5343]; + n191 -> n195 [pos="e,2555,29532 2555,29568 2555,29560 2555,29551 2555,29543"]; + n208 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2731,27768", + shape=ellipse, + style=solid, + width=2.817]; + n191 -> n208 [pos="e,2756.2,27815 2654.5,29606 2705.6,29596 2764.2,29576 2799,29532 2849,29470 2818,29433 2818,29353 2818,29353 2818,29353 2818,28031 \ +2818,27956 2785.8,27876 2760.6,27824"]; + n193 [color=deeppink, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2076,29352", + shape=ellipse, + style=solid, + width=3.5652]; + n192 -> n193 [pos="e,2072.3,29400 2069.6,29436 2070.3,29428 2070.9,29419 2071.6,29411"]; + n194 [color=deeppink, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2133,29088", + shape=ellipse, + style=solid, + width=2.6788]; + n193 -> n194 [pos="e,2122.7,29136 2086.3,29304 2095.9,29260 2110.3,29193 2120.6,29146"]; + n204 [color=dimgrey, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 139\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2524,28296", + shape=ellipse, + style=solid, + width=2.6788]; + n194 -> n204 [pos="e,2440.8,28321 2147.2,29040 2161.6,28988 2182,28901 2182,28825 2182,28825 2182,28825 2182,28559 2182,28433 2329.8,28359 2431.1,28324"]; + n196 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2547,29352", + shape=ellipse, + style=solid, + width=3.5652]; + n195 -> n196 [pos="e,2549.9,29400 2552.1,29436 2551.6,29428 2551.1,29419 2550.5,29411"]; + n197 [color=darkorchid2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2532,29220", + shape=ellipse, + style=solid, + width=2.6788]; + n196 -> n197 [pos="e,2537.4,29268 2541.5,29304 2540.6,29295 2539.6,29287 2538.6,29278"]; + n198 [color=gold1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 135\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,29088", + shape=ellipse, + style=solid, + width=7.3732]; + n197 -> n198 [pos="e,2526.9,29136 2529.1,29172 2528.6,29164 2528.1,29155 2527.5,29147"]; + n199 [color=darkorange2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,28956", + shape=ellipse, + style=solid, + width=3.5652]; + n198 -> n199 [pos="e,2524,29004 2524,29040 2524,29032 2524,29023 2524,29015"]; + n200 [color=darkorange2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2524,28824", + shape=ellipse, + style=solid, + width=2.6788]; + n199 -> n200 [pos="e,2524,28872 2524,28908 2524,28900 2524,28891 2524,28883"]; + n201 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 137\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28692", + shape=ellipse, + style=solid, + width=7.3732]; + n200 -> n201 [pos="e,2524,28740 2524,28776 2524,28768 2524,28759 2524,28751"]; + n202 [color=chartreuse, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28560", + shape=ellipse, + style=solid, + width=3.5652]; + n201 -> n202 [pos="e,2524,28608 2524,28644 2524,28636 2524,28627 2524,28619"]; + n203 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2524,28428", + shape=ellipse, + style=solid, + width=2.6788]; + n202 -> n203 [pos="e,2524,28476 2524,28512 2524,28504 2524,28495 2524,28487"]; + n203 -> n204 [pos="e,2524,28344 2524,28380 2524,28372 2524,28363 2524,28355"]; + n205 [color=coral3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 140\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2540,28164", + shape=ellipse, + style=solid, + width=6.5343]; + n204 -> n205 [pos="e,2534.2,28212 2529.8,28248 2530.8,28240 2531.9,28231 2532.9,28223"]; + n206 [color=coral2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 141\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2601,28032", + shape=ellipse, + style=solid, + width=3.5652]; + n205 -> n206 [pos="e,2579.2,28079 2562,28116 2566.2,28107 2570.7,28098 2575,28088"]; + n207 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 142\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2645,27900", + shape=ellipse, + style=solid, + width=2.817]; + n206 -> n207 [pos="e,2629.1,27948 2616.9,27984 2619.8,27975 2622.9,27966 2625.9,27957"]; + n207 -> n208 [pos="e,2701.2,27814 2674.8,27854 2681.5,27844 2688.6,27833 2695.5,27823"]; + n209 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2731,27636", + shape=ellipse, + style=solid, + width=2.817]; + n208 -> n209 [pos="e,2731,27684 2731,27720 2731,27712 2731,27703 2731,27695"]; + n210 [color=firebrick3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 143\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2242,27504", + shape=ellipse, + style=solid, + width=6.5343]; + n209 -> n210 [pos="e,2383.1,27543 2643.3,27612 2573.8,27593 2475,27567 2392.8,27545"]; + n213 [color=cornsilk4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 145\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2731,27504", + shape=ellipse, + style=solid, + width=6.5343]; + n209 -> n213 [pos="e,2731,27552 2731,27588 2731,27580 2731,27571 2731,27563"]; + n226 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2907,25788", + shape=ellipse, + style=solid, + width=2.817]; + n209 -> n226 [pos="e,2932.2,25835 2830.5,27626 2881.6,27616 2940.2,27596 2975,27552 3025,27490 2994,27453 2994,27373 2994,27373 2994,27373 2994,26051 \ +2994,25976 2961.8,25896 2936.6,25844"]; + n211 [color=forestgreen, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2280,27372", + shape=ellipse, + style=solid, + width=3.5652]; + n210 -> n211 [pos="e,2266.2,27420 2255.8,27456 2258.3,27447 2260.9,27438 2263.4,27430"]; + n212 [color=forestgreen, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2309,27108", + shape=ellipse, + style=solid, + width=2.6788]; + n211 -> n212 [pos="e,2303.8,27156 2285.2,27324 2290.1,27280 2297.4,27213 2302.7,27166"]; + n222 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 151\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2700,26316", + shape=ellipse, + style=solid, + width=2.6788]; + n212 -> n222 [pos="e,2616.8,26341 2323.2,27060 2337.6,27008 2358,26921 2358,26845 2358,26845 2358,26845 2358,26579 2358,26453 2505.8,26379 2607.1,26344"]; + n214 [color=aquamarine, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2716,27372", + shape=ellipse, + style=solid, + width=3.5652]; + n213 -> n214 [pos="e,2721.4,27420 2725.5,27456 2724.6,27447 2723.6,27439 2722.6,27430"]; + n215 [color=aquamarine, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2708,27240", + shape=ellipse, + style=solid, + width=2.6788]; + n214 -> n215 [pos="e,2710.9,27288 2713.1,27324 2712.6,27316 2712.1,27307 2711.5,27299"]; + n216 [color=goldenrod4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 147\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,27108", + shape=ellipse, + style=solid, + width=7.3732]; + n215 -> n216 [pos="e,2702.9,27156 2705.1,27192 2704.6,27184 2704.1,27175 2703.5,27167"]; + n217 [color=cyan4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,26976", + shape=ellipse, + style=solid, + width=3.5652]; + n216 -> n217 [pos="e,2700,27024 2700,27060 2700,27052 2700,27043 2700,27035"]; + n218 [color=cyan4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2700,26844", + shape=ellipse, + style=solid, + width=2.6788]; + n217 -> n218 [pos="e,2700,26892 2700,26928 2700,26920 2700,26911 2700,26903"]; + n219 [color=darkorange4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 149\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26712", + shape=ellipse, + style=solid, + width=7.3732]; + n218 -> n219 [pos="e,2700,26760 2700,26796 2700,26788 2700,26779 2700,26771"]; + n220 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26580", + shape=ellipse, + style=solid, + width=3.5652]; + n219 -> n220 [pos="e,2700,26628 2700,26664 2700,26656 2700,26647 2700,26639"]; + n221 [color=darkslategray3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2700,26448", + shape=ellipse, + style=solid, + width=2.6788]; + n220 -> n221 [pos="e,2700,26496 2700,26532 2700,26524 2700,26515 2700,26507"]; + n221 -> n222 [pos="e,2700,26364 2700,26400 2700,26392 2700,26383 2700,26375"]; + n223 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 152\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2716,26184", + shape=ellipse, + style=solid, + width=6.5343]; + n222 -> n223 [pos="e,2710.2,26232 2705.8,26268 2706.8,26260 2707.9,26251 2708.9,26243"]; + n224 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 153\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2777,26052", + shape=ellipse, + style=solid, + width=3.5652]; + n223 -> n224 [pos="e,2755.2,26099 2738,26136 2742.2,26127 2746.7,26118 2751,26108"]; + n225 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 154\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2821,25920", + shape=ellipse, + style=solid, + width=2.817]; + n224 -> n225 [pos="e,2805.1,25968 2792.9,26004 2795.8,25995 2798.9,25986 2801.9,25977"]; + n225 -> n226 [pos="e,2877.2,25834 2850.8,25874 2857.5,25864 2864.6,25853 2871.5,25843"]; + n227 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2907,25656", + shape=ellipse, + style=solid, + width=2.817]; + n226 -> n227 [pos="e,2907,25704 2907,25740 2907,25732 2907,25723 2907,25715"]; + n228 [color=chocolate3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 155\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2418,25524", + shape=ellipse, + style=solid, + width=6.5343]; + n227 -> n228 [pos="e,2559.1,25563 2819.3,25632 2749.8,25613 2651,25587 2568.8,25565"]; + n231 [color=deepskyblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 157\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2907,25524", + shape=ellipse, + style=solid, + width=6.5343]; + n227 -> n231 [pos="e,2907,25572 2907,25608 2907,25600 2907,25591 2907,25583"]; + n244 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3083,23808", + shape=ellipse, + style=solid, + width=2.817]; + n227 -> n244 [pos="e,3108.2,23855 3006.5,25646 3057.6,25636 3116.2,25616 3151,25572 3201,25510 3170,25473 3170,25393 3170,25393 3170,25393 3170,24071 \ +3170,23996 3137.8,23916 3112.6,23864"]; + n229 [color=brown, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2428,25392", + shape=ellipse, + style=solid, + width=3.5652]; + n228 -> n229 [pos="e,2424.3,25440 2421.6,25476 2422.3,25468 2422.9,25459 2423.6,25451"]; + n230 [color=brown, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2485,25128", + shape=ellipse, + style=solid, + width=2.6788]; + n229 -> n230 [pos="e,2474.7,25176 2438.3,25344 2447.9,25300 2462.3,25233 2472.6,25186"]; + n240 [color=brown3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 163\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="2876,24336", + shape=ellipse, + style=solid, + width=2.6788]; + n230 -> n240 [pos="e,2792.8,24361 2499.2,25080 2513.6,25028 2534,24941 2534,24865 2534,24865 2534,24865 2534,24599 2534,24473 2681.8,24399 2783.1,24364"]; + n232 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2899,25392", + shape=ellipse, + style=solid, + width=3.5652]; + n231 -> n232 [pos="e,2901.9,25440 2904.1,25476 2903.6,25468 2903.1,25459 2902.5,25451"]; + n233 [color=darkseagreen4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="2884,25260", + shape=ellipse, + style=solid, + width=2.6788]; + n232 -> n233 [pos="e,2889.4,25308 2893.5,25344 2892.6,25335 2891.6,25327 2890.6,25318"]; + n234 [color=dimgrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 159\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,25128", + shape=ellipse, + style=solid, + width=7.3732]; + n233 -> n234 [pos="e,2878.9,25176 2881.1,25212 2880.6,25204 2880.1,25195 2879.5,25187"]; + n235 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,24996", + shape=ellipse, + style=solid, + width=3.5652]; + n234 -> n235 [pos="e,2876,25044 2876,25080 2876,25072 2876,25063 2876,25055"]; + n236 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="2876,24864", + shape=ellipse, + style=solid, + width=2.6788]; + n235 -> n236 [pos="e,2876,24912 2876,24948 2876,24940 2876,24931 2876,24923"]; + n237 [color=cadetblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 161\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24732", + shape=ellipse, + style=solid, + width=7.3732]; + n236 -> n237 [pos="e,2876,24780 2876,24816 2876,24808 2876,24799 2876,24791"]; + n238 [color=burlywood, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24600", + shape=ellipse, + style=solid, + width=3.5652]; + n237 -> n238 [pos="e,2876,24648 2876,24684 2876,24676 2876,24667 2876,24659"]; + n239 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2876,24468", + shape=ellipse, + style=solid, + width=2.6788]; + n238 -> n239 [pos="e,2876,24516 2876,24552 2876,24544 2876,24535 2876,24527"]; + n239 -> n240 [pos="e,2876,24384 2876,24420 2876,24412 2876,24403 2876,24395"]; + n241 [color=brown3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 164\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2892,24204", + shape=ellipse, + style=solid, + width=6.5343]; + n240 -> n241 [pos="e,2886.2,24252 2881.8,24288 2882.8,24280 2883.9,24271 2884.9,24263"]; + n242 [color=blue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 165\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2953,24072", + shape=ellipse, + style=solid, + width=3.5652]; + n241 -> n242 [pos="e,2931.2,24119 2914,24156 2918.2,24147 2922.7,24138 2927,24128"]; + n243 [color=cyan3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 166\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="2997,23940", + shape=ellipse, + style=solid, + width=2.817]; + n242 -> n243 [pos="e,2981.1,23988 2968.9,24024 2971.8,24015 2974.9,24006 2977.9,23997"]; + n243 -> n244 [pos="e,3053.2,23854 3026.8,23894 3033.5,23884 3040.6,23873 3047.5,23863"]; + n245 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3083,23676", + shape=ellipse, + style=solid, + width=2.817]; + n244 -> n245 [pos="e,3083,23724 3083,23760 3083,23752 3083,23743 3083,23735"]; + n246 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 167\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2594,23544", + shape=ellipse, + style=solid, + width=6.5343]; + n245 -> n246 [pos="e,2735.1,23583 2995.3,23652 2925.8,23633 2827,23607 2744.8,23585"]; + n249 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 169\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3083,23544", + shape=ellipse, + style=solid, + width=6.5343]; + n245 -> n249 [pos="e,3083,23592 3083,23628 3083,23620 3083,23611 3083,23603"]; + n262 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3259,21828", + shape=ellipse, + style=solid, + width=2.817]; + n245 -> n262 [pos="e,3284.2,21875 3182.5,23666 3233.6,23656 3292.2,23636 3327,23592 3377,23530 3346,23493 3346,23413 3346,23413 3346,23413 3346,22091 \ +3346,22016 3313.8,21936 3288.6,21884"]; + n247 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2604,23412", + shape=ellipse, + style=solid, + width=3.5652]; + n246 -> n247 [pos="e,2600.3,23460 2597.6,23496 2598.3,23488 2598.9,23479 2599.6,23471"]; + n248 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2661,23148", + shape=ellipse, + style=solid, + width=2.6788]; + n247 -> n248 [pos="e,2650.7,23196 2614.3,23364 2623.9,23320 2638.3,23253 2648.6,23206"]; + n258 [color=deeppink, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 175\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3052,22356", + shape=ellipse, + style=solid, + width=2.6788]; + n248 -> n258 [pos="e,2968.8,22381 2675.2,23100 2689.6,23048 2710,22961 2710,22885 2710,22885 2710,22885 2710,22619 2710,22493 2857.8,22419 2959.1,22384"]; + n250 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3075,23412", + shape=ellipse, + style=solid, + width=3.5652]; + n249 -> n250 [pos="e,3077.9,23460 3080.1,23496 3079.6,23488 3079.1,23479 3078.5,23471"]; + n251 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3060,23280", + shape=ellipse, + style=solid, + width=2.6788]; + n250 -> n251 [pos="e,3065.4,23328 3069.5,23364 3068.6,23355 3067.6,23347 3066.6,23338"]; + n252 [color=burlywood2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 171\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,23148", + shape=ellipse, + style=solid, + width=7.3732]; + n251 -> n252 [pos="e,3054.9,23196 3057.1,23232 3056.6,23224 3056.1,23215 3055.5,23207"]; + n253 [color=antiquewhite2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,23016", + shape=ellipse, + style=solid, + width=3.5652]; + n252 -> n253 [pos="e,3052,23064 3052,23100 3052,23092 3052,23083 3052,23075"]; + n254 [color=antiquewhite2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3052,22884", + shape=ellipse, + style=solid, + width=2.6788]; + n253 -> n254 [pos="e,3052,22932 3052,22968 3052,22960 3052,22951 3052,22943"]; + n255 [color=bisque2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 173\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22752", + shape=ellipse, + style=solid, + width=7.3732]; + n254 -> n255 [pos="e,3052,22800 3052,22836 3052,22828 3052,22819 3052,22811"]; + n256 [color=crimson, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22620", + shape=ellipse, + style=solid, + width=3.5652]; + n255 -> n256 [pos="e,3052,22668 3052,22704 3052,22696 3052,22687 3052,22679"]; + n257 [color=crimson, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3052,22488", + shape=ellipse, + style=solid, + width=2.6788]; + n256 -> n257 [pos="e,3052,22536 3052,22572 3052,22564 3052,22555 3052,22547"]; + n257 -> n258 [pos="e,3052,22404 3052,22440 3052,22432 3052,22423 3052,22415"]; + n259 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 176\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3068,22224", + shape=ellipse, + style=solid, + width=6.5343]; + n258 -> n259 [pos="e,3062.2,22272 3057.8,22308 3058.8,22300 3059.9,22291 3060.9,22283"]; + n260 [color=firebrick3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 177\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3129,22092", + shape=ellipse, + style=solid, + width=3.5652]; + n259 -> n260 [pos="e,3107.2,22139 3090,22176 3094.2,22167 3098.7,22158 3103,22148"]; + n261 [color=darkseagreen1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 178\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3173,21960", + shape=ellipse, + style=solid, + width=2.817]; + n260 -> n261 [pos="e,3157.1,22008 3144.9,22044 3147.8,22035 3150.9,22026 3153.9,22017"]; + n261 -> n262 [pos="e,3229.2,21874 3202.8,21914 3209.5,21904 3216.6,21893 3223.5,21883"]; + n263 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3259,21696", + shape=ellipse, + style=solid, + width=2.817]; + n262 -> n263 [pos="e,3259,21744 3259,21780 3259,21772 3259,21763 3259,21755"]; + n264 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 179\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2770,21564", + shape=ellipse, + style=solid, + width=6.5343]; + n263 -> n264 [pos="e,2911.1,21603 3171.3,21672 3101.8,21653 3003,21627 2920.8,21605"]; + n267 [color=dimgrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 181\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3259,21564", + shape=ellipse, + style=solid, + width=6.5343]; + n263 -> n267 [pos="e,3259,21612 3259,21648 3259,21640 3259,21631 3259,21623"]; + n280 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3435,19848", + shape=ellipse, + style=solid, + width=2.817]; + n263 -> n280 [pos="e,3460.2,19895 3358.5,21686 3409.6,21676 3468.2,21656 3503,21612 3553,21550 3522,21513 3522,21433 3522,21433 3522,21433 3522,20111 \ +3522,20036 3489.8,19956 3464.6,19904"]; + n265 [color=gold2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2780,21432", + shape=ellipse, + style=solid, + width=3.5652]; + n264 -> n265 [pos="e,2776.3,21480 2773.6,21516 2774.3,21508 2774.9,21499 2775.6,21491"]; + n266 [color=gold2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2837,21168", + shape=ellipse, + style=solid, + width=2.6788]; + n265 -> n266 [pos="e,2826.7,21216 2790.3,21384 2799.9,21340 2814.3,21273 2824.6,21226"]; + n276 [color=darkseagreen3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 187\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3228,20376", + shape=ellipse, + style=solid, + width=2.6788]; + n266 -> n276 [pos="e,3144.8,20401 2851.2,21120 2865.6,21068 2886,20981 2886,20905 2886,20905 2886,20905 2886,20639 2886,20513 3033.8,20439 3135.1,20404"]; + n268 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3244,21432", + shape=ellipse, + style=solid, + width=3.5652]; + n267 -> n268 [pos="e,3249.4,21480 3253.5,21516 3252.6,21507 3251.6,21499 3250.6,21490"]; + n269 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3236,21300", + shape=ellipse, + style=solid, + width=2.6788]; + n268 -> n269 [pos="e,3238.9,21348 3241.1,21384 3240.6,21376 3240.1,21367 3239.5,21359"]; + n270 [color=deeppink, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 183\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,21168", + shape=ellipse, + style=solid, + width=7.3732]; + n269 -> n270 [pos="e,3230.9,21216 3233.1,21252 3232.6,21244 3232.1,21235 3231.5,21227"]; + n271 [color=gold1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,21036", + shape=ellipse, + style=solid, + width=3.5652]; + n270 -> n271 [pos="e,3228,21084 3228,21120 3228,21112 3228,21103 3228,21095"]; + n272 [color=gold1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3228,20904", + shape=ellipse, + style=solid, + width=2.6788]; + n271 -> n272 [pos="e,3228,20952 3228,20988 3228,20980 3228,20971 3228,20963"]; + n273 [color=antiquewhite2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 185\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20772", + shape=ellipse, + style=solid, + width=7.3732]; + n272 -> n273 [pos="e,3228,20820 3228,20856 3228,20848 3228,20839 3228,20831"]; + n274 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20640", + shape=ellipse, + style=solid, + width=3.5652]; + n273 -> n274 [pos="e,3228,20688 3228,20724 3228,20716 3228,20707 3228,20699"]; + n275 [color=deepskyblue1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3228,20508", + shape=ellipse, + style=solid, + width=2.6788]; + n274 -> n275 [pos="e,3228,20556 3228,20592 3228,20584 3228,20575 3228,20567"]; + n275 -> n276 [pos="e,3228,20424 3228,20460 3228,20452 3228,20443 3228,20435"]; + n277 [color=cornsilk2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 188\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3244,20244", + shape=ellipse, + style=solid, + width=6.5343]; + n276 -> n277 [pos="e,3238.2,20292 3233.8,20328 3234.8,20320 3235.9,20311 3236.9,20303"]; + n278 [color=bisque2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 189\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3305,20112", + shape=ellipse, + style=solid, + width=3.5652]; + n277 -> n278 [pos="e,3283.2,20159 3266,20196 3270.2,20187 3274.7,20178 3279,20168"]; + n279 [color=darkorchid4, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 190\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3348,19980", + shape=ellipse, + style=solid, + width=2.817]; + n278 -> n279 [pos="e,3332.5,20028 3320.5,20064 3323.4,20055 3326.4,20046 3329.3,20037"]; + n279 -> n280 [pos="e,3404.9,19894 3378.2,19934 3384.9,19924 3392.2,19913 3399.1,19903"]; + n281 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3435,19716", + shape=ellipse, + style=solid, + width=2.817]; + n280 -> n281 [pos="e,3435,19764 3435,19800 3435,19792 3435,19783 3435,19775"]; + n282 [color=cadetblue3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 191\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2946,19584", + shape=ellipse, + style=solid, + width=6.5343]; + n281 -> n282 [pos="e,3087.1,19623 3347.3,19692 3277.8,19673 3179,19647 3096.8,19625"]; + n285 [color=darkorchid4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 193\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3435,19584", + shape=ellipse, + style=solid, + width=6.5343]; + n281 -> n285 [pos="e,3435,19632 3435,19668 3435,19660 3435,19651 3435,19643"]; + n298 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3611,17868", + shape=ellipse, + style=solid, + width=2.817]; + n281 -> n298 [pos="e,3636.2,17915 3534.5,19706 3585.6,19696 3644.2,19676 3679,19632 3729,19570 3698,19533 3698,19453 3698,19453 3698,19453 3698,18131 \ +3698,18056 3665.8,17976 3640.6,17924"]; + n283 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="2956,19452", + shape=ellipse, + style=solid, + width=3.5652]; + n282 -> n283 [pos="e,2952.3,19500 2949.6,19536 2950.3,19528 2950.9,19519 2951.6,19511"]; + n284 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3013,19188", + shape=ellipse, + style=solid, + width=2.6788]; + n283 -> n284 [pos="e,3002.7,19236 2966.3,19404 2975.9,19360 2990.3,19293 3000.6,19246"]; + n294 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 199\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3404,18396", + shape=ellipse, + style=solid, + width=2.6788]; + n284 -> n294 [pos="e,3320.8,18421 3027.2,19140 3041.6,19088 3062,19001 3062,18925 3062,18925 3062,18925 3062,18659 3062,18533 3209.8,18459 3311.1,18424"]; + n286 [color=gold1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3427,19452", + shape=ellipse, + style=solid, + width=3.5652]; + n285 -> n286 [pos="e,3429.9,19500 3432.1,19536 3431.6,19528 3431.1,19519 3430.5,19511"]; + n287 [color=gold1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3412,19320", + shape=ellipse, + style=solid, + width=2.6788]; + n286 -> n287 [pos="e,3417.4,19368 3421.5,19404 3420.6,19395 3419.6,19387 3418.6,19378"]; + n288 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 195\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,19188", + shape=ellipse, + style=solid, + width=7.3732]; + n287 -> n288 [pos="e,3406.9,19236 3409.1,19272 3408.6,19264 3408.1,19255 3407.5,19247"]; + n289 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,19056", + shape=ellipse, + style=solid, + width=3.5652]; + n288 -> n289 [pos="e,3404,19104 3404,19140 3404,19132 3404,19123 3404,19115"]; + n290 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3404,18924", + shape=ellipse, + style=solid, + width=2.6788]; + n289 -> n290 [pos="e,3404,18972 3404,19008 3404,19000 3404,18991 3404,18983"]; + n291 [color=coral1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 197\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18792", + shape=ellipse, + style=solid, + width=7.3732]; + n290 -> n291 [pos="e,3404,18840 3404,18876 3404,18868 3404,18859 3404,18851"]; + n292 [color=burlywood2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18660", + shape=ellipse, + style=solid, + width=3.5652]; + n291 -> n292 [pos="e,3404,18708 3404,18744 3404,18736 3404,18727 3404,18719"]; + n293 [color=burlywood2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3404,18528", + shape=ellipse, + style=solid, + width=2.6788]; + n292 -> n293 [pos="e,3404,18576 3404,18612 3404,18604 3404,18595 3404,18587"]; + n293 -> n294 [pos="e,3404,18444 3404,18480 3404,18472 3404,18463 3404,18455"]; + n295 [color=darkorchid1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 200\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3420,18264", + shape=ellipse, + style=solid, + width=6.5343]; + n294 -> n295 [pos="e,3414.2,18312 3409.8,18348 3410.8,18340 3411.9,18331 3412.9,18323"]; + n296 [color=cyan2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 201\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3481,18132", + shape=ellipse, + style=solid, + width=3.5652]; + n295 -> n296 [pos="e,3459.2,18179 3442,18216 3446.2,18207 3450.7,18198 3455,18188"]; + n297 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 202\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3524,18000", + shape=ellipse, + style=solid, + width=2.817]; + n296 -> n297 [pos="e,3508.5,18048 3496.5,18084 3499.4,18075 3502.4,18066 3505.3,18057"]; + n297 -> n298 [pos="e,3580.9,17914 3554.2,17954 3560.9,17944 3568.2,17933 3575.1,17923"]; + n299 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3611,17736", + shape=ellipse, + style=solid, + width=2.817]; + n298 -> n299 [pos="e,3611,17784 3611,17820 3611,17812 3611,17803 3611,17795"]; + n300 [color=coral, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 203\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3122,17604", + shape=ellipse, + style=solid, + width=6.5343]; + n299 -> n300 [pos="e,3263.1,17643 3523.3,17712 3453.8,17693 3355,17667 3272.8,17645"]; + n303 [color=brown2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 205\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3611,17604", + shape=ellipse, + style=solid, + width=6.5343]; + n299 -> n303 [pos="e,3611,17652 3611,17688 3611,17680 3611,17671 3611,17663"]; + n316 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3787,15888", + shape=ellipse, + style=solid, + width=2.817]; + n299 -> n316 [pos="e,3812.2,15935 3710.5,17726 3761.6,17716 3820.2,17696 3855,17652 3905,17590 3874,17553 3874,17473 3874,17473 3874,17473 3874,16151 \ +3874,16076 3841.8,15996 3816.6,15944"]; + n301 [color=deeppink4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3132,17472", + shape=ellipse, + style=solid, + width=3.5652]; + n300 -> n301 [pos="e,3128.3,17520 3125.6,17556 3126.3,17548 3126.9,17539 3127.6,17531"]; + n302 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3189,17208", + shape=ellipse, + style=solid, + width=2.6788]; + n301 -> n302 [pos="e,3178.7,17256 3142.3,17424 3151.9,17380 3166.3,17313 3176.6,17266"]; + n312 [color=blue, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 211\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3580,16416", + shape=ellipse, + style=solid, + width=2.6788]; + n302 -> n312 [pos="e,3496.5,16440 3202.9,17160 3217.1,17108 3237,17021 3237,16945 3237,16945 3237,16945 3237,16679 3237,16553 3385.3,16479 3486.9,16444"]; + n304 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3603,17472", + shape=ellipse, + style=solid, + width=3.5652]; + n303 -> n304 [pos="e,3605.9,17520 3608.1,17556 3607.6,17548 3607.1,17539 3606.5,17531"]; + n305 [color=deepskyblue2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3588,17340", + shape=ellipse, + style=solid, + width=2.6788]; + n304 -> n305 [pos="e,3593.4,17388 3597.5,17424 3596.6,17415 3595.6,17407 3594.6,17398"]; + n306 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 207\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,17208", + shape=ellipse, + style=solid, + width=7.3732]; + n305 -> n306 [pos="e,3582.9,17256 3585.1,17292 3584.6,17284 3584.1,17275 3583.5,17267"]; + n307 [color=burlywood, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,17076", + shape=ellipse, + style=solid, + width=3.5652]; + n306 -> n307 [pos="e,3580,17124 3580,17160 3580,17152 3580,17143 3580,17135"]; + n308 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3580,16944", + shape=ellipse, + style=solid, + width=2.6788]; + n307 -> n308 [pos="e,3580,16992 3580,17028 3580,17020 3580,17011 3580,17003"]; + n309 [color=aquamarine3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 209\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16812", + shape=ellipse, + style=solid, + width=7.3732]; + n308 -> n309 [pos="e,3580,16860 3580,16896 3580,16888 3580,16879 3580,16871"]; + n310 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16680", + shape=ellipse, + style=solid, + width=3.5652]; + n309 -> n310 [pos="e,3580,16728 3580,16764 3580,16756 3580,16747 3580,16739"]; + n311 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3580,16548", + shape=ellipse, + style=solid, + width=2.6788]; + n310 -> n311 [pos="e,3580,16596 3580,16632 3580,16624 3580,16615 3580,16607"]; + n311 -> n312 [pos="e,3580,16464 3580,16500 3580,16492 3580,16483 3580,16475"]; + n313 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 212\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3596,16284", + shape=ellipse, + style=solid, + width=6.5343]; + n312 -> n313 [pos="e,3590.2,16332 3585.8,16368 3586.8,16360 3587.9,16351 3588.9,16343"]; + n314 [color=cyan3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 213\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3657,16152", + shape=ellipse, + style=solid, + width=3.5652]; + n313 -> n314 [pos="e,3635.2,16199 3618,16236 3622.2,16227 3626.7,16218 3631,16208"]; + n315 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 214\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3701,16020", + shape=ellipse, + style=solid, + width=2.817]; + n314 -> n315 [pos="e,3685.1,16068 3672.9,16104 3675.8,16095 3678.9,16086 3681.9,16077"]; + n315 -> n316 [pos="e,3757.2,15934 3730.8,15974 3737.5,15964 3744.6,15953 3751.5,15943"]; + n317 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3787,15756", + shape=ellipse, + style=solid, + width=2.817]; + n316 -> n317 [pos="e,3787,15804 3787,15840 3787,15832 3787,15823 3787,15815"]; + n318 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 215\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3298,15624", + shape=ellipse, + style=solid, + width=6.5343]; + n317 -> n318 [pos="e,3439.1,15663 3699.3,15732 3629.8,15713 3531,15687 3448.8,15665"]; + n321 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 217\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3787,15624", + shape=ellipse, + style=solid, + width=6.5343]; + n317 -> n321 [pos="e,3787,15672 3787,15708 3787,15700 3787,15691 3787,15683"]; + n334 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3963,13908", + shape=ellipse, + style=solid, + width=2.817]; + n317 -> n334 [pos="e,3988.2,13955 3886.5,15746 3937.6,15736 3996.2,15716 4031,15672 4081,15610 4050,15573 4050,15493 4050,15493 4050,15493 4050,14171 \ +4050,14096 4017.8,14016 3992.6,13964"]; + n319 [color=darkolivegreen3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3308,15492", + shape=ellipse, + style=solid, + width=3.5652]; + n318 -> n319 [pos="e,3304.3,15540 3301.6,15576 3302.3,15568 3302.9,15559 3303.6,15551"]; + n320 [color=darkolivegreen3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3365,15228", + shape=ellipse, + style=solid, + width=2.6788]; + n319 -> n320 [pos="e,3354.7,15276 3318.3,15444 3327.9,15400 3342.3,15333 3352.6,15286"]; + n330 [color=crimson, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 223\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3756,14436", + shape=ellipse, + style=solid, + width=2.6788]; + n320 -> n330 [pos="e,3672.8,14461 3379.2,15180 3393.6,15128 3414,15041 3414,14965 3414,14965 3414,14965 3414,14699 3414,14573 3561.8,14499 3663.1,14464"]; + n322 [color=deeppink3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3779,15492", + shape=ellipse, + style=solid, + width=3.5652]; + n321 -> n322 [pos="e,3781.9,15540 3784.1,15576 3783.6,15568 3783.1,15559 3782.5,15551"]; + n323 [color=deeppink3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3764,15360", + shape=ellipse, + style=solid, + width=2.6788]; + n322 -> n323 [pos="e,3769.4,15408 3773.5,15444 3772.6,15435 3771.6,15427 3770.6,15418"]; + n324 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 219\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,15228", + shape=ellipse, + style=solid, + width=7.3732]; + n323 -> n324 [pos="e,3758.9,15276 3761.1,15312 3760.6,15304 3760.1,15295 3759.5,15287"]; + n325 [color=brown4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,15096", + shape=ellipse, + style=solid, + width=3.5652]; + n324 -> n325 [pos="e,3756,15144 3756,15180 3756,15172 3756,15163 3756,15155"]; + n326 [color=brown4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3756,14964", + shape=ellipse, + style=solid, + width=2.6788]; + n325 -> n326 [pos="e,3756,15012 3756,15048 3756,15040 3756,15031 3756,15023"]; + n327 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 221\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14832", + shape=ellipse, + style=solid, + width=7.3732]; + n326 -> n327 [pos="e,3756,14880 3756,14916 3756,14908 3756,14899 3756,14891"]; + n328 [color=darkorange1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14700", + shape=ellipse, + style=solid, + width=3.5652]; + n327 -> n328 [pos="e,3756,14748 3756,14784 3756,14776 3756,14767 3756,14759"]; + n329 [color=darkorange1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3756,14568", + shape=ellipse, + style=solid, + width=2.6788]; + n328 -> n329 [pos="e,3756,14616 3756,14652 3756,14644 3756,14635 3756,14627"]; + n329 -> n330 [pos="e,3756,14484 3756,14520 3756,14512 3756,14503 3756,14495"]; + n331 [color=aliceblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 224\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3772,14304", + shape=ellipse, + style=solid, + width=6.5343]; + n330 -> n331 [pos="e,3766.2,14352 3761.8,14388 3762.8,14380 3763.9,14371 3764.9,14363"]; + n332 [color=goldenrod, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 225\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3833,14172", + shape=ellipse, + style=solid, + width=3.5652]; + n331 -> n332 [pos="e,3811.2,14219 3794,14256 3798.2,14247 3802.7,14238 3807,14228"]; + n333 [color=gold3, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 226\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3877,14040", + shape=ellipse, + style=solid, + width=2.817]; + n332 -> n333 [pos="e,3861.1,14088 3848.9,14124 3851.8,14115 3854.9,14106 3857.9,14097"]; + n333 -> n334 [pos="e,3933.2,13954 3906.8,13994 3913.5,13984 3920.6,13973 3927.5,13963"]; + n335 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3963,13776", + shape=ellipse, + style=solid, + width=2.817]; + n334 -> n335 [pos="e,3963,13824 3963,13860 3963,13852 3963,13843 3963,13835"]; + n336 [color=blue4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 227\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3474,13644", + shape=ellipse, + style=solid, + width=6.5343]; + n335 -> n336 [pos="e,3615.1,13683 3875.3,13752 3805.8,13733 3707,13707 3624.8,13685"]; + n339 [color=darkgoldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 229\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3963,13644", + shape=ellipse, + style=solid, + width=6.5343]; + n335 -> n339 [pos="e,3963,13692 3963,13728 3963,13720 3963,13711 3963,13703"]; + n352 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4139,11928", + shape=ellipse, + style=solid, + width=2.817]; + n335 -> n352 [pos="e,4164.2,11975 4062.5,13766 4113.6,13756 4172.2,13736 4207,13692 4257,13630 4226,13593 4226,13513 4226,13513 4226,13513 4226,12191 \ +4226,12116 4193.8,12036 4168.6,11984"]; + n337 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3484,13512", + shape=ellipse, + style=solid, + width=3.5652]; + n336 -> n337 [pos="e,3480.3,13560 3477.6,13596 3478.3,13588 3478.9,13579 3479.6,13571"]; + n338 [color=dodgerblue3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3541,13248", + shape=ellipse, + style=solid, + width=2.6788]; + n337 -> n338 [pos="e,3530.7,13296 3494.3,13464 3503.9,13420 3518.3,13353 3528.6,13306"]; + n348 [color=dodgerblue1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 235\nSize: {1, 384, 17, 17}\nMem size: 110976", + pos="3932,12456", + shape=ellipse, + style=solid, + width=2.6788]; + n338 -> n348 [pos="e,3848.8,12481 3555.2,13200 3569.6,13148 3590,13061 3590,12985 3590,12985 3590,12985 3590,12719 3590,12593 3737.8,12519 3839.1,12484"]; + n340 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3955,13512", + shape=ellipse, + style=solid, + width=3.5652]; + n339 -> n340 [pos="e,3957.9,13560 3960.1,13596 3959.6,13588 3959.1,13579 3958.5,13571"]; + n341 [color=goldenrod3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", + pos="3940,13380", + shape=ellipse, + style=solid, + width=2.6788]; + n340 -> n341 [pos="e,3945.4,13428 3949.5,13464 3948.6,13455 3947.6,13447 3946.6,13438"]; + n342 [color=burlywood1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 231\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,13248", + shape=ellipse, + style=solid, + width=7.3732]; + n341 -> n342 [pos="e,3934.9,13296 3937.1,13332 3936.6,13324 3936.1,13315 3935.5,13307"]; + n343 [color=deeppink, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,13116", + shape=ellipse, + style=solid, + width=3.5652]; + n342 -> n343 [pos="e,3932,13164 3932,13200 3932,13192 3932,13183 3932,13175"]; + n344 [color=deeppink, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", + pos="3932,12984", + shape=ellipse, + style=solid, + width=2.6788]; + n343 -> n344 [pos="e,3932,13032 3932,13068 3932,13060 3932,13051 3932,13043"]; + n345 [color=deeppink, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 233\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12852", + shape=ellipse, + style=solid, + width=7.3732]; + n344 -> n345 [pos="e,3932,12900 3932,12936 3932,12928 3932,12919 3932,12911"]; + n346 [color=burlywood2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12720", + shape=ellipse, + style=solid, + width=3.5652]; + n345 -> n346 [pos="e,3932,12768 3932,12804 3932,12796 3932,12787 3932,12779"]; + n347 [color=burlywood2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", + pos="3932,12588", + shape=ellipse, + style=solid, + width=2.6788]; + n346 -> n347 [pos="e,3932,12636 3932,12672 3932,12664 3932,12655 3932,12647"]; + n347 -> n348 [pos="e,3932,12504 3932,12540 3932,12532 3932,12523 3932,12515"]; + n349 [color=blue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 236\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="3948,12324", + shape=ellipse, + style=solid, + width=6.5343]; + n348 -> n349 [pos="e,3942.2,12372 3937.8,12408 3938.8,12400 3939.9,12391 3940.9,12383"]; + n350 [color=coral2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 237\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4008,12192", + shape=ellipse, + style=solid, + width=3.5652]; + n349 -> n350 [pos="e,3986.6,12239 3969.7,12276 3973.7,12267 3978.1,12258 3982.3,12249"]; + n351 [color=brown1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 238\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4052,12060", + shape=ellipse, + style=solid, + width=2.817]; + n350 -> n351 [pos="e,4036.1,12108 4023.9,12144 4026.8,12135 4029.9,12126 4032.9,12117"]; + n351 -> n352 [pos="e,4108.9,11974 4082.2,12014 4088.9,12004 4096.2,11993 4103.1,11983"]; + n353 [color=darkorchid3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", + pos="4139,11796", + shape=ellipse, + style=solid, + width=2.817]; + n352 -> n353 [pos="e,4139,11844 4139,11880 4139,11872 4139,11863 4139,11855"]; + n354 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 239\nSize: {1, 1152, 8, 8}\nMem size: 73728", + pos="3817,10872", + shape=ellipse, + style=solid, + width=3.7843]; + n353 -> n354 [pos="e,3784.9,10919 4043.1,11780 3916.1,11755 3708,11689 3708,11533 3708,11533 3708,11533 3708,11135 3708,11059 3748,10979 3779.5,10927"]; + n355 [color=darkolivegreen3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 240\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4005,11532", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n355 [pos="e,4001.3,11580 4073.3,11759 4055.9,11747 4038.9,11731 4028,11712 4006.6,11675 4001.4,11627 4001.3,11590"]; + n361 [color=darkseagreen1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 244\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4272,11664", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n361 [pos="e,4224.7,11711 4182.5,11752 4193.7,11742 4205.9,11730 4217.5,11718"]; + n367 [color=darkorange, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 248\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4762,11664", + shape=ellipse, + style=solid, + width=6.5343]; + n353 -> n367 [pos="e,4599.7,11699 4231.3,11776 4325.4,11756 4473.7,11725 4589.6,11701"]; + n376 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4246,10476", + shape=ellipse, + style=solid, + width=2.5643]; + n354 -> n376 [pos="e,4201.3,10518 3865,10827 3946.1,10752 4110,10602 4193.9,10525"]; + n356 [color=bisque4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4005,11400", + shape=ellipse, + style=solid, + width=3.5652]; + n355 -> n356 [pos="e,4005,11448 4005,11484 4005,11476 4005,11467 4005,11459"]; + n357 [color=bisque4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="3988,11268", + shape=ellipse, + style=solid, + width=2.6788]; + n356 -> n357 [pos="e,3994.2,11316 3998.8,11352 3997.7,11343 3996.6,11335 3995.5,11326"]; + n358 [color=aquamarine3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 242\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="3984,11136", + shape=ellipse, + style=solid, + width=6.8916]; + n357 -> n358 [pos="e,3985.5,11184 3986.5,11220 3986.3,11212 3986,11203 3985.8,11195"]; + n359 [color=chartreuse3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="4044,11004", + shape=ellipse, + style=solid, + width=3.5652]; + n358 -> n359 [pos="e,4022.6,11051 4005.7,11088 4009.7,11079 4014.1,11070 4018.3,11061"]; + n360 [color=chartreuse3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", + pos="4173,10740", + shape=ellipse, + style=solid, + width=2.4261]; + n359 -> n360 [pos="e,4150.5,10787 4066.9,10956 4089,10912 4122.6,10843 4146,10796"]; + n360 -> n376 [pos="e,4229.7,10523 4183.7,10692 4192.3,10656 4204.9,10604 4218,10560 4220.6,10551 4223.5,10542 4226.4,10533"]; + n362 [color=darkorange, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4484,11400", + shape=ellipse, + style=solid, + width=3.5652]; + n361 -> n362 [pos="e,4447.4,11446 4309.7,11616 4346.4,11571 4402.4,11502 4440.9,11454"]; + n363 [color=darkorange, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4492,11268", + shape=ellipse, + style=solid, + width=2.6788]; + n362 -> n363 [pos="e,4489.1,11316 4486.9,11352 4487.4,11344 4487.9,11335 4488.5,11327"]; + n364 [color=chocolate4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 246\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4498,11136", + shape=ellipse, + style=solid, + width=6.8916]; + n363 -> n364 [pos="e,4495.8,11184 4494.2,11220 4494.6,11212 4495,11203 4495.3,11195"]; + n365 [color=deeppink3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4438,11004", + shape=ellipse, + style=solid, + width=3.5652]; + n364 -> n365 [pos="e,4459.4,11051 4476.3,11088 4472.3,11079 4467.9,11070 4463.7,11061"]; + n366 [color=deeppink3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4315,10608", + shape=ellipse, + style=solid, + width=2.4261]; + n365 -> n366 [pos="e,4329.5,10656 4423.3,10956 4400.5,10883 4356.9,10743 4332.6,10665"]; + n366 -> n376 [pos="e,4270.2,10523 4290.9,10562 4285.7,10552 4280.3,10542 4275,10532"]; + n368 [color=blue3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4762,11532", + shape=ellipse, + style=solid, + width=3.5652]; + n367 -> n368 [pos="e,4762,11580 4762,11616 4762,11608 4762,11599 4762,11591"]; + n369 [color=blue3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4796,11400", + shape=ellipse, + style=solid, + width=2.6788]; + n368 -> n369 [pos="e,4783.7,11448 4774.3,11484 4776.5,11476 4778.9,11466 4781.2,11458"]; + n370 [color=firebrick4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 250\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4889,11268", + shape=ellipse, + style=solid, + width=7.3732]; + n369 -> n370 [pos="e,4855.4,11316 4828,11354 4834.9,11345 4842.3,11334 4849.4,11324"]; + n371 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4893,11136", + shape=ellipse, + style=solid, + width=3.5652]; + n370 -> n371 [pos="e,4891.5,11184 4890.5,11220 4890.7,11212 4891,11203 4891.2,11195"]; + n372 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", + pos="4817,11004", + shape=ellipse, + style=solid, + width=2.6788]; + n371 -> n372 [pos="e,4843.7,11051 4866,11089 4860.4,11079 4854.5,11069 4848.8,11059"]; + n373 [color=ghostwhite, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 252\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4811,10872", + shape=ellipse, + style=solid, + width=6.8916]; + n372 -> n373 [pos="e,4813.2,10920 4814.8,10956 4814.4,10948 4814,10939 4813.7,10931"]; + n374 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4560,10740", + shape=ellipse, + style=solid, + width=3.5652]; + n373 -> n374 [pos="e,4634.2,10779 4725.7,10827 4699.1,10813 4669.8,10798 4643.4,10784"]; + n375 [color=darkslategray4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4518,10608", + shape=ellipse, + style=solid, + width=2.4261]; + n374 -> n375 [pos="e,4533,10655 4544.8,10692 4542,10683 4539,10674 4536.1,10665"]; + n375 -> n376 [pos="e,4313.5,10509 4452.8,10576 4413.7,10557 4363.7,10533 4322.6,10514"]; + n377 [color=darkslategrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 255\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3757,10344", + shape=ellipse, + style=solid, + width=6.5343]; + n376 -> n377 [pos="e,3898.2,10383 4164.4,10453 4094.7,10435 3992.5,10408 3907.9,10385"]; + n380 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 257\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4246,10344", + shape=ellipse, + style=solid, + width=6.5343]; + n376 -> n380 [pos="e,4246,10392 4246,10428 4246,10420 4246,10411 4246,10403"]; + n393 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4424,8628", + shape=ellipse, + style=solid, + width=2.5643]; + n376 -> n393 [pos="e,4448.4,8674.4 4337.2,10468 4390.1,10459 4453.3,10438 4490,10392 4540,10330 4509,10293 4509,10213 4509,10213 4509,10213 4509,8891 \ +4509,8816.3 4477.4,8735.3 4452.7,8683.4"]; + n378 [color=floralwhite, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3768,10212", + shape=ellipse, + style=solid, + width=3.5652]; + n377 -> n378 [pos="e,3764,10260 3761,10296 3761.7,10288 3762.4,10279 3763.1,10271"]; + n379 [color=floralwhite, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3832,9948", + shape=ellipse, + style=solid, + width=2.4261]; + n378 -> n379 [pos="e,3820.6,9995.8 3779.5,10164 3790.4,10119 3806.6,10053 3818.2,10006"]; + n389 [color=chartreuse1, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 263\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4215,9156", + shape=ellipse, + style=solid, + width=2.4261]; + n379 -> n389 [pos="e,4138.1,9178.8 3844.7,9900.1 3857.7,9847.9 3876,9761.1 3876,9685 3876,9685 3876,9685 3876,9419 3876,9291.5 4028.1,9217.2 4128.5,\ +9182.1"]; + n381 [color=coral4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4231,10212", + shape=ellipse, + style=solid, + width=3.5652]; + n380 -> n381 [pos="e,4236.4,10260 4240.5,10296 4239.6,10287 4238.6,10279 4237.6,10270"]; + n382 [color=coral4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4223,10080", + shape=ellipse, + style=solid, + width=2.4261]; + n381 -> n382 [pos="e,4225.9,10128 4228.1,10164 4227.6,10156 4227.1,10147 4226.5,10139"]; + n383 [color=aquamarine1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 259\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9948", + shape=ellipse, + style=solid, + width=7.3732]; + n382 -> n383 [pos="e,4217.9,9996.5 4220.1,10032 4219.6,10024 4219.1,10015 4218.5,10007"]; + n384 [color=darkorange3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9816", + shape=ellipse, + style=solid, + width=3.5652]; + n383 -> n384 [pos="e,4215,9864.5 4215,9899.7 4215,9891.5 4215,9883 4215,9874.6"]; + n385 [color=darkorange3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4215,9684", + shape=ellipse, + style=solid, + width=2.4261]; + n384 -> n385 [pos="e,4215,9732.5 4215,9767.7 4215,9759.5 4215,9751 4215,9742.6"]; + n386 [color=darkseagreen2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 261\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9552", + shape=ellipse, + style=solid, + width=7.3732]; + n385 -> n386 [pos="e,4215,9600.5 4215,9635.7 4215,9627.5 4215,9619 4215,9610.6"]; + n387 [color=cyan4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9420", + shape=ellipse, + style=solid, + width=3.5652]; + n386 -> n387 [pos="e,4215,9468.5 4215,9503.7 4215,9495.5 4215,9487 4215,9478.6"]; + n388 [color=cyan4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4215,9288", + shape=ellipse, + style=solid, + width=2.4261]; + n387 -> n388 [pos="e,4215,9336.5 4215,9371.7 4215,9363.5 4215,9355 4215,9346.6"]; + n388 -> n389 [pos="e,4215,9204.5 4215,9239.7 4215,9231.5 4215,9223 4215,9214.6"]; + n390 [color=deeppink2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 264\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4231,9024", + shape=ellipse, + style=solid, + width=6.5343]; + n389 -> n390 [pos="e,4225.2,9072.5 4220.8,9107.7 4221.8,9099.5 4222.9,9091 4223.9,9082.6"]; + n391 [color=cyan4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 265\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4292,8892", + shape=ellipse, + style=solid, + width=3.5652]; + n390 -> n391 [pos="e,4270.2,8939.4 4253,8976.1 4257.2,8967.1 4261.7,8957.7 4266,8948.5"]; + n392 [color=darkslategray1, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 266\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4340,8760", + shape=ellipse, + style=solid, + width=2.5643]; + n391 -> n392 [pos="e,4322.9,8807.4 4309.3,8844.1 4312.6,8835.3 4316,8826.1 4319.3,8817.1"]; + n392 -> n393 [pos="e,4395,8673.8 4368.9,8714.3 4375.6,8703.9 4382.7,8692.9 4389.6,8682.2"]; + n394 [color=darksalmon, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4424,8496", + shape=ellipse, + style=solid, + width=2.5643]; + n393 -> n394 [pos="e,4424,8544.5 4424,8579.7 4424,8571.5 4424,8563 4424,8554.6"]; + n395 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 267\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3935,8364", + shape=ellipse, + style=solid, + width=6.5343]; + n394 -> n395 [pos="e,4076.2,8402.5 4342.4,8473.3 4272.7,8454.8 4170.5,8427.6 4085.9,8405.1"]; + n398 [color=brown2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 269\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4424,8364", + shape=ellipse, + style=solid, + width=6.5343]; + n394 -> n398 [pos="e,4424,8412.5 4424,8447.7 4424,8439.5 4424,8431 4424,8422.6"]; + n411 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4602,6648", + shape=ellipse, + style=solid, + width=2.5643]; + n394 -> n411 [pos="e,4626.4,6694.4 4515.2,8487.8 4568.1,8478.5 4631.3,8457.9 4668,8412 4718,8349.5 4687,8313 4687,8233 4687,8233 4687,8233 4687,6911 \ +4687,6836.3 4655.4,6755.3 4630.7,6703.4"]; + n396 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="3946,8232", + shape=ellipse, + style=solid, + width=3.5652]; + n395 -> n396 [pos="e,3942,8280.5 3939,8315.7 3939.7,8307.5 3940.4,8299 3941.1,8290.6"]; + n397 [color=blanchedalmond, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4010,7968", + shape=ellipse, + style=solid, + width=2.4261]; + n396 -> n397 [pos="e,3998.6,8015.8 3957.5,8183.9 3968.4,8139.4 3984.6,8072.8 3996.2,8025.5"]; + n407 [color=darkolivegreen2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 275\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4393,7176", + shape=ellipse, + style=solid, + width=2.4261]; + n397 -> n407 [pos="e,4315.9,7199.1 4023,7920.1 4036.3,7868 4055,7781.2 4055,7705 4055,7705 4055,7705 4055,7439 4055,7311.9 4206.2,7237.6 4306.4,7202.3"]; + n399 [color=coral4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4416,8232", + shape=ellipse, + style=solid, + width=3.5652]; + n398 -> n399 [pos="e,4418.9,8280.5 4421.1,8315.7 4420.6,8307.5 4420.1,8299 4419.5,8290.6"]; + n400 [color=coral4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4401,8100", + shape=ellipse, + style=solid, + width=2.4261]; + n399 -> n400 [pos="e,4406.4,8148 4410.5,8183.7 4409.6,8175.4 4408.6,8166.6 4407.6,8158"]; + n401 [color=cornsilk, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 271\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7968", + shape=ellipse, + style=solid, + width=7.3732]; + n400 -> n401 [pos="e,4395.9,8016.5 4398.1,8051.7 4397.6,8043.5 4397.1,8035 4396.5,8026.6"]; + n402 [color=burlywood, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7836", + shape=ellipse, + style=solid, + width=3.5652]; + n401 -> n402 [pos="e,4393,7884.5 4393,7919.7 4393,7911.5 4393,7903 4393,7894.6"]; + n403 [color=burlywood, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4393,7704", + shape=ellipse, + style=solid, + width=2.4261]; + n402 -> n403 [pos="e,4393,7752.5 4393,7787.7 4393,7779.5 4393,7771 4393,7762.6"]; + n404 [color=cyan4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 273\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7572", + shape=ellipse, + style=solid, + width=7.3732]; + n403 -> n404 [pos="e,4393,7620.5 4393,7655.7 4393,7647.5 4393,7639 4393,7630.6"]; + n405 [color=firebrick, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7440", + shape=ellipse, + style=solid, + width=3.5652]; + n404 -> n405 [pos="e,4393,7488.5 4393,7523.7 4393,7515.5 4393,7507 4393,7498.6"]; + n406 [color=firebrick, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4393,7308", + shape=ellipse, + style=solid, + width=2.4261]; + n405 -> n406 [pos="e,4393,7356.5 4393,7391.7 4393,7383.5 4393,7375 4393,7366.6"]; + n406 -> n407 [pos="e,4393,7224.5 4393,7259.7 4393,7251.5 4393,7243 4393,7234.6"]; + n408 [color=goldenrod1, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 276\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4409,7044", + shape=ellipse, + style=solid, + width=6.5343]; + n407 -> n408 [pos="e,4403.2,7092.5 4398.8,7127.7 4399.8,7119.5 4400.9,7111 4401.9,7102.6"]; + n409 [color=burlywood4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 277\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4470,6912", + shape=ellipse, + style=solid, + width=3.5652]; + n408 -> n409 [pos="e,4448.2,6959.4 4431,6996.1 4435.2,6987.1 4439.7,6977.7 4444,6968.5"]; + n410 [color=azure, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 278\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4518,6780", + shape=ellipse, + style=solid, + width=2.5643]; + n409 -> n410 [pos="e,4500.9,6827.4 4487.3,6864.1 4490.6,6855.3 4494,6846.1 4497.3,6837.1"]; + n410 -> n411 [pos="e,4573,6693.8 4546.9,6734.3 4553.6,6723.9 4560.7,6712.9 4567.6,6702.2"]; + n412 [color=darksalmon, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4602,6516", + shape=ellipse, + style=solid, + width=2.5643]; + n411 -> n412 [pos="e,4602,6564.5 4602,6599.7 4602,6591.5 4602,6583 4602,6574.6"]; + n413 [color=darksalmon, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 279\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4113,6384", + shape=ellipse, + style=solid, + width=6.5343]; + n412 -> n413 [pos="e,4254.2,6422.5 4520.4,6493.3 4450.7,6474.8 4348.5,6447.6 4263.9,6425.1"]; + n416 [color=cornsilk3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 281\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4602,6384", + shape=ellipse, + style=solid, + width=6.5343]; + n412 -> n416 [pos="e,4602,6432.5 4602,6467.7 4602,6459.5 4602,6451 4602,6442.6"]; + n429 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4780,4668", + shape=ellipse, + style=solid, + width=2.5643]; + n412 -> n429 [pos="e,4804.4,4714.4 4693.2,6507.8 4746.1,6498.5 4809.3,6477.9 4846,6432 4896,6369.5 4865,6333 4865,6253 4865,6253 4865,6253 4865,4931 \ +4865,4856.3 4833.4,4775.3 4808.7,4723.4"]; + n414 [color=coral3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4124,6252", + shape=ellipse, + style=solid, + width=3.5652]; + n413 -> n414 [pos="e,4120,6300.5 4117,6335.7 4117.7,6327.5 4118.4,6319 4119.1,6310.6"]; + n415 [color=coral3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4188,5988", + shape=ellipse, + style=solid, + width=2.4261]; + n414 -> n415 [pos="e,4176.6,6035.8 4135.5,6203.9 4146.4,6159.4 4162.6,6092.8 4174.2,6045.5"]; + n425 [color=blue3, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 287\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4571,5196", + shape=ellipse, + style=solid, + width=2.4261]; + n415 -> n425 [pos="e,4493.9,5219.1 4201,5940.1 4214.3,5888 4233,5801.2 4233,5725 4233,5725 4233,5725 4233,5459 4233,5331.9 4384.2,5257.6 4484.4,5222.3"]; + n417 [color=azure3, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4594,6252", + shape=ellipse, + style=solid, + width=3.5652]; + n416 -> n417 [pos="e,4596.9,6300.5 4599.1,6335.7 4598.6,6327.5 4598.1,6319 4597.5,6310.6"]; + n418 [color=azure3, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4579,6120", + shape=ellipse, + style=solid, + width=2.4261]; + n417 -> n418 [pos="e,4584.4,6168 4588.5,6203.7 4587.6,6195.4 4586.6,6186.6 4585.6,6178"]; + n419 [color=bisque3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 283\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5988", + shape=ellipse, + style=solid, + width=7.3732]; + n418 -> n419 [pos="e,4573.9,6036.5 4576.1,6071.7 4575.6,6063.5 4575.1,6055 4574.5,6046.6"]; + n420 [color=deeppink4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5856", + shape=ellipse, + style=solid, + width=3.5652]; + n419 -> n420 [pos="e,4571,5904.5 4571,5939.7 4571,5931.5 4571,5923 4571,5914.6"]; + n421 [color=deeppink4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4571,5724", + shape=ellipse, + style=solid, + width=2.4261]; + n420 -> n421 [pos="e,4571,5772.5 4571,5807.7 4571,5799.5 4571,5791 4571,5782.6"]; + n422 [color=deeppink3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 285\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4571,5592", + shape=ellipse, + style=solid, + width=7.3732]; + n421 -> n422 [pos="e,4571,5640.5 4571,5675.7 4571,5667.5 4571,5659 4571,5650.6"]; + n423 [color=chartreuse4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 286\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4571,5460", + shape=ellipse, + style=solid, + width=3.5652]; + n422 -> n423 [pos="e,4571,5508.5 4571,5543.7 4571,5535.5 4571,5527 4571,5518.6"]; + n424 [color=chartreuse4, + fontsize=14, + 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[color=burlywood, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 290\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4696,4800", + shape=ellipse, + style=solid, + width=2.5643]; + n427 -> n428 [pos="e,4678.9,4847.4 4665.3,4884.1 4668.6,4875.3 4672,4866.1 4675.3,4857.1"]; + n428 -> n429 [pos="e,4751,4713.8 4724.9,4754.3 4731.6,4743.9 4738.7,4732.9 4745.6,4722.2"]; + n430 [color=darksalmon, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4780,4536", + shape=ellipse, + style=solid, + width=2.5643]; + n429 -> n430 [pos="e,4780,4584.5 4780,4619.7 4780,4611.5 4780,4603 4780,4594.6"]; + n431 [color=darkslategray2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 291\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4291,4404", + shape=ellipse, + style=solid, + width=6.5343]; + n430 -> n431 [pos="e,4432.2,4442.5 4698.4,4513.3 4628.7,4494.8 4526.5,4467.6 4441.9,4445.1"]; + n434 [color=cornflowerblue, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 293\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4780,4404", + shape=ellipse, + style=solid, + width=6.5343]; + n430 -> n434 [pos="e,4780,4452.5 4780,4487.7 4780,4479.5 4780,4471 4780,4462.6"]; + n447 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4958,2688", + shape=ellipse, + style=solid, + width=2.5643]; + n430 -> n447 [pos="e,4982.4,2734.4 4871.2,4527.8 4924.1,4518.5 4987.3,4497.9 5024,4452 5074,4389.5 5043,4353 5043,4273 5043,4273 5043,4273 5043,2951 \ +5043,2876.3 5011.4,2795.3 4986.7,2743.4"]; + n432 [color=azure1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4302,4272", + shape=ellipse, + style=solid, + width=3.5652]; + n431 -> n432 [pos="e,4298,4320.5 4295,4355.7 4295.7,4347.5 4296.4,4339 4297.1,4330.6"]; + n433 [color=azure1, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4366,4008", + shape=ellipse, + style=solid, + width=2.4261]; + n432 -> n433 [pos="e,4354.6,4055.8 4313.5,4223.9 4324.4,4179.4 4340.6,4112.8 4352.2,4065.5"]; + n443 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 299\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4749,3216", + shape=ellipse, + style=solid, + width=2.4261]; + n433 -> n443 [pos="e,4671.9,3239.1 4379,3960.1 4392.3,3908 4411,3821.2 4411,3745 4411,3745 4411,3745 4411,3479 4411,3351.9 4562.2,3277.6 4662.4,3242.3"]; + n435 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4772,4272", + shape=ellipse, + style=solid, + width=3.5652]; + n434 -> n435 [pos="e,4774.9,4320.5 4777.1,4355.7 4776.6,4347.5 4776.1,4339 4775.5,4330.6"]; + n436 [color=darkolivegreen, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4757,4140", + shape=ellipse, + style=solid, + width=2.4261]; + n435 -> n436 [pos="e,4762.4,4188 4766.5,4223.7 4765.6,4215.4 4764.6,4206.6 4763.6,4198"]; + n437 [color=chartreuse, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 295\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,4008", + shape=ellipse, + style=solid, + width=7.3732]; + n436 -> n437 [pos="e,4751.9,4056.5 4754.1,4091.7 4753.6,4083.5 4753.1,4075 4752.5,4066.6"]; + n438 [color=azure2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,3876", + shape=ellipse, + style=solid, + width=3.5652]; + n437 -> n438 [pos="e,4749,3924.5 4749,3959.7 4749,3951.5 4749,3943 4749,3934.6"]; + n439 [color=azure2, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4749,3744", + shape=ellipse, + style=solid, + width=2.4261]; + n438 -> n439 [pos="e,4749,3792.5 4749,3827.7 4749,3819.5 4749,3811 4749,3802.6"]; + n440 [color=cadetblue2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 297\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3612", + shape=ellipse, + style=solid, + width=7.3732]; + n439 -> n440 [pos="e,4749,3660.5 4749,3695.7 4749,3687.5 4749,3679 4749,3670.6"]; + n441 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3480", + shape=ellipse, + style=solid, + width=3.5652]; + n440 -> n441 [pos="e,4749,3528.5 4749,3563.7 4749,3555.5 4749,3547 4749,3538.6"]; + n442 [color=deepskyblue, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", + pos="4749,3348", + shape=ellipse, + style=solid, + width=2.4261]; + n441 -> n442 [pos="e,4749,3396.5 4749,3431.7 4749,3423.5 4749,3415 4749,3406.6"]; + n442 -> n443 [pos="e,4749,3264.5 4749,3299.7 4749,3291.5 4749,3283 4749,3274.6"]; + n444 [color=dimgrey, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 300\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4765,3084", + shape=ellipse, + style=solid, + width=6.5343]; + n443 -> n444 [pos="e,4759.2,3132.5 4754.8,3167.7 4755.8,3159.5 4756.9,3151 4757.9,3142.6"]; + n445 [color=cyan1, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 301\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4826,2952", + shape=ellipse, + style=solid, + width=3.5652]; + n444 -> n445 [pos="e,4804.2,2999.4 4787,3036.1 4791.2,3027.1 4795.7,3017.7 4800,3008.5"]; + n446 [color=deepskyblue4, + fontsize=14, + height=1.3356, + label="nn.MulConstant\nStorage id: 302\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4874,2820", + shape=ellipse, + style=solid, + width=2.5643]; + n445 -> n446 [pos="e,4856.9,2867.4 4843.3,2904.1 4846.6,2895.3 4850,2886.1 4853.3,2877.1"]; + n446 -> n447 [pos="e,4929,2733.8 4902.9,2774.3 4909.6,2763.9 4916.7,2752.9 4923.6,2742.2"]; + n448 [color=darksalmon, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="4958,2556", + shape=ellipse, + style=solid, + width=2.5643]; + n447 -> n448 [pos="e,4958,2604.5 4958,2639.7 4958,2631.5 4958,2623 4958,2614.6"]; + n449 [color=deeppink2, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 303\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4469,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n448 -> n449 [pos="e,4610.2,2462.5 4876.4,2533.3 4806.7,2514.8 4704.5,2487.6 4619.9,2465.1"]; + n452 [color=brown3, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 305\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4958,2424", + shape=ellipse, + style=solid, + width=6.5343]; + n448 -> n452 [pos="e,4958,2472.5 4958,2507.7 4958,2499.5 4958,2491 4958,2482.6"]; + n465 [color=darksalmon, + fontsize=14, + height=1.3356, + label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", + pos="5136,708", + shape=ellipse, + style=solid, + width=2.5643]; + n448 -> n465 [pos="e,5160.4,754.41 5049.2,2547.8 5102.1,2538.5 5165.3,2517.9 5202,2472 5252,2409.5 5221,2373 5221,2293 5221,2293 5221,2293 5221,971 \ +5221,896.34 5189.4,815.3 5164.7,763.43"]; + n450 [color=cornsilk4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4480,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n449 -> n450 [pos="e,4476,2340.5 4473,2375.7 4473.7,2367.5 4474.4,2359 4475.1,2350.6"]; + n451 [color=cornsilk4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4544,2028", + shape=ellipse, + style=solid, + width=2.4261]; + n450 -> n451 [pos="e,4532.6,2075.8 4491.5,2243.9 4502.4,2199.4 4518.6,2132.8 4530.2,2085.5"]; + n461 [color=goldenrod, + fontsize=14, + height=1.3356, + label="nn.JoinTable\nStorage id: 311\nSize: {1, 448, 8, 8}\nMem size: 28672", + pos="4927,1236", + shape=ellipse, + style=solid, + width=2.4261]; + n451 -> n461 [pos="e,4849.9,1259.1 4557,1980.1 4570.3,1928 4589,1841.2 4589,1765 4589,1765 4589,1765 4589,1499 4589,1371.9 4740.2,1297.6 4840.4,1262.3"]; + n453 [color=firebrick4, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4950,2292", + shape=ellipse, + style=solid, + width=3.5652]; + n452 -> n453 [pos="e,4952.9,2340.5 4955.1,2375.7 4954.6,2367.5 4954.1,2359 4953.5,2350.6"]; + n454 [color=firebrick4, + fontsize=14, + height=1.3356, + label="cudnn.ReLU\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", + pos="4935,2160", + shape=ellipse, + style=solid, + width=2.4261]; + n453 -> n454 [pos="e,4940.4,2208 4944.5,2243.7 4943.6,2235.4 4942.6,2226.6 4941.6,2218"]; + n455 [color=darkgoldenrod4, + fontsize=14, + height=1.3356, + label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 307\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4927,2028", + shape=ellipse, + style=solid, + width=7.3732]; + n454 -> n455 [pos="e,4929.9,2076.5 4932.1,2111.7 4931.6,2103.5 4931.1,2095 4930.5,2086.6"]; + n456 [color=deeppink2, + fontsize=14, + height=1.3356, + label="nn.SpatialBatchNormalization\nStorage id: 308\nSize: {1, 224, 8, 8}\nMem size: 14336", + pos="4927,1896", + shape=ellipse, + style=solid, + width=3.5652]; + n455 -> n456 [pos="e,4927,1944.5 4927,1979.7 4927,1971.5 4927,1963 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+ +n122->n123 + + + + +n124 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 85 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n123->n124 + + + + +n125 + +nn.SpatialBatchNormalization +Storage id: 86 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n124->n125 + + + + +n126 + +cudnn.ReLU +Storage id: 86 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n125->n126 + + + + +n127 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 87 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n126->n127 + + + + +n128 + +nn.SpatialBatchNormalization +Storage id: 88 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n127->n128 + + + + +n129 + +cudnn.ReLU +Storage id: 88 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n128->n129 + + + + +n129->n130 + + + + +n131 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 90 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n130->n131 + + + + +n132 + +nn.SpatialBatchNormalization +Storage id: 91 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n131->n132 + + + + +n133 + +nn.MulConstant +Storage id: 92 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n132->n133 + + + + +n133->n134 + + + + +n135 + +cudnn.ReLU +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n134->n135 + + + + +n136 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 93 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n136 + + + + +n139 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 95 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n139 + + + + +n145 + +cudnn.SpatialConvolution(384 -> 32, 1x1) without bias +Storage id: 99 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n135->n145 + + + + +n158 + +nn.CAddTable +Storage id: 28 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n135->n158 + + + + +n137 + +nn.SpatialBatchNormalization +Storage id: 94 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n136->n137 + + + + +n138 + +cudnn.ReLU +Storage id: 94 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n137->n138 + + + + +n154 + +nn.JoinTable +Storage id: 105 +Size: {1, 128, 35, 35} +Mem size: 156800 + + +n138->n154 + + + + +n140 + +nn.SpatialBatchNormalization +Storage id: 96 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n139->n140 + + + + +n141 + +cudnn.ReLU +Storage id: 96 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n140->n141 + + + + +n142 + +cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias +Storage id: 97 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n141->n142 + + + + +n143 + +nn.SpatialBatchNormalization +Storage id: 98 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n142->n143 + + + + +n144 + +cudnn.ReLU +Storage id: 98 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n143->n144 + + + + +n144->n154 + + + + +n146 + +nn.SpatialBatchNormalization +Storage id: 100 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n145->n146 + + + + +n147 + +cudnn.ReLU +Storage id: 100 +Size: {1, 32, 35, 35} +Mem size: 39200 + + +n146->n147 + + + + +n148 + +cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias +Storage id: 101 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n147->n148 + + + + +n149 + +nn.SpatialBatchNormalization +Storage id: 102 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n148->n149 + + + + +n150 + +cudnn.ReLU +Storage id: 102 +Size: {1, 48, 35, 35} +Mem size: 58800 + + +n149->n150 + + + + +n151 + +cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias +Storage id: 103 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n150->n151 + + + + +n152 + +nn.SpatialBatchNormalization +Storage id: 104 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n151->n152 + + + + +n153 + +cudnn.ReLU +Storage id: 104 +Size: {1, 64, 35, 35} +Mem size: 78400 + + +n152->n153 + + + + +n153->n154 + + + + +n155 + +cudnn.SpatialConvolution(128 -> 384, 1x1) without bias +Storage id: 106 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n154->n155 + + + + +n156 + +nn.SpatialBatchNormalization +Storage id: 107 +Size: {1, 384, 35, 35} +Mem size: 470400 + + +n155->n156 + + + + 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1,1, 0,3) without bias +Storage id: 123 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n179->n180 + + + + +n181 + +nn.SpatialBatchNormalization +Storage id: 124 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n180->n181 + + + + +n182 + +cudnn.ReLU +Storage id: 124 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n181->n182 + + + + +n183 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 125 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n182->n183 + + + + +n184 + +nn.SpatialBatchNormalization +Storage id: 126 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n183->n184 + + + + +n185 + +cudnn.ReLU +Storage id: 126 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n184->n185 + + + + +n185->n186 + + + + +n187 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 128 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n186->n187 + + + + +n188 + +nn.SpatialBatchNormalization +Storage id: 129 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n187->n188 + + + + +n189 + +nn.MulConstant +Storage id: 130 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n188->n189 + + + + +n189->n190 + + + + +n191 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n190->n191 + + + + +n192 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 131 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n191->n192 + + + + +n195 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 133 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n191->n195 + + + + +n208 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n191->n208 + + + + +n193 + +nn.SpatialBatchNormalization +Storage id: 132 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n192->n193 + + + + +n194 + +cudnn.ReLU +Storage id: 132 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n193->n194 + + + + +n204 + +nn.JoinTable +Storage id: 139 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n194->n204 + + + + +n196 + +nn.SpatialBatchNormalization +Storage id: 134 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n195->n196 + + + + +n197 + +cudnn.ReLU +Storage id: 134 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n196->n197 + + + + +n198 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 135 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n197->n198 + + + + +n199 + +nn.SpatialBatchNormalization +Storage id: 136 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n198->n199 + + + + +n200 + +cudnn.ReLU +Storage id: 136 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n199->n200 + + + + +n201 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 137 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n200->n201 + + + + +n202 + +nn.SpatialBatchNormalization +Storage id: 138 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n201->n202 + + + + +n203 + +cudnn.ReLU +Storage id: 138 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n202->n203 + + + + +n203->n204 + + + + +n205 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 140 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n204->n205 + + + + +n206 + +nn.SpatialBatchNormalization +Storage id: 141 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n205->n206 + + + + +n207 + +nn.MulConstant +Storage id: 142 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n206->n207 + + + + +n207->n208 + + + + +n209 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n208->n209 + + + + +n210 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 143 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n209->n210 + + + + +n213 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 145 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n209->n213 + + + + +n226 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n209->n226 + + + + +n211 + +nn.SpatialBatchNormalization +Storage id: 144 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n210->n211 + + + + +n212 + +cudnn.ReLU +Storage id: 144 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n211->n212 + + + + +n222 + +nn.JoinTable +Storage id: 151 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n212->n222 + + + + +n214 + +nn.SpatialBatchNormalization +Storage id: 146 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n213->n214 + + + + +n215 + +cudnn.ReLU +Storage id: 146 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n214->n215 + + + + +n216 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 147 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n215->n216 + + + + +n217 + +nn.SpatialBatchNormalization +Storage id: 148 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n216->n217 + + + + +n218 + +cudnn.ReLU +Storage id: 148 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n217->n218 + + + + +n219 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 149 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n218->n219 + + + + +n220 + +nn.SpatialBatchNormalization +Storage id: 150 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n219->n220 + + + + +n221 + +cudnn.ReLU +Storage id: 150 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n220->n221 + + + + +n221->n222 + + + + +n223 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 152 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n222->n223 + + + + +n224 + +nn.SpatialBatchNormalization +Storage id: 153 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n223->n224 + + + + +n225 + +nn.MulConstant +Storage id: 154 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n224->n225 + + + + +n225->n226 + + + + +n227 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n226->n227 + + + + +n228 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 155 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n227->n228 + + + + +n231 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 157 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n227->n231 + + + + 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size: 46240 + + +n235->n236 + + + + +n237 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 161 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n236->n237 + + + + +n238 + +nn.SpatialBatchNormalization +Storage id: 162 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n237->n238 + + + + +n239 + +cudnn.ReLU +Storage id: 162 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n238->n239 + + + + +n239->n240 + + + + +n241 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 164 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n240->n241 + + + + +n242 + +nn.SpatialBatchNormalization +Storage id: 165 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n241->n242 + + + + +n243 + +nn.MulConstant +Storage id: 166 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n242->n243 + + + + +n243->n244 + + + + +n245 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n244->n245 + + + + +n246 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 167 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n245->n246 + + + + +n249 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 169 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n245->n249 + + + + +n262 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n245->n262 + + + + +n247 + +nn.SpatialBatchNormalization +Storage id: 168 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n246->n247 + + + + +n248 + +cudnn.ReLU +Storage id: 168 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n247->n248 + + + + +n258 + +nn.JoinTable +Storage id: 175 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n248->n258 + + + + +n250 + +nn.SpatialBatchNormalization +Storage id: 170 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n249->n250 + + + + +n251 + +cudnn.ReLU +Storage id: 170 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n250->n251 + + + + +n252 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 171 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n251->n252 + + + + +n253 + +nn.SpatialBatchNormalization +Storage id: 172 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n252->n253 + + + + +n254 + +cudnn.ReLU +Storage id: 172 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n253->n254 + + + + +n255 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 173 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n254->n255 + + + + +n256 + +nn.SpatialBatchNormalization +Storage id: 174 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n255->n256 + + + + +n257 + +cudnn.ReLU +Storage id: 174 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n256->n257 + + + + +n257->n258 + + + + +n259 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 176 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n258->n259 + + + + +n260 + +nn.SpatialBatchNormalization +Storage id: 177 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n259->n260 + + + + +n261 + +nn.MulConstant +Storage id: 178 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n260->n261 + + + + +n261->n262 + + + + +n263 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n262->n263 + + + + +n264 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 179 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n263->n264 + + + + +n267 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 181 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n263->n267 + + + + +n280 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n263->n280 + + + + +n265 + +nn.SpatialBatchNormalization +Storage id: 180 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n264->n265 + + + + +n266 + +cudnn.ReLU +Storage id: 180 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n265->n266 + + + + +n276 + +nn.JoinTable +Storage id: 187 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n266->n276 + + + + +n268 + +nn.SpatialBatchNormalization +Storage id: 182 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n267->n268 + + + + +n269 + +cudnn.ReLU +Storage id: 182 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n268->n269 + + + + +n270 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 183 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n269->n270 + + + + +n271 + +nn.SpatialBatchNormalization +Storage id: 184 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n270->n271 + + + + +n272 + +cudnn.ReLU +Storage id: 184 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n271->n272 + + + + +n273 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 185 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n272->n273 + + + + +n274 + +nn.SpatialBatchNormalization +Storage id: 186 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n273->n274 + + + + +n275 + +cudnn.ReLU +Storage id: 186 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n274->n275 + + + + +n275->n276 + + + + +n277 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 188 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n276->n277 + + + + +n278 + +nn.SpatialBatchNormalization +Storage id: 189 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n277->n278 + + + + +n279 + +nn.MulConstant +Storage id: 190 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n278->n279 + + + + +n279->n280 + + + + +n281 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n280->n281 + + + + +n282 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 191 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n281->n282 + + + + +n285 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 193 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n281->n285 + + + + +n298 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n281->n298 + + + + +n283 + +nn.SpatialBatchNormalization +Storage id: 192 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n282->n283 + + + + +n284 + +cudnn.ReLU +Storage id: 192 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n283->n284 + + + + +n294 + +nn.JoinTable +Storage id: 199 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n284->n294 + + + + +n286 + +nn.SpatialBatchNormalization +Storage id: 194 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n285->n286 + + + + +n287 + +cudnn.ReLU +Storage id: 194 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n286->n287 + + + + +n288 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 195 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n287->n288 + + + + +n289 + +nn.SpatialBatchNormalization +Storage id: 196 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n288->n289 + + + + +n290 + +cudnn.ReLU +Storage id: 196 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n289->n290 + + + + +n291 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 197 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n290->n291 + + + + +n292 + +nn.SpatialBatchNormalization +Storage id: 198 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n291->n292 + + + + +n293 + +cudnn.ReLU +Storage id: 198 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n292->n293 + + + + +n293->n294 + + + + +n295 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 200 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n294->n295 + + + + +n296 + +nn.SpatialBatchNormalization +Storage id: 201 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n295->n296 + + + + +n297 + +nn.MulConstant +Storage id: 202 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n296->n297 + + + + +n297->n298 + + + + +n299 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n298->n299 + + + + +n300 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 203 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n299->n300 + + + + +n303 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 205 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n299->n303 + + + + +n316 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n299->n316 + + + + +n301 + +nn.SpatialBatchNormalization +Storage id: 204 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n300->n301 + + + + +n302 + +cudnn.ReLU +Storage id: 204 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n301->n302 + + + + +n312 + +nn.JoinTable +Storage id: 211 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n302->n312 + + + + +n304 + +nn.SpatialBatchNormalization +Storage id: 206 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n303->n304 + + + + +n305 + +cudnn.ReLU +Storage id: 206 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n304->n305 + + + + +n306 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 207 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n305->n306 + + + + +n307 + +nn.SpatialBatchNormalization +Storage id: 208 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n306->n307 + + + + +n308 + +cudnn.ReLU +Storage id: 208 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n307->n308 + + + + +n309 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 209 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n308->n309 + + + + +n310 + +nn.SpatialBatchNormalization +Storage id: 210 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n309->n310 + + + + +n311 + +cudnn.ReLU +Storage id: 210 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n310->n311 + + + + +n311->n312 + + + + +n313 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 212 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n312->n313 + + + + +n314 + +nn.SpatialBatchNormalization +Storage id: 213 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n313->n314 + + + + +n315 + +nn.MulConstant +Storage id: 214 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n314->n315 + + + + +n315->n316 + + + + +n317 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n316->n317 + + + + +n318 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 215 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n317->n318 + + + + +n321 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 217 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n317->n321 + + + + +n334 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n317->n334 + + + + +n319 + +nn.SpatialBatchNormalization +Storage id: 216 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n318->n319 + + + + +n320 + +cudnn.ReLU +Storage id: 216 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n319->n320 + + + + +n330 + +nn.JoinTable +Storage id: 223 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n320->n330 + + + + +n322 + +nn.SpatialBatchNormalization +Storage id: 218 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n321->n322 + + + + +n323 + +cudnn.ReLU +Storage id: 218 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n322->n323 + + + + +n324 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 219 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n323->n324 + + + + +n325 + +nn.SpatialBatchNormalization +Storage id: 220 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n324->n325 + + + + +n326 + +cudnn.ReLU +Storage id: 220 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n325->n326 + + + + +n327 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 221 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n326->n327 + + + + +n328 + +nn.SpatialBatchNormalization +Storage id: 222 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n327->n328 + + + + +n329 + +cudnn.ReLU +Storage id: 222 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n328->n329 + + + + +n329->n330 + + + + +n331 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 224 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n330->n331 + + + + +n332 + +nn.SpatialBatchNormalization +Storage id: 225 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n331->n332 + + + + +n333 + +nn.MulConstant +Storage id: 226 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n332->n333 + + + + +n333->n334 + + + + +n335 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n334->n335 + + + + +n336 + +cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias +Storage id: 227 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n335->n336 + + + + +n339 + +cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias +Storage id: 229 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n335->n339 + + + + +n352 + +nn.CAddTable +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n335->n352 + + + + +n337 + +nn.SpatialBatchNormalization +Storage id: 228 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n336->n337 + + + + +n338 + +cudnn.ReLU +Storage id: 228 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n337->n338 + + + + +n348 + +nn.JoinTable +Storage id: 235 +Size: {1, 384, 17, 17} +Mem size: 110976 + + +n338->n348 + + + + +n340 + +nn.SpatialBatchNormalization +Storage id: 230 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n339->n340 + + + + +n341 + +cudnn.ReLU +Storage id: 230 +Size: {1, 128, 17, 17} +Mem size: 36992 + + +n340->n341 + + + + +n342 + +cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias +Storage id: 231 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n341->n342 + + + + +n343 + +nn.SpatialBatchNormalization +Storage id: 232 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n342->n343 + + + + +n344 + +cudnn.ReLU +Storage id: 232 +Size: {1, 160, 17, 17} +Mem size: 46240 + + +n343->n344 + + + + +n345 + +cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias +Storage id: 233 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n344->n345 + + + + +n346 + +nn.SpatialBatchNormalization +Storage id: 234 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n345->n346 + + + + +n347 + +cudnn.ReLU +Storage id: 234 +Size: {1, 192, 17, 17} +Mem size: 55488 + + +n346->n347 + + + + +n347->n348 + + + + +n349 + +cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias +Storage id: 236 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n348->n349 + + + + +n350 + +nn.SpatialBatchNormalization +Storage id: 237 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n349->n350 + + + + +n351 + +nn.MulConstant +Storage id: 238 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n350->n351 + + + + +n351->n352 + + + + +n353 + +cudnn.ReLU +Storage id: 118 +Size: {1, 1152, 17, 17} +Mem size: 332928 + + +n352->n353 + + + + +n354 + +nn.SpatialMaxPooling(3x3, 2,2) +Storage id: 239 +Size: {1, 1152, 8, 8} +Mem size: 73728 + + +n353->n354 + + + + +n355 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 240 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n355 + + + + +n361 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 244 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n361 + + + + +n367 + +cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias +Storage id: 248 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n353->n367 + + + + +n376 + +nn.JoinTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n354->n376 + + + + +n356 + +nn.SpatialBatchNormalization +Storage id: 241 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n355->n356 + + + + +n357 + +cudnn.ReLU +Storage id: 241 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n356->n357 + + + + +n358 + +cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias +Storage id: 242 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n357->n358 + + + + +n359 + +nn.SpatialBatchNormalization +Storage id: 243 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n358->n359 + + + + +n360 + +cudnn.ReLU +Storage id: 243 +Size: {1, 384, 8, 8} +Mem size: 24576 + + +n359->n360 + + + + +n360->n376 + + + + +n362 + +nn.SpatialBatchNormalization +Storage id: 245 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n361->n362 + + + + +n363 + +cudnn.ReLU +Storage id: 245 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n362->n363 + + + + +n364 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias +Storage id: 246 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n363->n364 + + + + +n365 + +nn.SpatialBatchNormalization +Storage id: 247 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n364->n365 + + + + +n366 + +cudnn.ReLU +Storage id: 247 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n365->n366 + + + + +n366->n376 + + + + +n368 + +nn.SpatialBatchNormalization +Storage id: 249 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n367->n368 + + + + +n369 + +cudnn.ReLU +Storage id: 249 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n368->n369 + + + + +n370 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias +Storage id: 250 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n369->n370 + + + + +n371 + +nn.SpatialBatchNormalization +Storage id: 251 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n370->n371 + + + + +n372 + +cudnn.ReLU +Storage id: 251 +Size: {1, 256, 17, 17} +Mem size: 73984 + + +n371->n372 + + + + +n373 + +cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias +Storage id: 252 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n372->n373 + + + + +n374 + +nn.SpatialBatchNormalization +Storage id: 253 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n373->n374 + + + + +n375 + +cudnn.ReLU +Storage id: 253 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n374->n375 + + + + +n375->n376 + + + + +n377 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 255 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n376->n377 + + + + +n380 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 257 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n376->n380 + + + + +n393 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n376->n393 + + + + +n378 + +nn.SpatialBatchNormalization +Storage id: 256 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n377->n378 + + + + +n379 + +cudnn.ReLU +Storage id: 256 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n378->n379 + + + + +n389 + +nn.JoinTable +Storage id: 263 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n379->n389 + + + + +n381 + +nn.SpatialBatchNormalization +Storage id: 258 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n380->n381 + + + + +n382 + +cudnn.ReLU +Storage id: 258 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n381->n382 + + + + +n383 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 259 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n382->n383 + + + + +n384 + +nn.SpatialBatchNormalization +Storage id: 260 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n383->n384 + + + + +n385 + +cudnn.ReLU +Storage id: 260 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n384->n385 + + + + +n386 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 261 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n385->n386 + + + + +n387 + +nn.SpatialBatchNormalization +Storage id: 262 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n386->n387 + + + + +n388 + +cudnn.ReLU +Storage id: 262 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n387->n388 + + + + +n388->n389 + + + + +n390 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 264 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n389->n390 + + + + +n391 + +nn.SpatialBatchNormalization +Storage id: 265 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n390->n391 + + + + +n392 + +nn.MulConstant +Storage id: 266 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n391->n392 + + + + +n392->n393 + + + + +n394 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n393->n394 + + + + +n395 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 267 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n394->n395 + + + + +n398 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 269 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n394->n398 + + + + +n411 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n394->n411 + + + + +n396 + +nn.SpatialBatchNormalization +Storage id: 268 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n395->n396 + + + + +n397 + +cudnn.ReLU +Storage id: 268 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n396->n397 + + + + +n407 + +nn.JoinTable +Storage id: 275 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n397->n407 + + + + +n399 + +nn.SpatialBatchNormalization +Storage id: 270 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n398->n399 + + + + +n400 + +cudnn.ReLU +Storage id: 270 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n399->n400 + + + + +n401 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 271 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n400->n401 + + + + +n402 + +nn.SpatialBatchNormalization +Storage id: 272 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n401->n402 + + + + +n403 + +cudnn.ReLU +Storage id: 272 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n402->n403 + + + + +n404 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 273 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n403->n404 + + + + +n405 + +nn.SpatialBatchNormalization +Storage id: 274 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n404->n405 + + + + 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+ + + + +n414 + +nn.SpatialBatchNormalization +Storage id: 280 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n413->n414 + + + + +n415 + +cudnn.ReLU +Storage id: 280 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n414->n415 + + + + +n425 + +nn.JoinTable +Storage id: 287 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n415->n425 + + + + +n417 + +nn.SpatialBatchNormalization +Storage id: 282 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n416->n417 + + + + +n418 + +cudnn.ReLU +Storage id: 282 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n417->n418 + + + + +n419 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 283 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n418->n419 + + + + +n420 + +nn.SpatialBatchNormalization +Storage id: 284 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n419->n420 + + + + +n421 + +cudnn.ReLU +Storage id: 284 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n420->n421 + + + + +n422 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 285 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n421->n422 + + + + +n423 + +nn.SpatialBatchNormalization +Storage id: 286 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n422->n423 + + + + +n424 + +cudnn.ReLU +Storage id: 286 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n423->n424 + + + + +n424->n425 + + + + +n426 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 288 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n425->n426 + + + + +n427 + +nn.SpatialBatchNormalization +Storage id: 289 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n426->n427 + + + + +n428 + +nn.MulConstant +Storage id: 290 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n427->n428 + + + + +n428->n429 + + + + +n430 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n429->n430 + + + + +n431 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 291 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n430->n431 + + + + +n434 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 293 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n430->n434 + + + + +n447 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n430->n447 + + + + +n432 + +nn.SpatialBatchNormalization +Storage id: 292 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n431->n432 + + + + +n433 + +cudnn.ReLU +Storage id: 292 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n432->n433 + + + + +n443 + +nn.JoinTable +Storage id: 299 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n433->n443 + + + + +n435 + +nn.SpatialBatchNormalization +Storage id: 294 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n434->n435 + + + + +n436 + +cudnn.ReLU +Storage id: 294 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n435->n436 + + + + +n437 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 295 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n436->n437 + + + + +n438 + +nn.SpatialBatchNormalization +Storage id: 296 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n437->n438 + + + + +n439 + +cudnn.ReLU +Storage id: 296 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n438->n439 + + + + +n440 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 297 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n439->n440 + + + + +n441 + +nn.SpatialBatchNormalization +Storage id: 298 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n440->n441 + + + + +n442 + +cudnn.ReLU +Storage id: 298 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n441->n442 + + + + +n442->n443 + + + + +n444 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 300 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n443->n444 + + + + +n445 + +nn.SpatialBatchNormalization +Storage id: 301 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n444->n445 + + + + +n446 + +nn.MulConstant +Storage id: 302 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n445->n446 + + + + +n446->n447 + + + + +n448 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n447->n448 + + + + +n449 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 303 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n448->n449 + + + + +n452 + +cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias +Storage id: 305 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n448->n452 + + + + +n465 + +nn.CAddTable +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n448->n465 + + + + +n450 + +nn.SpatialBatchNormalization +Storage id: 304 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n449->n450 + + + + +n451 + +cudnn.ReLU +Storage id: 304 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n450->n451 + + + + +n461 + +nn.JoinTable +Storage id: 311 +Size: {1, 448, 8, 8} +Mem size: 28672 + + +n451->n461 + + + + +n453 + +nn.SpatialBatchNormalization +Storage id: 306 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n452->n453 + + + + +n454 + +cudnn.ReLU +Storage id: 306 +Size: {1, 192, 8, 8} +Mem size: 12288 + + +n453->n454 + + + + +n455 + +cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias +Storage id: 307 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n454->n455 + + + + +n456 + +nn.SpatialBatchNormalization +Storage id: 308 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n455->n456 + + + + +n457 + +cudnn.ReLU +Storage id: 308 +Size: {1, 224, 8, 8} +Mem size: 14336 + + +n456->n457 + + + + +n458 + +cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias +Storage id: 309 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n457->n458 + + + + +n459 + +nn.SpatialBatchNormalization +Storage id: 310 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n458->n459 + + + + +n460 + +cudnn.ReLU +Storage id: 310 +Size: {1, 256, 8, 8} +Mem size: 16384 + + +n459->n460 + + + + +n460->n461 + + + + +n462 + +cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias +Storage id: 312 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n461->n462 + + + + +n463 + +nn.SpatialBatchNormalization +Storage id: 313 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n462->n463 + + + + +n464 + +nn.MulConstant +Storage id: 314 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n463->n464 + + + + +n464->n465 + + + + +n466 + +cudnn.ReLU +Storage id: 254 +Size: {1, 2048, 8, 8} +Mem size: 131072 + + +n465->n466 + + + + +n467 + +cudnn.SpatialAveragePooling(8x8, 1,1) +Storage id: 315 +Size: {1, 2048, 1, 1} +Mem size: 2048 + + +n466->n467 + + + + +n468 + +nn.View(2048) +Storage id: 315 +Size: {1, 2048} +Mem size: 2048 + + +n467->n468 + + + + +n469 + +nn.Dropout(0.200000) +Storage id: 316 +Size: {1, 2048} +Mem size: 2048 + + +n468->n469 + + + + +n470 + +nn.Linear(2048 -> 1000) +Storage id: 317 +Size: {1, 1000} +Mem size: 1000 + + +n469->n470 + + + + + diff --git a/main.lua b/main.lua index c1708cd9b..5e827c0f5 100644 --- a/main.lua +++ b/main.lua @@ -59,7 +59,7 @@ for epoch = startEpoch, opt.nEpochs do print(' * Best model ', testTop1, testTop5) end - checkpoints.save(epoch, model, trainer.optimState, bestModel) + checkpoints.save(epoch, model, trainer.optimState, bestModel, opt) end print(string.format(' * Finished top1: %6.3f top5: %6.3f', bestTop1, bestTop5)) diff --git a/main.sh b/main.sh new file mode 100755 index 000000000..d567621cb --- /dev/null +++ b/main.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +filename=$(date +"%Y:%m:%d-%H:%M:%S:%N")".log" + +#th main.lua -depth 50 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/ + +th main.lua -netType inceptionv4 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inceptionv4/ 2>&1 | tee /media/data0/cache/log/$filename + +#th main.lua -netType inceptionv4aux -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inceptionv4/ diff --git a/models/inceptionv4.lua b/models/inceptionv4.lua new file mode 100644 index 000000000..80b1958cf --- /dev/null +++ b/models/inceptionv4.lua @@ -0,0 +1,320 @@ + +local nn = require 'nn' +require 'cunn' +local cudnn = require 'cudnn' + +local Convolution = cudnn.SpatialConvolution +local Avg = cudnn.SpatialAveragePooling +local ReLU = cudnn.ReLU +local Max = nn.SpatialMaxPooling +local SBatchNorm = nn.SpatialBatchNormalization + +local function addConv(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + :add(ReLU(true)) + return layer +end + +local function addConvLinear(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) -- addConv without ReLU + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + return layer +end + +local function Stem(fs_start) -- fig 3 + local fs0 = {32, 32, 64}; fs0[0] = fs_start + local net = nn.Sequential() + ------------------- nInputPlane, nOutputPlane, k, k, s, s, p, p + net:add(addConv(fs0[0], fs0[1], 3, 3, 2, 2, 0, 0)) + net:add(addConv(fs0[1], fs0[2], 3, 3, 1, 1, 0, 0)) + net:add(addConv(fs0[2], fs0[3], 3, 3, 1, 1, 1, 1)) + + local fs1a = {}; fs1a[0] = fs0[#fs0] + local fs1b = {96}; fs1b[0] = fs0[#fs0] + local concat1 = nn.ConcatTable() + concat1:add(Max(3, 3, 2, 2, 0, 0)) + concat1:add(addConv(fs1b[0], fs1b[1], 3, 3, 2, 2, 0, 0)) + + net:add(concat1) + net:add(nn.JoinTable(2, 4)) + + local fs2a = {64, 96}; fs2a[0] = fs1a[#fs1a] + fs1b[#fs1b] + local fs2b = {64, 64, 64, 96}; fs2b[0] = fs1a[#fs1a] + fs1b[#fs1b] + local concat2 = nn.ConcatTable() + concat2:add(nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2a[1], fs2a[2], 3, 3, 1, 1, 0, 0)) + ) + concat2:add(nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 7, 1, 1, 1, 3, 0)) + :add(addConv(fs2b[2], fs2b[3], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2b[3], fs2b[4], 3, 3, 1, 1, 0, 0)) + ) + net:add(concat2) + net:add(nn.JoinTable(2, 4)) + + local fs3a = {192}; fs3a[0] = fs2a[#fs2a] + fs2b[#fs2b] + local fs3b = {}; fs3b[0] = fs2a[#fs2a] + fs2b[#fs2b] + local concat3 = nn.ConcatTable() + concat3:add(addConv(fs3a[0], fs3a[1], 3, 3, 2, 2, 0, 0)) + :add(Max(3, 3, 2, 2, 0, 0)) + net:add(concat3) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs3a[#fs3a] + fs3b[#fs3b] + return net, fs_final +end + +local function InceptionResnetA(fs_start) -- fig 16 + local path1 = nn.Identity() + + local fs2a = {32}; fs2a[0] = fs_start + local fs2b = {32, 32}; fs2b[0] = fs_start + local fs2c = {32, 48, 64}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {384}; + fs2[0] = fs2a[#fs2a] + fs2b[#fs2b] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2b = nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 3, 3, 1, 1, 1, 1)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 3, 1, 1, 1, 1)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2b) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionA(fs_start, k, l, m, n) -- fig 7 + local net = nn.Sequential() + + local concat = nn.ConcatTable() + concat:add(Max(3, 3, 2, 2, 0, 0)) -- path1 + concat:add(addConv(fs_start, n, 3, 3, 2, 2, 0, 0)) -- path2 + concat:add(nn.Sequential():add(addConv(fs_start, k, 1, 1, 1, 1, 0, 0)) -- path3 + :add(addConv(k, l, 3, 3, 1, 1, 1, 1)) + :add(addConv(l, m, 3, 3, 2, 2, 0, 0))) + net:add(concat) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs_start + n + m + + return net, fs_final +end + +local function InceptionResnetB(fs_start) -- fig 17 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {128, 160, 192}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {1152}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2c[2], fs2c[3], 7, 1, 1, 1, 3, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionB(fs_start) -- fig 18 + local path1 = Max(3, 3, 2, 2, 0, 0) + local fs2 = {256, 384}; fs2[0] = fs_start + local path2 = nn.Sequential():add(addConv(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2[1], fs2[2], 3, 3, 2, 2, 0, 0)) + --local fs3 = {256, 288}; fs3[0] = fs_start -- in the paper, but seems to have typo + local fs3 = {256, 256}; fs3[0] = fs_start + local path3 = nn.Sequential():add(addConv(fs3[0], fs3[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs3[1], fs3[2], 3, 3, 2, 2, 0, 0)) + --local fs4 = {256, 288, 320}; fs4[0] = fs_start -- in the paper, but seems to have typo + local fs4 = {256, 256, 256}; fs4[0] = fs_start + local path4 = nn.Sequential():add(addConv(fs4[0], fs4[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs4[1], fs4[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs4[2], fs4[3], 3, 3, 2, 2, 0, 0)) + + local concat = nn.ConcatTable() + concat:add(path1) + concat:add(path2) + concat:add(path3) + concat:add(path4) + + local net = nn.Sequential() + net:add(concat) + net:add(nn.JoinTable(2,4)) + + local fs_final = fs_start + fs2[#fs2] + fs3[#fs3] + fs4[#fs4] + + return net, fs_final +end + +local function InceptionResnetC(fs_start) -- fig 19 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {192, 224, 256}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {2048}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 3, 1, 1, 0, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 1, 1, 1, 1, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + + + +local function createModel(opt) + local model = nn.Sequential() + + -- Add Stem module + local stem, fs = Stem(3) + model:add(stem) + + -- Add Inception-resnet-A modules (x5) + for i = 1, 5 do + local irA, fs = InceptionResnetA(fs) + model:add(irA) + end + + -- Add Reduction-A module + local rA, fs = ReductionA(fs, 256, 256, 384, 384) + model:add(rA) + + -- Add Inception-resnet-B modules (x10) + for i = 1, 10 do + local irB, fs = InceptionResnetB(fs) + model:add(irB) + end + + -- Add Reduction-B module + local rB, fs = ReductionB(fs) + model:add(rB) + + -- Add Inception-resnet-C modules (x5) + for i = 1, 5 do + local irC, fs = InceptionResnetC(fs) + model:add(irC) + end + + local nFeatures = fs -- set final channels + + -- Add Average Pooling + model:add(Avg(8, 8, 1, 1)) + model:add(nn.View(nFeatures):setNumInputDims(3)) + + -- Add Dropout (keep 0.8) + model:add(nn.Dropout(0.2)) + + -- Add Classifier + model:add(nn.Linear(nFeatures, 1000)) + + -- Init + local function ConvInit(name) + for k,v in pairs(model:findModules(name)) do + local n = v.kW*v.kH*v.nOutputPlane + v.weight:normal(0,math.sqrt(2/n)) + if cudnn.version >= 4000 then + v.bias = nil + v.gradBias = nil + else + v.bias:zero() + end + end + end + local function BNInit(name) + for k,v in pairs(model:findModules(name)) do + v.weight:fill(1) + v.bias:zero() + end + end + + ConvInit('cudnn.SpatialConvolution') + ConvInit('nn.SpatialConvolution') + BNInit('fbnn.SpatialBatchNormalization') + BNInit('cudnn.SpatialBatchNormalization') + BNInit('nn.SpatialBatchNormalization') + for k,v in pairs(model:findModules('nn.Linear')) do + v.bias:zero() + end + + -- Convert to cuda + model:cuda() + + if opt.cudnn == 'deterministic' then + model:apply(function(m) + if m.setMode then m:setMode(1,1,1) end + end) + end + + model:get(1).gradInput = nil + + return model +end + +return createModel diff --git a/models/inceptionv4aux.lua b/models/inceptionv4aux.lua new file mode 100644 index 000000000..c93ac371f --- /dev/null +++ b/models/inceptionv4aux.lua @@ -0,0 +1,399 @@ + +local nn = require 'nn' +require 'cunn' +local cudnn = require 'cudnn' + +local Convolution = cudnn.SpatialConvolution +local Avg = cudnn.SpatialAveragePooling +local ReLU = cudnn.ReLU +local Max = nn.SpatialMaxPooling +local SBatchNorm = nn.SpatialBatchNormalization +local BatchNorm = nn.BatchNormalization + +local function addConv(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + :add(ReLU(true)) + return layer +end + +local function addConvLinear(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) -- addConv without ReLU + assert(not (nInputPlane == nil)) + assert(not (nOutputPlane == nil)) + assert(not (kh == nil)) + assert(not (kw == nil)) + local sh = sh or 1 + local sw = sw or 1 + local ph = ph or 0 + local pw = pw or 0 + + local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) + :add(SBatchNorm(nOutputPlane)) + return layer +end + +local function Stem(fs_start) -- fig 3 + local fs0 = {32, 32, 64}; fs0[0] = fs_start + local net = nn.Sequential() + ------------------- nInputPlane, nOutputPlane, k, k, s, s, p, p + net:add(addConv(fs0[0], fs0[1], 3, 3, 2, 2, 0, 0)) + net:add(addConv(fs0[1], fs0[2], 3, 3, 1, 1, 0, 0)) + net:add(addConv(fs0[2], fs0[3], 3, 3, 1, 1, 1, 1)) + + local fs1a = {}; fs1a[0] = fs0[#fs0] + local fs1b = {96}; fs1b[0] = fs0[#fs0] + local concat1 = nn.ConcatTable() + concat1:add(Max(3, 3, 2, 2, 0, 0)) + concat1:add(addConv(fs1b[0], fs1b[1], 3, 3, 2, 2, 0, 0)) + + net:add(concat1) + net:add(nn.JoinTable(2, 4)) + + local fs2a = {64, 96}; fs2a[0] = fs1a[#fs1a] + fs1b[#fs1b] + local fs2b = {64, 64, 64, 96}; fs2b[0] = fs1a[#fs1a] + fs1b[#fs1b] + local concat2 = nn.ConcatTable() + concat2:add(nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2a[1], fs2a[2], 3, 3, 1, 1, 0, 0)) + ) + concat2:add(nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 7, 1, 1, 1, 3, 0)) + :add(addConv(fs2b[2], fs2b[3], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2b[3], fs2b[4], 3, 3, 1, 1, 0, 0)) + ) + net:add(concat2) + net:add(nn.JoinTable(2, 4)) + + local fs3a = {192}; fs3a[0] = fs2a[#fs2a] + fs2b[#fs2b] + local fs3b = {}; fs3b[0] = fs2a[#fs2a] + fs2b[#fs2b] + local concat3 = nn.ConcatTable() + concat3:add(addConv(fs3a[0], fs3a[1], 3, 3, 2, 2, 0, 0)) + :add(Max(3, 3, 2, 2, 0, 0)) + net:add(concat3) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs3a[#fs3a] + fs3b[#fs3b] + return net, fs_final +end + +local function InceptionResnetA(fs_start) -- fig 16 + local path1 = nn.Identity() + + local fs2a = {32}; fs2a[0] = fs_start + local fs2b = {32, 32}; fs2b[0] = fs_start + local fs2c = {32, 48, 64}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {384}; + fs2[0] = fs2a[#fs2a] + fs2b[#fs2b] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2b = nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2b[1], fs2b[2], 3, 3, 1, 1, 1, 1)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 3, 1, 1, 1, 1)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2b) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionA(fs_start, k, l, m, n) -- fig 7 + local net = nn.Sequential() + + local concat = nn.ConcatTable() + concat:add(Max(3, 3, 2, 2, 0, 0)) -- path1 + concat:add(addConv(fs_start, n, 3, 3, 2, 2, 0, 0)) -- path2 + concat:add(nn.Sequential():add(addConv(fs_start, k, 1, 1, 1, 1, 0, 0)) -- path3 + :add(addConv(k, l, 3, 3, 1, 1, 1, 1)) + :add(addConv(l, m, 3, 3, 2, 2, 0, 0))) + net:add(concat) + net:add(nn.JoinTable(2, 4)) + + local fs_final = fs_start + n + m + + return net, fs_final +end + +local function InceptionResnetB(fs_start) -- fig 17 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {128, 160, 192}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {1152}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 7, 1, 1, 0, 3)) + :add(addConv(fs2c[2], fs2c[3], 7, 1, 1, 1, 3, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function ReductionB(fs_start) -- fig 18 + local path1 = Max(3, 3, 2, 2, 0, 0) + local fs2 = {256, 384}; fs2[0] = fs_start + local path2 = nn.Sequential():add(addConv(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2[1], fs2[2], 3, 3, 2, 2, 0, 0)) + --local fs3 = {256, 288}; fs3[0] = fs_start -- in the paper, but seems to have typo + local fs3 = {256, 256}; fs3[0] = fs_start + local path3 = nn.Sequential():add(addConv(fs3[0], fs3[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs3[1], fs3[2], 3, 3, 2, 2, 0, 0)) + --local fs4 = {256, 288, 320}; fs4[0] = fs_start -- in the paper, but seems to have typo + local fs4 = {256, 256, 256}; fs4[0] = fs_start + local path4 = nn.Sequential():add(addConv(fs4[0], fs4[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs4[1], fs4[2], 3, 3, 1, 1, 1, 1)) + :add(addConv(fs4[2], fs4[3], 3, 3, 2, 2, 0, 0)) + + local concat = nn.ConcatTable() + concat:add(path1) + concat:add(path2) + concat:add(path3) + concat:add(path4) + + local net = nn.Sequential() + net:add(concat) + net:add(nn.JoinTable(2,4)) + + local fs_final = fs_start + fs2[#fs2] + fs3[#fs3] + fs4[#fs4] + + return net, fs_final +end + +local function InceptionResnetC(fs_start) -- fig 19 + local path1 = nn.Identity() + + local fs2a = {192}; fs2a[0] = fs_start + local fs2c = {192, 224, 256}; fs2c[0] = fs_start + local fs2 = {fs_start}; --local fs2 = {2048}; + fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] + local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) + local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) + :add(addConv(fs2c[1], fs2c[2], 1, 3, 1, 1, 0, 1)) + :add(addConv(fs2c[2], fs2c[3], 3, 1, 1, 1, 1, 0)) + local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) + :add(path2c) + ) + :add(nn.JoinTable(2, 4)) + :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) + :add(nn.MulConstant(0.1)) + + local net = nn.Sequential() + net:add(nn.ConcatTable():add(path1) + :add(path2) + ) + net:add(nn.CAddTable(true)) + net:add(ReLU(true)) + + local fs_final = fs2[#fs2] + assert(fs_final == fs_start) + + return net, fs_final +end + +local function AuxClassifier1(fs_start, nClasses) + fs = {128, 128*4*4, 1024}; fs[0] = fs_start + + local model = nn.Sequential() + model:add(Avg(5,5,4,4):ceil()) + model:add(addConv(fs[0], fs[1], 1, 1, 1, 1, 0, 0)) + model:add(nn.View(fs[2]):setNumInputDims(3)) + + local lin = nn.Linear(fs[2], fs[3]) + if cudnn.version >= 4000 then + lin.bias = nil + lin.gradBias = nil + else + lin.bias:zero() + end + model:add(lin) + model:add(BatchNorm(fs[3])) + model:add(nn.ReLU()) + model:add(nn.Linear(fs[3], nClasses)) + return model +end + +local function AuxClassifier2(fs_start, nClasses) + fs = {256, 256*2*2, 1024}; fs[0] = fs_start + + local model = nn.Sequential() + model:add(Avg(5,5,3,3):ceil()) + model:add(addConv(fs[0], fs[1], 1, 1, 1, 1, 0, 0)) + model:add(nn.View(fs[2]):setNumInputDims(3)) + + local lin = nn.Linear(fs[2], fs[3]) + if cudnn.version >= 4000 then + lin.bias = nil + lin.gradBias = nil + else + lin.bias:zero() + end + model:add(lin) + model:add(BatchNorm(fs[3])) + model:add(nn.ReLU()) + model:add(nn.Linear(fs[3], nClasses)) + return model +end + +local function Classifier(fs_start, nClasses) + local nFeatures = fs_start -- set final channels + local model = nn.Sequential() + + -- Add Average Pooling + model:add(Avg(8, 8, 1, 1)) + model:add(nn.View(nFeatures):setNumInputDims(3)) + + -- Add Dropout (keep 0.8) + model:add(nn.Dropout(0.2)) + + -- Add Classifier + model:add(nn.Linear(nFeatures, nClasses)) + + return model +end + +local function createModel(opt) + local nClasses = 1000 + + local feature = nn.Sequential() + -- Add Stem module + local stem, fs = Stem(3) + feature:add(stem) + + -- Add Inception-resnet-A modules (x5) + for i = 1, 5 do + local irA, fs = InceptionResnetA(fs) + feature:add(irA) + end + + -- Add Reduction-A module + local rA, fs = ReductionA(fs, 256, 256, 384, 384) + feature:add(rA) + + -- Add auxiliary classifier + local auxcls1 = AuxClassifier1(fs, nClasses) + + local path2 = nn.Sequential() + -- Add Inception-resnet-B modules (x10) + for i = 1, 10 do + local irB, fs = InceptionResnetB(fs) + path2:add(irB) + end + + -- Add Reduction-B module + local rB, fs = ReductionB(fs) + path2:add(rB) + + -- Add auxiliary classifier + local auxcls2 = AuxClassifier2(fs, nClasses) + + local path3 = nn.Sequential() + -- Add Inception-resnet-C modules (x5) + for i = 1, 5 do + local irC, fs = InceptionResnetC(fs) + path3:add(irC) + end + + -- Add final classifier + local cls = Classifier(fs, nClasses) + path3:add(cls) + + -- first branch + local concat1 = nn.ConcatTable() + local concat2 = nn.ConcatTable() + + concat2:add(path3) + :add(auxcls2) + path2:add(concat2) + concat1:add(path2) + :add(auxcls1) + + local model = nn.Sequential() + model:add(feature) + :add(concat1) + :add(nn.FlattenTable()) + + -- Init + local function ConvInit(name) + for k,v in pairs(model:findModules(name)) do + local n = v.kW*v.kH*v.nOutputPlane + v.weight:normal(0,math.sqrt(2/n)) + if cudnn.version >= 4000 then + v.bias = nil + v.gradBias = nil + else + v.bias:zero() + end + end + end + local function BNInit(name) + for k,v in pairs(model:findModules(name)) do + v.weight:fill(1) + v.bias:zero() + end + end + + ConvInit('cudnn.SpatialConvolution') + ConvInit('nn.SpatialConvolution') + BNInit('fbnn.SpatialBatchNormalization') + BNInit('cudnn.SpatialBatchNormalization') + BNInit('nn.SpatialBatchNormalization') + BNInit('nn.BatchNormalization') + for k,v in pairs(model:findModules('nn.Linear')) do + if v.bias then + v.bias:zero() + end + end + + -- Convert to cuda + model:cuda() + + if opt.cudnn == 'deterministic' then + model:apply(function(m) + if m.setMode then m:setMode(1,1,1) end + end) + end + + model:get(1).gradInput = nil + + return model +end + +return createModel diff --git a/models/init.lua b/models/init.lua index 553453902..03dbf0292 100644 --- a/models/init.lua +++ b/models/init.lua @@ -110,7 +110,13 @@ function M.setup(opt, checkpoint) model = dpt:cuda() end - local criterion = nn.CrossEntropyCriterion():cuda() + local criterion + if opt.netType == 'inceptionv4aux' then + local CE = nn.CrossEntropyCriterion() + criterion = nn.ParallelCriterion(true):add(CE):add(CE,0.3):add(CE,0.3):cuda() + else + criterion = nn.CrossEntropyCriterion():cuda() + end return model, criterion end diff --git a/opts.lua b/opts.lua index 4c5112d24..34c6ead64 100644 --- a/opts.lua +++ b/opts.lua @@ -36,8 +36,10 @@ function M.parse(arg) cmd:option('-LR', 0.1, 'initial learning rate') cmd:option('-momentum', 0.9, 'momentum') cmd:option('-weightDecay', 1e-4, 'weight decay') + cmd:option('-gamma', 0.96, 'gamma for learning rate policy (step)') + cmd:option('-step', 6400, 'step for learning rate policy (step)') ---------- Model options ---------------------------------- - cmd:option('-netType', 'resnet', 'Options: resnet | preresnet') + cmd:option('-netType', 'resnet', 'Options: resnet | preresnet | inceptionv4 | inceptionv4aux') cmd:option('-depth', 34, 'ResNet depth: 18 | 34 | 50 | 101 | ...', 'number') cmd:option('-shortcutType', '', 'Options: A | B | C') cmd:option('-retrain', 'none', 'Path to model to retrain with') @@ -55,6 +57,12 @@ function M.parse(arg) opt.shareGradInput = opt.shareGradInput ~= 'false' opt.resetClassifier = opt.resetClassifier ~= 'false' + if opt.netType == 'inceptionv4' or opt.netType == 'inceptionv4aux' then + opt.momentum = 0.4737 + opt.LR = 0.045 --45 + opt.step = 12800 + end + if opt.dataset == 'imagenet' then -- Handle the most common case of missing -data flag local trainDir = paths.concat(opt.data, 'train') diff --git a/train.lua b/train.lua index 7843a1f8f..1e394ec44 100644 --- a/train.lua +++ b/train.lua @@ -31,7 +31,7 @@ end function Trainer:train(epoch, dataloader) -- Trains the model for a single epoch - self.optimState.learningRate = self:learningRate(epoch) + --self.optimState.learningRate = self:learningRate(epoch) local timer = torch.Timer() local dataTimer = torch.Timer() @@ -50,10 +50,21 @@ function Trainer:train(epoch, dataloader) for n, sample in dataloader:run() do local dataTime = dataTimer:time().real + -- Update learningRate + self.optimState.learningRate = self:learningRateStep(epoch, n, trainSize, self.opt.gamma, self.opt.step) + -- Copy input and target to the GPU self:copyInputs(sample) local output = self.model:forward(self.input):float() + --local output = self.model:forward(self.input) + --if type(output) == 'table' then + -- for i = 1, #output do + -- output[i]:float() + -- end + --else + -- output:float() + --end local loss = self.criterion:forward(self.model.output, self.target) self.model:zeroGradParameters() @@ -63,6 +74,12 @@ function Trainer:train(epoch, dataloader) optim.sgd(feval, self.params, self.optimState) local top1, top5 = self:computeScore(output, sample.target, 1) + --local top1, top5 + --if type(output) == 'table' then + -- top1, top5 = self:computeScore(output[1], sample.target, 1) + --else + -- top1, top5 = self:computeScore(output, sample.target, 1) + --end top1Sum = top1Sum + top1 top5Sum = top5Sum + top5 lossSum = lossSum + loss @@ -100,9 +117,23 @@ function Trainer:test(epoch, dataloader) self:copyInputs(sample) local output = self.model:forward(self.input):float() + --local output = self.model:forward(self.input) + --if type(output) == 'table' then + -- for i = 1, #output do + -- output[i]:float() + -- end + --else + -- output:float() + --end local loss = self.criterion:forward(self.model.output, self.target) local top1, top5 = self:computeScore(output, sample.target, nCrops) + --local top1, top5 + --if type(output) == 'table' then + -- top1, top5 = self:computeScore(output[1], sample.target, nCrops) + --else + -- top1, top5 = self:computeScore(output, sample.target, nCrops) + --end top1Sum = top1Sum + top1 top5Sum = top5Sum + top5 N = N + 1 @@ -171,4 +202,14 @@ function Trainer:learningRate(epoch) return self.opt.LR * math.pow(0.1, decay) end +function Trainer:learningRateStep(epoch, iter, size, gamma, step) + -- epoch = current epoch + -- iter = current iter number + -- size = num of data / batchsize + local iter_ = (epoch-1) * size + iter + + -- Training schedule + return self.opt.LR * math.pow(gamma, torch.floor(iter_ / step)) +end + return M.Trainer From 1d2f028ac1282eb448b729cec73442cd8d9294d4 Mon Sep 17 00:00:00 2001 From: Jaehyun Lim Date: Thu, 9 Jun 2016 19:22:55 +0900 Subject: [PATCH 2/5] change the names of networks from inceptionv4 to inception-resnet-v2 --- dataloader.lua | 2 +- datasets/imagenet.lua | 2 +- drawnet.lua | 8 +- ...ptionv4-cls.dot => inception-resnet-v2.dot | 938 ++++---- ...ptionv4-cls.svg => inception-resnet-v2.svg | 938 ++++---- inceptionv4-cls-aux.dot | 1445 ----------- inceptionv4-cls-aux.svg | 2137 ----------------- main.sh | 4 +- ...nv4aux.lua => inception-resnet-v2-aux.lua} | 0 ...nceptionv4.lua => inception-resnet-v2.lua} | 0 models/init.lua | 2 +- opts.lua | 4 +- 12 files changed, 949 insertions(+), 4531 deletions(-) rename inceptionv4-cls.dot => inception-resnet-v2.dot (92%) rename inceptionv4-cls.svg => inception-resnet-v2.svg (93%) delete mode 100644 inceptionv4-cls-aux.dot delete mode 100644 inceptionv4-cls-aux.svg rename models/{inceptionv4aux.lua => inception-resnet-v2-aux.lua} (100%) rename models/{inceptionv4.lua => inception-resnet-v2.lua} (100%) diff --git a/dataloader.lua b/dataloader.lua index fa49301b1..223b42e25 100644 --- a/dataloader.lua +++ b/dataloader.lua @@ -39,7 +39,7 @@ function DataLoader:__init(dataset, opt, split) end torch.setnumthreads(1) _G.dataset = dataset - if opt.netType == 'inceptionv4' or opt.netType == 'inceptionv4aux' then + if opt.netType == 'inception-resnet-v2' or opt.netType == 'inception-resnet-v2-aux' then _G.preprocess = dataset:preprocess(328, 299) else _G.preprocess = dataset:preprocess(256, 224) diff --git a/datasets/imagenet.lua b/datasets/imagenet.lua index 8f0581c7a..a26f745a4 100644 --- a/datasets/imagenet.lua +++ b/datasets/imagenet.lua @@ -79,7 +79,7 @@ local pca = { function ImagenetDataset:preprocess(minSize, cropSize) -- minSize : 256, cropSize : 224 for resnet - -- minSize : 328, cropSize : 299 for inceptionv4 + -- minSize : 328, cropSize : 299 for inception-resnet-v2 if self.split == 'train' then return t.Compose{ diff --git a/drawnet.lua b/drawnet.lua index d28427ece..dc30cc1ac 100644 --- a/drawnet.lua +++ b/drawnet.lua @@ -84,16 +84,16 @@ input_rois = torch.cat(torch.floor(torch.rand(n_rois,1) * batch_size) + 1, input -- Create net w/ options print('Create network architectures') ---local createModel = paths.dofile('models/inceptionv4.lua') -local createModel = paths.dofile('models/inceptionv4aux.lua') +local createModel = paths.dofile('models/inception-resnet-v2.lua') +--local createModel = paths.dofile('models/inception-resnet-v2-aux.lua') local opt = {}; opt.cudnn = 'fastest' local model = createModel(opt) ---local g = drawModel(model, input_image, 'inceptionv4-cls') -local g = drawModel(model, input_image, 'inceptionv4-cls-aux') +local g = drawModel(model, input_image, 'inception-resnet-v2') +--local g = drawModel(model, input_image, 'inception-resnet-v2-aux') --local output = model:forward(input_image:cuda()) --print(output) diff --git a/inceptionv4-cls.dot b/inception-resnet-v2.dot similarity index 92% rename from inceptionv4-cls.dot rename to inception-resnet-v2.dot index fac43be45..3cac08206 100644 --- a/inceptionv4-cls.dot +++ b/inception-resnet-v2.dot @@ -3,7 +3,7 @@ digraph G { node [label="\N", shape=oval ]; - n1 [color=darksalmon, + n1 [color=cyan3, fontsize=14, height=1.3356, label="Input\nStorage id: 1\nSize: {1, 3, 299, 299}\nMem size: 268203", @@ -11,7 +11,7 @@ digraph G { shape=ellipse, style=solid, width=2.6788]; - n2 [color=aquamarine2, + n2 [color=burlywood1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(3 -> 32, 3x3, 2,2) without bias\nStorage id: 2\nSize: {1, 32, 149, 149}\nMem size: 710432", @@ -20,7 +20,7 @@ digraph G { style=solid, width=6.5007]; n1 -> n2 [pos="e,666,46692 666,46728 666,46720 666,46711 666,46703"]; - n3 [color=dodgerblue, + n3 [color=antiquewhite, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 3\nSize: {1, 32, 149, 149}\nMem size: 710432", @@ -29,7 +29,7 @@ digraph G { style=solid, width=3.5652]; n2 -> n3 [pos="e,666,46560 666,46596 666,46588 666,46579 666,46571"]; - n4 [color=dodgerblue, + n4 [color=antiquewhite, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 3\nSize: {1, 32, 149, 149}\nMem size: 710432", @@ -38,7 +38,7 @@ digraph G { style=solid, width=2.817]; n3 -> n4 [pos="e,666,46428 666,46464 666,46456 666,46447 666,46439"]; - n5 [color=brown1, + n5 [color=darkgoldenrod2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3) without bias\nStorage id: 4\nSize: {1, 32, 147, 147}\nMem size: 691488", @@ -47,7 +47,7 @@ digraph G { style=solid, width=6.1434]; n4 -> n5 [pos="e,666,46296 666,46332 666,46324 666,46315 666,46307"]; - n6 [color=darkolivegreen1, + n6 [color=darkgoldenrod2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 5\nSize: {1, 32, 147, 147}\nMem size: 691488", @@ -56,7 +56,7 @@ digraph G { style=solid, width=3.5652]; n5 -> n6 [pos="e,666,46164 666,46200 666,46192 666,46183 666,46175"]; - n7 [color=darkolivegreen1, + n7 [color=darkgoldenrod2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 5\nSize: {1, 32, 147, 147}\nMem size: 691488", @@ -65,7 +65,7 @@ digraph G { style=solid, width=2.817]; n6 -> n7 [pos="e,666,46032 666,46068 666,46060 666,46051 666,46043"]; - n8 [color=darkseagreen1, + n8 [color=blue3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 6\nSize: {1, 64, 147, 147}\nMem size: 1382976", @@ -74,7 +74,7 @@ digraph G { style=solid, width=7.0968]; n7 -> n8 [pos="e,666,45900 666,45936 666,45928 666,45919 666,45911"]; - n9 [color=bisque, + n9 [color=deepskyblue3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 7\nSize: {1, 64, 147, 147}\nMem size: 1382976", @@ -83,7 +83,7 @@ digraph G { style=solid, width=3.5652]; n8 -> n9 [pos="e,666,45768 666,45804 666,45796 666,45787 666,45779"]; - n10 [color=bisque, + n10 [color=deepskyblue3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 7\nSize: {1, 64, 147, 147}\nMem size: 1382976", @@ -92,7 +92,7 @@ digraph G { style=solid, width=2.817]; n9 -> n10 [pos="e,666,45636 666,45672 666,45664 666,45655 666,45647"]; - n11 [color=darksalmon, + n11 [color=cornsilk, fontsize=14, height=1.3356, label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 8\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -101,7 +101,7 @@ digraph G { style=solid, width=3.7843]; n10 -> n11 [pos="e,523.91,45372 601.38,45551 584.14,45538 567.22,45522 556,45504 533.77,45468 526.38,45420 524.35,45383"]; - n12 [color=deepskyblue2, + n12 [color=darkturquoise, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(64 -> 96, 3x3, 2,2) without bias\nStorage id: 9\nSize: {1, 96, 73, 73}\nMem size: 511584", @@ -110,7 +110,7 @@ digraph G { style=solid, width=6.6389]; n10 -> n12 [pos="e,754.65,45503 710.8,45545 722.41,45534 735.05,45522 747.18,45511"]; - n15 [color=dodgerblue2, + n15 [color=cornsilk1, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 11\nSize: {1, 160, 73, 73}\nMem size: 852640", @@ -119,7 +119,7 @@ digraph G { style=solid, width=2.6788]; n11 -> n15 [pos="e,658.14,45106 553.41,45277 581.2,45232 623.51,45163 652.82,45115"]; - n13 [color=darkorange, + n13 [color=gainsboro, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 10\nSize: {1, 96, 73, 73}\nMem size: 511584", @@ -128,7 +128,7 @@ digraph G { style=solid, width=3.5652]; n12 -> n13 [pos="e,806.54,45372 805.46,45408 805.71,45400 805.97,45391 806.23,45383"]; - n14 [color=darkorange, + n14 [color=gainsboro, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 10\nSize: {1, 96, 73, 73}\nMem size: 511584", @@ -138,7 +138,7 @@ digraph G { width=2.5643]; n13 -> n14 [pos="e,781.78,45240 793.2,45276 790.5,45268 787.66,45258 784.88,45250"]; n14 -> n15 [pos="e,714.05,45106 738.9,45146 732.61,45136 725.89,45125 719.42,45115"]; - n16 [color=deepskyblue3, + n16 [color=beige, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 64, 1x1) without bias\nStorage id: 12\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -147,7 +147,7 @@ digraph G { style=solid, width=6.2816]; n15 -> n16 [pos="e,530.62,44973 622.41,45024 596.89,45010 567.05,44993 539.48,44978"]; - n22 [color=deepskyblue3, + n22 [color=chocolate3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 64, 1x1) without bias\nStorage id: 16\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -156,7 +156,7 @@ digraph G { style=solid, width=6.2816]; n15 -> n22 [pos="e,850.32,44973 751.48,45025 778.92,45010 811.33,44993 841.16,44978"]; - n17 [color=coral4, + n17 [color=chocolate1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 13\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -165,7 +165,7 @@ digraph G { style=solid, width=3.5652]; n16 -> n17 [pos="e,447.19,44844 448.82,44880 448.44,44872 448.05,44863 447.66,44855"]; - n18 [color=coral4, + n18 [color=chocolate1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 13\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -174,7 +174,7 @@ digraph G { style=solid, width=2.5643]; n17 -> n18 [pos="e,444.37,44712 444.64,44748 444.57,44740 444.51,44731 444.44,44723"]; - n19 [color=bisque3, + n19 [color=bisque, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(64 -> 96, 3x3) without bias\nStorage id: 14\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -183,7 +183,7 @@ digraph G { style=solid, width=6.1434]; n18 -> n19 [pos="e,444,44580 444,44616 444,44608 444,44599 444,44591"]; - n20 [color=dodgerblue1, + n20 [color=chocolate2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 15\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -192,7 +192,7 @@ digraph G { style=solid, width=3.5652]; n19 -> n20 [pos="e,485.14,44447 467.11,44484 471.51,44475 476.16,44466 480.68,44456"]; - n21 [color=dodgerblue1, + n21 [color=chocolate2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 15\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -201,7 +201,7 @@ digraph G { style=solid, width=2.5643]; n20 -> n21 [pos="e,537.51,44184 514.48,44352 520.55,44308 529.64,44241 536.13,44194"]; - n34 [color=gold4, + n34 [color=darkgoldenrod, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 24\nSize: {1, 192, 71, 71}\nMem size: 967872", @@ -210,7 +210,7 @@ digraph G { style=solid, width=2.6788]; n21 -> n34 [pos="e,661.57,43391 561.35,44088 579.07,44037 604,43950 604,43873 604,43873 604,43873 604,43607 604,43533 633.82,43452 657.27,43400"]; - n23 [color=blue, + n23 [color=cornsilk3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -219,7 +219,7 @@ digraph G { style=solid, width=3.5652]; n22 -> n23 [pos="e,935.9,44844 935.09,44880 935.28,44872 935.48,44863 935.67,44855"]; - n24 [color=blue, + n24 [color=cornsilk3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 17\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -228,7 +228,7 @@ digraph G { style=solid, width=2.5643]; n23 -> n24 [pos="e,937.63,44712 937.36,44748 937.43,44740 937.49,44731 937.56,44723"]; - n25 [color=brown4, + n25 [color=aquamarine3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(64 -> 64, 7x1, 1,1, 3,0) without bias\nStorage id: 18\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -237,7 +237,7 @@ digraph G { style=solid, width=7.0968]; n24 -> n25 [pos="e,938.63,44580 938.36,44616 938.43,44608 938.49,44599 938.56,44591"]; - n26 [color=burlywood2, + n26 [color=aliceblue, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -246,7 +246,7 @@ digraph G { style=solid, width=3.5652]; n25 -> n26 [pos="e,937.73,44448 938.27,44484 938.15,44476 938.02,44467 937.89,44459"]; - n27 [color=burlywood2, + n27 [color=aliceblue, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 19\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -255,7 +255,7 @@ digraph G { style=solid, width=2.5643]; n26 -> n27 [pos="e,926.79,44316 931.18,44352 930.15,44343 929.07,44335 928.02,44326"]; - n28 [color=chartreuse, + n28 [color=gold4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(64 -> 64, 1x7, 1,1, 0,3) without bias\nStorage id: 20\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -264,7 +264,7 @@ digraph G { style=solid, width=7.0968]; n27 -> n28 [pos="e,915.92,44184 918.09,44220 917.59,44212 917.06,44203 916.54,44195"]; - n29 [color=bisque4, + n29 [color=beige, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -273,7 +273,7 @@ digraph G { style=solid, width=3.5652]; n28 -> n29 [pos="e,907.92,44052 910.09,44088 909.59,44080 909.06,44071 908.54,44063"]; - n30 [color=bisque4, + n30 [color=beige, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 21\nSize: {1, 64, 73, 73}\nMem size: 341056", @@ -282,7 +282,7 @@ digraph G { style=solid, width=2.5643]; n29 -> n30 [pos="e,885.81,43920 894.17,43956 892.19,43948 890.11,43938 888.08,43930"]; - n31 [color=burlywood4, + n31 [color=deepskyblue3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(64 -> 96, 3x3) without bias\nStorage id: 22\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -291,7 +291,7 @@ digraph G { style=solid, width=6.1434]; n30 -> n31 [pos="e,865.48,43788 869.54,43824 868.6,43816 867.61,43807 866.65,43799"]; - n32 [color=bisque4, + n32 [color=deepskyblue3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -300,7 +300,7 @@ digraph G { style=solid, width=3.5652]; n31 -> n32 [pos="e,828.02,43656 841.95,43692 838.62,43683 835.11,43674 831.69,43665"]; - n33 [color=bisque4, + n33 [color=deepskyblue3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 23\nSize: {1, 96, 71, 71}\nMem size: 483936", @@ -310,7 +310,7 @@ digraph G { width=2.5643]; n32 -> n33 [pos="e,782.36,43523 794.48,43560 791.58,43551 788.52,43542 785.55,43533"]; n33 -> n34 [pos="e,713.4,43390 738.55,43430 732.19,43420 725.38,43409 718.83,43399"]; - n35 [color=gold1, + n35 [color=cornsilk, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(192 -> 192, 3x3, 2,2) without bias\nStorage id: 25\nSize: {1, 192, 35, 35}\nMem size: 235200", @@ -319,7 +319,7 @@ digraph G { style=solid, width=6.8916]; n34 -> n35 [pos="e,596.12,43259 640.58,43301 628.83,43290 615.99,43278 603.69,43266"]; - n38 [color=darkgoldenrod2, + n38 [color=cyan3, fontsize=14, height=1.3356, label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 27\nSize: {1, 192, 35, 35}\nMem size: 235200", @@ -328,7 +328,7 @@ digraph G { style=solid, width=3.7843]; n34 -> n38 [pos="e,831.68,43128 752.84,43310 772.36,43297 791.67,43280 804,43260 826.16,43224 831.56,43176 831.76,43138"]; - n36 [color=bisque3, + n36 [color=darkorange4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", @@ -337,7 +337,7 @@ digraph G { style=solid, width=3.5652]; n35 -> n36 [pos="e,545.73,43128 546.27,43164 546.15,43156 546.02,43147 545.89,43139"]; - n37 [color=bisque3, + n37 [color=darkorange4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 26\nSize: {1, 192, 35, 35}\nMem size: 235200", @@ -346,7 +346,7 @@ digraph G { style=solid, width=2.6788]; n36 -> n37 [pos="e,596.55,42994 573.07,43033 578.97,43023 585.25,43013 591.3,43003"]; - n39 [color=azure2, + n39 [color=firebrick3, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -356,7 +356,7 @@ digraph G { width=2.6788]; n37 -> n39 [pos="e,677.6,42862 652.45,42902 658.81,42892 665.62,42881 672.17,42871"]; n38 -> n39 [pos="e,727.47,42863 806.33,43032 785.47,42988 753.9,42920 731.77,42872"]; - n40 [color=darkolivegreen3, + n40 [color=firebrick2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 29\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -365,7 +365,7 @@ digraph G { style=solid, width=6.2816]; n39 -> n40 [pos="e,363.74,42722 621.84,42792 553.37,42774 455.09,42747 373.59,42725"]; - n43 [color=gold2, + n43 [color=blanchedalmond, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 31\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -374,7 +374,7 @@ digraph G { style=solid, width=6.2816]; n39 -> n43 [pos="e,553.2,42600 668.58,42771 658.33,42759 647.45,42745 638,42732 609.32,42692 579.95,42645 558.48,42609"]; - n49 [color=goldenrod1, + n49 [color=antiquewhite, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 35\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -383,7 +383,7 @@ digraph G { style=solid, width=6.2816]; n39 -> n49 [pos="e,814.4,42731 757.47,42775 772.95,42763 790.17,42749 806.48,42737"]; - n62 [color=azure2, + n62 [color=firebrick3, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -393,7 +393,7 @@ digraph G { width=2.6788]; n39 -> n62 [pos="e,1080.1,41014 800.96,42807 905.56,42796 1064.7,42773 1108,42732 1167.5,42675 1154,42635 1154,42553 1154,42553 1154,42553 1154,41231 \ 1154,41155 1115.4,41074 1085.2,41023"]; - n41 [color=deeppink, + n41 [color=chocolate, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -402,7 +402,7 @@ digraph G { style=solid, width=3.5652]; n40 -> n41 [pos="e,179.07,42599 199.65,42636 194.57,42627 189.21,42617 184,42608"]; - n42 [color=deeppink, + n42 [color=chocolate, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 30\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -411,7 +411,7 @@ digraph G { style=solid, width=2.5643]; n41 -> n42 [pos="e,265.49,41938 165.73,42504 178.72,42452 197,42365 197,42289 197,42289 197,42289 197,42155 197,42079 232.71,41999 260.57,41947"]; - n58 [color=cadetblue1, + n58 [color=deepskyblue3, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 41\nSize: {1, 128, 35, 35}\nMem size: 156800", @@ -420,7 +420,7 @@ digraph G { style=solid, width=2.6788]; n42 -> n58 [pos="e,607.29,41539 325.41,41847 353.07,41810 394.52,41756 434,41712 487.74,41652 554.33,41588 599.88,41546"]; - n44 [color=deepskyblue4, + n44 [color=darkolivegreen4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -429,7 +429,7 @@ digraph G { style=solid, width=3.5652]; n43 -> n44 [pos="e,513.88,42468 519.09,42504 517.87,42495 516.58,42487 515.33,42478"]; - n45 [color=deepskyblue4, + n45 [color=darkolivegreen4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 32\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -438,7 +438,7 @@ digraph G { style=solid, width=2.5643]; n44 -> n45 [pos="e,496.15,42336 500.81,42372 499.72,42363 498.58,42355 497.45,42346"]; - n46 [color=firebrick2, + n46 [color=darkolivegreen4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 33\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -447,7 +447,7 @@ digraph G { style=solid, width=7.0968]; n45 -> n46 [pos="e,486.19,42204 487.82,42240 487.44,42232 487.05,42223 486.66,42215"]; - n47 [color=darkgoldenrod, + n47 [color=blueviolet, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -456,7 +456,7 @@ digraph G { style=solid, width=3.5652]; n46 -> n47 [pos="e,503.14,42072 494.92,42108 496.84,42099 498.87,42091 500.85,42082"]; - n48 [color=darkgoldenrod, + n48 [color=blueviolet, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -466,7 +466,7 @@ digraph G { width=2.5643]; n47 -> n48 [pos="e,532.04,41808 517.96,41976 521.67,41932 527.23,41865 531.19,41818"]; n48 -> n58 [pos="e,632.31,41543 556.51,41713 576.48,41668 606.85,41600 628.18,41553"]; - n50 [color=brown3, + n50 [color=darkorange, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -475,7 +475,7 @@ digraph G { style=solid, width=3.5652]; n49 -> n50 [pos="e,889.59,42600 882.46,42636 884.13,42627 885.88,42619 887.6,42610"]; - n51 [color=brown3, + n51 [color=darkorange, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -484,7 +484,7 @@ digraph G { style=solid, width=2.5643]; n50 -> n51 [pos="e,896.46,42468 897.54,42504 897.29,42496 897.03,42487 896.77,42479"]; - n52 [color=darkseagreen4, + n52 [color=black, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 37\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -493,7 +493,7 @@ digraph G { style=solid, width=7.0968]; n51 -> n52 [pos="e,879.1,42336 885.97,42372 884.37,42364 882.67,42355 881.02,42346"]; - n53 [color=darkorchid3, + n53 [color=cyan2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -502,7 +502,7 @@ digraph G { style=solid, width=3.5652]; n52 -> n53 [pos="e,880.85,42204 876.19,42240 877.28,42231 878.42,42223 879.55,42214"]; - n54 [color=darkorchid3, + n54 [color=cyan2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -511,7 +511,7 @@ digraph G { style=solid, width=2.5643]; n53 -> n54 [pos="e,881.92,42072 884.09,42108 883.59,42100 883.06,42091 882.54,42083"]; - n55 [color=aquamarine2, + n55 [color=deepskyblue2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 39\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -520,7 +520,7 @@ digraph G { style=solid, width=7.0968]; n54 -> n55 [pos="e,841.3,41940 858.03,41977 853.96,41968 849.65,41958 845.45,41949"]; - n56 [color=deeppink2, + n56 [color=cadetblue2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -529,7 +529,7 @@ digraph G { style=solid, width=3.5652]; n55 -> n56 [pos="e,800.86,41808 809.08,41844 807.16,41835 805.13,41827 803.15,41818"]; - n57 [color=deeppink2, + n57 [color=cadetblue2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -539,7 +539,7 @@ digraph G { width=2.5643]; n56 -> n57 [pos="e,768.9,41676 778.09,41712 775.91,41704 773.62,41694 771.39,41686"]; n57 -> n58 [pos="e,688.36,41541 722.08,41583 713.34,41572 703.88,41561 694.85,41549"]; - n59 [color=darkolivegreen, + n59 [color=cadetblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 42\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -548,7 +548,7 @@ digraph G { style=solid, width=6.3961]; n58 -> n59 [pos="e,701.53,41412 679.58,41450 685.02,41440 690.81,41430 696.43,41421"]; - n60 [color=cornflowerblue, + n60 [color=gold4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 43\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -557,7 +557,7 @@ digraph G { style=solid, width=3.5652]; n59 -> n60 [pos="e,817.33,41277 776.62,41317 787.48,41306 799.09,41295 810.15,41284"]; - n61 [color=darkolivegreen, + n61 [color=darkseagreen3, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 44\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -567,7 +567,7 @@ digraph G { width=2.6788]; n60 -> n61 [pos="e,917.28,41146 892.26,41185 898.61,41175 905.37,41165 911.88,41154"]; n61 -> n62 [pos="e,1014.8,41013 981.21,41055 989.93,41044 999.37,41032 1008.4,41021"]; - n63 [color=azure2, + n63 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -576,7 +576,7 @@ digraph G { style=solid, width=2.6788]; n62 -> n63 [pos="e,1050,40884 1050,40920 1050,40912 1050,40903 1050,40895"]; - n64 [color=bisque2, + n64 [color=gold4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 45\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -585,7 +585,7 @@ digraph G { style=solid, width=6.2816]; n63 -> n64 [pos="e,707.74,40742 965.84,40812 897.37,40794 799.09,40767 717.59,40745"]; - n67 [color=darkkhaki, + n67 [color=aquamarine2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 47\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -594,7 +594,7 @@ digraph G { style=solid, width=6.2816]; n63 -> n67 [pos="e,860.69,40620 999.56,40794 984.81,40782 969.13,40767 956,40752 921.87,40713 889.15,40665 866.05,40628"]; - n73 [color=dodgerblue2, + n73 [color=cyan3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 51\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -603,7 +603,7 @@ digraph G { style=solid, width=6.2816]; n63 -> n73 [pos="e,1140.8,40751 1095.4,40793 1107.5,40782 1120.8,40770 1133.4,40758"]; - n86 [color=azure2, + n86 [color=firebrick3, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -613,7 +613,7 @@ digraph G { width=2.6788]; n63 -> n86 [pos="e,1391.9,39035 1144.7,40826 1243.1,40815 1387.9,40792 1426,40752 1482,40694 1458,40654 1458,40573 1458,40573 1458,40573 1458,39251 \ 1458,39176 1423.6,39095 1396.7,39044"]; - n65 [color=darkorange1, + n65 [color=gold4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -622,7 +622,7 @@ digraph G { style=solid, width=3.5652]; n64 -> n65 [pos="e,498.02,40618 530.6,40656 522.15,40646 513.16,40636 504.54,40626"]; - n66 [color=darkorange1, + n66 [color=gold4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 46\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -631,7 +631,7 @@ digraph G { style=solid, width=2.5643]; n65 -> n66 [pos="e,582.47,39957 471.28,40524 482.8,40472 499,40385 499,40309 499,40309 499,40309 499,40175 499,40097 542.7,40017 576.68,39966"]; - n82 [color=darkslategrey, + n82 [color=darkgoldenrod3, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 57\nSize: {1, 128, 35, 35}\nMem size: 156800", @@ -641,7 +641,7 @@ digraph G { width=2.6788]; n66 -> n82 [pos="e,986.5,39555 649.8,39867 679.23,39830 723.83,39775 767,39732 830.83,39668 849.94,39655 922,39600 939.79,39586 959.64,39573 978.12,\ 39560"]; - n68 [color=cornsilk3, + n68 [color=cyan1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -650,7 +650,7 @@ digraph G { style=solid, width=3.5652]; n67 -> n68 [pos="e,819.6,40488 825.36,40524 824.01,40515 822.59,40507 821.21,40498"]; - n69 [color=cornsilk3, + n69 [color=cyan1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 48\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -659,7 +659,7 @@ digraph G { style=solid, width=2.5643]; n68 -> n69 [pos="e,799.88,40356 805.09,40392 803.87,40383 802.58,40375 801.33,40366"]; - n70 [color=deepskyblue4, + n70 [color=gainsboro, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 49\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -668,7 +668,7 @@ digraph G { style=solid, width=7.0968]; n69 -> n70 [pos="e,788.56,40224 790.45,40260 790.01,40252 789.55,40243 789.1,40235"]; - n71 [color=deepskyblue1, + n71 [color=darkslategray3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -677,7 +677,7 @@ digraph G { style=solid, width=3.5652]; n70 -> n71 [pos="e,819.35,40092 804.92,40128 808.38,40119 812.01,40110 815.56,40101"]; - n72 [color=deepskyblue1, + n72 [color=darkslategray3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 50\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -687,7 +687,7 @@ digraph G { width=2.5643]; n71 -> n72 [pos="e,863.41,39828 843.58,39996 848.81,39952 856.64,39885 862.22,39838"]; n72 -> n82 [pos="e,1014.8,39562 899.06,39734 929.53,39689 976.68,39619 1009.1,39570"]; - n74 [color=darkolivegreen2, + n74 [color=darkorange, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -696,7 +696,7 @@ digraph G { style=solid, width=3.5652]; n73 -> n74 [pos="e,1200.6,40620 1196.5,40656 1197.4,40647 1198.4,40639 1199.4,40630"]; - n75 [color=darkolivegreen2, + n75 [color=darkorange, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 52\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -705,7 +705,7 @@ digraph G { style=solid, width=2.5643]; n74 -> n75 [pos="e,1203.5,40488 1204.5,40524 1204.3,40516 1204,40507 1203.8,40499"]; - n76 [color=chocolate, + n76 [color=darkolivegreen1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 53\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -714,7 +714,7 @@ digraph G { style=solid, width=7.0968]; n75 -> n76 [pos="e,1184.2,40356 1191.9,40392 1190.1,40384 1188.2,40375 1186.3,40366"]; - n77 [color=aliceblue, + n77 [color=bisque2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -723,7 +723,7 @@ digraph G { style=solid, width=3.5652]; n76 -> n77 [pos="e,1183.6,40224 1179.5,40260 1180.4,40251 1181.4,40243 1182.4,40234"]; - n78 [color=aliceblue, + n78 [color=bisque2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 54\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -732,7 +732,7 @@ digraph G { style=solid, width=2.5643]; n77 -> n78 [pos="e,1175.6,40092 1181.4,40128 1180,40119 1178.6,40111 1177.2,40102"]; - n79 [color=deeppink1, + n79 [color=darkkhaki, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 55\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -741,7 +741,7 @@ digraph G { style=solid, width=7.0968]; n78 -> n79 [pos="e,1163.6,39960 1165.5,39996 1165,39988 1164.6,39979 1164.1,39971"]; - n80 [color=darkgoldenrod1, + n80 [color=gainsboro, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -750,7 +750,7 @@ digraph G { style=solid, width=3.5652]; n79 -> n80 [pos="e,1149.5,39828 1154.5,39864 1153.3,39855 1152.1,39847 1150.9,39838"]; - n81 [color=darkgoldenrod1, + n81 [color=gainsboro, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 56\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -760,7 +760,7 @@ digraph G { width=2.5643]; n80 -> n81 [pos="e,1118.7,39696 1129.3,39732 1126.8,39724 1124.1,39714 1121.6,39706"]; n81 -> n82 [pos="e,1066.4,39563 1083.8,39601 1079.6,39592 1075,39582 1070.6,39572"]; - n83 [color=chocolate2, + n83 [color=goldenrod3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 58\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -769,7 +769,7 @@ digraph G { style=solid, width=6.3961]; n82 -> n83 [pos="e,1094.2,39432 1071.9,39470 1077.4,39460 1083.3,39450 1089,39441"]; - n84 [color=darkorchid4, + n84 [color=brown1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 59\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -778,7 +778,7 @@ digraph G { style=solid, width=3.5652]; n83 -> n84 [pos="e,1180.2,39299 1154.5,39336 1161,39327 1167.8,39317 1174.4,39307"]; - n85 [color=darkorange, + n85 [color=cornsilk2, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 60\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -788,7 +788,7 @@ digraph G { width=2.6788]; n84 -> n85 [pos="e,1250.7,39167 1233.5,39204 1237.7,39195 1242.1,39186 1246.4,39176"]; n85 -> n86 [pos="e,1332.9,39034 1304,39074 1311.4,39064 1319.3,39053 1326.9,39042"]; - n87 [color=azure2, + n87 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -797,7 +797,7 @@ digraph G { style=solid, width=2.6788]; n86 -> n87 [pos="e,1365,38904 1365,38940 1365,38932 1365,38923 1365,38915"]; - n88 [color=gold, + n88 [color=brown4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 61\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -806,7 +806,7 @@ digraph G { style=solid, width=6.2816]; n87 -> n88 [pos="e,1029.9,38763 1281.3,38832 1214.5,38813 1119.3,38787 1040,38765"]; - n91 [color=burlywood3, + n91 [color=cyan3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 63\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -815,7 +815,7 @@ digraph G { style=solid, width=6.2816]; n87 -> n91 [pos="e,1215,38640 1322.2,38812 1310.7,38800 1298.8,38786 1289,38772 1261.7,38733 1236.9,38686 1219.5,38650"]; - n97 [color=darkseagreen, + n97 [color=gainsboro, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 67\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -824,7 +824,7 @@ digraph G { style=solid, width=6.2816]; n87 -> n97 [pos="e,1468.2,38771 1414.4,38815 1429,38803 1445,38790 1460.3,38777"]; - n110 [color=azure2, + n110 [color=firebrick3, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -834,7 +834,7 @@ digraph G { width=2.6788]; n87 -> n110 [pos="e,1736.1,37054 1459.9,38846 1562.5,38835 1716.9,38812 1759,38772 1818.3,38715 1805,38675 1805,38593 1805,38593 1805,38593 1805,37271 \ 1805,37196 1769.1,37115 1741,37063"]; - n89 [color=chartreuse, + n89 [color=darkslategray1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -843,7 +843,7 @@ digraph G { style=solid, width=3.5652]; n88 -> n89 [pos="e,847.07,38639 867.65,38676 862.57,38667 857.21,38657 852,38648"]; - n90 [color=chartreuse, + n90 [color=darkslategray1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 62\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -852,7 +852,7 @@ digraph G { style=solid, width=2.5643]; n89 -> n90 [pos="e,1009.8,37966 823.02,38544 825.09,38491 828,38404 828,38329 828,38329 828,38329 828,38195 828,38093 928.28,38015 1001.1,37971"]; - n106 [color=bisque2, + n106 [color=bisque, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 73\nSize: {1, 128, 35, 35}\nMem size: 156800", @@ -861,7 +861,7 @@ digraph G { style=solid, width=2.6788]; n90 -> n106 [pos="e,1259.6,37579 1081.1,37884 1087.3,37846 1099.3,37794 1121,37752 1154.5,37688 1210.6,37626 1252.3,37586"]; - n92 [color=darkolivegreen2, + n92 [color=chartreuse1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -870,7 +870,7 @@ digraph G { style=solid, width=3.5652]; n91 -> n92 [pos="e,1185.1,38508 1188.9,38544 1188,38535 1187.1,38527 1186.1,38518"]; - n93 [color=darkolivegreen2, + n93 [color=chartreuse1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 64\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -879,7 +879,7 @@ digraph G { style=solid, width=2.5643]; n92 -> n93 [pos="e,1151.7,38375 1164.1,38412 1161.2,38403 1158,38394 1155,38385"]; - n94 [color=deepskyblue1, + n94 [color=bisque4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 65\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -888,7 +888,7 @@ digraph G { style=solid, width=7.0968]; n93 -> n94 [pos="e,1130.3,38244 1132.7,38280 1132.2,38272 1131.6,38263 1131,38255"]; - n95 [color=darkslategray4, + n95 [color=crimson, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -897,7 +897,7 @@ digraph G { style=solid, width=3.5652]; n94 -> n95 [pos="e,1162.3,38112 1147,38148 1150.7,38139 1154.6,38130 1158.4,38121"]; - n96 [color=darkslategray4, + n96 [color=crimson, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 66\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -907,7 +907,7 @@ digraph G { width=2.5643]; n95 -> n96 [pos="e,1215.6,37848 1189.4,38016 1196.3,37972 1206.6,37905 1214,37858"]; n96 -> n106 [pos="e,1291.3,37584 1237.7,37752 1251.8,37708 1273.1,37641 1288.1,37593"]; - n98 [color=brown1, + n98 [color=bisque1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -916,7 +916,7 @@ digraph G { style=solid, width=3.5652]; n97 -> n98 [pos="e,1551.4,38640 1539.6,38676 1542.4,38667 1545.4,38658 1548.2,38650"]; - n99 [color=brown1, + n99 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 68\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -925,7 +925,7 @@ digraph G { style=solid, width=2.5643]; n98 -> n99 [pos="e,1561.9,38508 1564.1,38544 1563.6,38536 1563.1,38527 1562.5,38519"]; - n100 [color=aquamarine4, + n100 [color=beige, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 69\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -934,7 +934,7 @@ digraph G { style=solid, width=7.0968]; n99 -> n100 [pos="e,1534.8,38376 1545.4,38412 1542.9,38404 1540.2,38395 1537.7,38386"]; - n101 [color=goldenrod4, + n101 [color=dodgerblue3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -943,7 +943,7 @@ digraph G { style=solid, width=3.5652]; n100 -> n101 [pos="e,1526.7,38244 1524.3,38280 1524.8,38272 1525.4,38263 1526,38255"]; - n102 [color=goldenrod4, + n102 [color=dodgerblue3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 70\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -952,7 +952,7 @@ digraph G { style=solid, width=2.5643]; n101 -> n102 [pos="e,1517.9,38112 1523.1,38148 1521.9,38139 1520.6,38131 1519.3,38122"]; - n103 [color=goldenrod1, + n103 [color=goldenrod3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 71\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -961,7 +961,7 @@ digraph G { style=solid, width=7.0968]; n102 -> n103 [pos="e,1508.5,37980 1509.5,38016 1509.3,38008 1509,37999 1508.8,37991"]; - n104 [color=darkorchid1, + n104 [color=darkgoldenrod3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -970,7 +970,7 @@ digraph G { style=solid, width=3.5652]; n103 -> n104 [pos="e,1484,37848 1493.9,37884 1491.6,37875 1489.1,37866 1486.7,37858"]; - n105 [color=darkorchid1, + n105 [color=darkgoldenrod3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 72\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -980,7 +980,7 @@ digraph G { width=2.5643]; n104 -> n105 [pos="e,1453.1,37716 1460.8,37752 1459,37743 1457.1,37735 1455.3,37726"]; n105 -> n106 [pos="e,1350.2,37579 1399.3,37626 1386.1,37613 1371.4,37599 1357.7,37586"]; - n107 [color=gold3, + n107 [color=darkorchid3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 74\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -989,7 +989,7 @@ digraph G { style=solid, width=6.3961]; n106 -> n107 [pos="e,1392.1,37451 1349.5,37493 1360.8,37482 1373.1,37470 1384.8,37459"]; - n108 [color=bisque1, + n108 [color=azure1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 75\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -998,7 +998,7 @@ digraph G { style=solid, width=3.5652]; n107 -> n108 [pos="e,1507.7,37318 1477.3,37356 1485.1,37347 1493.4,37336 1501.4,37326"]; - n109 [color=black, + n109 [color=blueviolet, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 76\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1008,7 +1008,7 @@ digraph G { width=2.6788]; n108 -> n109 [pos="e,1588,37187 1568.4,37224 1573.2,37215 1578.3,37205 1583.3,37196"]; n109 -> n110 [pos="e,1675.1,37054 1644.8,37095 1652.6,37084 1660.9,37073 1669,37062"]; - n111 [color=azure2, + n111 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1017,7 +1017,7 @@ digraph G { style=solid, width=2.6788]; n110 -> n111 [pos="e,1708,36924 1708,36960 1708,36952 1708,36943 1708,36935"]; - n112 [color=chocolate1, + n112 [color=cornsilk4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 77\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1026,7 +1026,7 @@ digraph G { style=solid, width=6.2816]; n111 -> n112 [pos="e,1365.7,36782 1623.8,36852 1555.4,36834 1457.1,36807 1375.6,36785"]; - n115 [color=coral2, + n115 [color=deepskyblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 79\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1035,7 +1035,7 @@ digraph G { style=solid, width=6.2816]; n111 -> n115 [pos="e,1533.5,36660 1656.8,36835 1642,36822 1626.5,36807 1614,36792 1583.3,36754 1556.5,36706 1538,36669"]; - n121 [color=burlywood1, + n121 [color=cornsilk2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 83\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1044,7 +1044,7 @@ digraph G { style=solid, width=6.2816]; n111 -> n121 [pos="e,1798.8,36791 1753.4,36833 1765.5,36822 1778.8,36810 1791.4,36798"]; - n134 [color=azure2, + n134 [color=firebrick3, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1054,7 +1054,7 @@ digraph G { width=2.6788]; n111 -> n134 [pos="e,2049.9,35075 1802.7,36866 1901.1,36855 2045.9,36832 2084,36792 2140,36734 2116,36694 2116,36613 2116,36613 2116,36613 2116,35291 \ 2116,35216 2081.6,35135 2054.7,35084"]; - n113 [color=chartreuse2, + n113 [color=gold1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1063,7 +1063,7 @@ digraph G { style=solid, width=3.5652]; n112 -> n113 [pos="e,1170.5,36659 1195.9,36696 1189.5,36687 1182.7,36677 1176.2,36667"]; - n114 [color=chartreuse2, + n114 [color=gold1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 78\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1072,7 +1072,7 @@ digraph G { style=solid, width=2.5643]; n113 -> n114 [pos="e,1230.3,35997 1140.4,36564 1141.9,36511 1144,36424 1144,36349 1144,36349 1144,36349 1144,36215 1144,36136 1189.4,36056 1224.6,36005"]; - n130 [color=coral4, + n130 [color=azure2, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 89\nSize: {1, 128, 35, 35}\nMem size: 156800", @@ -1082,7 +1082,7 @@ digraph G { width=2.6788]; n114 -> n130 [pos="e,1643.3,35594 1300.4,35908 1331.4,35870 1378.4,35816 1423,35772 1487.5,35709 1505.8,35695 1578,35640 1596,35626 1616.1,35612 1634.8,\ 35600"]; - n116 [color=darkseagreen4, + n116 [color=chartreuse, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1091,7 +1091,7 @@ digraph G { style=solid, width=3.5652]; n115 -> n116 [pos="e,1483.9,36528 1496,36564 1493.1,36555 1490.1,36546 1487.2,36538"]; - n117 [color=darkseagreen4, + n117 [color=chartreuse, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 80\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1100,7 +1100,7 @@ digraph G { style=solid, width=2.5643]; n116 -> n117 [pos="e,1454.6,36396 1460.4,36432 1459,36423 1457.6,36415 1456.2,36406"]; - n118 [color=goldenrod1, + n118 [color=chocolate1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 81\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1109,7 +1109,7 @@ digraph G { style=solid, width=7.0968]; n117 -> n118 [pos="e,1441.3,36264 1443.7,36300 1443.2,36292 1442.6,36283 1442,36275"]; - n119 [color=darkviolet, + n119 [color=darkseagreen4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1118,7 +1118,7 @@ digraph G { style=solid, width=3.5652]; n118 -> n119 [pos="e,1473.3,36132 1458,36168 1461.7,36159 1465.6,36150 1469.4,36141"]; - n120 [color=darkviolet, + n120 [color=darkseagreen4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 82\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1128,7 +1128,7 @@ digraph G { width=2.5643]; n119 -> n120 [pos="e,1519.2,35868 1498.8,36036 1504.2,35992 1512.2,35925 1518,35878"]; n120 -> n130 [pos="e,1671.6,35602 1555.2,35774 1585.9,35729 1633.3,35659 1665.9,35610"]; - n122 [color=cyan, + n122 [color=deeppink2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1137,7 +1137,7 @@ digraph G { style=solid, width=3.5652]; n121 -> n122 [pos="e,1872,36660 1862.1,36696 1864.4,36687 1866.9,36678 1869.3,36670"]; - n123 [color=cyan, + n123 [color=deeppink2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 84\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1146,7 +1146,7 @@ digraph G { style=solid, width=2.5643]; n122 -> n123 [pos="e,1881.2,36528 1882.8,36564 1882.4,36556 1882,36547 1881.7,36539"]; - n124 [color=floralwhite, + n124 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 85\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1155,7 +1155,7 @@ digraph G { style=solid, width=7.0968]; n123 -> n124 [pos="e,1849,36396 1862.2,36432 1859,36424 1855.7,36415 1852.5,36406"]; - n125 [color=dodgerblue, + n125 [color=deepskyblue4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1164,7 +1164,7 @@ digraph G { style=solid, width=3.5652]; n124 -> n125 [pos="e,1837.7,36264 1835.3,36300 1835.8,36292 1836.4,36283 1837,36275"]; - n126 [color=dodgerblue, + n126 [color=deepskyblue4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 86\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1173,7 +1173,7 @@ digraph G { style=solid, width=2.5643]; n125 -> n126 [pos="e,1828.9,36132 1834.1,36168 1832.9,36159 1831.6,36151 1830.3,36142"]; - n127 [color=cornflowerblue, + n127 [color=chartreuse2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 87\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1182,7 +1182,7 @@ digraph G { style=solid, width=7.0968]; n126 -> n127 [pos="e,1819.5,36000 1820.5,36036 1820.3,36028 1820,36019 1819.8,36011"]; - n128 [color=deepskyblue, + n128 [color=aquamarine1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1191,7 +1191,7 @@ digraph G { style=solid, width=3.5652]; n127 -> n128 [pos="e,1806.5,35868 1811.5,35904 1810.3,35895 1809.1,35887 1807.9,35878"]; - n129 [color=deepskyblue, + n129 [color=aquamarine1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 88\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1201,7 +1201,7 @@ digraph G { width=2.5643]; n128 -> n129 [pos="e,1775.7,35736 1786.3,35772 1783.8,35764 1781.1,35754 1778.6,35746"]; n129 -> n130 [pos="e,1723.4,35603 1740.8,35641 1736.6,35632 1732,35622 1727.6,35612"]; - n131 [color=blueviolet, + n131 [color=aquamarine2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 90\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1210,7 +1210,7 @@ digraph G { style=solid, width=6.3961]; n130 -> n131 [pos="e,1751.8,35472 1729.3,35510 1734.9,35500 1740.8,35490 1746.6,35481"]; - n132 [color=aquamarine3, + n132 [color=gold, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 91\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1219,7 +1219,7 @@ digraph G { style=solid, width=3.5652]; n131 -> n132 [pos="e,1838.2,35339 1812.5,35376 1819,35367 1825.8,35357 1832.4,35347"]; - n133 [color=cadetblue1, + n133 [color=azure1, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 92\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1229,7 +1229,7 @@ digraph G { width=2.6788]; n132 -> n133 [pos="e,1909.3,35207 1891.9,35244 1896.1,35235 1900.6,35226 1905,35216"]; n133 -> n134 [pos="e,1991.3,35074 1962.7,35114 1970,35104 1977.8,35093 1985.3,35082"]; - n135 [color=azure2, + n135 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1238,7 +1238,7 @@ digraph G { style=solid, width=2.6788]; n134 -> n135 [pos="e,2023,34944 2023,34980 2023,34972 2023,34963 2023,34955"]; - n136 [color=dimgray, + n136 [color=deepskyblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 93\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1247,7 +1247,7 @@ digraph G { style=solid, width=6.2816]; n135 -> n136 [pos="e,1680.7,34802 1938.8,34872 1870.4,34854 1772.1,34827 1690.6,34805"]; - n139 [color=forestgreen, + n139 [color=goldenrod2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 95\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1256,7 +1256,7 @@ digraph G { style=solid, width=6.2816]; n135 -> n139 [pos="e,1870.2,34680 1985.6,34851 1975.3,34839 1964.5,34825 1955,34812 1926.3,34772 1896.9,34725 1875.5,34689"]; - n145 [color=darkgreen, + n145 [color=black, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 99\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1265,7 +1265,7 @@ digraph G { style=solid, width=6.2816]; n135 -> n145 [pos="e,2131.4,34811 2074.5,34855 2090,34843 2107.2,34829 2123.5,34817"]; - n158 [color=azure2, + n158 [color=firebrick3, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1275,7 +1275,7 @@ digraph G { width=2.6788]; n135 -> n158 [pos="e,2405.6,33095 2118,34887 2222.6,34876 2381.7,34853 2425,34812 2484.5,34755 2471,34715 2471,34633 2471,34633 2471,34633 2471,33311 \ 2471,33236 2437,33155 2410.3,33104"]; - n137 [color=darkturquoise, + n137 [color=bisque1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1284,7 +1284,7 @@ digraph G { style=solid, width=3.5652]; n136 -> n137 [pos="e,1496.1,34679 1516.6,34716 1511.6,34707 1506.2,34697 1501,34688"]; - n138 [color=darkturquoise, + n138 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 94\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1293,7 +1293,7 @@ digraph G { style=solid, width=2.5643]; n137 -> n138 [pos="e,1608.5,34016 1482.7,34584 1495.7,34532 1514,34445 1514,34369 1514,34369 1514,34369 1514,34235 1514,34155 1563.8,34074 1602.2,34024"]; - n154 [color=chartreuse1, + n154 [color=gold2, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 105\nSize: {1, 128, 35, 35}\nMem size: 156800", @@ -1302,7 +1302,7 @@ digraph G { style=solid, width=2.6788]; n138 -> n154 [pos="e,1950.2,33619 1676.9,33927 1703.2,33890 1742.9,33836 1781,33792 1833,33732 1898,33668 1942.6,33626"]; - n140 [color=darkorchid2, + n140 [color=goldenrod4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1311,7 +1311,7 @@ digraph G { style=solid, width=3.5652]; n139 -> n140 [pos="e,1830.9,34548 1836.1,34584 1834.9,34575 1833.6,34567 1832.3,34558"]; - n141 [color=darkorchid2, + n141 [color=goldenrod4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 96\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1320,7 +1320,7 @@ digraph G { style=solid, width=2.5643]; n140 -> n141 [pos="e,1813.2,34416 1817.8,34452 1816.7,34443 1815.6,34435 1814.5,34426"]; - n142 [color=burlywood2, + n142 [color=cornsilk3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias\nStorage id: 97\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1329,7 +1329,7 @@ digraph G { style=solid, width=7.0968]; n141 -> n142 [pos="e,1803.2,34284 1804.8,34320 1804.4,34312 1804,34303 1803.7,34295"]; - n143 [color=aliceblue, + n143 [color=deepskyblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1338,7 +1338,7 @@ digraph G { style=solid, width=3.5652]; n142 -> n143 [pos="e,1833.7,34152 1819.6,34188 1822.9,34179 1826.5,34170 1830,34161"]; - n144 [color=aliceblue, + n144 [color=deepskyblue1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 98\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1348,7 +1348,7 @@ digraph G { width=2.5643]; n143 -> n144 [pos="e,1877.4,33888 1857.6,34056 1862.8,34012 1870.6,33945 1876.2,33898"]; n144 -> n154 [pos="e,1975.2,33623 1902.8,33793 1921.9,33748 1950.9,33680 1971.3,33632"]; - n146 [color=ghostwhite, + n146 [color=cyan1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1357,7 +1357,7 @@ digraph G { style=solid, width=3.5652]; n145 -> n146 [pos="e,2206.6,34680 2199.5,34716 2201.1,34707 2202.9,34699 2204.6,34690"]; - n147 [color=ghostwhite, + n147 [color=cyan1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 100\nSize: {1, 32, 35, 35}\nMem size: 39200", @@ -1366,7 +1366,7 @@ digraph G { style=solid, width=2.5643]; n146 -> n147 [pos="e,2213.5,34548 2214.5,34584 2214.3,34576 2214,34567 2213.8,34559"]; - n148 [color=deepskyblue4, + n148 [color=aliceblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 101\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1375,7 +1375,7 @@ digraph G { style=solid, width=7.0968]; n147 -> n148 [pos="e,2196.1,34416 2203,34452 2201.4,34444 2199.7,34435 2198,34426"]; - n149 [color=gold2, + n149 [color=coral2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1384,7 +1384,7 @@ digraph G { style=solid, width=3.5652]; n148 -> n149 [pos="e,2197.8,34284 2193.2,34320 2194.3,34311 2195.4,34303 2196.5,34294"]; - n150 [color=gold2, + n150 [color=coral2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 102\nSize: {1, 48, 35, 35}\nMem size: 58800", @@ -1393,7 +1393,7 @@ digraph G { style=solid, width=2.5643]; n149 -> n150 [pos="e,2190,34152 2196,34188 2194.6,34179 2193.1,34171 2191.6,34162"]; - n151 [color=burlywood1, + n151 [color=gold2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 103\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1402,7 +1402,7 @@ digraph G { style=solid, width=7.0968]; n150 -> n151 [pos="e,2176.9,34020 2179.1,34056 2178.6,34048 2178.1,34039 2177.5,34031"]; - n152 [color=darkorchid, + n152 [color=dodgerblue4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1411,7 +1411,7 @@ digraph G { style=solid, width=3.5652]; n151 -> n152 [pos="e,2151.7,33888 2161.3,33924 2159,33915 2156.6,33906 2154.3,33898"]; - n153 [color=darkorchid, + n153 [color=dodgerblue4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 104\nSize: {1, 64, 35, 35}\nMem size: 78400", @@ -1421,7 +1421,7 @@ digraph G { width=2.5643]; n152 -> n153 [pos="e,2095.6,33755 2115,33792 2110.2,33783 2105.2,33773 2100.3,33764"]; n153 -> n154 [pos="e,2021.9,33622 2045.3,33662 2039.4,33652 2033.2,33641 2027.2,33631"]; - n155 [color=blue4, + n155 [color=darkseagreen4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 106\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1430,7 +1430,7 @@ digraph G { style=solid, width=6.3961]; n154 -> n155 [pos="e,2089.9,33491 2041.5,33534 2054.5,33522 2068.8,33510 2082.4,33498"]; - n156 [color=darkgoldenrod4, + n156 [color=cadetblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 107\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1439,7 +1439,7 @@ digraph G { style=solid, width=3.5652]; n155 -> n156 [pos="e,2197.6,33359 2173,33396 2179.2,33387 2185.6,33377 2191.9,33368"]; - n157 [color=darkolivegreen, + n157 [color=blue1, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 108\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1449,7 +1449,7 @@ digraph G { width=2.6788]; n156 -> n157 [pos="e,2266,33227 2249.1,33264 2253.3,33255 2257.6,33246 2261.8,33236"]; n157 -> n158 [pos="e,2347.3,33094 2318.7,33134 2326,33124 2333.8,33113 2341.3,33102"]; - n159 [color=azure2, + n159 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", @@ -1458,7 +1458,7 @@ digraph G { style=solid, width=2.6788]; n158 -> n159 [pos="e,2379,32964 2379,33000 2379,32992 2379,32983 2379,32975"]; - n160 [color=chartreuse3, + n160 [color=darkseagreen3, fontsize=14, height=1.3356, label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 109\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1467,7 +1467,7 @@ digraph G { style=solid, width=3.7843]; n159 -> n160 [pos="e,2138.6,32436 2312,32881 2249.7,32846 2160.9,32783 2122,32700 2083.5,32618 2111.2,32510 2134.9,32445"]; - n161 [color=darkslategray3, + n161 [color=darkorange4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias\nStorage id: 110\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1476,7 +1476,7 @@ digraph G { style=solid, width=6.8916]; n159 -> n161 [pos="e,2379,32700 2379,32868 2379,32824 2379,32757 2379,32710"]; - n164 [color=blue, + n164 [color=darkkhaki, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 256, 1x1) without bias\nStorage id: 112\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1485,7 +1485,7 @@ digraph G { style=solid, width=6.3961]; n159 -> n164 [pos="e,2600.7,32827 2453.6,32885 2494.3,32869 2545.7,32849 2591.4,32831"]; - n173 [color=darkorchid3, + n173 [color=coral, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1494,7 +1494,7 @@ digraph G { style=solid, width=2.817]; n160 -> n173 [pos="e,2329.7,31638 2172.9,32340 2187.1,32288 2207,32201 2207,32125 2207,32125 2207,32125 2207,31859 2207,31773 2272.3,31694 2322.4,31645"]; - n162 [color=blue1, + n162 [color=cornsilk2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1503,7 +1503,7 @@ digraph G { style=solid, width=3.5652]; n161 -> n162 [pos="e,2379,32568 2379,32604 2379,32596 2379,32587 2379,32579"]; - n163 [color=blue1, + n163 [color=cornsilk2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 111\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1513,7 +1513,7 @@ digraph G { width=2.6788]; n162 -> n163 [pos="e,2379,32304 2379,32472 2379,32428 2379,32361 2379,32314"]; n163 -> n173 [pos="e,2379,31644 2379,32208 2379,32155 2379,32068 2379,31993 2379,31993 2379,31993 2379,31859 2379,31789 2379,31708 2379,31655"]; - n165 [color=aquamarine1, + n165 [color=gold2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1522,7 +1522,7 @@ digraph G { style=solid, width=3.5652]; n164 -> n165 [pos="e,2749.7,32699 2730.5,32736 2735.2,32727 2740.2,32718 2745,32708"]; - n166 [color=aquamarine1, + n166 [color=gold2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 113\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1531,7 +1531,7 @@ digraph G { style=solid, width=2.6788]; n165 -> n166 [pos="e,2757.4,32568 2764.5,32604 2762.9,32595 2761.1,32587 2759.4,32578"]; - n167 [color=darkorange2, + n167 [color=firebrick, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 114\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1540,7 +1540,7 @@ digraph G { style=solid, width=7.3732]; n166 -> n167 [pos="e,2716.1,32436 2730.1,32472 2726.8,32464 2723.2,32455 2719.8,32446"]; - n168 [color=chartreuse4, + n168 [color=blue2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1549,7 +1549,7 @@ digraph G { style=solid, width=3.5652]; n167 -> n168 [pos="e,2694.2,32304 2695.8,32340 2695.4,32332 2695,32323 2694.7,32315"]; - n169 [color=chartreuse4, + n169 [color=blue2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 115\nSize: {1, 256, 35, 35}\nMem size: 313600", @@ -1558,7 +1558,7 @@ digraph G { style=solid, width=2.6788]; n168 -> n169 [pos="e,2688.8,32172 2690.2,32208 2689.9,32200 2689.5,32191 2689.2,32183"]; - n170 [color=azure1, + n170 [color=darkgreen, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 116\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1567,7 +1567,7 @@ digraph G { style=solid, width=6.8916]; n169 -> n170 [pos="e,2673.7,32040 2679.4,32076 2678,32068 2676.7,32059 2675.3,32051"]; - n171 [color=dimgray, + n171 [color=aquamarine, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1576,7 +1576,7 @@ digraph G { style=solid, width=3.5652]; n170 -> n171 [pos="e,2624.2,31907 2642.5,31944 2638.1,31935 2633.3,31926 2628.7,31916"]; - n172 [color=dimgray, + n172 [color=aquamarine, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 117\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1586,7 +1586,7 @@ digraph G { width=2.6788]; n171 -> n172 [pos="e,2569.5,31775 2583.3,31812 2580,31803 2576.5,31794 2573.1,31785"]; n172 -> n173 [pos="e,2432.4,31637 2499.2,31687 2480.6,31673 2459.7,31658 2440.5,31643"]; - n174 [color=darkolivegreen2, + n174 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 119\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1595,7 +1595,7 @@ digraph G { style=solid, width=6.5343]; n173 -> n174 [pos="e,2031.1,31503 2291.3,31572 2221.8,31553 2123,31527 2040.8,31505"]; - n177 [color=deeppink3, + n177 [color=burlywood, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 121\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1604,7 +1604,7 @@ digraph G { style=solid, width=6.5343]; n173 -> n177 [pos="e,2379,31512 2379,31548 2379,31540 2379,31531 2379,31523"]; - n190 [color=darkorchid3, + n190 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1614,7 +1614,7 @@ digraph G { width=2.817]; n173 -> n190 [pos="e,2580.2,29795 2478.5,31586 2529.6,31576 2588.2,31556 2623,31512 2673,31450 2642,31413 2642,31333 2642,31333 2642,31333 2642,30011 \ 2642,29936 2609.8,29856 2584.6,29804"]; - n175 [color=coral, + n175 [color=blanchedalmond, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1623,7 +1623,7 @@ digraph G { style=solid, width=3.5652]; n174 -> n175 [pos="e,1896.3,31380 1893.6,31416 1894.3,31408 1894.9,31399 1895.6,31391"]; - n176 [color=coral, + n176 [color=blanchedalmond, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 120\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1632,7 +1632,7 @@ digraph G { style=solid, width=2.6788]; n175 -> n176 [pos="e,1946.7,31116 1910.3,31284 1919.9,31240 1934.3,31173 1944.6,31126"]; - n186 [color=cadetblue3, + n186 [color=floralwhite, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 127\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1641,7 +1641,7 @@ digraph G { style=solid, width=2.6788]; n176 -> n186 [pos="e,2264.8,30301 1971.2,31020 1985.6,30968 2006,30881 2006,30805 2006,30805 2006,30805 2006,30539 2006,30413 2153.8,30339 2255.1,30304"]; - n178 [color=darkorange2, + n178 [color=gold1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1650,7 +1650,7 @@ digraph G { style=solid, width=3.5652]; n177 -> n178 [pos="e,2373.9,31380 2376.1,31416 2375.6,31408 2375.1,31399 2374.5,31391"]; - n179 [color=darkorange2, + n179 [color=gold1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 122\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1659,7 +1659,7 @@ digraph G { style=solid, width=2.6788]; n178 -> n179 [pos="e,2361.4,31248 2365.5,31284 2364.6,31275 2363.6,31267 2362.6,31258"]; - n180 [color=bisque4, + n180 [color=darkolivegreen1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 123\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1668,7 +1668,7 @@ digraph G { style=solid, width=7.3732]; n179 -> n180 [pos="e,2350.9,31116 2353.1,31152 2352.6,31144 2352.1,31135 2351.5,31127"]; - n181 [color=antiquewhite1, + n181 [color=chartreuse1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1677,7 +1677,7 @@ digraph G { style=solid, width=3.5652]; n180 -> n181 [pos="e,2348,30984 2348,31020 2348,31012 2348,31003 2348,30995"]; - n182 [color=antiquewhite1, + n182 [color=chartreuse1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 124\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1686,7 +1686,7 @@ digraph G { style=solid, width=2.6788]; n181 -> n182 [pos="e,2348,30852 2348,30888 2348,30880 2348,30871 2348,30863"]; - n183 [color=burlywood, + n183 [color=dodgerblue4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 125\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1695,7 +1695,7 @@ digraph G { style=solid, width=7.3732]; n182 -> n183 [pos="e,2348,30720 2348,30756 2348,30748 2348,30739 2348,30731"]; - n184 [color=floralwhite, + n184 [color=azure, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1704,7 +1704,7 @@ digraph G { style=solid, width=3.5652]; n183 -> n184 [pos="e,2348,30588 2348,30624 2348,30616 2348,30607 2348,30599"]; - n185 [color=floralwhite, + n185 [color=azure, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 126\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1714,7 +1714,7 @@ digraph G { width=2.6788]; n184 -> n185 [pos="e,2348,30456 2348,30492 2348,30484 2348,30475 2348,30467"]; n185 -> n186 [pos="e,2348,30324 2348,30360 2348,30352 2348,30343 2348,30335"]; - n187 [color=deeppink1, + n187 [color=firebrick2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 128\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1723,7 +1723,7 @@ digraph G { style=solid, width=6.5343]; n186 -> n187 [pos="e,2358.2,30192 2353.8,30228 2354.8,30220 2355.9,30211 2356.9,30203"]; - n188 [color=coral4, + n188 [color=darkolivegreen4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 129\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1732,7 +1732,7 @@ digraph G { style=solid, width=3.5652]; n187 -> n188 [pos="e,2403.2,30059 2386,30096 2390.2,30087 2394.7,30078 2399,30068"]; - n189 [color=darkorange1, + n189 [color=forestgreen, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 130\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1742,7 +1742,7 @@ digraph G { width=2.817]; n188 -> n189 [pos="e,2453.1,29928 2440.9,29964 2443.8,29955 2446.9,29946 2449.9,29937"]; n189 -> n190 [pos="e,2525.2,29794 2498.8,29834 2505.5,29824 2512.6,29813 2519.5,29803"]; - n191 [color=darkorchid3, + n191 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1751,7 +1751,7 @@ digraph G { style=solid, width=2.817]; n190 -> n191 [pos="e,2555,29664 2555,29700 2555,29692 2555,29683 2555,29675"]; - n192 [color=darkslategray3, + n192 [color=darkseagreen3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 131\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1760,7 +1760,7 @@ digraph G { style=solid, width=6.5343]; n191 -> n192 [pos="e,2207.1,29523 2467.3,29592 2397.8,29573 2299,29547 2216.8,29525"]; - n195 [color=blue3, + n195 [color=aliceblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 133\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1769,7 +1769,7 @@ digraph G { style=solid, width=6.5343]; n191 -> n195 [pos="e,2555,29532 2555,29568 2555,29560 2555,29551 2555,29543"]; - n208 [color=darkorchid3, + n208 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1779,7 +1779,7 @@ digraph G { width=2.817]; n191 -> n208 [pos="e,2756.2,27815 2654.5,29606 2705.6,29596 2764.2,29576 2799,29532 2849,29470 2818,29433 2818,29353 2818,29353 2818,29353 2818,28031 \ 2818,27956 2785.8,27876 2760.6,27824"]; - n193 [color=deeppink, + n193 [color=azure, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1788,7 +1788,7 @@ digraph G { style=solid, width=3.5652]; n192 -> n193 [pos="e,2072.3,29400 2069.6,29436 2070.3,29428 2070.9,29419 2071.6,29411"]; - n194 [color=deeppink, + n194 [color=azure, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 132\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1797,7 +1797,7 @@ digraph G { style=solid, width=2.6788]; n193 -> n194 [pos="e,2122.7,29136 2086.3,29304 2095.9,29260 2110.3,29193 2120.6,29146"]; - n204 [color=dimgrey, + n204 [color=burlywood4, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 139\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1806,7 +1806,7 @@ digraph G { style=solid, width=2.6788]; n194 -> n204 [pos="e,2440.8,28321 2147.2,29040 2161.6,28988 2182,28901 2182,28825 2182,28825 2182,28825 2182,28559 2182,28433 2329.8,28359 2431.1,28324"]; - n196 [color=darkorchid2, + n196 [color=beige, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1815,7 +1815,7 @@ digraph G { style=solid, width=3.5652]; n195 -> n196 [pos="e,2549.9,29400 2552.1,29436 2551.6,29428 2551.1,29419 2550.5,29411"]; - n197 [color=darkorchid2, + n197 [color=beige, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 134\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1824,7 +1824,7 @@ digraph G { style=solid, width=2.6788]; n196 -> n197 [pos="e,2537.4,29268 2541.5,29304 2540.6,29295 2539.6,29287 2538.6,29278"]; - n198 [color=gold1, + n198 [color=dodgerblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 135\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1833,7 +1833,7 @@ digraph G { style=solid, width=7.3732]; n197 -> n198 [pos="e,2526.9,29136 2529.1,29172 2528.6,29164 2528.1,29155 2527.5,29147"]; - n199 [color=darkorange2, + n199 [color=chartreuse1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1842,7 +1842,7 @@ digraph G { style=solid, width=3.5652]; n198 -> n199 [pos="e,2524,29004 2524,29040 2524,29032 2524,29023 2524,29015"]; - n200 [color=darkorange2, + n200 [color=chartreuse1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 136\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1851,7 +1851,7 @@ digraph G { style=solid, width=2.6788]; n199 -> n200 [pos="e,2524,28872 2524,28908 2524,28900 2524,28891 2524,28883"]; - n201 [color=cornsilk2, + n201 [color=chartreuse4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 137\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1860,7 +1860,7 @@ digraph G { style=solid, width=7.3732]; n200 -> n201 [pos="e,2524,28740 2524,28776 2524,28768 2524,28759 2524,28751"]; - n202 [color=chartreuse, + n202 [color=blue4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1869,7 +1869,7 @@ digraph G { style=solid, width=3.5652]; n201 -> n202 [pos="e,2524,28608 2524,28644 2524,28636 2524,28627 2524,28619"]; - n203 [color=chartreuse, + n203 [color=blue4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 138\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1879,7 +1879,7 @@ digraph G { width=2.6788]; n202 -> n203 [pos="e,2524,28476 2524,28512 2524,28504 2524,28495 2524,28487"]; n203 -> n204 [pos="e,2524,28344 2524,28380 2524,28372 2524,28363 2524,28355"]; - n205 [color=coral3, + n205 [color=darkorange2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 140\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1888,7 +1888,7 @@ digraph G { style=solid, width=6.5343]; n204 -> n205 [pos="e,2534.2,28212 2529.8,28248 2530.8,28240 2531.9,28231 2532.9,28223"]; - n206 [color=coral2, + n206 [color=aquamarine, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 141\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1897,7 +1897,7 @@ digraph G { style=solid, width=3.5652]; n205 -> n206 [pos="e,2579.2,28079 2562,28116 2566.2,28107 2570.7,28098 2575,28088"]; - n207 [color=bisque2, + n207 [color=dodgerblue1, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 142\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1907,7 +1907,7 @@ digraph G { width=2.817]; n206 -> n207 [pos="e,2629.1,27948 2616.9,27984 2619.8,27975 2622.9,27966 2625.9,27957"]; n207 -> n208 [pos="e,2701.2,27814 2674.8,27854 2681.5,27844 2688.6,27833 2695.5,27823"]; - n209 [color=darkorchid3, + n209 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1916,7 +1916,7 @@ digraph G { style=solid, width=2.817]; n208 -> n209 [pos="e,2731,27684 2731,27720 2731,27712 2731,27703 2731,27695"]; - n210 [color=firebrick3, + n210 [color=gold, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 143\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1925,7 +1925,7 @@ digraph G { style=solid, width=6.5343]; n209 -> n210 [pos="e,2383.1,27543 2643.3,27612 2573.8,27593 2475,27567 2392.8,27545"]; - n213 [color=cornsilk4, + n213 [color=antiquewhite3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 145\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1934,7 +1934,7 @@ digraph G { style=solid, width=6.5343]; n209 -> n213 [pos="e,2731,27552 2731,27588 2731,27580 2731,27571 2731,27563"]; - n226 [color=darkorchid3, + n226 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -1944,7 +1944,7 @@ digraph G { width=2.817]; n209 -> n226 [pos="e,2932.2,25835 2830.5,27626 2881.6,27616 2940.2,27596 2975,27552 3025,27490 2994,27453 2994,27373 2994,27373 2994,27373 2994,26051 \ 2994,25976 2961.8,25896 2936.6,25844"]; - n211 [color=forestgreen, + n211 [color=cadetblue4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1953,7 +1953,7 @@ digraph G { style=solid, width=3.5652]; n210 -> n211 [pos="e,2266.2,27420 2255.8,27456 2258.3,27447 2260.9,27438 2263.4,27430"]; - n212 [color=forestgreen, + n212 [color=cadetblue4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 144\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -1962,7 +1962,7 @@ digraph G { style=solid, width=2.6788]; n211 -> n212 [pos="e,2303.8,27156 2285.2,27324 2290.1,27280 2297.4,27213 2302.7,27166"]; - n222 [color=goldenrod4, + n222 [color=darkgoldenrod, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 151\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -1971,7 +1971,7 @@ digraph G { style=solid, width=2.6788]; n212 -> n222 [pos="e,2616.8,26341 2323.2,27060 2337.6,27008 2358,26921 2358,26845 2358,26845 2358,26845 2358,26579 2358,26453 2505.8,26379 2607.1,26344"]; - n214 [color=aquamarine, + n214 [color=burlywood4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1980,7 +1980,7 @@ digraph G { style=solid, width=3.5652]; n213 -> n214 [pos="e,2721.4,27420 2725.5,27456 2724.6,27447 2723.6,27439 2722.6,27430"]; - n215 [color=aquamarine, + n215 [color=burlywood4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 146\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -1989,7 +1989,7 @@ digraph G { style=solid, width=2.6788]; n214 -> n215 [pos="e,2710.9,27288 2713.1,27324 2712.6,27316 2712.1,27307 2711.5,27299"]; - n216 [color=goldenrod4, + n216 [color=burlywood1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 147\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -1998,7 +1998,7 @@ digraph G { style=solid, width=7.3732]; n215 -> n216 [pos="e,2702.9,27156 2705.1,27192 2704.6,27184 2704.1,27175 2703.5,27167"]; - n217 [color=cyan4, + n217 [color=brown, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2007,7 +2007,7 @@ digraph G { style=solid, width=3.5652]; n216 -> n217 [pos="e,2700,27024 2700,27060 2700,27052 2700,27043 2700,27035"]; - n218 [color=cyan4, + n218 [color=brown, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 148\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2016,7 +2016,7 @@ digraph G { style=solid, width=2.6788]; n217 -> n218 [pos="e,2700,26892 2700,26928 2700,26920 2700,26911 2700,26903"]; - n219 [color=darkorange4, + n219 [color=firebrick, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 149\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2025,7 +2025,7 @@ digraph G { style=solid, width=7.3732]; n218 -> n219 [pos="e,2700,26760 2700,26796 2700,26788 2700,26779 2700,26771"]; - n220 [color=darkslategray3, + n220 [color=deeppink4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2034,7 +2034,7 @@ digraph G { style=solid, width=3.5652]; n219 -> n220 [pos="e,2700,26628 2700,26664 2700,26656 2700,26647 2700,26639"]; - n221 [color=darkslategray3, + n221 [color=deeppink4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 150\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2044,7 +2044,7 @@ digraph G { width=2.6788]; n220 -> n221 [pos="e,2700,26496 2700,26532 2700,26524 2700,26515 2700,26507"]; n221 -> n222 [pos="e,2700,26364 2700,26400 2700,26392 2700,26383 2700,26375"]; - n223 [color=azure2, + n223 [color=brown, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 152\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2053,7 +2053,7 @@ digraph G { style=solid, width=6.5343]; n222 -> n223 [pos="e,2710.2,26232 2705.8,26268 2706.8,26260 2707.9,26251 2708.9,26243"]; - n224 [color=darkorchid3, + n224 [color=cadetblue2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 153\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2062,7 +2062,7 @@ digraph G { style=solid, width=3.5652]; n223 -> n224 [pos="e,2755.2,26099 2738,26136 2742.2,26127 2746.7,26118 2751,26108"]; - n225 [color=deepskyblue2, + n225 [color=deeppink3, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 154\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2072,7 +2072,7 @@ digraph G { width=2.817]; n224 -> n225 [pos="e,2805.1,25968 2792.9,26004 2795.8,25995 2798.9,25986 2801.9,25977"]; n225 -> n226 [pos="e,2877.2,25834 2850.8,25874 2857.5,25864 2864.6,25853 2871.5,25843"]; - n227 [color=darkorchid3, + n227 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2081,7 +2081,7 @@ digraph G { style=solid, width=2.817]; n226 -> n227 [pos="e,2907,25704 2907,25740 2907,25732 2907,25723 2907,25715"]; - n228 [color=chocolate3, + n228 [color=chartreuse1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 155\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2090,7 +2090,7 @@ digraph G { style=solid, width=6.5343]; n227 -> n228 [pos="e,2559.1,25563 2819.3,25632 2749.8,25613 2651,25587 2568.8,25565"]; - n231 [color=deepskyblue3, + n231 [color=firebrick4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 157\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2099,7 +2099,7 @@ digraph G { style=solid, width=6.5343]; n227 -> n231 [pos="e,2907,25572 2907,25608 2907,25600 2907,25591 2907,25583"]; - n244 [color=darkorchid3, + n244 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2109,7 +2109,7 @@ digraph G { width=2.817]; n227 -> n244 [pos="e,3108.2,23855 3006.5,25646 3057.6,25636 3116.2,25616 3151,25572 3201,25510 3170,25473 3170,25393 3170,25393 3170,25393 3170,24071 \ 3170,23996 3137.8,23916 3112.6,23864"]; - n229 [color=brown, + n229 [color=darkkhaki, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2118,7 +2118,7 @@ digraph G { style=solid, width=3.5652]; n228 -> n229 [pos="e,2424.3,25440 2421.6,25476 2422.3,25468 2422.9,25459 2423.6,25451"]; - n230 [color=brown, + n230 [color=darkkhaki, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 156\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2127,7 +2127,7 @@ digraph G { style=solid, width=2.6788]; n229 -> n230 [pos="e,2474.7,25176 2438.3,25344 2447.9,25300 2462.3,25233 2472.6,25186"]; - n240 [color=brown3, + n240 [color=bisque2, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 163\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2136,7 +2136,7 @@ digraph G { style=solid, width=2.6788]; n230 -> n240 [pos="e,2792.8,24361 2499.2,25080 2513.6,25028 2534,24941 2534,24865 2534,24865 2534,24865 2534,24599 2534,24473 2681.8,24399 2783.1,24364"]; - n232 [color=darkseagreen4, + n232 [color=azure3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2145,7 +2145,7 @@ digraph G { style=solid, width=3.5652]; n231 -> n232 [pos="e,2901.9,25440 2904.1,25476 2903.6,25468 2903.1,25459 2902.5,25451"]; - n233 [color=darkseagreen4, + n233 [color=azure3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 158\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2154,7 +2154,7 @@ digraph G { style=solid, width=2.6788]; n232 -> n233 [pos="e,2889.4,25308 2893.5,25344 2892.6,25335 2891.6,25327 2890.6,25318"]; - n234 [color=dimgrey, + n234 [color=dodgerblue3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 159\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2163,7 +2163,7 @@ digraph G { style=solid, width=7.3732]; n233 -> n234 [pos="e,2878.9,25176 2881.1,25212 2880.6,25204 2880.1,25195 2879.5,25187"]; - n235 [color=chartreuse4, + n235 [color=black, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2172,7 +2172,7 @@ digraph G { style=solid, width=3.5652]; n234 -> n235 [pos="e,2876,25044 2876,25080 2876,25072 2876,25063 2876,25055"]; - n236 [color=chartreuse4, + n236 [color=black, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 160\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2181,7 +2181,7 @@ digraph G { style=solid, width=2.6788]; n235 -> n236 [pos="e,2876,24912 2876,24948 2876,24940 2876,24931 2876,24923"]; - n237 [color=cadetblue3, + n237 [color=darkorange3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 161\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2190,7 +2190,7 @@ digraph G { style=solid, width=7.3732]; n236 -> n237 [pos="e,2876,24780 2876,24816 2876,24808 2876,24799 2876,24791"]; - n238 [color=burlywood, + n238 [color=coral3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2199,7 +2199,7 @@ digraph G { style=solid, width=3.5652]; n237 -> n238 [pos="e,2876,24648 2876,24684 2876,24676 2876,24667 2876,24659"]; - n239 [color=burlywood, + n239 [color=coral3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 162\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2209,7 +2209,7 @@ digraph G { width=2.6788]; n238 -> n239 [pos="e,2876,24516 2876,24552 2876,24544 2876,24535 2876,24527"]; n239 -> n240 [pos="e,2876,24384 2876,24420 2876,24412 2876,24403 2876,24395"]; - n241 [color=brown3, + n241 [color=blue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 164\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2218,7 +2218,7 @@ digraph G { style=solid, width=6.5343]; n240 -> n241 [pos="e,2886.2,24252 2881.8,24288 2882.8,24280 2883.9,24271 2884.9,24263"]; - n242 [color=blue1, + n242 [color=darksalmon, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 165\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2227,7 +2227,7 @@ digraph G { style=solid, width=3.5652]; n241 -> n242 [pos="e,2931.2,24119 2914,24156 2918.2,24147 2922.7,24138 2927,24128"]; - n243 [color=cyan3, + n243 [color=gainsboro, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 166\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2237,7 +2237,7 @@ digraph G { width=2.817]; n242 -> n243 [pos="e,2981.1,23988 2968.9,24024 2971.8,24015 2974.9,24006 2977.9,23997"]; n243 -> n244 [pos="e,3053.2,23854 3026.8,23894 3033.5,23884 3040.6,23873 3047.5,23863"]; - n245 [color=darkorchid3, + n245 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2246,7 +2246,7 @@ digraph G { style=solid, width=2.817]; n244 -> n245 [pos="e,3083,23724 3083,23760 3083,23752 3083,23743 3083,23735"]; - n246 [color=cornsilk2, + n246 [color=blueviolet, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 167\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2255,7 +2255,7 @@ digraph G { style=solid, width=6.5343]; n245 -> n246 [pos="e,2735.1,23583 2995.3,23652 2925.8,23633 2827,23607 2744.8,23585"]; - n249 [color=dodgerblue1, + n249 [color=chocolate4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 169\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2264,7 +2264,7 @@ digraph G { style=solid, width=6.5343]; n245 -> n249 [pos="e,3083,23592 3083,23628 3083,23620 3083,23611 3083,23603"]; - n262 [color=darkorchid3, + n262 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2274,7 +2274,7 @@ digraph G { width=2.817]; n245 -> n262 [pos="e,3284.2,21875 3182.5,23666 3233.6,23656 3292.2,23636 3327,23592 3377,23530 3346,23493 3346,23413 3346,23413 3346,23413 3346,22091 \ 3346,22016 3313.8,21936 3288.6,21884"]; - n247 [color=darkolivegreen2, + n247 [color=darkseagreen2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2283,7 +2283,7 @@ digraph G { style=solid, width=3.5652]; n246 -> n247 [pos="e,2600.3,23460 2597.6,23496 2598.3,23488 2598.9,23479 2599.6,23471"]; - n248 [color=darkolivegreen2, + n248 [color=darkseagreen2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 168\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2292,7 +2292,7 @@ digraph G { style=solid, width=2.6788]; n247 -> n248 [pos="e,2650.7,23196 2614.3,23364 2623.9,23320 2638.3,23253 2648.6,23206"]; - n258 [color=deeppink, + n258 [color=dodgerblue2, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 175\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2301,7 +2301,7 @@ digraph G { style=solid, width=2.6788]; n248 -> n258 [pos="e,2968.8,22381 2675.2,23100 2689.6,23048 2710,22961 2710,22885 2710,22885 2710,22885 2710,22619 2710,22493 2857.8,22419 2959.1,22384"]; - n250 [color=deepskyblue2, + n250 [color=dodgerblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2310,7 +2310,7 @@ digraph G { style=solid, width=3.5652]; n249 -> n250 [pos="e,3077.9,23460 3080.1,23496 3079.6,23488 3079.1,23479 3078.5,23471"]; - n251 [color=deepskyblue2, + n251 [color=dodgerblue1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 170\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2319,7 +2319,7 @@ digraph G { style=solid, width=2.6788]; n250 -> n251 [pos="e,3065.4,23328 3069.5,23364 3068.6,23355 3067.6,23347 3066.6,23338"]; - n252 [color=burlywood2, + n252 [color=brown3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 171\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2328,7 +2328,7 @@ digraph G { style=solid, width=7.3732]; n251 -> n252 [pos="e,3054.9,23196 3057.1,23232 3056.6,23224 3056.1,23215 3055.5,23207"]; - n253 [color=antiquewhite2, + n253 [color=brown2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2337,7 +2337,7 @@ digraph G { style=solid, width=3.5652]; n252 -> n253 [pos="e,3052,23064 3052,23100 3052,23092 3052,23083 3052,23075"]; - n254 [color=antiquewhite2, + n254 [color=brown2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 172\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2346,7 +2346,7 @@ digraph G { style=solid, width=2.6788]; n253 -> n254 [pos="e,3052,22932 3052,22968 3052,22960 3052,22951 3052,22943"]; - n255 [color=bisque2, + n255 [color=darkgoldenrod4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 173\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2355,7 +2355,7 @@ digraph G { style=solid, width=7.3732]; n254 -> n255 [pos="e,3052,22800 3052,22836 3052,22828 3052,22819 3052,22811"]; - n256 [color=crimson, + n256 [color=antiquewhite, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2364,7 +2364,7 @@ digraph G { style=solid, width=3.5652]; n255 -> n256 [pos="e,3052,22668 3052,22704 3052,22696 3052,22687 3052,22679"]; - n257 [color=crimson, + n257 [color=antiquewhite, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 174\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2374,7 +2374,7 @@ digraph G { width=2.6788]; n256 -> n257 [pos="e,3052,22536 3052,22572 3052,22564 3052,22555 3052,22547"]; n257 -> n258 [pos="e,3052,22404 3052,22440 3052,22432 3052,22423 3052,22415"]; - n259 [color=darkolivegreen, + n259 [color=deeppink4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 176\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2383,7 +2383,7 @@ digraph G { style=solid, width=6.5343]; n258 -> n259 [pos="e,3062.2,22272 3057.8,22308 3058.8,22300 3059.9,22291 3060.9,22283"]; - n260 [color=firebrick3, + n260 [color=brown, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 177\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2392,7 +2392,7 @@ digraph G { style=solid, width=3.5652]; n259 -> n260 [pos="e,3107.2,22139 3090,22176 3094.2,22167 3098.7,22158 3103,22148"]; - n261 [color=darkseagreen1, + n261 [color=bisque2, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 178\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2402,7 +2402,7 @@ digraph G { width=2.817]; n260 -> n261 [pos="e,3157.1,22008 3144.9,22044 3147.8,22035 3150.9,22026 3153.9,22017"]; n261 -> n262 [pos="e,3229.2,21874 3202.8,21914 3209.5,21904 3216.6,21893 3223.5,21883"]; - n263 [color=darkorchid3, + n263 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2411,7 +2411,7 @@ digraph G { style=solid, width=2.817]; n262 -> n263 [pos="e,3259,21744 3259,21780 3259,21772 3259,21763 3259,21755"]; - n264 [color=aliceblue, + n264 [color=coral2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 179\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2420,7 +2420,7 @@ digraph G { style=solid, width=6.5343]; n263 -> n264 [pos="e,2911.1,21603 3171.3,21672 3101.8,21653 3003,21627 2920.8,21605"]; - n267 [color=dimgrey, + n267 [color=antiquewhite1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 181\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2429,7 +2429,7 @@ digraph G { style=solid, width=6.5343]; n263 -> n267 [pos="e,3259,21612 3259,21648 3259,21640 3259,21631 3259,21623"]; - n280 [color=darkorchid3, + n280 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2439,7 +2439,7 @@ digraph G { width=2.817]; n263 -> n280 [pos="e,3460.2,19895 3358.5,21686 3409.6,21676 3468.2,21656 3503,21612 3553,21550 3522,21513 3522,21433 3522,21433 3522,21433 3522,20111 \ 3522,20036 3489.8,19956 3464.6,19904"]; - n265 [color=gold2, + n265 [color=darkseagreen4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2448,7 +2448,7 @@ digraph G { style=solid, width=3.5652]; n264 -> n265 [pos="e,2776.3,21480 2773.6,21516 2774.3,21508 2774.9,21499 2775.6,21491"]; - n266 [color=gold2, + n266 [color=darkseagreen4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 180\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2457,7 +2457,7 @@ digraph G { style=solid, width=2.6788]; n265 -> n266 [pos="e,2826.7,21216 2790.3,21384 2799.9,21340 2814.3,21273 2824.6,21226"]; - n276 [color=darkseagreen3, + n276 [color=deeppink3, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 187\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2466,7 +2466,7 @@ digraph G { style=solid, width=2.6788]; n266 -> n276 [pos="e,3144.8,20401 2851.2,21120 2865.6,21068 2886,20981 2886,20905 2886,20905 2886,20905 2886,20639 2886,20513 3033.8,20439 3135.1,20404"]; - n268 [color=chartreuse1, + n268 [color=gold, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2475,7 +2475,7 @@ digraph G { style=solid, width=3.5652]; n267 -> n268 [pos="e,3249.4,21480 3253.5,21516 3252.6,21507 3251.6,21499 3250.6,21490"]; - n269 [color=chartreuse1, + n269 [color=gold, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 182\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2484,7 +2484,7 @@ digraph G { style=solid, width=2.6788]; n268 -> n269 [pos="e,3238.9,21348 3241.1,21384 3240.6,21376 3240.1,21367 3239.5,21359"]; - n270 [color=deeppink, + n270 [color=cornsilk3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 183\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2493,7 +2493,7 @@ digraph G { style=solid, width=7.3732]; n269 -> n270 [pos="e,3230.9,21216 3233.1,21252 3232.6,21244 3232.1,21235 3231.5,21227"]; - n271 [color=gold1, + n271 [color=deepskyblue, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2502,7 +2502,7 @@ digraph G { style=solid, width=3.5652]; n270 -> n271 [pos="e,3228,21084 3228,21120 3228,21112 3228,21103 3228,21095"]; - n272 [color=gold1, + n272 [color=deepskyblue, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 184\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2511,7 +2511,7 @@ digraph G { style=solid, width=2.6788]; n271 -> n272 [pos="e,3228,20952 3228,20988 3228,20980 3228,20971 3228,20963"]; - n273 [color=antiquewhite2, + n273 [color=firebrick4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 185\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2520,7 +2520,7 @@ digraph G { style=solid, width=7.3732]; n272 -> n273 [pos="e,3228,20820 3228,20856 3228,20848 3228,20839 3228,20831"]; - n274 [color=deepskyblue1, + n274 [color=deeppink1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2529,7 +2529,7 @@ digraph G { style=solid, width=3.5652]; n273 -> n274 [pos="e,3228,20688 3228,20724 3228,20716 3228,20707 3228,20699"]; - n275 [color=deepskyblue1, + n275 [color=deeppink1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 186\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2539,7 +2539,7 @@ digraph G { width=2.6788]; n274 -> n275 [pos="e,3228,20556 3228,20592 3228,20584 3228,20575 3228,20567"]; n275 -> n276 [pos="e,3228,20424 3228,20460 3228,20452 3228,20443 3228,20435"]; - n277 [color=cornsilk2, + n277 [color=gold3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 188\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2548,7 +2548,7 @@ digraph G { style=solid, width=6.5343]; n276 -> n277 [pos="e,3238.2,20292 3233.8,20328 3234.8,20320 3235.9,20311 3236.9,20303"]; - n278 [color=bisque2, + n278 [color=chocolate, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 189\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2557,7 +2557,7 @@ digraph G { style=solid, width=3.5652]; n277 -> n278 [pos="e,3283.2,20159 3266,20196 3270.2,20187 3274.7,20178 3279,20168"]; - n279 [color=darkorchid4, + n279 [color=darkturquoise, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 190\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2567,7 +2567,7 @@ digraph G { width=2.817]; n278 -> n279 [pos="e,3332.5,20028 3320.5,20064 3323.4,20055 3326.4,20046 3329.3,20037"]; n279 -> n280 [pos="e,3404.9,19894 3378.2,19934 3384.9,19924 3392.2,19913 3399.1,19903"]; - n281 [color=darkorchid3, + n281 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2576,7 +2576,7 @@ digraph G { style=solid, width=2.817]; n280 -> n281 [pos="e,3435,19764 3435,19800 3435,19792 3435,19783 3435,19775"]; - n282 [color=cadetblue3, + n282 [color=chartreuse2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 191\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2585,7 +2585,7 @@ digraph G { style=solid, width=6.5343]; n281 -> n282 [pos="e,3087.1,19623 3347.3,19692 3277.8,19673 3179,19647 3096.8,19625"]; - n285 [color=darkorchid4, + n285 [color=chartreuse4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 193\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2594,7 +2594,7 @@ digraph G { style=solid, width=6.5343]; n281 -> n285 [pos="e,3435,19632 3435,19668 3435,19660 3435,19651 3435,19643"]; - n298 [color=darkorchid3, + n298 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2604,7 +2604,7 @@ digraph G { width=2.817]; n281 -> n298 [pos="e,3636.2,17915 3534.5,19706 3585.6,19696 3644.2,19676 3679,19632 3729,19570 3698,19533 3698,19453 3698,19453 3698,19453 3698,18131 \ 3698,18056 3665.8,17976 3640.6,17924"]; - n283 [color=goldenrod3, + n283 [color=dodgerblue2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2613,7 +2613,7 @@ digraph G { style=solid, width=3.5652]; n282 -> n283 [pos="e,2952.3,19500 2949.6,19536 2950.3,19528 2950.9,19519 2951.6,19511"]; - n284 [color=goldenrod3, + n284 [color=dodgerblue2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 192\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2622,7 +2622,7 @@ digraph G { style=solid, width=2.6788]; n283 -> n284 [pos="e,3002.7,19236 2966.3,19404 2975.9,19360 2990.3,19293 3000.6,19246"]; - n294 [color=darkgoldenrod3, + n294 [color=azure1, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 199\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2631,7 +2631,7 @@ digraph G { style=solid, width=2.6788]; n284 -> n294 [pos="e,3320.8,18421 3027.2,19140 3041.6,19088 3062,19001 3062,18925 3062,18925 3062,18925 3062,18659 3062,18533 3209.8,18459 3311.1,18424"]; - n286 [color=gold1, + n286 [color=darkorchid3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2640,7 +2640,7 @@ digraph G { style=solid, width=3.5652]; n285 -> n286 [pos="e,3429.9,19500 3432.1,19536 3431.6,19528 3431.1,19519 3430.5,19511"]; - n287 [color=gold1, + n287 [color=darkorchid3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 194\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2649,7 +2649,7 @@ digraph G { style=solid, width=2.6788]; n286 -> n287 [pos="e,3417.4,19368 3421.5,19404 3420.6,19395 3419.6,19387 3418.6,19378"]; - n288 [color=cornsilk, + n288 [color=coral1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 195\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2658,7 +2658,7 @@ digraph G { style=solid, width=7.3732]; n287 -> n288 [pos="e,3406.9,19236 3409.1,19272 3408.6,19264 3408.1,19255 3407.5,19247"]; - n289 [color=deepskyblue2, + n289 [color=bisque2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2667,7 +2667,7 @@ digraph G { style=solid, width=3.5652]; n288 -> n289 [pos="e,3404,19104 3404,19140 3404,19132 3404,19123 3404,19115"]; - n290 [color=deepskyblue2, + n290 [color=bisque2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 196\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2676,7 +2676,7 @@ digraph G { style=solid, width=2.6788]; n289 -> n290 [pos="e,3404,18972 3404,19008 3404,19000 3404,18991 3404,18983"]; - n291 [color=coral1, + n291 [color=blue4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 197\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2685,7 +2685,7 @@ digraph G { style=solid, width=7.3732]; n290 -> n291 [pos="e,3404,18840 3404,18876 3404,18868 3404,18859 3404,18851"]; - n292 [color=burlywood2, + n292 [color=deepskyblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2694,7 +2694,7 @@ digraph G { style=solid, width=3.5652]; n291 -> n292 [pos="e,3404,18708 3404,18744 3404,18736 3404,18727 3404,18719"]; - n293 [color=burlywood2, + n293 [color=deepskyblue1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 198\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2704,7 +2704,7 @@ digraph G { width=2.6788]; n292 -> n293 [pos="e,3404,18576 3404,18612 3404,18604 3404,18595 3404,18587"]; n293 -> n294 [pos="e,3404,18444 3404,18480 3404,18472 3404,18463 3404,18455"]; - n295 [color=darkorchid1, + n295 [color=ghostwhite, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 200\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2713,7 +2713,7 @@ digraph G { style=solid, width=6.5343]; n294 -> n295 [pos="e,3414.2,18312 3409.8,18348 3410.8,18340 3411.9,18331 3412.9,18323"]; - n296 [color=cyan2, + n296 [color=dodgerblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 201\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2722,7 +2722,7 @@ digraph G { style=solid, width=3.5652]; n295 -> n296 [pos="e,3459.2,18179 3442,18216 3446.2,18207 3450.7,18198 3455,18188"]; - n297 [color=deepskyblue2, + n297 [color=forestgreen, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 202\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2732,7 +2732,7 @@ digraph G { width=2.817]; n296 -> n297 [pos="e,3508.5,18048 3496.5,18084 3499.4,18075 3502.4,18066 3505.3,18057"]; n297 -> n298 [pos="e,3580.9,17914 3554.2,17954 3560.9,17944 3568.2,17933 3575.1,17923"]; - n299 [color=darkorchid3, + n299 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2741,7 +2741,7 @@ digraph G { style=solid, width=2.817]; n298 -> n299 [pos="e,3611,17784 3611,17820 3611,17812 3611,17803 3611,17795"]; - n300 [color=coral, + n300 [color=ghostwhite, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 203\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2750,7 +2750,7 @@ digraph G { style=solid, width=6.5343]; n299 -> n300 [pos="e,3263.1,17643 3523.3,17712 3453.8,17693 3355,17667 3272.8,17645"]; - n303 [color=brown2, + n303 [color=darkgreen, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 205\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2759,7 +2759,7 @@ digraph G { style=solid, width=6.5343]; n299 -> n303 [pos="e,3611,17652 3611,17688 3611,17680 3611,17671 3611,17663"]; - n316 [color=darkorchid3, + n316 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2769,7 +2769,7 @@ digraph G { width=2.817]; n299 -> n316 [pos="e,3812.2,15935 3710.5,17726 3761.6,17716 3820.2,17696 3855,17652 3905,17590 3874,17553 3874,17473 3874,17473 3874,17473 3874,16151 \ 3874,16076 3841.8,15996 3816.6,15944"]; - n301 [color=deeppink4, + n301 [color=darkorange4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2778,7 +2778,7 @@ digraph G { style=solid, width=3.5652]; n300 -> n301 [pos="e,3128.3,17520 3125.6,17556 3126.3,17548 3126.9,17539 3127.6,17531"]; - n302 [color=deeppink4, + n302 [color=darkorange4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 204\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2787,7 +2787,7 @@ digraph G { style=solid, width=2.6788]; n301 -> n302 [pos="e,3178.7,17256 3142.3,17424 3151.9,17380 3166.3,17313 3176.6,17266"]; - n312 [color=blue, + n312 [color=cornsilk1, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 211\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2796,7 +2796,7 @@ digraph G { style=solid, width=2.6788]; n302 -> n312 [pos="e,3496.5,16440 3202.9,17160 3217.1,17108 3237,17021 3237,16945 3237,16945 3237,16945 3237,16679 3237,16553 3385.3,16479 3486.9,16444"]; - n304 [color=deepskyblue2, + n304 [color=dodgerblue, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2805,7 +2805,7 @@ digraph G { style=solid, width=3.5652]; n303 -> n304 [pos="e,3605.9,17520 3608.1,17556 3607.6,17548 3607.1,17539 3606.5,17531"]; - n305 [color=deepskyblue2, + n305 [color=dodgerblue, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 206\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2814,7 +2814,7 @@ digraph G { style=solid, width=2.6788]; n304 -> n305 [pos="e,3593.4,17388 3597.5,17424 3596.6,17415 3595.6,17407 3594.6,17398"]; - n306 [color=cornsilk, + n306 [color=gold, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 207\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2823,7 +2823,7 @@ digraph G { style=solid, width=7.3732]; n305 -> n306 [pos="e,3582.9,17256 3585.1,17292 3584.6,17284 3584.1,17275 3583.5,17267"]; - n307 [color=burlywood, + n307 [color=darkgoldenrod1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2832,7 +2832,7 @@ digraph G { style=solid, width=3.5652]; n306 -> n307 [pos="e,3580,17124 3580,17160 3580,17152 3580,17143 3580,17135"]; - n308 [color=burlywood, + n308 [color=darkgoldenrod1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 208\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2841,7 +2841,7 @@ digraph G { style=solid, width=2.6788]; n307 -> n308 [pos="e,3580,16992 3580,17028 3580,17020 3580,17011 3580,17003"]; - n309 [color=aquamarine3, + n309 [color=chartreuse, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 209\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2850,7 +2850,7 @@ digraph G { style=solid, width=7.3732]; n308 -> n309 [pos="e,3580,16860 3580,16896 3580,16888 3580,16879 3580,16871"]; - n310 [color=darkslategray1, + n310 [color=darkgoldenrod2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2859,7 +2859,7 @@ digraph G { style=solid, width=3.5652]; n309 -> n310 [pos="e,3580,16728 3580,16764 3580,16756 3580,16747 3580,16739"]; - n311 [color=darkslategray1, + n311 [color=darkgoldenrod2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 210\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2869,7 +2869,7 @@ digraph G { width=2.6788]; n310 -> n311 [pos="e,3580,16596 3580,16632 3580,16624 3580,16615 3580,16607"]; n311 -> n312 [pos="e,3580,16464 3580,16500 3580,16492 3580,16483 3580,16475"]; - n313 [color=bisque4, + n313 [color=dodgerblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 212\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2878,7 +2878,7 @@ digraph G { style=solid, width=6.5343]; n312 -> n313 [pos="e,3590.2,16332 3585.8,16368 3586.8,16360 3587.9,16351 3588.9,16343"]; - n314 [color=cyan3, + n314 [color=brown4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 213\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2887,7 +2887,7 @@ digraph G { style=solid, width=3.5652]; n313 -> n314 [pos="e,3635.2,16199 3618,16236 3622.2,16227 3626.7,16218 3631,16208"]; - n315 [color=brown1, + n315 [color=chartreuse1, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 214\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2897,7 +2897,7 @@ digraph G { width=2.817]; n314 -> n315 [pos="e,3685.1,16068 3672.9,16104 3675.8,16095 3678.9,16086 3681.9,16077"]; n315 -> n316 [pos="e,3757.2,15934 3730.8,15974 3737.5,15964 3744.6,15953 3751.5,15943"]; - n317 [color=darkorchid3, + n317 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2906,7 +2906,7 @@ digraph G { style=solid, width=2.817]; n316 -> n317 [pos="e,3787,15804 3787,15840 3787,15832 3787,15823 3787,15815"]; - n318 [color=blanchedalmond, + n318 [color=cyan3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 215\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2915,7 +2915,7 @@ digraph G { style=solid, width=6.5343]; n317 -> n318 [pos="e,3439.1,15663 3699.3,15732 3629.8,15713 3531,15687 3448.8,15665"]; - n321 [color=darkslategray4, + n321 [color=azure4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 217\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2924,7 +2924,7 @@ digraph G { style=solid, width=6.5343]; n317 -> n321 [pos="e,3787,15672 3787,15708 3787,15700 3787,15691 3787,15683"]; - n334 [color=darkorchid3, + n334 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -2934,7 +2934,7 @@ digraph G { width=2.817]; n317 -> n334 [pos="e,3988.2,13955 3886.5,15746 3937.6,15736 3996.2,15716 4031,15672 4081,15610 4050,15573 4050,15493 4050,15493 4050,15493 4050,14171 \ 4050,14096 4017.8,14016 3992.6,13964"]; - n319 [color=darkolivegreen3, + n319 [color=darkkhaki, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2943,7 +2943,7 @@ digraph G { style=solid, width=3.5652]; n318 -> n319 [pos="e,3304.3,15540 3301.6,15576 3302.3,15568 3302.9,15559 3303.6,15551"]; - n320 [color=darkolivegreen3, + n320 [color=darkkhaki, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 216\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -2952,7 +2952,7 @@ digraph G { style=solid, width=2.6788]; n319 -> n320 [pos="e,3354.7,15276 3318.3,15444 3327.9,15400 3342.3,15333 3352.6,15286"]; - n330 [color=crimson, + n330 [color=darkseagreen1, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 223\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -2961,7 +2961,7 @@ digraph G { style=solid, width=2.6788]; n320 -> n330 [pos="e,3672.8,14461 3379.2,15180 3393.6,15128 3414,15041 3414,14965 3414,14965 3414,14965 3414,14699 3414,14573 3561.8,14499 3663.1,14464"]; - n322 [color=deeppink3, + n322 [color=darkseagreen, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2970,7 +2970,7 @@ digraph G { style=solid, width=3.5652]; n321 -> n322 [pos="e,3781.9,15540 3784.1,15576 3783.6,15568 3783.1,15559 3782.5,15551"]; - n323 [color=deeppink3, + n323 [color=darkseagreen, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 218\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -2979,7 +2979,7 @@ digraph G { style=solid, width=2.6788]; n322 -> n323 [pos="e,3769.4,15408 3773.5,15444 3772.6,15435 3771.6,15427 3770.6,15418"]; - n324 [color=goldenrod3, + n324 [color=burlywood3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 219\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2988,7 +2988,7 @@ digraph G { style=solid, width=7.3732]; n323 -> n324 [pos="e,3758.9,15276 3761.1,15312 3760.6,15304 3760.1,15295 3759.5,15287"]; - n325 [color=brown4, + n325 [color=goldenrod, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -2997,7 +2997,7 @@ digraph G { style=solid, width=3.5652]; n324 -> n325 [pos="e,3756,15144 3756,15180 3756,15172 3756,15163 3756,15155"]; - n326 [color=brown4, + n326 [color=goldenrod, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 220\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -3006,7 +3006,7 @@ digraph G { style=solid, width=2.6788]; n325 -> n326 [pos="e,3756,15012 3756,15048 3756,15040 3756,15031 3756,15023"]; - n327 [color=aquamarine1, + n327 [color=darkgoldenrod, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 221\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3015,7 +3015,7 @@ digraph G { style=solid, width=7.3732]; n326 -> n327 [pos="e,3756,14880 3756,14916 3756,14908 3756,14899 3756,14891"]; - n328 [color=darkorange1, + n328 [color=floralwhite, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3024,7 +3024,7 @@ digraph G { style=solid, width=3.5652]; n327 -> n328 [pos="e,3756,14748 3756,14784 3756,14776 3756,14767 3756,14759"]; - n329 [color=darkorange1, + n329 [color=floralwhite, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 222\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3034,7 +3034,7 @@ digraph G { width=2.6788]; n328 -> n329 [pos="e,3756,14616 3756,14652 3756,14644 3756,14635 3756,14627"]; n329 -> n330 [pos="e,3756,14484 3756,14520 3756,14512 3756,14503 3756,14495"]; - n331 [color=aliceblue, + n331 [color=coral4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 224\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3043,7 +3043,7 @@ digraph G { style=solid, width=6.5343]; n330 -> n331 [pos="e,3766.2,14352 3761.8,14388 3762.8,14380 3763.9,14371 3764.9,14363"]; - n332 [color=goldenrod, + n332 [color=antiquewhite4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 225\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3052,7 +3052,7 @@ digraph G { style=solid, width=3.5652]; n331 -> n332 [pos="e,3811.2,14219 3794,14256 3798.2,14247 3802.7,14238 3807,14228"]; - n333 [color=gold3, + n333 [color=brown, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 226\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3062,7 +3062,7 @@ digraph G { width=2.817]; n332 -> n333 [pos="e,3861.1,14088 3848.9,14124 3851.8,14115 3854.9,14106 3857.9,14097"]; n333 -> n334 [pos="e,3933.2,13954 3906.8,13994 3913.5,13984 3920.6,13973 3927.5,13963"]; - n335 [color=darkorchid3, + n335 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3071,7 +3071,7 @@ digraph G { style=solid, width=2.817]; n334 -> n335 [pos="e,3963,13824 3963,13860 3963,13852 3963,13843 3963,13835"]; - n336 [color=blue4, + n336 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 227\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3080,7 +3080,7 @@ digraph G { style=solid, width=6.5343]; n335 -> n336 [pos="e,3615.1,13683 3875.3,13752 3805.8,13733 3707,13707 3624.8,13685"]; - n339 [color=darkgoldenrod3, + n339 [color=burlywood2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 229\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -3089,7 +3089,7 @@ digraph G { style=solid, width=6.5343]; n335 -> n339 [pos="e,3963,13692 3963,13728 3963,13720 3963,13711 3963,13703"]; - n352 [color=darkorchid3, + n352 [color=coral, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3099,7 +3099,7 @@ digraph G { width=2.817]; n335 -> n352 [pos="e,4164.2,11975 4062.5,13766 4113.6,13756 4172.2,13736 4207,13692 4257,13630 4226,13593 4226,13513 4226,13513 4226,13513 4226,12191 \ 4226,12116 4193.8,12036 4168.6,11984"]; - n337 [color=dodgerblue3, + n337 [color=cornsilk, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3108,7 +3108,7 @@ digraph G { style=solid, width=3.5652]; n336 -> n337 [pos="e,3480.3,13560 3477.6,13596 3478.3,13588 3478.9,13579 3479.6,13571"]; - n338 [color=dodgerblue3, + n338 [color=cornsilk, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 228\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3117,7 +3117,7 @@ digraph G { style=solid, width=2.6788]; n337 -> n338 [pos="e,3530.7,13296 3494.3,13464 3503.9,13420 3518.3,13353 3528.6,13306"]; - n348 [color=dodgerblue1, + n348 [color=darkslategray3, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 235\nSize: {1, 384, 17, 17}\nMem size: 110976", @@ -3126,7 +3126,7 @@ digraph G { style=solid, width=2.6788]; n338 -> n348 [pos="e,3848.8,12481 3555.2,13200 3569.6,13148 3590,13061 3590,12985 3590,12985 3590,12985 3590,12719 3590,12593 3737.8,12519 3839.1,12484"]; - n340 [color=goldenrod3, + n340 [color=cyan3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -3135,7 +3135,7 @@ digraph G { style=solid, width=3.5652]; n339 -> n340 [pos="e,3957.9,13560 3960.1,13596 3959.6,13588 3959.1,13579 3958.5,13571"]; - n341 [color=goldenrod3, + n341 [color=cyan3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 230\nSize: {1, 128, 17, 17}\nMem size: 36992", @@ -3144,7 +3144,7 @@ digraph G { style=solid, width=2.6788]; n340 -> n341 [pos="e,3945.4,13428 3949.5,13464 3948.6,13455 3947.6,13447 3946.6,13438"]; - n342 [color=burlywood1, + n342 [color=dodgerblue2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 231\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -3153,7 +3153,7 @@ digraph G { style=solid, width=7.3732]; n341 -> n342 [pos="e,3934.9,13296 3937.1,13332 3936.6,13324 3936.1,13315 3935.5,13307"]; - n343 [color=deeppink, + n343 [color=crimson, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -3162,7 +3162,7 @@ digraph G { style=solid, width=3.5652]; n342 -> n343 [pos="e,3932,13164 3932,13200 3932,13192 3932,13183 3932,13175"]; - n344 [color=deeppink, + n344 [color=crimson, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 232\nSize: {1, 160, 17, 17}\nMem size: 46240", @@ -3171,7 +3171,7 @@ digraph G { style=solid, width=2.6788]; n343 -> n344 [pos="e,3932,13032 3932,13068 3932,13060 3932,13051 3932,13043"]; - n345 [color=deeppink, + n345 [color=darkolivegreen, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 233\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3180,7 +3180,7 @@ digraph G { style=solid, width=7.3732]; n344 -> n345 [pos="e,3932,12900 3932,12936 3932,12928 3932,12919 3932,12911"]; - n346 [color=burlywood2, + n346 [color=beige, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3189,7 +3189,7 @@ digraph G { style=solid, width=3.5652]; n345 -> n346 [pos="e,3932,12768 3932,12804 3932,12796 3932,12787 3932,12779"]; - n347 [color=burlywood2, + n347 [color=beige, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 234\nSize: {1, 192, 17, 17}\nMem size: 55488", @@ -3199,7 +3199,7 @@ digraph G { width=2.6788]; n346 -> n347 [pos="e,3932,12636 3932,12672 3932,12664 3932,12655 3932,12647"]; n347 -> n348 [pos="e,3932,12504 3932,12540 3932,12532 3932,12523 3932,12515"]; - n349 [color=blue2, + n349 [color=deeppink4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 236\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3208,7 +3208,7 @@ digraph G { style=solid, width=6.5343]; n348 -> n349 [pos="e,3942.2,12372 3937.8,12408 3938.8,12400 3939.9,12391 3940.9,12383"]; - n350 [color=coral2, + n350 [color=brown2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 237\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3217,7 +3217,7 @@ digraph G { style=solid, width=3.5652]; n349 -> n350 [pos="e,3986.6,12239 3969.7,12276 3973.7,12267 3978.1,12258 3982.3,12249"]; - n351 [color=brown1, + n351 [color=aliceblue, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 238\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3227,7 +3227,7 @@ digraph G { width=2.817]; n350 -> n351 [pos="e,4036.1,12108 4023.9,12144 4026.8,12135 4029.9,12126 4032.9,12117"]; n351 -> n352 [pos="e,4108.9,11974 4082.2,12014 4088.9,12004 4096.2,11993 4103.1,11983"]; - n353 [color=darkorchid3, + n353 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 118\nSize: {1, 1152, 17, 17}\nMem size: 332928", @@ -3236,7 +3236,7 @@ digraph G { style=solid, width=2.817]; n352 -> n353 [pos="e,4139,11844 4139,11880 4139,11872 4139,11863 4139,11855"]; - n354 [color=cadetblue2, + n354 [color=brown1, fontsize=14, height=1.3356, label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 239\nSize: {1, 1152, 8, 8}\nMem size: 73728", @@ -3245,7 +3245,7 @@ digraph G { style=solid, width=3.7843]; n353 -> n354 [pos="e,3784.9,10919 4043.1,11780 3916.1,11755 3708,11689 3708,11533 3708,11533 3708,11533 3708,11135 3708,11059 3748,10979 3779.5,10927"]; - n355 [color=darkolivegreen3, + n355 [color=firebrick3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 240\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3254,7 +3254,7 @@ digraph G { style=solid, width=6.5343]; n353 -> n355 [pos="e,4001.3,11580 4073.3,11759 4055.9,11747 4038.9,11731 4028,11712 4006.6,11675 4001.4,11627 4001.3,11590"]; - n361 [color=darkseagreen1, + n361 [color=antiquewhite, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 244\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3263,7 +3263,7 @@ digraph G { style=solid, width=6.5343]; n353 -> n361 [pos="e,4224.7,11711 4182.5,11752 4193.7,11742 4205.9,11730 4217.5,11718"]; - n367 [color=darkorange, + n367 [color=bisque4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 248\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3272,7 +3272,7 @@ digraph G { style=solid, width=6.5343]; n353 -> n367 [pos="e,4599.7,11699 4231.3,11776 4325.4,11756 4473.7,11725 4589.6,11701"]; - n376 [color=darksalmon, + n376 [color=brown4, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3281,7 +3281,7 @@ digraph G { style=solid, width=2.5643]; n354 -> n376 [pos="e,4201.3,10518 3865,10827 3946.1,10752 4110,10602 4193.9,10525"]; - n356 [color=bisque4, + n356 [color=deepskyblue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3290,7 +3290,7 @@ digraph G { style=solid, width=3.5652]; n355 -> n356 [pos="e,4005,11448 4005,11484 4005,11476 4005,11467 4005,11459"]; - n357 [color=bisque4, + n357 [color=deepskyblue1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 241\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3299,7 +3299,7 @@ digraph G { style=solid, width=2.6788]; n356 -> n357 [pos="e,3994.2,11316 3998.8,11352 3997.7,11343 3996.6,11335 3995.5,11326"]; - n358 [color=aquamarine3, + n358 [color=azure, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 242\nSize: {1, 384, 8, 8}\nMem size: 24576", @@ -3308,7 +3308,7 @@ digraph G { style=solid, width=6.8916]; n357 -> n358 [pos="e,3985.5,11184 3986.5,11220 3986.3,11212 3986,11203 3985.8,11195"]; - n359 [color=chartreuse3, + n359 [color=coral1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", @@ -3317,7 +3317,7 @@ digraph G { style=solid, width=3.5652]; n358 -> n359 [pos="e,4022.6,11051 4005.7,11088 4009.7,11079 4014.1,11070 4018.3,11061"]; - n360 [color=chartreuse3, + n360 [color=coral1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 243\nSize: {1, 384, 8, 8}\nMem size: 24576", @@ -3327,7 +3327,7 @@ digraph G { width=2.4261]; n359 -> n360 [pos="e,4150.5,10787 4066.9,10956 4089,10912 4122.6,10843 4146,10796"]; n360 -> n376 [pos="e,4229.7,10523 4183.7,10692 4192.3,10656 4204.9,10604 4218,10560 4220.6,10551 4223.5,10542 4226.4,10533"]; - n362 [color=darkorange, + n362 [color=blue2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3336,7 +3336,7 @@ digraph G { style=solid, width=3.5652]; n361 -> n362 [pos="e,4447.4,11446 4309.7,11616 4346.4,11571 4402.4,11502 4440.9,11454"]; - n363 [color=darkorange, + n363 [color=blue2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 245\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3345,7 +3345,7 @@ digraph G { style=solid, width=2.6788]; n362 -> n363 [pos="e,4489.1,11316 4486.9,11352 4487.4,11344 4487.9,11335 4488.5,11327"]; - n364 [color=chocolate4, + n364 [color=dodgerblue3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 246\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3354,7 +3354,7 @@ digraph G { style=solid, width=6.8916]; n363 -> n364 [pos="e,4495.8,11184 4494.2,11220 4494.6,11212 4495,11203 4495.3,11195"]; - n365 [color=deeppink3, + n365 [color=gainsboro, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3363,7 +3363,7 @@ digraph G { style=solid, width=3.5652]; n364 -> n365 [pos="e,4459.4,11051 4476.3,11088 4472.3,11079 4467.9,11070 4463.7,11061"]; - n366 [color=deeppink3, + n366 [color=gainsboro, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 247\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3373,7 +3373,7 @@ digraph G { width=2.4261]; n365 -> n366 [pos="e,4329.5,10656 4423.3,10956 4400.5,10883 4356.9,10743 4332.6,10665"]; n366 -> n376 [pos="e,4270.2,10523 4290.9,10562 4285.7,10552 4280.3,10542 4275,10532"]; - n368 [color=blue3, + n368 [color=cyan, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3382,7 +3382,7 @@ digraph G { style=solid, width=3.5652]; n367 -> n368 [pos="e,4762,11580 4762,11616 4762,11608 4762,11599 4762,11591"]; - n369 [color=blue3, + n369 [color=cyan, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 249\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3391,7 +3391,7 @@ digraph G { style=solid, width=2.6788]; n368 -> n369 [pos="e,4783.7,11448 4774.3,11484 4776.5,11476 4778.9,11466 4781.2,11458"]; - n370 [color=firebrick4, + n370 [color=brown2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 250\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3400,7 +3400,7 @@ digraph G { style=solid, width=7.3732]; n369 -> n370 [pos="e,4855.4,11316 4828,11354 4834.9,11345 4842.3,11334 4849.4,11324"]; - n371 [color=darkseagreen2, + n371 [color=chartreuse4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3409,7 +3409,7 @@ digraph G { style=solid, width=3.5652]; n370 -> n371 [pos="e,4891.5,11184 4890.5,11220 4890.7,11212 4891,11203 4891.2,11195"]; - n372 [color=darkseagreen2, + n372 [color=chartreuse4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 251\nSize: {1, 256, 17, 17}\nMem size: 73984", @@ -3418,7 +3418,7 @@ digraph G { style=solid, width=2.6788]; n371 -> n372 [pos="e,4843.7,11051 4866,11089 4860.4,11079 4854.5,11069 4848.8,11059"]; - n373 [color=ghostwhite, + n373 [color=darkseagreen, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 252\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3427,7 +3427,7 @@ digraph G { style=solid, width=6.8916]; n372 -> n373 [pos="e,4813.2,10920 4814.8,10956 4814.4,10948 4814,10939 4813.7,10931"]; - n374 [color=darkslategray4, + n374 [color=chartreuse, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3436,7 +3436,7 @@ digraph G { style=solid, width=3.5652]; n373 -> n374 [pos="e,4634.2,10779 4725.7,10827 4699.1,10813 4669.8,10798 4643.4,10784"]; - n375 [color=darkslategray4, + n375 [color=chartreuse, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 253\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3446,7 +3446,7 @@ digraph G { width=2.4261]; n374 -> n375 [pos="e,4533,10655 4544.8,10692 4542,10683 4539,10674 4536.1,10665"]; n375 -> n376 [pos="e,4313.5,10509 4452.8,10576 4413.7,10557 4363.7,10533 4322.6,10514"]; - n377 [color=darkslategrey, + n377 [color=coral4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 255\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3455,7 +3455,7 @@ digraph G { style=solid, width=6.5343]; n376 -> n377 [pos="e,3898.2,10383 4164.4,10453 4094.7,10435 3992.5,10408 3907.9,10385"]; - n380 [color=darkslategray1, + n380 [color=cornsilk, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 257\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3464,7 +3464,7 @@ digraph G { style=solid, width=6.5343]; n376 -> n380 [pos="e,4246,10392 4246,10428 4246,10420 4246,10411 4246,10403"]; - n393 [color=darksalmon, + n393 [color=brown4, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3474,7 +3474,7 @@ digraph G { width=2.5643]; n376 -> n393 [pos="e,4448.4,8674.4 4337.2,10468 4390.1,10459 4453.3,10438 4490,10392 4540,10330 4509,10293 4509,10213 4509,10213 4509,10213 4509,8891 \ 4509,8816.3 4477.4,8735.3 4452.7,8683.4"]; - n378 [color=floralwhite, + n378 [color=darkorange, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3483,7 +3483,7 @@ digraph G { style=solid, width=3.5652]; n377 -> n378 [pos="e,3764,10260 3761,10296 3761.7,10288 3762.4,10279 3763.1,10271"]; - n379 [color=floralwhite, + n379 [color=darkorange, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 256\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3492,7 +3492,7 @@ digraph G { style=solid, width=2.4261]; n378 -> n379 [pos="e,3820.6,9995.8 3779.5,10164 3790.4,10119 3806.6,10053 3818.2,10006"]; - n389 [color=chartreuse1, + n389 [color=darkgreen, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 263\nSize: {1, 448, 8, 8}\nMem size: 28672", @@ -3502,7 +3502,7 @@ digraph G { width=2.4261]; n379 -> n389 [pos="e,4138.1,9178.8 3844.7,9900.1 3857.7,9847.9 3876,9761.1 3876,9685 3876,9685 3876,9685 3876,9419 3876,9291.5 4028.1,9217.2 4128.5,\ 9182.1"]; - n381 [color=coral4, + n381 [color=firebrick2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3511,7 +3511,7 @@ digraph G { style=solid, width=3.5652]; n380 -> n381 [pos="e,4236.4,10260 4240.5,10296 4239.6,10287 4238.6,10279 4237.6,10270"]; - n382 [color=coral4, + n382 [color=firebrick2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 258\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3520,7 +3520,7 @@ digraph G { style=solid, width=2.4261]; n381 -> n382 [pos="e,4225.9,10128 4228.1,10164 4227.6,10156 4227.1,10147 4226.5,10139"]; - n383 [color=aquamarine1, + n383 [color=aliceblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 259\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3529,7 +3529,7 @@ digraph G { style=solid, width=7.3732]; n382 -> n383 [pos="e,4217.9,9996.5 4220.1,10032 4219.6,10024 4219.1,10015 4218.5,10007"]; - n384 [color=darkorange3, + n384 [color=blanchedalmond, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3538,7 +3538,7 @@ digraph G { style=solid, width=3.5652]; n383 -> n384 [pos="e,4215,9864.5 4215,9899.7 4215,9891.5 4215,9883 4215,9874.6"]; - n385 [color=darkorange3, + n385 [color=blanchedalmond, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 260\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3547,7 +3547,7 @@ digraph G { style=solid, width=2.4261]; n384 -> n385 [pos="e,4215,9732.5 4215,9767.7 4215,9759.5 4215,9751 4215,9742.6"]; - n386 [color=darkseagreen2, + n386 [color=beige, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 261\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3556,7 +3556,7 @@ digraph G { style=solid, width=7.3732]; n385 -> n386 [pos="e,4215,9600.5 4215,9635.7 4215,9627.5 4215,9619 4215,9610.6"]; - n387 [color=cyan4, + n387 [color=coral, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3565,7 +3565,7 @@ digraph G { style=solid, width=3.5652]; n386 -> n387 [pos="e,4215,9468.5 4215,9503.7 4215,9495.5 4215,9487 4215,9478.6"]; - n388 [color=cyan4, + n388 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 262\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3575,7 +3575,7 @@ digraph G { width=2.4261]; n387 -> n388 [pos="e,4215,9336.5 4215,9371.7 4215,9363.5 4215,9355 4215,9346.6"]; n388 -> n389 [pos="e,4215,9204.5 4215,9239.7 4215,9231.5 4215,9223 4215,9214.6"]; - n390 [color=deeppink2, + n390 [color=blueviolet, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 264\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3584,7 +3584,7 @@ digraph G { style=solid, width=6.5343]; n389 -> n390 [pos="e,4225.2,9072.5 4220.8,9107.7 4221.8,9099.5 4222.9,9091 4223.9,9082.6"]; - n391 [color=cyan4, + n391 [color=bisque4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 265\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3593,7 +3593,7 @@ digraph G { style=solid, width=3.5652]; n390 -> n391 [pos="e,4270.2,8939.4 4253,8976.1 4257.2,8967.1 4261.7,8957.7 4266,8948.5"]; - n392 [color=darkslategray1, + n392 [color=cornsilk3, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 266\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3603,7 +3603,7 @@ digraph G { width=2.5643]; n391 -> n392 [pos="e,4322.9,8807.4 4309.3,8844.1 4312.6,8835.3 4316,8826.1 4319.3,8817.1"]; n392 -> n393 [pos="e,4395,8673.8 4368.9,8714.3 4375.6,8703.9 4382.7,8692.9 4389.6,8682.2"]; - n394 [color=darksalmon, + n394 [color=brown4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3612,7 +3612,7 @@ digraph G { style=solid, width=2.5643]; n393 -> n394 [pos="e,4424,8544.5 4424,8579.7 4424,8571.5 4424,8563 4424,8554.6"]; - n395 [color=darkolivegreen, + n395 [color=cornsilk4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 267\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3621,7 +3621,7 @@ digraph G { style=solid, width=6.5343]; n394 -> n395 [pos="e,4076.2,8402.5 4342.4,8473.3 4272.7,8454.8 4170.5,8427.6 4085.9,8405.1"]; - n398 [color=brown2, + n398 [color=azure1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 269\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3630,7 +3630,7 @@ digraph G { style=solid, width=6.5343]; n394 -> n398 [pos="e,4424,8412.5 4424,8447.7 4424,8439.5 4424,8431 4424,8422.6"]; - n411 [color=darksalmon, + n411 [color=brown4, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3640,7 +3640,7 @@ digraph G { width=2.5643]; n394 -> n411 [pos="e,4626.4,6694.4 4515.2,8487.8 4568.1,8478.5 4631.3,8457.9 4668,8412 4718,8349.5 4687,8313 4687,8233 4687,8233 4687,8233 4687,6911 \ 4687,6836.3 4655.4,6755.3 4630.7,6703.4"]; - n396 [color=blanchedalmond, + n396 [color=darkorchid1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3649,7 +3649,7 @@ digraph G { style=solid, width=3.5652]; n395 -> n396 [pos="e,3942,8280.5 3939,8315.7 3939.7,8307.5 3940.4,8299 3941.1,8290.6"]; - n397 [color=blanchedalmond, + n397 [color=darkorchid1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 268\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3658,7 +3658,7 @@ digraph G { style=solid, width=2.4261]; n396 -> n397 [pos="e,3998.6,8015.8 3957.5,8183.9 3968.4,8139.4 3984.6,8072.8 3996.2,8025.5"]; - n407 [color=darkolivegreen2, + n407 [color=forestgreen, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 275\nSize: {1, 448, 8, 8}\nMem size: 28672", @@ -3667,7 +3667,7 @@ digraph G { style=solid, width=2.4261]; n397 -> n407 [pos="e,4315.9,7199.1 4023,7920.1 4036.3,7868 4055,7781.2 4055,7705 4055,7705 4055,7705 4055,7439 4055,7311.9 4206.2,7237.6 4306.4,7202.3"]; - n399 [color=coral4, + n399 [color=darkslategrey, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3676,7 +3676,7 @@ digraph G { style=solid, width=3.5652]; n398 -> n399 [pos="e,4418.9,8280.5 4421.1,8315.7 4420.6,8307.5 4420.1,8299 4419.5,8290.6"]; - n400 [color=coral4, + n400 [color=darkslategrey, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 270\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3685,7 +3685,7 @@ digraph G { style=solid, width=2.4261]; n399 -> n400 [pos="e,4406.4,8148 4410.5,8183.7 4409.6,8175.4 4408.6,8166.6 4407.6,8158"]; - n401 [color=cornsilk, + n401 [color=azure3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 271\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3694,7 +3694,7 @@ digraph G { style=solid, width=7.3732]; n400 -> n401 [pos="e,4395.9,8016.5 4398.1,8051.7 4397.6,8043.5 4397.1,8035 4396.5,8026.6"]; - n402 [color=burlywood, + n402 [color=burlywood3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3703,7 +3703,7 @@ digraph G { style=solid, width=3.5652]; n401 -> n402 [pos="e,4393,7884.5 4393,7919.7 4393,7911.5 4393,7903 4393,7894.6"]; - n403 [color=burlywood, + n403 [color=burlywood3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 272\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3712,7 +3712,7 @@ digraph G { style=solid, width=2.4261]; n402 -> n403 [pos="e,4393,7752.5 4393,7787.7 4393,7779.5 4393,7771 4393,7762.6"]; - n404 [color=cyan4, + n404 [color=dodgerblue, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 273\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3721,7 +3721,7 @@ digraph G { style=solid, width=7.3732]; n403 -> n404 [pos="e,4393,7620.5 4393,7655.7 4393,7647.5 4393,7639 4393,7630.6"]; - n405 [color=firebrick, + n405 [color=burlywood, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3730,7 +3730,7 @@ digraph G { style=solid, width=3.5652]; n404 -> n405 [pos="e,4393,7488.5 4393,7523.7 4393,7515.5 4393,7507 4393,7498.6"]; - n406 [color=firebrick, + n406 [color=burlywood, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 274\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3740,7 +3740,7 @@ digraph G { width=2.4261]; n405 -> n406 [pos="e,4393,7356.5 4393,7391.7 4393,7383.5 4393,7375 4393,7366.6"]; n406 -> n407 [pos="e,4393,7224.5 4393,7259.7 4393,7251.5 4393,7243 4393,7234.6"]; - n408 [color=goldenrod1, + n408 [color=bisque1, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 276\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3749,7 +3749,7 @@ digraph G { style=solid, width=6.5343]; n407 -> n408 [pos="e,4403.2,7092.5 4398.8,7127.7 4399.8,7119.5 4400.9,7111 4401.9,7102.6"]; - n409 [color=burlywood4, + n409 [color=darkorchid3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 277\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3758,7 +3758,7 @@ digraph G { style=solid, width=3.5652]; n408 -> n409 [pos="e,4448.2,6959.4 4431,6996.1 4435.2,6987.1 4439.7,6977.7 4444,6968.5"]; - n410 [color=azure, + n410 [color=dodgerblue4, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 278\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3768,7 +3768,7 @@ digraph G { width=2.5643]; n409 -> n410 [pos="e,4500.9,6827.4 4487.3,6864.1 4490.6,6855.3 4494,6846.1 4497.3,6837.1"]; n410 -> n411 [pos="e,4573,6693.8 4546.9,6734.3 4553.6,6723.9 4560.7,6712.9 4567.6,6702.2"]; - n412 [color=darksalmon, + n412 [color=brown4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3777,7 +3777,7 @@ digraph G { style=solid, width=2.5643]; n411 -> n412 [pos="e,4602,6564.5 4602,6599.7 4602,6591.5 4602,6583 4602,6574.6"]; - n413 [color=darksalmon, + n413 [color=bisque4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 279\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3786,7 +3786,7 @@ digraph G { style=solid, width=6.5343]; n412 -> n413 [pos="e,4254.2,6422.5 4520.4,6493.3 4450.7,6474.8 4348.5,6447.6 4263.9,6425.1"]; - n416 [color=cornsilk3, + n416 [color=deepskyblue3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 281\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3795,7 +3795,7 @@ digraph G { style=solid, width=6.5343]; n412 -> n416 [pos="e,4602,6432.5 4602,6467.7 4602,6459.5 4602,6451 4602,6442.6"]; - n429 [color=darksalmon, + n429 [color=brown4, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3805,7 +3805,7 @@ digraph G { width=2.5643]; n412 -> n429 [pos="e,4804.4,4714.4 4693.2,6507.8 4746.1,6498.5 4809.3,6477.9 4846,6432 4896,6369.5 4865,6333 4865,6253 4865,6253 4865,6253 4865,4931 \ 4865,4856.3 4833.4,4775.3 4808.7,4723.4"]; - n414 [color=coral3, + n414 [color=bisque, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3814,7 +3814,7 @@ digraph G { style=solid, width=3.5652]; n413 -> n414 [pos="e,4120,6300.5 4117,6335.7 4117.7,6327.5 4118.4,6319 4119.1,6310.6"]; - n415 [color=coral3, + n415 [color=bisque, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 280\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3823,7 +3823,7 @@ digraph G { style=solid, width=2.4261]; n414 -> n415 [pos="e,4176.6,6035.8 4135.5,6203.9 4146.4,6159.4 4162.6,6092.8 4174.2,6045.5"]; - n425 [color=blue3, + n425 [color=darkolivegreen2, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 287\nSize: {1, 448, 8, 8}\nMem size: 28672", @@ -3832,7 +3832,7 @@ digraph G { style=solid, width=2.4261]; n415 -> n425 [pos="e,4493.9,5219.1 4201,5940.1 4214.3,5888 4233,5801.2 4233,5725 4233,5725 4233,5725 4233,5459 4233,5331.9 4384.2,5257.6 4484.4,5222.3"]; - n417 [color=azure3, + n417 [color=deeppink4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3841,7 +3841,7 @@ digraph G { style=solid, width=3.5652]; n416 -> n417 [pos="e,4596.9,6300.5 4599.1,6335.7 4598.6,6327.5 4598.1,6319 4597.5,6310.6"]; - n418 [color=azure3, + n418 [color=deeppink4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 282\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3850,7 +3850,7 @@ digraph G { style=solid, width=2.4261]; n417 -> n418 [pos="e,4584.4,6168 4588.5,6203.7 4587.6,6195.4 4586.6,6186.6 4585.6,6178"]; - n419 [color=bisque3, + n419 [color=darkolivegreen4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 283\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3859,7 +3859,7 @@ digraph G { style=solid, width=7.3732]; n418 -> n419 [pos="e,4573.9,6036.5 4576.1,6071.7 4575.6,6063.5 4575.1,6055 4574.5,6046.6"]; - n420 [color=deeppink4, + n420 [color=blueviolet, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3868,7 +3868,7 @@ digraph G { style=solid, width=3.5652]; n419 -> n420 [pos="e,4571,5904.5 4571,5939.7 4571,5931.5 4571,5923 4571,5914.6"]; - n421 [color=deeppink4, + n421 [color=blueviolet, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 284\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -3877,7 +3877,7 @@ digraph G { style=solid, width=2.4261]; n420 -> n421 [pos="e,4571,5772.5 4571,5807.7 4571,5799.5 4571,5791 4571,5782.6"]; - n422 [color=deeppink3, + n422 [color=darkorchid3, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 285\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3886,7 +3886,7 @@ digraph G { style=solid, width=7.3732]; n421 -> n422 [pos="e,4571,5640.5 4571,5675.7 4571,5667.5 4571,5659 4571,5650.6"]; - n423 [color=chartreuse4, + n423 [color=firebrick1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 286\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3895,7 +3895,7 @@ digraph G { style=solid, width=3.5652]; n422 -> n423 [pos="e,4571,5508.5 4571,5543.7 4571,5535.5 4571,5527 4571,5518.6"]; - n424 [color=chartreuse4, + n424 [color=firebrick1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 286\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -3905,7 +3905,7 @@ digraph G { width=2.4261]; n423 -> n424 [pos="e,4571,5376.5 4571,5411.7 4571,5403.5 4571,5395 4571,5386.6"]; n424 -> n425 [pos="e,4571,5244.5 4571,5279.7 4571,5271.5 4571,5263 4571,5254.6"]; - n426 [color=darkslategray3, + n426 [color=brown, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 288\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3914,7 +3914,7 @@ digraph G { style=solid, width=6.5343]; n425 -> n426 [pos="e,4581.2,5112.5 4576.8,5147.7 4577.8,5139.5 4578.9,5131 4579.9,5122.6"]; - n427 [color=firebrick1, + n427 [color=brown1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 289\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3923,7 +3923,7 @@ digraph G { style=solid, width=3.5652]; n426 -> n427 [pos="e,4626.2,4979.4 4609,5016.1 4613.2,5007.1 4617.7,4997.7 4622,4988.5"]; - n428 [color=burlywood, + n428 [color=goldenrod1, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 290\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3933,7 +3933,7 @@ digraph G { width=2.5643]; n427 -> n428 [pos="e,4678.9,4847.4 4665.3,4884.1 4668.6,4875.3 4672,4866.1 4675.3,4857.1"]; n428 -> n429 [pos="e,4751,4713.8 4724.9,4754.3 4731.6,4743.9 4738.7,4732.9 4745.6,4722.2"]; - n430 [color=darksalmon, + n430 [color=brown4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3942,7 +3942,7 @@ digraph G { style=solid, width=2.5643]; n429 -> n430 [pos="e,4780,4584.5 4780,4619.7 4780,4611.5 4780,4603 4780,4594.6"]; - n431 [color=darkslategray2, + n431 [color=darkolivegreen2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 291\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3951,7 +3951,7 @@ digraph G { style=solid, width=6.5343]; n430 -> n431 [pos="e,4432.2,4442.5 4698.4,4513.3 4628.7,4494.8 4526.5,4467.6 4441.9,4445.1"]; - n434 [color=cornflowerblue, + n434 [color=dimgrey, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 293\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3960,7 +3960,7 @@ digraph G { style=solid, width=6.5343]; n430 -> n434 [pos="e,4780,4452.5 4780,4487.7 4780,4479.5 4780,4471 4780,4462.6"]; - n447 [color=darksalmon, + n447 [color=brown4, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -3970,7 +3970,7 @@ digraph G { width=2.5643]; n430 -> n447 [pos="e,4982.4,2734.4 4871.2,4527.8 4924.1,4518.5 4987.3,4497.9 5024,4452 5074,4389.5 5043,4353 5043,4273 5043,4273 5043,4273 5043,2951 \ 5043,2876.3 5011.4,2795.3 4986.7,2743.4"]; - n432 [color=azure1, + n432 [color=darkorchid2, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3979,7 +3979,7 @@ digraph G { style=solid, width=3.5652]; n431 -> n432 [pos="e,4298,4320.5 4295,4355.7 4295.7,4347.5 4296.4,4339 4297.1,4330.6"]; - n433 [color=azure1, + n433 [color=darkorchid2, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 292\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -3988,7 +3988,7 @@ digraph G { style=solid, width=2.4261]; n432 -> n433 [pos="e,4354.6,4055.8 4313.5,4223.9 4324.4,4179.4 4340.6,4112.8 4352.2,4065.5"]; - n443 [color=cadetblue2, + n443 [color=chartreuse, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 299\nSize: {1, 448, 8, 8}\nMem size: 28672", @@ -3997,7 +3997,7 @@ digraph G { style=solid, width=2.4261]; n433 -> n443 [pos="e,4671.9,3239.1 4379,3960.1 4392.3,3908 4411,3821.2 4411,3745 4411,3745 4411,3745 4411,3479 4411,3351.9 4562.2,3277.6 4662.4,3242.3"]; - n435 [color=darkolivegreen, + n435 [color=darkgoldenrod3, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4006,7 +4006,7 @@ digraph G { style=solid, width=3.5652]; n434 -> n435 [pos="e,4774.9,4320.5 4777.1,4355.7 4776.6,4347.5 4776.1,4339 4775.5,4330.6"]; - n436 [color=darkolivegreen, + n436 [color=darkgoldenrod3, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 294\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4024,7 +4024,7 @@ digraph G { style=solid, width=7.3732]; n436 -> n437 [pos="e,4751.9,4056.5 4754.1,4091.7 4753.6,4083.5 4753.1,4075 4752.5,4066.6"]; - n438 [color=azure2, + n438 [color=cornsilk, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -4033,7 +4033,7 @@ digraph G { style=solid, width=3.5652]; n437 -> n438 [pos="e,4749,3924.5 4749,3959.7 4749,3951.5 4749,3943 4749,3934.6"]; - n439 [color=azure2, + n439 [color=cornsilk, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 296\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -4042,7 +4042,7 @@ digraph G { style=solid, width=2.4261]; n438 -> n439 [pos="e,4749,3792.5 4749,3827.7 4749,3819.5 4749,3811 4749,3802.6"]; - n440 [color=cadetblue2, + n440 [color=darkslategrey, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 297\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4051,7 +4051,7 @@ digraph G { style=solid, width=7.3732]; n439 -> n440 [pos="e,4749,3660.5 4749,3695.7 4749,3687.5 4749,3679 4749,3670.6"]; - n441 [color=deepskyblue, + n441 [color=chocolate1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4060,7 +4060,7 @@ digraph G { style=solid, width=3.5652]; n440 -> n441 [pos="e,4749,3528.5 4749,3563.7 4749,3555.5 4749,3547 4749,3538.6"]; - n442 [color=deepskyblue, + n442 [color=chocolate1, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 298\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4070,7 +4070,7 @@ digraph G { width=2.4261]; n441 -> n442 [pos="e,4749,3396.5 4749,3431.7 4749,3423.5 4749,3415 4749,3406.6"]; n442 -> n443 [pos="e,4749,3264.5 4749,3299.7 4749,3291.5 4749,3283 4749,3274.6"]; - n444 [color=dimgrey, + n444 [color=cornsilk, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 300\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4079,7 +4079,7 @@ digraph G { style=solid, width=6.5343]; n443 -> n444 [pos="e,4759.2,3132.5 4754.8,3167.7 4755.8,3159.5 4756.9,3151 4757.9,3142.6"]; - n445 [color=cyan1, + n445 [color=blue1, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 301\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4088,7 +4088,7 @@ digraph G { style=solid, width=3.5652]; n444 -> n445 [pos="e,4804.2,2999.4 4787,3036.1 4791.2,3027.1 4795.7,3017.7 4800,3008.5"]; - n446 [color=deepskyblue4, + n446 [color=cyan, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 302\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4098,7 +4098,7 @@ digraph G { width=2.5643]; n445 -> n446 [pos="e,4856.9,2867.4 4843.3,2904.1 4846.6,2895.3 4850,2886.1 4853.3,2877.1"]; n446 -> n447 [pos="e,4929,2733.8 4902.9,2774.3 4909.6,2763.9 4916.7,2752.9 4923.6,2742.2"]; - n448 [color=darksalmon, + n448 [color=brown4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4107,7 +4107,7 @@ digraph G { style=solid, width=2.5643]; n447 -> n448 [pos="e,4958,2604.5 4958,2639.7 4958,2631.5 4958,2623 4958,2614.6"]; - n449 [color=deeppink2, + n449 [color=chocolate4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 303\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4116,7 +4116,7 @@ digraph G { style=solid, width=6.5343]; n448 -> n449 [pos="e,4610.2,2462.5 4876.4,2533.3 4806.7,2514.8 4704.5,2487.6 4619.9,2465.1"]; - n452 [color=brown3, + n452 [color=coral, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 305\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4125,7 +4125,7 @@ digraph G { style=solid, width=6.5343]; n448 -> n452 [pos="e,4958,2472.5 4958,2507.7 4958,2499.5 4958,2491 4958,2482.6"]; - n465 [color=darksalmon, + n465 [color=brown4, fontsize=14, height=1.3356, label="nn.CAddTable\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4135,7 +4135,7 @@ digraph G { width=2.5643]; n448 -> n465 [pos="e,5160.4,754.41 5049.2,2547.8 5102.1,2538.5 5165.3,2517.9 5202,2472 5252,2409.5 5221,2373 5221,2293 5221,2293 5221,2293 5221,971 \ 5221,896.34 5189.4,815.3 5164.7,763.43"]; - n450 [color=cornsilk4, + n450 [color=aliceblue, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4144,7 +4144,7 @@ digraph G { style=solid, width=3.5652]; n449 -> n450 [pos="e,4476,2340.5 4473,2375.7 4473.7,2367.5 4474.4,2359 4475.1,2350.6"]; - n451 [color=cornsilk4, + n451 [color=aliceblue, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 304\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4153,7 +4153,7 @@ digraph G { style=solid, width=2.4261]; n450 -> n451 [pos="e,4532.6,2075.8 4491.5,2243.9 4502.4,2199.4 4518.6,2132.8 4530.2,2085.5"]; - n461 [color=goldenrod, + n461 [color=darkorange1, fontsize=14, height=1.3356, label="nn.JoinTable\nStorage id: 311\nSize: {1, 448, 8, 8}\nMem size: 28672", @@ -4162,7 +4162,7 @@ digraph G { style=solid, width=2.4261]; n451 -> n461 [pos="e,4849.9,1259.1 4557,1980.1 4570.3,1928 4589,1841.2 4589,1765 4589,1765 4589,1765 4589,1499 4589,1371.9 4740.2,1297.6 4840.4,1262.3"]; - n453 [color=firebrick4, + n453 [color=darkslategray4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4171,7 +4171,7 @@ digraph G { style=solid, width=3.5652]; n452 -> n453 [pos="e,4952.9,2340.5 4955.1,2375.7 4954.6,2367.5 4954.1,2359 4953.5,2350.6"]; - n454 [color=firebrick4, + n454 [color=darkslategray4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 306\nSize: {1, 192, 8, 8}\nMem size: 12288", @@ -4180,7 +4180,7 @@ digraph G { style=solid, width=2.4261]; n453 -> n454 [pos="e,4940.4,2208 4944.5,2243.7 4943.6,2235.4 4942.6,2226.6 4941.6,2218"]; - n455 [color=darkgoldenrod4, + n455 [color=deepskyblue2, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias\nStorage id: 307\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -4189,7 +4189,7 @@ digraph G { style=solid, width=7.3732]; n454 -> n455 [pos="e,4929.9,2076.5 4932.1,2111.7 4931.6,2103.5 4931.1,2095 4930.5,2086.6"]; - n456 [color=deeppink2, + n456 [color=coral, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 308\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -4198,7 +4198,7 @@ digraph G { style=solid, width=3.5652]; n455 -> n456 [pos="e,4927,1944.5 4927,1979.7 4927,1971.5 4927,1963 4927,1954.6"]; - n457 [color=deeppink2, + n457 [color=coral, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 308\nSize: {1, 224, 8, 8}\nMem size: 14336", @@ -4207,7 +4207,7 @@ digraph G { style=solid, width=2.4261]; n456 -> n457 [pos="e,4927,1812.5 4927,1847.7 4927,1839.5 4927,1831 4927,1822.6"]; - n458 [color=blue3, + n458 [color=floralwhite, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias\nStorage id: 309\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4216,7 +4216,7 @@ digraph G { style=solid, width=7.3732]; n457 -> n458 [pos="e,4927,1680.5 4927,1715.7 4927,1707.5 4927,1699 4927,1690.6"]; - n459 [color=blue, + n459 [color=blue4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 310\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4225,7 +4225,7 @@ digraph G { style=solid, width=3.5652]; n458 -> n459 [pos="e,4927,1548.5 4927,1583.7 4927,1575.5 4927,1567 4927,1558.6"]; - n460 [color=blue, + n460 [color=blue4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 310\nSize: {1, 256, 8, 8}\nMem size: 16384", @@ -4235,7 +4235,7 @@ digraph G { width=2.4261]; n459 -> n460 [pos="e,4927,1416.5 4927,1451.7 4927,1443.5 4927,1435 4927,1426.6"]; n460 -> n461 [pos="e,4927,1284.5 4927,1319.7 4927,1311.5 4927,1303 4927,1294.6"]; - n462 [color=firebrick1, + n462 [color=cadetblue4, fontsize=14, height=1.3356, label="cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias\nStorage id: 312\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4244,7 +4244,7 @@ digraph G { style=solid, width=6.5343]; n461 -> n462 [pos="e,4937.2,1152.5 4932.8,1187.7 4933.8,1179.5 4934.9,1171 4935.9,1162.6"]; - n463 [color=dimgray, + n463 [color=darkgoldenrod4, fontsize=14, height=1.3356, label="nn.SpatialBatchNormalization\nStorage id: 313\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4253,7 +4253,7 @@ digraph G { style=solid, width=3.5652]; n462 -> n463 [pos="e,4982.2,1019.4 4965,1056.1 4969.2,1047.1 4973.7,1037.7 4978,1028.5"]; - n464 [color=firebrick3, + n464 [color=chartreuse4, fontsize=14, height=1.3356, label="nn.MulConstant\nStorage id: 314\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4263,7 +4263,7 @@ digraph G { width=2.5643]; n463 -> n464 [pos="e,5034.9,887.43 5021.3,924.07 5024.6,915.31 5028,906.07 5031.3,897.07"]; n464 -> n465 [pos="e,5107,753.82 5080.9,794.26 5087.6,783.95 5094.7,772.87 5101.6,762.24"]; - n466 [color=darksalmon, + n466 [color=brown4, fontsize=14, height=1.3356, label="cudnn.ReLU\nStorage id: 254\nSize: {1, 2048, 8, 8}\nMem size: 131072", @@ -4272,7 +4272,7 @@ digraph G { style=solid, width=2.5643]; n465 -> n466 [pos="e,5136,624.48 5136,659.7 5136,651.54 5136,642.99 5136,634.6"]; - n467 [color=darkolivegreen, + n467 [color=darkslategray3, fontsize=14, height=1.3356, label="cudnn.SpatialAveragePooling(8x8, 1,1)\nStorage id: 315\nSize: {1, 2048, 1, 1}\nMem size: 2048", @@ -4281,7 +4281,7 @@ digraph G { style=solid, width=4.5661]; n466 -> n467 [pos="e,5136,492.48 5136,527.7 5136,519.54 5136,510.99 5136,502.6"]; - n468 [color=darkolivegreen, + n468 [color=darkslategray3, fontsize=14, height=1.3356, label="nn.View(2048)\nStorage id: 315\nSize: {1, 2048}\nMem size: 2048", @@ -4290,7 +4290,7 @@ digraph G { style=solid, width=2.0688]; n467 -> n468 [pos="e,5136,360.48 5136,395.7 5136,387.54 5136,378.99 5136,370.6"]; - n469 [color=brown, + n469 [color=bisque3, fontsize=14, height=1.3356, label="nn.Dropout(0.200000)\nStorage id: 316\nSize: {1, 2048}\nMem size: 2048", @@ -4299,7 +4299,7 @@ digraph G { style=solid, width=2.7262]; n468 -> n469 [pos="e,5136,228.48 5136,263.7 5136,255.54 5136,246.99 5136,238.6"]; - n470 [color=deepskyblue1, + n470 [color=aquamarine2, fontsize=14, height=1.3356, label="nn.Linear(2048 -> 1000)\nStorage id: 317\nSize: {1, 1000}\nMem size: 1000", diff --git a/inceptionv4-cls.svg b/inception-resnet-v2.svg similarity index 93% rename from inceptionv4-cls.svg rename to inception-resnet-v2.svg index 8b6b10904..c429924f7 100644 --- a/inceptionv4-cls.svg +++ b/inception-resnet-v2.svg @@ -11,7 +11,7 @@ n1 - + Input Storage id: 1 Size: {1, 3, 299, 299} @@ -19,7 +19,7 @@ n2 - + cudnn.SpatialConvolution(3 -> 32, 3x3, 2,2) without bias Storage id: 2 Size: {1, 32, 149, 149} @@ -32,7 +32,7 @@ n3 - + nn.SpatialBatchNormalization Storage id: 3 Size: {1, 32, 149, 149} @@ -45,7 +45,7 @@ n4 - + cudnn.ReLU Storage id: 3 Size: {1, 32, 149, 149} @@ -58,7 +58,7 @@ n5 - + cudnn.SpatialConvolution(32 -> 32, 3x3) without bias Storage id: 4 Size: {1, 32, 147, 147} @@ -71,7 +71,7 @@ n6 - + nn.SpatialBatchNormalization Storage id: 5 Size: {1, 32, 147, 147} @@ -84,7 +84,7 @@ n7 - + cudnn.ReLU Storage id: 5 Size: {1, 32, 147, 147} @@ -97,7 +97,7 @@ n8 - + cudnn.SpatialConvolution(32 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 6 Size: {1, 64, 147, 147} @@ -110,7 +110,7 @@ n9 - + nn.SpatialBatchNormalization Storage id: 7 Size: {1, 64, 147, 147} @@ -123,7 +123,7 @@ n10 - + cudnn.ReLU Storage id: 7 Size: {1, 64, 147, 147} @@ -136,7 +136,7 @@ n11 - + nn.SpatialMaxPooling(3x3, 2,2) Storage id: 8 Size: {1, 64, 73, 73} @@ -149,7 +149,7 @@ n12 - + cudnn.SpatialConvolution(64 -> 96, 3x3, 2,2) without bias Storage id: 9 Size: {1, 96, 73, 73} @@ -162,7 +162,7 @@ n15 - + nn.JoinTable Storage id: 11 Size: {1, 160, 73, 73} @@ -175,7 +175,7 @@ n13 - + nn.SpatialBatchNormalization Storage id: 10 Size: {1, 96, 73, 73} @@ -188,7 +188,7 @@ n14 - + cudnn.ReLU Storage id: 10 Size: {1, 96, 73, 73} @@ -206,7 +206,7 @@ n16 - + cudnn.SpatialConvolution(160 -> 64, 1x1) without bias Storage id: 12 Size: {1, 64, 73, 73} @@ -219,7 +219,7 @@ n22 - + cudnn.SpatialConvolution(160 -> 64, 1x1) without bias Storage id: 16 Size: {1, 64, 73, 73} @@ -232,7 +232,7 @@ n17 - + nn.SpatialBatchNormalization Storage id: 13 Size: {1, 64, 73, 73} @@ -245,7 +245,7 @@ n18 - + cudnn.ReLU Storage id: 13 Size: {1, 64, 73, 73} @@ -258,7 +258,7 @@ n19 - + cudnn.SpatialConvolution(64 -> 96, 3x3) without bias Storage id: 14 Size: {1, 96, 71, 71} @@ -271,7 +271,7 @@ n20 - + nn.SpatialBatchNormalization Storage id: 15 Size: {1, 96, 71, 71} @@ -284,7 +284,7 @@ n21 - + cudnn.ReLU Storage id: 15 Size: {1, 96, 71, 71} @@ -297,7 +297,7 @@ n34 - + nn.JoinTable Storage id: 24 Size: {1, 192, 71, 71} @@ -310,7 +310,7 @@ n23 - + nn.SpatialBatchNormalization Storage id: 17 Size: {1, 64, 73, 73} @@ -323,7 +323,7 @@ n24 - + cudnn.ReLU Storage id: 17 Size: {1, 64, 73, 73} @@ -336,7 +336,7 @@ n25 - + cudnn.SpatialConvolution(64 -> 64, 7x1, 1,1, 3,0) without bias Storage id: 18 Size: {1, 64, 73, 73} @@ -349,7 +349,7 @@ n26 - + nn.SpatialBatchNormalization Storage id: 19 Size: {1, 64, 73, 73} @@ -362,7 +362,7 @@ n27 - + cudnn.ReLU Storage id: 19 Size: {1, 64, 73, 73} @@ -375,7 +375,7 @@ n28 - + cudnn.SpatialConvolution(64 -> 64, 1x7, 1,1, 0,3) without bias Storage id: 20 Size: {1, 64, 73, 73} @@ -388,7 +388,7 @@ n29 - + nn.SpatialBatchNormalization Storage id: 21 Size: {1, 64, 73, 73} @@ -401,7 +401,7 @@ n30 - + cudnn.ReLU Storage id: 21 Size: {1, 64, 73, 73} @@ -414,7 +414,7 @@ n31 - + cudnn.SpatialConvolution(64 -> 96, 3x3) without bias Storage id: 22 Size: {1, 96, 71, 71} @@ -427,7 +427,7 @@ n32 - + nn.SpatialBatchNormalization Storage id: 23 Size: {1, 96, 71, 71} @@ -440,7 +440,7 @@ n33 - + cudnn.ReLU Storage id: 23 Size: {1, 96, 71, 71} @@ -458,7 +458,7 @@ n35 - + cudnn.SpatialConvolution(192 -> 192, 3x3, 2,2) without bias Storage id: 25 Size: {1, 192, 35, 35} @@ -471,7 +471,7 @@ n38 - + nn.SpatialMaxPooling(3x3, 2,2) Storage id: 27 Size: {1, 192, 35, 35} @@ -484,7 +484,7 @@ n36 - + nn.SpatialBatchNormalization Storage id: 26 Size: {1, 192, 35, 35} @@ -497,7 +497,7 @@ n37 - + cudnn.ReLU Storage id: 26 Size: {1, 192, 35, 35} @@ -510,7 +510,7 @@ n39 - + nn.JoinTable Storage id: 28 Size: {1, 384, 35, 35} @@ -528,7 +528,7 @@ n40 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 29 Size: {1, 32, 35, 35} @@ -541,7 +541,7 @@ n43 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 31 Size: {1, 32, 35, 35} @@ -554,7 +554,7 @@ n49 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 35 Size: {1, 32, 35, 35} @@ -567,7 +567,7 @@ n62 - + nn.CAddTable Storage id: 28 Size: {1, 384, 35, 35} @@ -580,7 +580,7 @@ n41 - + nn.SpatialBatchNormalization Storage id: 30 Size: {1, 32, 35, 35} @@ -593,7 +593,7 @@ n42 - + cudnn.ReLU Storage id: 30 Size: {1, 32, 35, 35} @@ -606,7 +606,7 @@ n58 - + nn.JoinTable Storage id: 41 Size: {1, 128, 35, 35} @@ -619,7 +619,7 @@ n44 - + nn.SpatialBatchNormalization Storage id: 32 Size: {1, 32, 35, 35} @@ -632,7 +632,7 @@ n45 - + cudnn.ReLU Storage id: 32 Size: {1, 32, 35, 35} @@ -645,7 +645,7 @@ n46 - + cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias Storage id: 33 Size: {1, 32, 35, 35} @@ -658,7 +658,7 @@ n47 - + nn.SpatialBatchNormalization Storage id: 34 Size: {1, 32, 35, 35} @@ -671,7 +671,7 @@ n48 - + cudnn.ReLU Storage id: 34 Size: {1, 32, 35, 35} @@ -689,7 +689,7 @@ n50 - + nn.SpatialBatchNormalization Storage id: 36 Size: {1, 32, 35, 35} @@ -702,7 +702,7 @@ n51 - + cudnn.ReLU Storage id: 36 Size: {1, 32, 35, 35} @@ -715,7 +715,7 @@ n52 - + cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias Storage id: 37 Size: {1, 48, 35, 35} @@ -728,7 +728,7 @@ n53 - + nn.SpatialBatchNormalization Storage id: 38 Size: {1, 48, 35, 35} @@ -741,7 +741,7 @@ n54 - + cudnn.ReLU Storage id: 38 Size: {1, 48, 35, 35} @@ -754,7 +754,7 @@ n55 - + cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 39 Size: {1, 64, 35, 35} @@ -767,7 +767,7 @@ n56 - + nn.SpatialBatchNormalization Storage id: 40 Size: {1, 64, 35, 35} @@ -780,7 +780,7 @@ n57 - + cudnn.ReLU Storage id: 40 Size: {1, 64, 35, 35} @@ -798,7 +798,7 @@ n59 - + cudnn.SpatialConvolution(128 -> 384, 1x1) without bias Storage id: 42 Size: {1, 384, 35, 35} @@ -811,7 +811,7 @@ n60 - + nn.SpatialBatchNormalization Storage id: 43 Size: {1, 384, 35, 35} @@ -824,7 +824,7 @@ n61 - + nn.MulConstant Storage id: 44 Size: {1, 384, 35, 35} @@ -842,7 +842,7 @@ n63 - + cudnn.ReLU Storage id: 28 Size: {1, 384, 35, 35} @@ -855,7 +855,7 @@ n64 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 45 Size: {1, 32, 35, 35} @@ -868,7 +868,7 @@ n67 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 47 Size: {1, 32, 35, 35} @@ -881,7 +881,7 @@ n73 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 51 Size: {1, 32, 35, 35} @@ -894,7 +894,7 @@ n86 - + nn.CAddTable Storage id: 28 Size: {1, 384, 35, 35} @@ -907,7 +907,7 @@ n65 - + nn.SpatialBatchNormalization Storage id: 46 Size: {1, 32, 35, 35} @@ -920,7 +920,7 @@ n66 - + cudnn.ReLU Storage id: 46 Size: {1, 32, 35, 35} @@ -933,7 +933,7 @@ n82 - + nn.JoinTable Storage id: 57 Size: {1, 128, 35, 35} @@ -946,7 +946,7 @@ n68 - + nn.SpatialBatchNormalization Storage id: 48 Size: {1, 32, 35, 35} @@ -959,7 +959,7 @@ n69 - + cudnn.ReLU Storage id: 48 Size: {1, 32, 35, 35} @@ -972,7 +972,7 @@ n70 - + cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias Storage id: 49 Size: {1, 32, 35, 35} @@ -985,7 +985,7 @@ n71 - + nn.SpatialBatchNormalization Storage id: 50 Size: {1, 32, 35, 35} @@ -998,7 +998,7 @@ n72 - + cudnn.ReLU Storage id: 50 Size: {1, 32, 35, 35} @@ -1016,7 +1016,7 @@ n74 - + nn.SpatialBatchNormalization Storage id: 52 Size: {1, 32, 35, 35} @@ -1029,7 +1029,7 @@ n75 - + cudnn.ReLU Storage id: 52 Size: {1, 32, 35, 35} @@ -1042,7 +1042,7 @@ n76 - + cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias Storage id: 53 Size: {1, 48, 35, 35} @@ -1055,7 +1055,7 @@ n77 - + nn.SpatialBatchNormalization Storage id: 54 Size: {1, 48, 35, 35} @@ -1068,7 +1068,7 @@ n78 - + cudnn.ReLU Storage id: 54 Size: {1, 48, 35, 35} @@ -1081,7 +1081,7 @@ n79 - + cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 55 Size: {1, 64, 35, 35} @@ -1094,7 +1094,7 @@ n80 - + nn.SpatialBatchNormalization Storage id: 56 Size: {1, 64, 35, 35} @@ -1107,7 +1107,7 @@ n81 - + cudnn.ReLU Storage id: 56 Size: {1, 64, 35, 35} @@ -1125,7 +1125,7 @@ n83 - + cudnn.SpatialConvolution(128 -> 384, 1x1) without bias Storage id: 58 Size: {1, 384, 35, 35} @@ -1138,7 +1138,7 @@ n84 - + nn.SpatialBatchNormalization Storage id: 59 Size: {1, 384, 35, 35} @@ -1151,7 +1151,7 @@ n85 - + nn.MulConstant Storage id: 60 Size: {1, 384, 35, 35} @@ -1169,7 +1169,7 @@ n87 - + cudnn.ReLU Storage id: 28 Size: {1, 384, 35, 35} @@ -1182,7 +1182,7 @@ n88 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 61 Size: {1, 32, 35, 35} @@ -1195,7 +1195,7 @@ n91 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 63 Size: {1, 32, 35, 35} @@ -1208,7 +1208,7 @@ n97 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 67 Size: {1, 32, 35, 35} @@ -1221,7 +1221,7 @@ n110 - + nn.CAddTable Storage id: 28 Size: {1, 384, 35, 35} @@ -1234,7 +1234,7 @@ n89 - + nn.SpatialBatchNormalization Storage id: 62 Size: {1, 32, 35, 35} @@ -1247,7 +1247,7 @@ n90 - + cudnn.ReLU Storage id: 62 Size: {1, 32, 35, 35} @@ -1260,7 +1260,7 @@ n106 - + nn.JoinTable Storage id: 73 Size: {1, 128, 35, 35} @@ -1273,7 +1273,7 @@ n92 - + nn.SpatialBatchNormalization Storage id: 64 Size: {1, 32, 35, 35} @@ -1286,7 +1286,7 @@ n93 - + cudnn.ReLU Storage id: 64 Size: {1, 32, 35, 35} @@ -1299,7 +1299,7 @@ n94 - + cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias Storage id: 65 Size: {1, 32, 35, 35} @@ -1312,7 +1312,7 @@ n95 - + nn.SpatialBatchNormalization Storage id: 66 Size: {1, 32, 35, 35} @@ -1325,7 +1325,7 @@ n96 - + cudnn.ReLU Storage id: 66 Size: {1, 32, 35, 35} @@ -1343,7 +1343,7 @@ n98 - + nn.SpatialBatchNormalization Storage id: 68 Size: {1, 32, 35, 35} @@ -1356,7 +1356,7 @@ n99 - + cudnn.ReLU Storage id: 68 Size: {1, 32, 35, 35} @@ -1369,7 +1369,7 @@ n100 - + cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias Storage id: 69 Size: {1, 48, 35, 35} @@ -1382,7 +1382,7 @@ n101 - + nn.SpatialBatchNormalization Storage id: 70 Size: {1, 48, 35, 35} @@ -1395,7 +1395,7 @@ n102 - + cudnn.ReLU Storage id: 70 Size: {1, 48, 35, 35} @@ -1408,7 +1408,7 @@ n103 - + cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 71 Size: {1, 64, 35, 35} @@ -1421,7 +1421,7 @@ n104 - + nn.SpatialBatchNormalization Storage id: 72 Size: {1, 64, 35, 35} @@ -1434,7 +1434,7 @@ n105 - + cudnn.ReLU Storage id: 72 Size: {1, 64, 35, 35} @@ -1452,7 +1452,7 @@ n107 - + cudnn.SpatialConvolution(128 -> 384, 1x1) without bias Storage id: 74 Size: {1, 384, 35, 35} @@ -1465,7 +1465,7 @@ n108 - + nn.SpatialBatchNormalization Storage id: 75 Size: {1, 384, 35, 35} @@ -1478,7 +1478,7 @@ n109 - + nn.MulConstant Storage id: 76 Size: {1, 384, 35, 35} @@ -1496,7 +1496,7 @@ n111 - + cudnn.ReLU Storage id: 28 Size: {1, 384, 35, 35} @@ -1509,7 +1509,7 @@ n112 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 77 Size: {1, 32, 35, 35} @@ -1522,7 +1522,7 @@ n115 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 79 Size: {1, 32, 35, 35} @@ -1535,7 +1535,7 @@ n121 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 83 Size: {1, 32, 35, 35} @@ -1548,7 +1548,7 @@ n134 - + nn.CAddTable Storage id: 28 Size: {1, 384, 35, 35} @@ -1561,7 +1561,7 @@ n113 - + nn.SpatialBatchNormalization Storage id: 78 Size: {1, 32, 35, 35} @@ -1574,7 +1574,7 @@ n114 - + cudnn.ReLU Storage id: 78 Size: {1, 32, 35, 35} @@ -1587,7 +1587,7 @@ n130 - + nn.JoinTable Storage id: 89 Size: {1, 128, 35, 35} @@ -1600,7 +1600,7 @@ n116 - + nn.SpatialBatchNormalization Storage id: 80 Size: {1, 32, 35, 35} @@ -1613,7 +1613,7 @@ n117 - + cudnn.ReLU Storage id: 80 Size: {1, 32, 35, 35} @@ -1626,7 +1626,7 @@ n118 - + cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias Storage id: 81 Size: {1, 32, 35, 35} @@ -1639,7 +1639,7 @@ n119 - + nn.SpatialBatchNormalization Storage id: 82 Size: {1, 32, 35, 35} @@ -1652,7 +1652,7 @@ n120 - + cudnn.ReLU Storage id: 82 Size: {1, 32, 35, 35} @@ -1670,7 +1670,7 @@ n122 - + nn.SpatialBatchNormalization Storage id: 84 Size: {1, 32, 35, 35} @@ -1683,7 +1683,7 @@ n123 - + cudnn.ReLU Storage id: 84 Size: {1, 32, 35, 35} @@ -1696,7 +1696,7 @@ n124 - + cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias Storage id: 85 Size: {1, 48, 35, 35} @@ -1709,7 +1709,7 @@ n125 - + nn.SpatialBatchNormalization Storage id: 86 Size: {1, 48, 35, 35} @@ -1722,7 +1722,7 @@ n126 - + cudnn.ReLU Storage id: 86 Size: {1, 48, 35, 35} @@ -1735,7 +1735,7 @@ n127 - + cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 87 Size: {1, 64, 35, 35} @@ -1748,7 +1748,7 @@ n128 - + nn.SpatialBatchNormalization Storage id: 88 Size: {1, 64, 35, 35} @@ -1761,7 +1761,7 @@ n129 - + cudnn.ReLU Storage id: 88 Size: {1, 64, 35, 35} @@ -1779,7 +1779,7 @@ n131 - + cudnn.SpatialConvolution(128 -> 384, 1x1) without bias Storage id: 90 Size: {1, 384, 35, 35} @@ -1792,7 +1792,7 @@ n132 - + nn.SpatialBatchNormalization Storage id: 91 Size: {1, 384, 35, 35} @@ -1805,7 +1805,7 @@ n133 - + nn.MulConstant Storage id: 92 Size: {1, 384, 35, 35} @@ -1823,7 +1823,7 @@ n135 - + cudnn.ReLU Storage id: 28 Size: {1, 384, 35, 35} @@ -1836,7 +1836,7 @@ n136 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 93 Size: {1, 32, 35, 35} @@ -1849,7 +1849,7 @@ n139 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 95 Size: {1, 32, 35, 35} @@ -1862,7 +1862,7 @@ n145 - + cudnn.SpatialConvolution(384 -> 32, 1x1) without bias Storage id: 99 Size: {1, 32, 35, 35} @@ -1875,7 +1875,7 @@ n158 - + nn.CAddTable Storage id: 28 Size: {1, 384, 35, 35} @@ -1888,7 +1888,7 @@ n137 - + nn.SpatialBatchNormalization Storage id: 94 Size: {1, 32, 35, 35} @@ -1901,7 +1901,7 @@ n138 - + cudnn.ReLU Storage id: 94 Size: {1, 32, 35, 35} @@ -1914,7 +1914,7 @@ n154 - + nn.JoinTable Storage id: 105 Size: {1, 128, 35, 35} @@ -1927,7 +1927,7 @@ n140 - + nn.SpatialBatchNormalization Storage id: 96 Size: {1, 32, 35, 35} @@ -1940,7 +1940,7 @@ n141 - + cudnn.ReLU Storage id: 96 Size: {1, 32, 35, 35} @@ -1953,7 +1953,7 @@ n142 - + cudnn.SpatialConvolution(32 -> 32, 3x3, 1,1, 1,1) without bias Storage id: 97 Size: {1, 32, 35, 35} @@ -1966,7 +1966,7 @@ n143 - + nn.SpatialBatchNormalization Storage id: 98 Size: {1, 32, 35, 35} @@ -1979,7 +1979,7 @@ n144 - + cudnn.ReLU Storage id: 98 Size: {1, 32, 35, 35} @@ -1997,7 +1997,7 @@ n146 - + nn.SpatialBatchNormalization Storage id: 100 Size: {1, 32, 35, 35} @@ -2010,7 +2010,7 @@ n147 - + cudnn.ReLU Storage id: 100 Size: {1, 32, 35, 35} @@ -2023,7 +2023,7 @@ n148 - + cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias Storage id: 101 Size: {1, 48, 35, 35} @@ -2036,7 +2036,7 @@ n149 - + nn.SpatialBatchNormalization Storage id: 102 Size: {1, 48, 35, 35} @@ -2049,7 +2049,7 @@ n150 - + cudnn.ReLU Storage id: 102 Size: {1, 48, 35, 35} @@ -2062,7 +2062,7 @@ n151 - + cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias Storage id: 103 Size: {1, 64, 35, 35} @@ -2075,7 +2075,7 @@ n152 - + nn.SpatialBatchNormalization Storage id: 104 Size: {1, 64, 35, 35} @@ -2088,7 +2088,7 @@ n153 - + cudnn.ReLU Storage id: 104 Size: {1, 64, 35, 35} @@ -2106,7 +2106,7 @@ n155 - + cudnn.SpatialConvolution(128 -> 384, 1x1) without bias Storage id: 106 Size: {1, 384, 35, 35} @@ -2119,7 +2119,7 @@ n156 - + nn.SpatialBatchNormalization Storage id: 107 Size: {1, 384, 35, 35} @@ -2132,7 +2132,7 @@ n157 - + nn.MulConstant Storage id: 108 Size: {1, 384, 35, 35} @@ -2150,7 +2150,7 @@ n159 - + cudnn.ReLU Storage id: 28 Size: {1, 384, 35, 35} @@ -2163,7 +2163,7 @@ n160 - + nn.SpatialMaxPooling(3x3, 2,2) Storage id: 109 Size: {1, 384, 17, 17} @@ -2176,7 +2176,7 @@ n161 - + cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias Storage id: 110 Size: {1, 384, 17, 17} @@ -2189,7 +2189,7 @@ n164 - + cudnn.SpatialConvolution(384 -> 256, 1x1) without bias Storage id: 112 Size: {1, 256, 35, 35} @@ -2202,7 +2202,7 @@ n173 - + nn.JoinTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -2215,7 +2215,7 @@ n162 - + nn.SpatialBatchNormalization Storage id: 111 Size: {1, 384, 17, 17} @@ -2228,7 +2228,7 @@ n163 - + cudnn.ReLU Storage id: 111 Size: {1, 384, 17, 17} @@ -2246,7 +2246,7 @@ n165 - + nn.SpatialBatchNormalization Storage id: 113 Size: {1, 256, 35, 35} @@ -2259,7 +2259,7 @@ n166 - + cudnn.ReLU Storage id: 113 Size: {1, 256, 35, 35} @@ -2272,7 +2272,7 @@ n167 - + cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias Storage id: 114 Size: {1, 256, 35, 35} @@ -2285,7 +2285,7 @@ n168 - + nn.SpatialBatchNormalization Storage id: 115 Size: {1, 256, 35, 35} @@ -2298,7 +2298,7 @@ n169 - + cudnn.ReLU Storage id: 115 Size: {1, 256, 35, 35} @@ -2311,7 +2311,7 @@ n170 - + cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias Storage id: 116 Size: {1, 384, 17, 17} @@ -2324,7 +2324,7 @@ n171 - + nn.SpatialBatchNormalization Storage id: 117 Size: {1, 384, 17, 17} @@ -2337,7 +2337,7 @@ n172 - + cudnn.ReLU Storage id: 117 Size: {1, 384, 17, 17} @@ -2355,7 +2355,7 @@ n174 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 119 Size: {1, 192, 17, 17} @@ -2368,7 +2368,7 @@ n177 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 121 Size: {1, 128, 17, 17} @@ -2381,7 +2381,7 @@ n190 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -2394,7 +2394,7 @@ n175 - + nn.SpatialBatchNormalization Storage id: 120 Size: {1, 192, 17, 17} @@ -2407,7 +2407,7 @@ n176 - + cudnn.ReLU Storage id: 120 Size: {1, 192, 17, 17} @@ -2420,7 +2420,7 @@ n186 - + nn.JoinTable Storage id: 127 Size: {1, 384, 17, 17} @@ -2433,7 +2433,7 @@ n178 - + nn.SpatialBatchNormalization Storage id: 122 Size: {1, 128, 17, 17} @@ -2446,7 +2446,7 @@ n179 - + cudnn.ReLU Storage id: 122 Size: {1, 128, 17, 17} @@ -2459,7 +2459,7 @@ n180 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 123 Size: {1, 160, 17, 17} @@ -2472,7 +2472,7 @@ n181 - + nn.SpatialBatchNormalization Storage id: 124 Size: {1, 160, 17, 17} @@ -2485,7 +2485,7 @@ n182 - + cudnn.ReLU Storage id: 124 Size: {1, 160, 17, 17} @@ -2498,7 +2498,7 @@ n183 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 125 Size: {1, 192, 17, 17} @@ -2511,7 +2511,7 @@ n184 - + nn.SpatialBatchNormalization Storage id: 126 Size: {1, 192, 17, 17} @@ -2524,7 +2524,7 @@ n185 - + cudnn.ReLU Storage id: 126 Size: {1, 192, 17, 17} @@ -2542,7 +2542,7 @@ n187 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 128 Size: {1, 1152, 17, 17} @@ -2555,7 +2555,7 @@ n188 - + nn.SpatialBatchNormalization Storage id: 129 Size: {1, 1152, 17, 17} @@ -2568,7 +2568,7 @@ n189 - + nn.MulConstant Storage id: 130 Size: {1, 1152, 17, 17} @@ -2586,7 +2586,7 @@ n191 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -2599,7 +2599,7 @@ n192 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 131 Size: {1, 192, 17, 17} @@ -2612,7 +2612,7 @@ n195 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 133 Size: {1, 128, 17, 17} @@ -2625,7 +2625,7 @@ n208 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -2638,7 +2638,7 @@ n193 - + nn.SpatialBatchNormalization Storage id: 132 Size: {1, 192, 17, 17} @@ -2651,7 +2651,7 @@ n194 - + cudnn.ReLU Storage id: 132 Size: {1, 192, 17, 17} @@ -2664,7 +2664,7 @@ n204 - + nn.JoinTable Storage id: 139 Size: {1, 384, 17, 17} @@ -2677,7 +2677,7 @@ n196 - + nn.SpatialBatchNormalization Storage id: 134 Size: {1, 128, 17, 17} @@ -2690,7 +2690,7 @@ n197 - + cudnn.ReLU Storage id: 134 Size: {1, 128, 17, 17} @@ -2703,7 +2703,7 @@ n198 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 135 Size: {1, 160, 17, 17} @@ -2716,7 +2716,7 @@ n199 - + nn.SpatialBatchNormalization Storage id: 136 Size: {1, 160, 17, 17} @@ -2729,7 +2729,7 @@ n200 - + cudnn.ReLU Storage id: 136 Size: {1, 160, 17, 17} @@ -2742,7 +2742,7 @@ n201 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 137 Size: {1, 192, 17, 17} @@ -2755,7 +2755,7 @@ n202 - + nn.SpatialBatchNormalization Storage id: 138 Size: {1, 192, 17, 17} @@ -2768,7 +2768,7 @@ n203 - + cudnn.ReLU Storage id: 138 Size: {1, 192, 17, 17} @@ -2786,7 +2786,7 @@ n205 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 140 Size: {1, 1152, 17, 17} @@ -2799,7 +2799,7 @@ n206 - + nn.SpatialBatchNormalization Storage id: 141 Size: {1, 1152, 17, 17} @@ -2812,7 +2812,7 @@ n207 - + nn.MulConstant Storage id: 142 Size: {1, 1152, 17, 17} @@ -2830,7 +2830,7 @@ n209 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -2843,7 +2843,7 @@ n210 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 143 Size: {1, 192, 17, 17} @@ -2856,7 +2856,7 @@ n213 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 145 Size: {1, 128, 17, 17} @@ -2869,7 +2869,7 @@ n226 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -2882,7 +2882,7 @@ n211 - + nn.SpatialBatchNormalization Storage id: 144 Size: {1, 192, 17, 17} @@ -2895,7 +2895,7 @@ n212 - + cudnn.ReLU Storage id: 144 Size: {1, 192, 17, 17} @@ -2908,7 +2908,7 @@ n222 - + nn.JoinTable Storage id: 151 Size: {1, 384, 17, 17} @@ -2921,7 +2921,7 @@ n214 - + nn.SpatialBatchNormalization Storage id: 146 Size: {1, 128, 17, 17} @@ -2934,7 +2934,7 @@ n215 - + cudnn.ReLU Storage id: 146 Size: {1, 128, 17, 17} @@ -2947,7 +2947,7 @@ n216 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 147 Size: {1, 160, 17, 17} @@ -2960,7 +2960,7 @@ n217 - + nn.SpatialBatchNormalization Storage id: 148 Size: {1, 160, 17, 17} @@ -2973,7 +2973,7 @@ n218 - + cudnn.ReLU Storage id: 148 Size: {1, 160, 17, 17} @@ -2986,7 +2986,7 @@ n219 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 149 Size: {1, 192, 17, 17} @@ -2999,7 +2999,7 @@ n220 - + nn.SpatialBatchNormalization Storage id: 150 Size: {1, 192, 17, 17} @@ -3012,7 +3012,7 @@ n221 - + cudnn.ReLU Storage id: 150 Size: {1, 192, 17, 17} @@ -3030,7 +3030,7 @@ n223 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 152 Size: {1, 1152, 17, 17} @@ -3043,7 +3043,7 @@ n224 - + nn.SpatialBatchNormalization Storage id: 153 Size: {1, 1152, 17, 17} @@ -3056,7 +3056,7 @@ n225 - + nn.MulConstant Storage id: 154 Size: {1, 1152, 17, 17} @@ -3074,7 +3074,7 @@ n227 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -3087,7 +3087,7 @@ n228 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 155 Size: {1, 192, 17, 17} @@ -3100,7 +3100,7 @@ n231 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 157 Size: {1, 128, 17, 17} @@ -3113,7 +3113,7 @@ n244 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -3126,7 +3126,7 @@ n229 - + nn.SpatialBatchNormalization Storage id: 156 Size: {1, 192, 17, 17} @@ -3139,7 +3139,7 @@ n230 - + cudnn.ReLU Storage id: 156 Size: {1, 192, 17, 17} @@ -3152,7 +3152,7 @@ n240 - + nn.JoinTable Storage id: 163 Size: {1, 384, 17, 17} @@ -3165,7 +3165,7 @@ n232 - + nn.SpatialBatchNormalization Storage id: 158 Size: {1, 128, 17, 17} @@ -3178,7 +3178,7 @@ n233 - + cudnn.ReLU Storage id: 158 Size: {1, 128, 17, 17} @@ -3191,7 +3191,7 @@ n234 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 159 Size: {1, 160, 17, 17} @@ -3204,7 +3204,7 @@ n235 - + nn.SpatialBatchNormalization Storage id: 160 Size: {1, 160, 17, 17} @@ -3217,7 +3217,7 @@ n236 - + cudnn.ReLU Storage id: 160 Size: {1, 160, 17, 17} @@ -3230,7 +3230,7 @@ n237 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 161 Size: {1, 192, 17, 17} @@ -3243,7 +3243,7 @@ n238 - + nn.SpatialBatchNormalization Storage id: 162 Size: {1, 192, 17, 17} @@ -3256,7 +3256,7 @@ n239 - + cudnn.ReLU Storage id: 162 Size: {1, 192, 17, 17} @@ -3274,7 +3274,7 @@ n241 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 164 Size: {1, 1152, 17, 17} @@ -3287,7 +3287,7 @@ n242 - + nn.SpatialBatchNormalization Storage id: 165 Size: {1, 1152, 17, 17} @@ -3300,7 +3300,7 @@ n243 - + nn.MulConstant Storage id: 166 Size: {1, 1152, 17, 17} @@ -3318,7 +3318,7 @@ n245 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -3331,7 +3331,7 @@ n246 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 167 Size: {1, 192, 17, 17} @@ -3344,7 +3344,7 @@ n249 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 169 Size: {1, 128, 17, 17} @@ -3357,7 +3357,7 @@ n262 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -3370,7 +3370,7 @@ n247 - + nn.SpatialBatchNormalization Storage id: 168 Size: {1, 192, 17, 17} @@ -3383,7 +3383,7 @@ n248 - + cudnn.ReLU Storage id: 168 Size: {1, 192, 17, 17} @@ -3396,7 +3396,7 @@ n258 - + nn.JoinTable Storage id: 175 Size: {1, 384, 17, 17} @@ -3409,7 +3409,7 @@ n250 - + nn.SpatialBatchNormalization Storage id: 170 Size: {1, 128, 17, 17} @@ -3422,7 +3422,7 @@ n251 - + cudnn.ReLU Storage id: 170 Size: {1, 128, 17, 17} @@ -3435,7 +3435,7 @@ n252 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 171 Size: {1, 160, 17, 17} @@ -3448,7 +3448,7 @@ n253 - + nn.SpatialBatchNormalization Storage id: 172 Size: {1, 160, 17, 17} @@ -3461,7 +3461,7 @@ n254 - + cudnn.ReLU Storage id: 172 Size: {1, 160, 17, 17} @@ -3474,7 +3474,7 @@ n255 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 173 Size: {1, 192, 17, 17} @@ -3487,7 +3487,7 @@ n256 - + nn.SpatialBatchNormalization Storage id: 174 Size: {1, 192, 17, 17} @@ -3500,7 +3500,7 @@ n257 - + cudnn.ReLU Storage id: 174 Size: {1, 192, 17, 17} @@ -3518,7 +3518,7 @@ n259 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 176 Size: {1, 1152, 17, 17} @@ -3531,7 +3531,7 @@ n260 - + nn.SpatialBatchNormalization Storage id: 177 Size: {1, 1152, 17, 17} @@ -3544,7 +3544,7 @@ n261 - + nn.MulConstant Storage id: 178 Size: {1, 1152, 17, 17} @@ -3562,7 +3562,7 @@ n263 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -3575,7 +3575,7 @@ n264 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 179 Size: {1, 192, 17, 17} @@ -3588,7 +3588,7 @@ n267 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 181 Size: {1, 128, 17, 17} @@ -3601,7 +3601,7 @@ n280 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -3614,7 +3614,7 @@ n265 - + nn.SpatialBatchNormalization Storage id: 180 Size: {1, 192, 17, 17} @@ -3627,7 +3627,7 @@ n266 - + cudnn.ReLU Storage id: 180 Size: {1, 192, 17, 17} @@ -3640,7 +3640,7 @@ n276 - + nn.JoinTable Storage id: 187 Size: {1, 384, 17, 17} @@ -3653,7 +3653,7 @@ n268 - + nn.SpatialBatchNormalization Storage id: 182 Size: {1, 128, 17, 17} @@ -3666,7 +3666,7 @@ n269 - + cudnn.ReLU Storage id: 182 Size: {1, 128, 17, 17} @@ -3679,7 +3679,7 @@ n270 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 183 Size: {1, 160, 17, 17} @@ -3692,7 +3692,7 @@ n271 - + nn.SpatialBatchNormalization Storage id: 184 Size: {1, 160, 17, 17} @@ -3705,7 +3705,7 @@ n272 - + cudnn.ReLU Storage id: 184 Size: {1, 160, 17, 17} @@ -3718,7 +3718,7 @@ n273 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 185 Size: {1, 192, 17, 17} @@ -3731,7 +3731,7 @@ n274 - + nn.SpatialBatchNormalization Storage id: 186 Size: {1, 192, 17, 17} @@ -3744,7 +3744,7 @@ n275 - + cudnn.ReLU Storage id: 186 Size: {1, 192, 17, 17} @@ -3762,7 +3762,7 @@ n277 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 188 Size: {1, 1152, 17, 17} @@ -3775,7 +3775,7 @@ n278 - + nn.SpatialBatchNormalization Storage id: 189 Size: {1, 1152, 17, 17} @@ -3788,7 +3788,7 @@ n279 - + nn.MulConstant Storage id: 190 Size: {1, 1152, 17, 17} @@ -3806,7 +3806,7 @@ n281 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -3819,7 +3819,7 @@ n282 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 191 Size: {1, 192, 17, 17} @@ -3832,7 +3832,7 @@ n285 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 193 Size: {1, 128, 17, 17} @@ -3845,7 +3845,7 @@ n298 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -3858,7 +3858,7 @@ n283 - + nn.SpatialBatchNormalization Storage id: 192 Size: {1, 192, 17, 17} @@ -3871,7 +3871,7 @@ n284 - + cudnn.ReLU Storage id: 192 Size: {1, 192, 17, 17} @@ -3884,7 +3884,7 @@ n294 - + nn.JoinTable Storage id: 199 Size: {1, 384, 17, 17} @@ -3897,7 +3897,7 @@ n286 - + nn.SpatialBatchNormalization Storage id: 194 Size: {1, 128, 17, 17} @@ -3910,7 +3910,7 @@ n287 - + cudnn.ReLU Storage id: 194 Size: {1, 128, 17, 17} @@ -3923,7 +3923,7 @@ n288 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 195 Size: {1, 160, 17, 17} @@ -3936,7 +3936,7 @@ n289 - + nn.SpatialBatchNormalization Storage id: 196 Size: {1, 160, 17, 17} @@ -3949,7 +3949,7 @@ n290 - + cudnn.ReLU Storage id: 196 Size: {1, 160, 17, 17} @@ -3962,7 +3962,7 @@ n291 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 197 Size: {1, 192, 17, 17} @@ -3975,7 +3975,7 @@ n292 - + nn.SpatialBatchNormalization Storage id: 198 Size: {1, 192, 17, 17} @@ -3988,7 +3988,7 @@ n293 - + cudnn.ReLU Storage id: 198 Size: {1, 192, 17, 17} @@ -4006,7 +4006,7 @@ n295 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 200 Size: {1, 1152, 17, 17} @@ -4019,7 +4019,7 @@ n296 - + nn.SpatialBatchNormalization Storage id: 201 Size: {1, 1152, 17, 17} @@ -4032,7 +4032,7 @@ n297 - + nn.MulConstant Storage id: 202 Size: {1, 1152, 17, 17} @@ -4050,7 +4050,7 @@ n299 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -4063,7 +4063,7 @@ n300 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 203 Size: {1, 192, 17, 17} @@ -4076,7 +4076,7 @@ n303 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 205 Size: {1, 128, 17, 17} @@ -4089,7 +4089,7 @@ n316 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -4102,7 +4102,7 @@ n301 - + nn.SpatialBatchNormalization Storage id: 204 Size: {1, 192, 17, 17} @@ -4115,7 +4115,7 @@ n302 - + cudnn.ReLU Storage id: 204 Size: {1, 192, 17, 17} @@ -4128,7 +4128,7 @@ n312 - + nn.JoinTable Storage id: 211 Size: {1, 384, 17, 17} @@ -4141,7 +4141,7 @@ n304 - + nn.SpatialBatchNormalization Storage id: 206 Size: {1, 128, 17, 17} @@ -4154,7 +4154,7 @@ n305 - + cudnn.ReLU Storage id: 206 Size: {1, 128, 17, 17} @@ -4167,7 +4167,7 @@ n306 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 207 Size: {1, 160, 17, 17} @@ -4180,7 +4180,7 @@ n307 - + nn.SpatialBatchNormalization Storage id: 208 Size: {1, 160, 17, 17} @@ -4193,7 +4193,7 @@ n308 - + cudnn.ReLU Storage id: 208 Size: {1, 160, 17, 17} @@ -4206,7 +4206,7 @@ n309 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 209 Size: {1, 192, 17, 17} @@ -4219,7 +4219,7 @@ n310 - + nn.SpatialBatchNormalization Storage id: 210 Size: {1, 192, 17, 17} @@ -4232,7 +4232,7 @@ n311 - + cudnn.ReLU Storage id: 210 Size: {1, 192, 17, 17} @@ -4250,7 +4250,7 @@ n313 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 212 Size: {1, 1152, 17, 17} @@ -4263,7 +4263,7 @@ n314 - + nn.SpatialBatchNormalization Storage id: 213 Size: {1, 1152, 17, 17} @@ -4276,7 +4276,7 @@ n315 - + nn.MulConstant Storage id: 214 Size: {1, 1152, 17, 17} @@ -4294,7 +4294,7 @@ n317 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -4307,7 +4307,7 @@ n318 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 215 Size: {1, 192, 17, 17} @@ -4320,7 +4320,7 @@ n321 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 217 Size: {1, 128, 17, 17} @@ -4333,7 +4333,7 @@ n334 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -4346,7 +4346,7 @@ n319 - + nn.SpatialBatchNormalization Storage id: 216 Size: {1, 192, 17, 17} @@ -4359,7 +4359,7 @@ n320 - + cudnn.ReLU Storage id: 216 Size: {1, 192, 17, 17} @@ -4372,7 +4372,7 @@ n330 - + nn.JoinTable Storage id: 223 Size: {1, 384, 17, 17} @@ -4385,7 +4385,7 @@ n322 - + nn.SpatialBatchNormalization Storage id: 218 Size: {1, 128, 17, 17} @@ -4398,7 +4398,7 @@ n323 - + cudnn.ReLU Storage id: 218 Size: {1, 128, 17, 17} @@ -4411,7 +4411,7 @@ n324 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 219 Size: {1, 160, 17, 17} @@ -4424,7 +4424,7 @@ n325 - + nn.SpatialBatchNormalization Storage id: 220 Size: {1, 160, 17, 17} @@ -4437,7 +4437,7 @@ n326 - + cudnn.ReLU Storage id: 220 Size: {1, 160, 17, 17} @@ -4450,7 +4450,7 @@ n327 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 221 Size: {1, 192, 17, 17} @@ -4463,7 +4463,7 @@ n328 - + nn.SpatialBatchNormalization Storage id: 222 Size: {1, 192, 17, 17} @@ -4476,7 +4476,7 @@ n329 - + cudnn.ReLU Storage id: 222 Size: {1, 192, 17, 17} @@ -4494,7 +4494,7 @@ n331 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 224 Size: {1, 1152, 17, 17} @@ -4507,7 +4507,7 @@ n332 - + nn.SpatialBatchNormalization Storage id: 225 Size: {1, 1152, 17, 17} @@ -4520,7 +4520,7 @@ n333 - + nn.MulConstant Storage id: 226 Size: {1, 1152, 17, 17} @@ -4538,7 +4538,7 @@ n335 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -4551,7 +4551,7 @@ n336 - + cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias Storage id: 227 Size: {1, 192, 17, 17} @@ -4564,7 +4564,7 @@ n339 - + cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias Storage id: 229 Size: {1, 128, 17, 17} @@ -4577,7 +4577,7 @@ n352 - + nn.CAddTable Storage id: 118 Size: {1, 1152, 17, 17} @@ -4590,7 +4590,7 @@ n337 - + nn.SpatialBatchNormalization Storage id: 228 Size: {1, 192, 17, 17} @@ -4603,7 +4603,7 @@ n338 - + cudnn.ReLU Storage id: 228 Size: {1, 192, 17, 17} @@ -4616,7 +4616,7 @@ n348 - + nn.JoinTable Storage id: 235 Size: {1, 384, 17, 17} @@ -4629,7 +4629,7 @@ n340 - + nn.SpatialBatchNormalization Storage id: 230 Size: {1, 128, 17, 17} @@ -4642,7 +4642,7 @@ n341 - + cudnn.ReLU Storage id: 230 Size: {1, 128, 17, 17} @@ -4655,7 +4655,7 @@ n342 - + cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias Storage id: 231 Size: {1, 160, 17, 17} @@ -4668,7 +4668,7 @@ n343 - + nn.SpatialBatchNormalization Storage id: 232 Size: {1, 160, 17, 17} @@ -4681,7 +4681,7 @@ n344 - + cudnn.ReLU Storage id: 232 Size: {1, 160, 17, 17} @@ -4694,7 +4694,7 @@ n345 - + cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias Storage id: 233 Size: {1, 192, 17, 17} @@ -4707,7 +4707,7 @@ n346 - + nn.SpatialBatchNormalization Storage id: 234 Size: {1, 192, 17, 17} @@ -4720,7 +4720,7 @@ n347 - + cudnn.ReLU Storage id: 234 Size: {1, 192, 17, 17} @@ -4738,7 +4738,7 @@ n349 - + cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias Storage id: 236 Size: {1, 1152, 17, 17} @@ -4751,7 +4751,7 @@ n350 - + nn.SpatialBatchNormalization Storage id: 237 Size: {1, 1152, 17, 17} @@ -4764,7 +4764,7 @@ n351 - + nn.MulConstant Storage id: 238 Size: {1, 1152, 17, 17} @@ -4782,7 +4782,7 @@ n353 - + cudnn.ReLU Storage id: 118 Size: {1, 1152, 17, 17} @@ -4795,7 +4795,7 @@ n354 - + nn.SpatialMaxPooling(3x3, 2,2) Storage id: 239 Size: {1, 1152, 8, 8} @@ -4808,7 +4808,7 @@ n355 - + cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias Storage id: 240 Size: {1, 256, 17, 17} @@ -4821,7 +4821,7 @@ n361 - + cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias Storage id: 244 Size: {1, 256, 17, 17} @@ -4834,7 +4834,7 @@ n367 - + cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias Storage id: 248 Size: {1, 256, 17, 17} @@ -4847,7 +4847,7 @@ n376 - + nn.JoinTable Storage id: 254 Size: {1, 2048, 8, 8} @@ -4860,7 +4860,7 @@ n356 - + nn.SpatialBatchNormalization Storage id: 241 Size: {1, 256, 17, 17} @@ -4873,7 +4873,7 @@ n357 - + cudnn.ReLU Storage id: 241 Size: {1, 256, 17, 17} @@ -4886,7 +4886,7 @@ n358 - + cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias Storage id: 242 Size: {1, 384, 8, 8} @@ -4899,7 +4899,7 @@ n359 - + nn.SpatialBatchNormalization Storage id: 243 Size: {1, 384, 8, 8} @@ -4912,7 +4912,7 @@ n360 - + cudnn.ReLU Storage id: 243 Size: {1, 384, 8, 8} @@ -4930,7 +4930,7 @@ n362 - + nn.SpatialBatchNormalization Storage id: 245 Size: {1, 256, 17, 17} @@ -4943,7 +4943,7 @@ n363 - + cudnn.ReLU Storage id: 245 Size: {1, 256, 17, 17} @@ -4956,7 +4956,7 @@ n364 - + cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias Storage id: 246 Size: {1, 256, 8, 8} @@ -4969,7 +4969,7 @@ n365 - + nn.SpatialBatchNormalization Storage id: 247 Size: {1, 256, 8, 8} @@ -4982,7 +4982,7 @@ n366 - + cudnn.ReLU Storage id: 247 Size: {1, 256, 8, 8} @@ -5000,7 +5000,7 @@ n368 - + nn.SpatialBatchNormalization Storage id: 249 Size: {1, 256, 17, 17} @@ -5013,7 +5013,7 @@ n369 - + cudnn.ReLU Storage id: 249 Size: {1, 256, 17, 17} @@ -5026,7 +5026,7 @@ n370 - + cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias Storage id: 250 Size: {1, 256, 17, 17} @@ -5039,7 +5039,7 @@ n371 - + nn.SpatialBatchNormalization Storage id: 251 Size: {1, 256, 17, 17} @@ -5052,7 +5052,7 @@ n372 - + cudnn.ReLU Storage id: 251 Size: {1, 256, 17, 17} @@ -5065,7 +5065,7 @@ n373 - + cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias Storage id: 252 Size: {1, 256, 8, 8} @@ -5078,7 +5078,7 @@ n374 - + nn.SpatialBatchNormalization Storage id: 253 Size: {1, 256, 8, 8} @@ -5091,7 +5091,7 @@ n375 - + cudnn.ReLU Storage id: 253 Size: {1, 256, 8, 8} @@ -5109,7 +5109,7 @@ n377 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 255 Size: {1, 192, 8, 8} @@ -5122,7 +5122,7 @@ n380 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 257 Size: {1, 192, 8, 8} @@ -5135,7 +5135,7 @@ n393 - + nn.CAddTable Storage id: 254 Size: {1, 2048, 8, 8} @@ -5148,7 +5148,7 @@ n378 - + nn.SpatialBatchNormalization Storage id: 256 Size: {1, 192, 8, 8} @@ -5161,7 +5161,7 @@ n379 - + cudnn.ReLU Storage id: 256 Size: {1, 192, 8, 8} @@ -5174,7 +5174,7 @@ n389 - + nn.JoinTable Storage id: 263 Size: {1, 448, 8, 8} @@ -5187,7 +5187,7 @@ n381 - + nn.SpatialBatchNormalization Storage id: 258 Size: {1, 192, 8, 8} @@ -5200,7 +5200,7 @@ n382 - + cudnn.ReLU Storage id: 258 Size: {1, 192, 8, 8} @@ -5213,7 +5213,7 @@ n383 - + cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias Storage id: 259 Size: {1, 224, 8, 8} @@ -5226,7 +5226,7 @@ n384 - + nn.SpatialBatchNormalization Storage id: 260 Size: {1, 224, 8, 8} @@ -5239,7 +5239,7 @@ n385 - + cudnn.ReLU Storage id: 260 Size: {1, 224, 8, 8} @@ -5252,7 +5252,7 @@ n386 - + cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias Storage id: 261 Size: {1, 256, 8, 8} @@ -5265,7 +5265,7 @@ n387 - + nn.SpatialBatchNormalization Storage id: 262 Size: {1, 256, 8, 8} @@ -5278,7 +5278,7 @@ n388 - + cudnn.ReLU Storage id: 262 Size: {1, 256, 8, 8} @@ -5296,7 +5296,7 @@ n390 - + cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias Storage id: 264 Size: {1, 2048, 8, 8} @@ -5309,7 +5309,7 @@ n391 - + nn.SpatialBatchNormalization Storage id: 265 Size: {1, 2048, 8, 8} @@ -5322,7 +5322,7 @@ n392 - + nn.MulConstant Storage id: 266 Size: {1, 2048, 8, 8} @@ -5340,7 +5340,7 @@ n394 - + cudnn.ReLU Storage id: 254 Size: {1, 2048, 8, 8} @@ -5353,7 +5353,7 @@ n395 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 267 Size: {1, 192, 8, 8} @@ -5366,7 +5366,7 @@ n398 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 269 Size: {1, 192, 8, 8} @@ -5379,7 +5379,7 @@ n411 - + nn.CAddTable Storage id: 254 Size: {1, 2048, 8, 8} @@ -5392,7 +5392,7 @@ n396 - + nn.SpatialBatchNormalization Storage id: 268 Size: {1, 192, 8, 8} @@ -5405,7 +5405,7 @@ n397 - + cudnn.ReLU Storage id: 268 Size: {1, 192, 8, 8} @@ -5418,7 +5418,7 @@ n407 - + nn.JoinTable Storage id: 275 Size: {1, 448, 8, 8} @@ -5431,7 +5431,7 @@ n399 - + nn.SpatialBatchNormalization Storage id: 270 Size: {1, 192, 8, 8} @@ -5444,7 +5444,7 @@ n400 - + cudnn.ReLU Storage id: 270 Size: {1, 192, 8, 8} @@ -5457,7 +5457,7 @@ n401 - + cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias Storage id: 271 Size: {1, 224, 8, 8} @@ -5470,7 +5470,7 @@ n402 - + nn.SpatialBatchNormalization Storage id: 272 Size: {1, 224, 8, 8} @@ -5483,7 +5483,7 @@ n403 - + cudnn.ReLU Storage id: 272 Size: {1, 224, 8, 8} @@ -5496,7 +5496,7 @@ n404 - + cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias Storage id: 273 Size: {1, 256, 8, 8} @@ -5509,7 +5509,7 @@ n405 - + nn.SpatialBatchNormalization Storage id: 274 Size: {1, 256, 8, 8} @@ -5522,7 +5522,7 @@ n406 - + cudnn.ReLU Storage id: 274 Size: {1, 256, 8, 8} @@ -5540,7 +5540,7 @@ n408 - + cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias Storage id: 276 Size: {1, 2048, 8, 8} @@ -5553,7 +5553,7 @@ n409 - + nn.SpatialBatchNormalization Storage id: 277 Size: {1, 2048, 8, 8} @@ -5566,7 +5566,7 @@ n410 - + nn.MulConstant Storage id: 278 Size: {1, 2048, 8, 8} @@ -5584,7 +5584,7 @@ n412 - + cudnn.ReLU Storage id: 254 Size: {1, 2048, 8, 8} @@ -5597,7 +5597,7 @@ n413 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 279 Size: {1, 192, 8, 8} @@ -5610,7 +5610,7 @@ n416 - + cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias Storage id: 281 Size: {1, 192, 8, 8} @@ -5623,7 +5623,7 @@ n429 - + nn.CAddTable Storage id: 254 Size: {1, 2048, 8, 8} @@ -5636,7 +5636,7 @@ n414 - + nn.SpatialBatchNormalization Storage id: 280 Size: {1, 192, 8, 8} @@ -5649,7 +5649,7 @@ n415 - + cudnn.ReLU Storage id: 280 Size: {1, 192, 8, 8} @@ -5662,7 +5662,7 @@ n425 - + nn.JoinTable Storage id: 287 Size: {1, 448, 8, 8} @@ -5675,7 +5675,7 @@ n417 - + nn.SpatialBatchNormalization Storage id: 282 Size: {1, 192, 8, 8} @@ -5688,7 +5688,7 @@ n418 - + cudnn.ReLU Storage id: 282 Size: {1, 192, 8, 8} @@ -5701,7 +5701,7 @@ n419 - + cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias Storage id: 283 Size: {1, 224, 8, 8} @@ -5714,7 +5714,7 @@ n420 - + nn.SpatialBatchNormalization Storage id: 284 Size: {1, 224, 8, 8} @@ -5727,7 +5727,7 @@ n421 - + cudnn.ReLU Storage id: 284 Size: {1, 224, 8, 8} @@ -5740,7 +5740,7 @@ n422 - + cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias Storage id: 285 Size: {1, 256, 8, 8} @@ -5753,7 +5753,7 @@ n423 - + nn.SpatialBatchNormalization Storage id: 286 Size: {1, 256, 8, 8} @@ -5766,7 +5766,7 @@ n424 - + cudnn.ReLU Storage id: 286 Size: {1, 256, 8, 8} @@ -5784,7 +5784,7 @@ n426 - + cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias Storage id: 288 Size: {1, 2048, 8, 8} @@ -5797,7 +5797,7 @@ n427 - + nn.SpatialBatchNormalization Storage id: 289 Size: {1, 2048, 8, 8} @@ -5810,7 +5810,7 @@ n428 - + nn.MulConstant Storage id: 290 Size: {1, 2048, 8, 8} @@ -5828,7 +5828,7 @@ n430 - + cudnn.ReLU Storage id: 254 Size: {1, 2048, 8, 8} @@ -5841,7 +5841,7 @@ n431 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566.97,9363.1 573.25,9352.8 579.3,9342.8"]; - n39 [color=darkgoldenrod2, - fontsize=14, - height=1.3356, - label="nn.JoinTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="694,9156", - shape=ellipse, - style=solid, - width=2.6788]; - n37 -> n39 [pos="e,665.6,9202 640.45,9241.9 646.81,9231.8 653.62,9221 660.17,9210.6"]; - n38 -> n39 [pos="e,715.47,9203.1 794.33,9372.5 773.47,9327.7 741.9,9259.9 719.77,9212.3"]; - n40 [color=deepskyblue3, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 29\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="226,9024", - shape=ellipse, - style=solid, - width=6.2816]; - n39 -> n40 [pos="e,361.38,9062.6 610.6,9131.8 544.32,9113.4 450.01,9087.2 371.38,9065.4"]; - n43 [color=antiquewhite3, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 31\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="526,8892", - shape=ellipse, - style=solid, - width=6.2816]; - n39 -> n43 [pos="e,544.37,8940.1 648.93,9113.3 636.73,9100.7 624.12,9086.4 614,9072 587.16,9033.8 564.16,8986.1 548.42,8949.6"]; - n49 [color=goldenrod, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 32, 1x1) without bias\nStorage id: 35\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="849,9024", - shape=ellipse, - style=solid, - width=6.2816]; - n39 -> n49 [pos="e,794.35,9070.8 742.62,9114.2 756.48,9102.6 771.77,9089.8 786.34,9077.6"]; - n62 [color=darkgoldenrod2, - fontsize=14, - height=1.3356, - label="nn.CAddTable\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="1030,7308", - shape=ellipse, - style=solid, - width=2.6788]; - n39 -> n62 [pos="e,1058.9,7354.3 788.88,9146.2 890.4,9134.9 1042.5,9111.8 1084,9072 1143.3,9015.1 1130,8975.1 1130,8893 1130,8893 1130,8893 1130,\ -7571 1130,7495.2 1093,7414.7 1064,7363.3"]; - n41 [color=cornsilk2, - fontsize=14, - height=1.3356, - 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34\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="507,8364", - shape=ellipse, - style=solid, - width=3.5652]; - n46 -> n47 [pos="e,487.28,8411.6 472.01,8447.7 475.7,8439 479.59,8429.8 483.38,8420.8"]; - n48 [color=aquamarine, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 34\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="548,8100", - shape=ellipse, - style=solid, - width=2.5643]; - n47 -> n48 [pos="e,540.61,8148.2 514.38,8315.9 521.29,8271.7 531.65,8205.5 539.04,8158.3"]; - n48 -> n58 [pos="e,653.58,7882.9 570.44,8053.1 592.36,8008.2 625.72,7939.9 649.06,7892.2"]; - n50 [color=antiquewhite2, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="899,8892", - shape=ellipse, - style=solid, - width=3.5652]; - n49 -> n50 [pos="e,880.98,8939.9 867.05,8976.1 870.38,8967.4 873.89,8958.3 877.31,8949.4"]; - n51 [color=antiquewhite2, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 36\nSize: {1, 32, 35, 35}\nMem size: 39200", - pos="893,8760", - shape=ellipse, - style=solid, - width=2.5643]; - n50 -> n51 [pos="e,895.19,8808.5 896.82,8843.7 896.44,8835.5 896.05,8827 895.66,8818.6"]; - n52 [color=goldenrod1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias\nStorage id: 37\nSize: {1, 48, 35, 35}\nMem size: 58800", - pos="846,8628", - shape=ellipse, - style=solid, - width=7.0968]; - n51 -> n52 [pos="e,863.03,8676.1 876.16,8712.4 873.03,8703.8 869.73,8694.6 866.5,8685.7"]; - n53 [color=darkorchid3, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", - pos="855,8496", - shape=ellipse, - style=solid, - width=3.5652]; - n52 -> n53 [pos="e,851.71,8544.5 849.27,8579.7 849.84,8571.5 850.43,8563 851.01,8554.6"]; - n54 [color=darkorchid3, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 38\nSize: {1, 48, 35, 35}\nMem size: 58800", - pos="836,8364", - shape=ellipse, - style=solid, - width=2.5643]; - n53 -> n54 [pos="e,842.88,8412 848.09,8447.7 846.87,8439.4 845.58,8430.6 844.33,8422"]; - n55 [color=forestgreen, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias\nStorage id: 39\nSize: {1, 64, 35, 35}\nMem size: 78400", - pos="832,8232", - shape=ellipse, - style=solid, - width=7.0968]; - n54 -> n55 [pos="e,833.46,8280.5 834.54,8315.7 834.29,8307.5 834.03,8299 833.77,8290.6"]; - n56 [color=ghostwhite, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", - pos="796,8100", - shape=ellipse, - style=solid, - width=3.5652]; - n55 -> n56 [pos="e,809.03,8148 818.9,8183.7 816.56,8175.3 814.11,8166.4 811.71,8157.7"]; - n57 [color=ghostwhite, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 40\nSize: {1, 64, 35, 35}\nMem size: 78400", - pos="764,7968", - shape=ellipse, - style=solid, - width=2.5643]; - n56 -> n57 [pos="e,775.54,8015.9 784.45,8052.1 782.34,8043.5 780.12,8034.5 777.95,8025.7"]; - n57 -> n58 [pos="e,706.34,7881.8 733.72,7922.3 726.73,7911.9 719.24,7900.9 712.04,7890.2"]; - n59 [color=deepskyblue4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(128 -> 384, 1x1) without bias\nStorage id: 42\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="741,7704", - shape=ellipse, - style=solid, - width=6.3961]; - n58 -> n59 [pos="e,717.41,7752.2 698.92,7789.2 703.41,7780.2 708.18,7770.6 712.82,7761.4"]; - n60 [color=cadetblue4, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 43\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="857,7572", - shape=ellipse, - style=solid, - width=3.5652]; - n59 -> n60 [pos="e,816.9,7617.9 782.55,7656.4 791.46,7646.4 800.94,7635.8 810.04,7625.6"]; - n61 [color=cyan3, - fontsize=14, - height=1.3356, - label="nn.MulConstant\nStorage id: 44\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="931,7440", - shape=ellipse, - style=solid, - width=2.6788]; - n60 -> n61 [pos="e,905.04,7486.6 883.3,7524.8 888.7,7515.3 894.45,7505.2 900,7495.5"]; - n61 -> n62 [pos="e,996.36,7353.2 964.79,7394.6 973,7383.9 981.84,7372.2 990.29,7361.1"]; - n63 [color=darkgoldenrod2, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 28\nSize: {1, 384, 35, 35}\nMem size: 470400", - pos="1030,7176", - shape=ellipse, - style=solid, - width=2.6788]; - n62 -> n63 [pos="e,1030,7224.5 1030,7259.7 1030,7251.5 1030,7243 1030,7234.6"]; - n64 [color=darkseagreen, - fontsize=14, - height=1.3356, - label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 45\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="810,6648", - shape=ellipse, - style=solid, - width=3.7843]; - n63 -> n64 [pos="e,789.55,6695.7 962.95,7141.2 900.68,7105.7 811.91,7043.3 773,6960 734.47,6877.5 762.15,6769.9 785.93,6705.3"]; - n65 [color=blue4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias\nStorage id: 46\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1030,6912", - shape=ellipse, - style=solid, - width=6.8916]; - n63 -> n65 [pos="e,1030,6960.2 1030,7127.9 1030,7083.7 1030,7017.5 1030,6970.3"]; - n68 [color=darkorchid4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 256, 1x1) without bias\nStorage id: 48\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1357,7044", - shape=ellipse, - style=solid, - width=6.3961]; - n63 -> n68 [pos="e,1251.7,7086.9 1104.6,7145.3 1145.3,7129.1 1196.7,7108.7 1242.4,7090.6"]; - n77 [color=aquamarine, - fontsize=14, - height=1.3356, - label="nn.JoinTable\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="1030,5856", - shape=ellipse, - style=solid, - width=2.817]; - n64 -> n77 [pos="e,980.71,5898.3 823.88,6600.1 838.06,6548.1 858,6461.3 858,6385 858,6385 858,6385 858,6119 858,6033.2 923.3,5953.6 973.44,5905.2"]; - n66 [color=goldenrod2, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 47\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1030,6780", - shape=ellipse, - style=solid, - width=3.5652]; - n65 -> n66 [pos="e,1030,6828.5 1030,6863.7 1030,6855.5 1030,6847 1030,6838.6"]; - n67 [color=goldenrod2, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 47\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1030,6516", - shape=ellipse, - style=solid, - width=2.6788]; - n66 -> n67 [pos="e,1030,6564.2 1030,6731.9 1030,6687.7 1030,6621.5 1030,6574.3"]; - n67 -> n77 [pos="e,1030,5904.3 1030,6467.7 1030,6415.2 1030,6328.1 1030,6253 1030,6253 1030,6253 1030,6119 1030,6048.6 1030,5967.6 1030,5914.6"]; - n69 [color=goldenrod3, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 49\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1425,6912", - shape=ellipse, - style=solid, - width=3.5652]; - n68 -> n69 [pos="e,1400.7,6959.4 1381.5,6996.1 1386.2,6987.1 1391.2,6977.7 1396,6968.5"]; - n70 [color=goldenrod3, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 49\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1399,6780", - shape=ellipse, - style=solid, - width=2.6788]; - n69 -> n70 [pos="e,1408.4,6828 1415.5,6863.7 1413.9,6855.4 1412.1,6846.6 1410.4,6838"]; - n71 [color=burlywood4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 50\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1349,6648", - shape=ellipse, - style=solid, - width=7.3732]; - n70 -> n71 [pos="e,1367.1,6696.1 1381.1,6732.4 1377.8,6723.8 1374.2,6714.6 1370.8,6705.7"]; - n72 [color=darkslategray3, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 51\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1343,6516", - shape=ellipse, - style=solid, - width=3.5652]; - n71 -> n72 [pos="e,1345.2,6564.5 1346.8,6599.7 1346.4,6591.5 1346,6583 1345.7,6574.6"]; - n73 [color=darkslategray3, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 51\nSize: {1, 256, 35, 35}\nMem size: 313600", - pos="1338,6384", - shape=ellipse, - style=solid, - width=2.6788]; - n72 -> n73 [pos="e,1339.8,6432.5 1341.2,6467.7 1340.9,6459.5 1340.5,6451 1340.2,6442.6"]; - n74 [color=bisque, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 52\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1317,6252", - shape=ellipse, - style=solid, - width=6.8916]; - n73 -> n74 [pos="e,1324.7,6300.5 1330.4,6335.7 1329,6327.5 1327.7,6319 1326.3,6310.6"]; - n75 [color=antiquewhite4, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 53\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1252,6120", - shape=ellipse, - style=solid, - width=3.5652]; - n74 -> n75 [pos="e,1275.2,6167.4 1293.5,6204.1 1289.1,6195.1 1284.3,6185.7 1279.7,6176.5"]; - n76 [color=antiquewhite4, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 53\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="1203,5988", - shape=ellipse, - style=solid, - width=2.6788]; - n75 -> n76 [pos="e,1220.5,6035.4 1234.3,6072.1 1231,6063.3 1227.5,6054.1 1224.1,6045.1"]; - n76 -> n77 [pos="e,1083.4,5897.1 1150.2,5947.3 1131.6,5933.4 1110.7,5917.6 1091.5,5903.2"]; - n78 [color=chocolate3, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias\nStorage id: 55\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="410,5724", - shape=ellipse, - style=solid, - width=6.5343]; - n77 -> n78 [pos="e,571.96,5759 937.85,5835.7 844.27,5816.1 697.13,5785.2 581.95,5761.1"]; - n81 [color=beige, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias\nStorage id: 57\nSize: {1, 128, 17, 17}\nMem size: 36992", - pos="899,5724", - shape=ellipse, - style=solid, - width=6.5343]; - n77 -> n81 [pos="e,945.82,5771.5 986.74,5812.1 975.98,5801.4 964.31,5789.8 953.11,5778.7"]; - n94 [color=aquamarine, - fontsize=14, - height=1.3356, - label="nn.CAddTable\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="1075,4008", - shape=ellipse, - style=solid, - width=2.817]; - n77 -> n94 [pos="e,1100.2,4054.8 1096.9,5819.7 1114.7,5807.1 1132,5791.1 1143,5772 1182.9,5702.6 1162,5673 1162,5593 1162,5593 1162,5593 1162,4271 \ -1162,4196.4 1129.8,4115.7 1104.6,4063.8"]; - n150 [color=goldenrod1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialAveragePooling(5x5, 4,4)\nStorage id: 105\nSize: {1, 1152, 4, 4}\nMem size: 18432", - pos="1390,5724", - shape=ellipse, - style=solid, - width=4.5661]; - n77 -> n150 [pos="e,1288.1,5761.8 1109.9,5826.1 1159.4,5808.3 1223.8,5785 1278.3,5765.3"]; - n79 [color=azure3, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 56\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="420,5592", - shape=ellipse, - style=solid, - width=3.5652]; - n78 -> n79 [pos="e,416.35,5640.5 413.64,5675.7 414.27,5667.5 414.92,5659 415.57,5650.6"]; - n80 [color=azure3, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 56\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="468,5328", - shape=ellipse, - style=solid, - width=2.6788]; - n79 -> n80 [pos="e,459.35,5376.2 428.64,5543.9 436.73,5499.7 448.86,5433.5 457.51,5386.3"]; - n90 [color=dodgerblue4, - fontsize=14, - height=1.3356, - label="nn.JoinTable\nStorage id: 63\nSize: {1, 384, 17, 17}\nMem size: 110976", - pos="868,4536", - shape=ellipse, - style=solid, - width=2.6788]; - n80 -> n90 [pos="e,784,4560 483.33,5280.2 498.98,5228.3 521,5141.6 521,5065 521,5065 521,5065 521,4799 521,4671.8 671.44,4598.5 774.21,4563.3"]; - n82 [color=darkorange2, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 58\nSize: {1, 128, 17, 17}\nMem size: 36992", - pos="891,5592", - shape=ellipse, - style=solid, - width=3.5652]; - n81 -> n82 [pos="e,893.92,5640.5 896.09,5675.7 895.59,5667.5 895.06,5659 894.54,5650.6"]; - n83 [color=darkorange2, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 58\nSize: {1, 128, 17, 17}\nMem size: 36992", - pos="876,5460", - shape=ellipse, - style=solid, - width=2.6788]; - n82 -> n83 [pos="e,881.43,5508 885.54,5543.7 884.58,5535.4 883.57,5526.6 882.58,5518"]; - n84 [color=firebrick1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias\nStorage id: 59\nSize: {1, 160, 17, 17}\nMem size: 46240", - pos="868,5328", - shape=ellipse, - style=solid, - width=7.3732]; - n83 -> n84 [pos="e,870.92,5376.5 873.09,5411.7 872.59,5403.5 872.06,5395 871.54,5386.6"]; - n85 [color=cyan4, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 60\nSize: {1, 160, 17, 17}\nMem size: 46240", - pos="868,5196", - shape=ellipse, - style=solid, - width=3.5652]; - n84 -> n85 [pos="e,868,5244.5 868,5279.7 868,5271.5 868,5263 868,5254.6"]; - n86 [color=cyan4, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 60\nSize: {1, 160, 17, 17}\nMem size: 46240", - pos="868,5064", - shape=ellipse, - style=solid, - width=2.6788]; - n85 -> n86 [pos="e,868,5112.5 868,5147.7 868,5139.5 868,5131 868,5122.6"]; - n87 [color=dodgerblue1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias\nStorage id: 61\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="868,4932", - shape=ellipse, - style=solid, - width=7.3732]; - n86 -> n87 [pos="e,868,4980.5 868,5015.7 868,5007.5 868,4999 868,4990.6"]; - n88 [color=darkslategray1, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 62\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="868,4800", - shape=ellipse, - style=solid, - width=3.5652]; - n87 -> n88 [pos="e,868,4848.5 868,4883.7 868,4875.5 868,4867 868,4858.6"]; - n89 [color=darkslategray1, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 62\nSize: {1, 192, 17, 17}\nMem size: 55488", - pos="868,4668", - shape=ellipse, - style=solid, - width=2.6788]; - n88 -> n89 [pos="e,868,4716.5 868,4751.7 868,4743.5 868,4735 868,4726.6"]; - n89 -> n90 [pos="e,868,4584.5 868,4619.7 868,4611.5 868,4603 868,4594.6"]; - n91 [color=azure1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias\nStorage id: 64\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="884,4404", - shape=ellipse, - style=solid, - width=6.5343]; - n90 -> n91 [pos="e,878.16,4452.5 873.82,4487.7 874.83,4479.5 875.88,4471 876.91,4462.6"]; - n92 [color=darkorange, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 65\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="945,4272", - shape=ellipse, - style=solid, - width=3.5652]; - n91 -> n92 [pos="e,923.21,4319.4 906.02,4356.1 910.22,4347.1 914.65,4337.7 918.96,4328.5"]; - n93 [color=gold1, - fontsize=14, - height=1.3356, - label="nn.MulConstant\nStorage id: 66\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="988,4140", - shape=ellipse, - style=solid, - width=2.817]; - n92 -> n93 [pos="e,972.5,4187.9 960.52,4224.1 963.39,4215.4 966.4,4206.3 969.34,4197.4"]; - n93 -> n94 [pos="e,1044.9,4054 1018.2,4093.9 1024.9,4083.8 1032.2,4073 1039.1,4062.6"]; - n95 [color=aquamarine, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 54\nSize: {1, 1152, 17, 17}\nMem size: 332928", - pos="1075,3876", - shape=ellipse, - style=solid, - width=2.817]; - n94 -> n95 [pos="e,1075,3924.5 1075,3959.7 1075,3951.5 1075,3943 1075,3934.6"]; - n96 [color=goldenrod3, - fontsize=14, - height=1.3356, - label="nn.SpatialMaxPooling(3x3, 2,2)\nStorage id: 67\nSize: {1, 1152, 8, 8}\nMem size: 73728", - pos="736,2952", - shape=ellipse, - style=solid, - width=3.7843]; - n95 -> n96 [pos="e,703.89,2998.9 977.79,3861.9 846.03,3838.1 627,3774.3 627,3613 627,3613 627,3613 627,3215 627,3138.6 666.97,3058.7 698.51,3007.5"]; - n97 [color=bisque3, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 68\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="916,3612", - shape=ellipse, - style=solid, - width=6.5343]; - n95 -> n97 [pos="e,926.24,3660.1 1020.6,3835.1 1006.3,3822.6 991.78,3807.8 981,3792 955.82,3755 939.1,3707.1 928.93,3670.2"]; - n103 [color=bisque4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 72\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1225,3744", - shape=ellipse, - style=solid, - width=6.5343]; - n95 -> n103 [pos="e,1171.9,3791.1 1122.9,3833.5 1136,3822.1 1150.5,3809.6 1164.3,3797.6"]; - n109 [color=azure2, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias\nStorage id: 76\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1733,3744", - shape=ellipse, - style=solid, - width=6.5343]; - n95 -> n109 [pos="e,1566.3,3777.9 1167.9,3856.6 1268.4,3836.8 1431.1,3804.6 1556.2,3779.9"]; - n118 [color=firebrick4, - fontsize=14, - height=1.3356, - label="nn.JoinTable\nStorage id: 82\nSize: {1, 2048, 8, 8}\nMem size: 131072", - pos="1165,2556", - shape=ellipse, - style=solid, - width=2.5643]; - n96 -> n118 [pos="e,1120.3,2598.1 783.96,2907 865.14,2832.4 1029,2681.9 1112.9,2604.9"]; - n98 [color=darkolivegreen4, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 69\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="911,3480", - shape=ellipse, - style=solid, - width=3.5652]; - n97 -> n98 [pos="e,912.83,3528.5 914.18,3563.7 913.87,3555.5 913.54,3547 913.22,3538.6"]; - n99 [color=darkolivegreen4, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 69\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="910,3348", - shape=ellipse, - style=solid, - width=2.6788]; - n98 -> n99 [pos="e,910.37,3396.5 910.64,3431.7 910.57,3423.5 910.51,3415 910.44,3406.6"]; - n100 [color=blue1, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias\nStorage id: 70\nSize: {1, 384, 8, 8}\nMem size: 24576", - pos="903,3216", - shape=ellipse, - style=solid, - width=6.8916]; - n99 -> n100 [pos="e,905.56,3264.5 907.45,3299.7 907.01,3291.5 906.55,3283 906.1,3274.6"]; - n101 [color=chartreuse, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 71\nSize: {1, 384, 8, 8}\nMem size: 24576", - pos="963,3084", - shape=ellipse, - style=solid, - width=3.5652]; - n100 -> n101 [pos="e,941.57,3131.4 924.66,3168.1 928.75,3159.2 933.06,3149.9 937.25,3140.8"]; - n102 [color=chartreuse, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 71\nSize: {1, 384, 8, 8}\nMem size: 24576", - pos="1092,2820", - shape=ellipse, - style=solid, - width=2.4261]; - n101 -> n102 [pos="e,1069.5,2866.7 985.91,3036.5 1008,2991.5 1041.6,2923.5 1065,2875.9"]; - n102 -> n118 [pos="e,1148.7,2603.3 1102.7,2772 1111.3,2735.7 1123.9,2684.4 1137,2640 1139.6,2631.2 1142.5,2622 1145.4,2613"]; - n104 [color=azure4, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 73\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1349,3480", - shape=ellipse, - style=solid, - width=3.5652]; - n103 -> n104 [pos="e,1326.9,3527.8 1247.3,3695.9 1268.4,3651.2 1300.1,3584.2 1322.5,3536.9"]; - n105 [color=azure4, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 73\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1374,3348", - shape=ellipse, - style=solid, - width=2.6788]; - n104 -> n105 [pos="e,1365,3396 1358.1,3431.7 1359.7,3423.4 1361.4,3414.6 1363,3406"]; - n106 [color=chocolate, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 74\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1417,3216", - shape=ellipse, - style=solid, - width=6.8916]; - n105 -> n106 [pos="e,1401.4,3264.1 1389.4,3300.4 1392.3,3291.8 1395.3,3282.6 1398.2,3273.7"]; - n107 [color=deeppink1, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 75\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1357,3084", - shape=ellipse, - style=solid, - width=3.5652]; - n106 -> n107 [pos="e,1378.4,3131.4 1395.3,3168.1 1391.3,3159.2 1386.9,3149.9 1382.7,3140.8"]; - n108 [color=deeppink1, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 75\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1234,2688", - shape=ellipse, - style=solid, - width=2.4261]; - n107 -> n108 [pos="e,1248.5,2735.6 1342.3,3035.9 1319.5,2963 1275.9,2823.3 1251.6,2745.2"]; - n108 -> n118 [pos="e,1189.2,2602.7 1209.9,2641.5 1204.7,2631.9 1199.3,2621.6 1194,2611.7"]; - n110 [color=darkolivegreen1, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 77\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1735,3612", - shape=ellipse, - style=solid, - width=3.5652]; - n109 -> n110 [pos="e,1734.3,3660.5 1733.7,3695.7 1733.9,3687.5 1734,3679 1734.1,3670.6"]; - n111 [color=darkolivegreen1, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 77\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1736,3480", - shape=ellipse, - style=solid, - width=2.6788]; - n110 -> n111 [pos="e,1735.6,3528.5 1735.4,3563.7 1735.4,3555.5 1735.5,3547 1735.6,3538.6"]; - n112 [color=chartreuse, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias\nStorage id: 78\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1804,3348", - shape=ellipse, - style=solid, - width=7.3732]; - n111 -> n112 [pos="e,1779.3,3396.2 1760,3433.2 1764.7,3424.1 1769.8,3414.4 1774.7,3405.1"]; - n113 [color=coral3, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 79\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1812,3216", - shape=ellipse, - style=solid, - width=3.5652]; - n112 -> n113 [pos="e,1809.1,3264.5 1806.9,3299.7 1807.4,3291.5 1807.9,3283 1808.5,3274.6"]; - n114 [color=coral3, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 79\nSize: {1, 256, 17, 17}\nMem size: 73984", - pos="1736,3084", - shape=ellipse, - style=solid, - width=2.6788]; - n113 -> n114 [pos="e,1762.7,3130.6 1785,3168.8 1779.4,3159.3 1773.5,3149.2 1767.8,3139.5"]; - n115 [color=coral4, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias\nStorage id: 80\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1730,2952", - shape=ellipse, - style=solid, - width=6.8916]; - n114 -> n115 [pos="e,1732.2,3000.5 1733.8,3035.7 1733.4,3027.5 1733,3019 1732.7,3010.6"]; - n116 [color=firebrick1, - fontsize=14, - height=1.3356, - label="nn.SpatialBatchNormalization\nStorage id: 81\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1479,2820", - shape=ellipse, - style=solid, - width=3.5652]; - n115 -> n116 [pos="e,1553.2,2859.4 1644.7,2906.8 1618.1,2893 1588.8,2877.9 1562.4,2864.2"]; - n117 [color=firebrick1, - fontsize=14, - height=1.3356, - label="cudnn.ReLU\nStorage id: 81\nSize: {1, 256, 8, 8}\nMem size: 16384", - pos="1437,2688", - shape=ellipse, - style=solid, - width=2.4261]; - n116 -> n117 [pos="e,1452,2735.4 1463.8,2772.1 1461,2763.3 1458,2754.1 1455.1,2745.1"]; - n117 -> n118 [pos="e,1232.5,2589.2 1371.8,2655.9 1332.7,2637.1 1282.7,2613.2 1241.6,2593.6"]; - n119 [color=darkslategrey, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 83\nSize: {1, 192, 8, 8}\nMem size: 12288", - pos="545,2424", - shape=ellipse, - style=solid, - width=6.5343]; - n118 -> n119 [pos="e,707,2459 1079.8,2537.1 986.63,2517.6 834.95,2485.8 716.87,2461"]; - n122 [color=blue2, - fontsize=14, - height=1.3356, - label="cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias\nStorage id: 85\nSize: {1, 192, 8, 8}\nMem size: 12288", - pos="1034,2424", - shape=ellipse, - style=solid, - width=6.5343]; - n118 -> n122 [pos="e,1080.6,2471.3 1122.8,2513.2 1111.7,2502.1 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-n49->n50 - - - - -n51 - -cudnn.ReLU -Storage id: 36 -Size: {1, 32, 35, 35} -Mem size: 39200 - - -n50->n51 - - - - -n52 - -cudnn.SpatialConvolution(32 -> 48, 3x3, 1,1, 1,1) without bias -Storage id: 37 -Size: {1, 48, 35, 35} -Mem size: 58800 - - -n51->n52 - - - - -n53 - -nn.SpatialBatchNormalization -Storage id: 38 -Size: {1, 48, 35, 35} -Mem size: 58800 - - -n52->n53 - - - - -n54 - -cudnn.ReLU -Storage id: 38 -Size: {1, 48, 35, 35} -Mem size: 58800 - - -n53->n54 - - - - -n55 - -cudnn.SpatialConvolution(48 -> 64, 3x3, 1,1, 1,1) without bias -Storage id: 39 -Size: {1, 64, 35, 35} -Mem size: 78400 - - -n54->n55 - - - - -n56 - -nn.SpatialBatchNormalization -Storage id: 40 -Size: {1, 64, 35, 35} -Mem size: 78400 - - -n55->n56 - - - - -n57 - -cudnn.ReLU -Storage id: 40 -Size: {1, 64, 35, 35} -Mem size: 78400 - - -n56->n57 - - - - -n57->n58 - - - - -n59 - -cudnn.SpatialConvolution(128 -> 384, 1x1) without bias -Storage id: 42 -Size: {1, 384, 35, 35} -Mem size: 470400 - - -n58->n59 - - - - -n60 - -nn.SpatialBatchNormalization -Storage id: 43 -Size: {1, 384, 35, 35} -Mem size: 470400 - - -n59->n60 - - - - -n61 - -nn.MulConstant -Storage id: 44 -Size: {1, 384, 35, 35} -Mem size: 470400 - - -n60->n61 - - - - -n61->n62 - - - - -n63 - -cudnn.ReLU -Storage id: 28 -Size: {1, 384, 35, 35} -Mem size: 470400 - - -n62->n63 - - - - -n64 - -nn.SpatialMaxPooling(3x3, 2,2) -Storage id: 45 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n63->n64 - - - - -n65 - -cudnn.SpatialConvolution(384 -> 384, 3x3, 2,2) without bias -Storage id: 46 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n63->n65 - - - - -n68 - -cudnn.SpatialConvolution(384 -> 256, 1x1) without bias -Storage id: 48 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n63->n68 - - - - -n77 - -nn.JoinTable -Storage id: 54 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n64->n77 - - - - -n66 - -nn.SpatialBatchNormalization -Storage id: 47 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n65->n66 - - - - -n67 - -cudnn.ReLU -Storage id: 47 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n66->n67 - - - - -n67->n77 - - - - -n69 - -nn.SpatialBatchNormalization -Storage id: 49 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n68->n69 - - - - -n70 - -cudnn.ReLU -Storage id: 49 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n69->n70 - - - - -n71 - -cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias -Storage id: 50 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n70->n71 - - - - -n72 - -nn.SpatialBatchNormalization -Storage id: 51 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n71->n72 - - - - -n73 - -cudnn.ReLU -Storage id: 51 -Size: {1, 256, 35, 35} -Mem size: 313600 - - -n72->n73 - - - - -n74 - -cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias -Storage id: 52 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n73->n74 - - - - -n75 - -nn.SpatialBatchNormalization -Storage id: 53 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n74->n75 - - - - -n76 - -cudnn.ReLU -Storage id: 53 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n75->n76 - - - - -n76->n77 - - - - -n78 - -cudnn.SpatialConvolution(1152 -> 192, 1x1) without bias -Storage id: 55 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n77->n78 - - - - -n81 - -cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias -Storage id: 57 -Size: {1, 128, 17, 17} -Mem size: 36992 - - -n77->n81 - - - - -n94 - -nn.CAddTable -Storage id: 54 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n77->n94 - - - - -n150 - -cudnn.SpatialAveragePooling(5x5, 4,4) -Storage id: 105 -Size: {1, 1152, 4, 4} -Mem size: 18432 - - -n77->n150 - - - - -n79 - -nn.SpatialBatchNormalization -Storage id: 56 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n78->n79 - - - - -n80 - -cudnn.ReLU -Storage id: 56 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n79->n80 - - - - -n90 - -nn.JoinTable -Storage id: 63 -Size: {1, 384, 17, 17} -Mem size: 110976 - - -n80->n90 - - - - -n82 - -nn.SpatialBatchNormalization -Storage id: 58 -Size: {1, 128, 17, 17} -Mem size: 36992 - - -n81->n82 - - - - -n83 - -cudnn.ReLU -Storage id: 58 -Size: {1, 128, 17, 17} -Mem size: 36992 - - -n82->n83 - - - - -n84 - -cudnn.SpatialConvolution(128 -> 160, 1x7, 1,1, 0,3) without bias -Storage id: 59 -Size: {1, 160, 17, 17} -Mem size: 46240 - - -n83->n84 - - - - -n85 - -nn.SpatialBatchNormalization -Storage id: 60 -Size: {1, 160, 17, 17} -Mem size: 46240 - - -n84->n85 - - - - -n86 - -cudnn.ReLU -Storage id: 60 -Size: {1, 160, 17, 17} -Mem size: 46240 - - -n85->n86 - - - - -n87 - -cudnn.SpatialConvolution(160 -> 192, 7x1, 1,1, 3,0) without bias -Storage id: 61 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n86->n87 - - - - -n88 - -nn.SpatialBatchNormalization -Storage id: 62 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n87->n88 - - - - -n89 - -cudnn.ReLU -Storage id: 62 -Size: {1, 192, 17, 17} -Mem size: 55488 - - -n88->n89 - - - - -n89->n90 - - - - -n91 - -cudnn.SpatialConvolution(384 -> 1152, 1x1) without bias -Storage id: 64 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n90->n91 - - - - -n92 - -nn.SpatialBatchNormalization -Storage id: 65 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n91->n92 - - - - -n93 - -nn.MulConstant -Storage id: 66 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n92->n93 - - - - -n93->n94 - - - - -n95 - -cudnn.ReLU -Storage id: 54 -Size: {1, 1152, 17, 17} -Mem size: 332928 - - -n94->n95 - - - - -n96 - -nn.SpatialMaxPooling(3x3, 2,2) -Storage id: 67 -Size: {1, 1152, 8, 8} -Mem size: 73728 - - -n95->n96 - - - - -n97 - -cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias -Storage id: 68 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n95->n97 - - - - -n103 - -cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias -Storage id: 72 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n95->n103 - - - - -n109 - -cudnn.SpatialConvolution(1152 -> 256, 1x1) without bias -Storage id: 76 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n95->n109 - - - - -n118 - -nn.JoinTable -Storage id: 82 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n96->n118 - - - - -n98 - -nn.SpatialBatchNormalization -Storage id: 69 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n97->n98 - - - - -n99 - -cudnn.ReLU -Storage id: 69 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n98->n99 - - - - -n100 - -cudnn.SpatialConvolution(256 -> 384, 3x3, 2,2) without bias -Storage id: 70 -Size: {1, 384, 8, 8} -Mem size: 24576 - - -n99->n100 - - - - -n101 - -nn.SpatialBatchNormalization -Storage id: 71 -Size: {1, 384, 8, 8} -Mem size: 24576 - - -n100->n101 - - - - -n102 - -cudnn.ReLU -Storage id: 71 -Size: {1, 384, 8, 8} -Mem size: 24576 - - -n101->n102 - - - - -n102->n118 - - - - -n104 - -nn.SpatialBatchNormalization -Storage id: 73 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n103->n104 - - - - -n105 - -cudnn.ReLU -Storage id: 73 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n104->n105 - - - - -n106 - -cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias -Storage id: 74 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n105->n106 - - - - -n107 - -nn.SpatialBatchNormalization -Storage id: 75 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n106->n107 - - - - -n108 - -cudnn.ReLU -Storage id: 75 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n107->n108 - - - - -n108->n118 - - - - -n110 - -nn.SpatialBatchNormalization -Storage id: 77 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n109->n110 - - - - -n111 - -cudnn.ReLU -Storage id: 77 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n110->n111 - - - - -n112 - -cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) without bias -Storage id: 78 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n111->n112 - - - - -n113 - -nn.SpatialBatchNormalization -Storage id: 79 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n112->n113 - - - - -n114 - -cudnn.ReLU -Storage id: 79 -Size: {1, 256, 17, 17} -Mem size: 73984 - - -n113->n114 - - - - -n115 - -cudnn.SpatialConvolution(256 -> 256, 3x3, 2,2) without bias -Storage id: 80 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n114->n115 - - - - -n116 - -nn.SpatialBatchNormalization -Storage id: 81 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n115->n116 - - - - -n117 - -cudnn.ReLU -Storage id: 81 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n116->n117 - - - - -n117->n118 - - - - -n119 - -cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias -Storage id: 83 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n118->n119 - - - - -n122 - -cudnn.SpatialConvolution(2048 -> 192, 1x1) without bias -Storage id: 85 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n118->n122 - - - - -n135 - -nn.CAddTable -Storage id: 82 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n118->n135 - - - - -n141 - -cudnn.SpatialAveragePooling(5x5, 3,3) -Storage id: 98 -Size: {1, 2048, 2, 2} -Mem size: 8192 - - -n118->n141 - - - - -n120 - -nn.SpatialBatchNormalization -Storage id: 84 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n119->n120 - - - - -n121 - -cudnn.ReLU -Storage id: 84 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n120->n121 - - - - -n131 - -nn.JoinTable -Storage id: 91 -Size: {1, 448, 8, 8} -Mem size: 28672 - - -n121->n131 - - - - -n123 - -nn.SpatialBatchNormalization -Storage id: 86 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n122->n123 - - - - -n124 - -cudnn.ReLU -Storage id: 86 -Size: {1, 192, 8, 8} -Mem size: 12288 - - -n123->n124 - - - - -n125 - -cudnn.SpatialConvolution(192 -> 224, 1x3, 1,1, 0,1) without bias -Storage id: 87 -Size: {1, 224, 8, 8} -Mem size: 14336 - - -n124->n125 - - - - -n126 - -nn.SpatialBatchNormalization -Storage id: 88 -Size: {1, 224, 8, 8} -Mem size: 14336 - - -n125->n126 - - - - -n127 - -cudnn.ReLU -Storage id: 88 -Size: {1, 224, 8, 8} -Mem size: 14336 - - -n126->n127 - - - - -n128 - -cudnn.SpatialConvolution(224 -> 256, 3x1, 1,1, 1,0) without bias -Storage id: 89 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n127->n128 - - - - -n129 - -nn.SpatialBatchNormalization -Storage id: 90 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n128->n129 - - - - -n130 - -cudnn.ReLU -Storage id: 90 -Size: {1, 256, 8, 8} -Mem size: 16384 - - -n129->n130 - - - - -n130->n131 - - - - -n132 - -cudnn.SpatialConvolution(448 -> 2048, 1x1) without bias -Storage id: 92 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n131->n132 - - - - -n133 - -nn.SpatialBatchNormalization -Storage id: 93 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n132->n133 - - - - -n134 - -nn.MulConstant -Storage id: 94 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n133->n134 - - - - -n134->n135 - - - - -n136 - -cudnn.ReLU -Storage id: 82 -Size: {1, 2048, 8, 8} -Mem size: 131072 - - -n135->n136 - - - - -n137 - -cudnn.SpatialAveragePooling(8x8, 1,1) -Storage id: 95 -Size: {1, 2048, 1, 1} -Mem size: 2048 - - -n136->n137 - - - - -n138 - -nn.View(2048) -Storage id: 95 -Size: {1, 2048} -Mem size: 2048 - - -n137->n138 - - - - -n139 - -nn.Dropout(0.200000) -Storage id: 96 -Size: {1, 2048} -Mem size: 2048 - - -n138->n139 - - - - -n140 - -nn.Linear(2048 -> 1003) -Storage id: 97 -Size: {1, 1003} -Mem size: 1003 - - -n139->n140 - - - - -n142 - -cudnn.SpatialConvolution(2048 -> 256, 1x1) without bias -Storage id: 99 -Size: {1, 256, 2, 2} -Mem size: 1024 - - -n141->n142 - - - - -n143 - -nn.SpatialBatchNormalization -Storage id: 100 -Size: {1, 256, 2, 2} -Mem size: 1024 - - -n142->n143 - - - - -n144 - -cudnn.ReLU -Storage id: 100 -Size: {1, 256, 2, 2} -Mem size: 1024 - - -n143->n144 - - - - -n145 - -nn.View(1024) -Storage id: 100 -Size: {1, 1024} -Mem size: 1024 - - -n144->n145 - - - - -n146 - -nn.Linear(1024 -> 1024) without bias -Storage id: 101 -Size: {1, 1024} -Mem size: 1024 - - -n145->n146 - - - - -n147 - -nn.BatchNormalization -Storage id: 102 -Size: {1, 1024} -Mem size: 1024 - - -n146->n147 - - - - -n148 - -nn.ReLU -Storage id: 103 -Size: {1, 1024} -Mem size: 1024 - - -n147->n148 - - - - -n149 - -nn.Linear(1024 -> 1002) -Storage id: 104 -Size: {1, 1002} -Mem size: 1002 - - -n148->n149 - - - - -n151 - -cudnn.SpatialConvolution(1152 -> 128, 1x1) without bias -Storage id: 106 -Size: {1, 128, 4, 4} -Mem size: 2048 - - -n150->n151 - - - - -n152 - -nn.SpatialBatchNormalization -Storage id: 107 -Size: {1, 128, 4, 4} -Mem size: 2048 - - -n151->n152 - - - - -n153 - -cudnn.ReLU -Storage id: 107 -Size: {1, 128, 4, 4} -Mem size: 2048 - - -n152->n153 - - - - -n154 - -nn.View(2048) -Storage id: 107 -Size: {1, 2048} -Mem size: 2048 - - -n153->n154 - - - - -n155 - -nn.Linear(2048 -> 1024) without bias -Storage id: 108 -Size: {1, 1024} -Mem size: 1024 - - -n154->n155 - - - - -n156 - -nn.BatchNormalization -Storage id: 109 -Size: {1, 1024} -Mem size: 1024 - - -n155->n156 - - - - -n157 - -nn.ReLU -Storage id: 110 -Size: {1, 1024} -Mem size: 1024 - - -n156->n157 - - - - -n158 - -nn.Linear(1024 -> 1001) -Storage id: 111 -Size: {1, 1001} -Mem size: 1001 - - -n157->n158 - - - - - diff --git a/main.sh b/main.sh index d567621cb..b49dca1e3 100755 --- a/main.sh +++ b/main.sh @@ -4,6 +4,6 @@ filename=$(date +"%Y:%m:%d-%H:%M:%S:%N")".log" #th main.lua -depth 50 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/ -th main.lua -netType inceptionv4 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inceptionv4/ 2>&1 | tee /media/data0/cache/log/$filename +th main.lua -netType inception-resnet-v2 -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inception-resnet-v2/ 2>&1 | tee /media/data0/cache/log/$filename -#th main.lua -netType inceptionv4aux -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inceptionv4/ +#th main.lua -netType inception-resnet-v2-aux -batchSize 64 -nGPU 2 -nThreads 16 -shareGradInput true -data /media/data1/image/ilsvrc15/ILSVRC2015/Data/CLS-LOC/ -resume /media/data0/cache/inception-resnet-v2/ diff --git a/models/inceptionv4aux.lua b/models/inception-resnet-v2-aux.lua similarity index 100% rename from models/inceptionv4aux.lua rename to models/inception-resnet-v2-aux.lua diff --git a/models/inceptionv4.lua b/models/inception-resnet-v2.lua similarity index 100% rename from models/inceptionv4.lua rename to models/inception-resnet-v2.lua diff --git a/models/init.lua b/models/init.lua index 03dbf0292..3171679ac 100644 --- a/models/init.lua +++ b/models/init.lua @@ -111,7 +111,7 @@ function M.setup(opt, checkpoint) end local criterion - if opt.netType == 'inceptionv4aux' then + if opt.netType == 'inception-resnet-v2-aux' then local CE = nn.CrossEntropyCriterion() criterion = nn.ParallelCriterion(true):add(CE):add(CE,0.3):add(CE,0.3):cuda() else diff --git a/opts.lua b/opts.lua index 34c6ead64..c7ba12e5f 100644 --- a/opts.lua +++ b/opts.lua @@ -39,7 +39,7 @@ function M.parse(arg) cmd:option('-gamma', 0.96, 'gamma for learning rate policy (step)') cmd:option('-step', 6400, 'step for learning rate policy (step)') ---------- Model options ---------------------------------- - cmd:option('-netType', 'resnet', 'Options: resnet | preresnet | inceptionv4 | inceptionv4aux') + cmd:option('-netType', 'resnet', 'Options: resnet | preresnet | inception-resnet-v2 | inception-resnet-v2-aux') cmd:option('-depth', 34, 'ResNet depth: 18 | 34 | 50 | 101 | ...', 'number') cmd:option('-shortcutType', '', 'Options: A | B | C') cmd:option('-retrain', 'none', 'Path to model to retrain with') @@ -57,7 +57,7 @@ function M.parse(arg) opt.shareGradInput = opt.shareGradInput ~= 'false' opt.resetClassifier = opt.resetClassifier ~= 'false' - if opt.netType == 'inceptionv4' or opt.netType == 'inceptionv4aux' then + if opt.netType == 'inception-resnet-v2' or opt.netType == 'inception-resnet-v2-aux' then opt.momentum = 0.4737 opt.LR = 0.045 --45 opt.step = 12800 From 5b02c42406855192ebd45d8c2740ab90b4d5ac7e Mon Sep 17 00:00:00 2001 From: Jaehyun Lim Date: Thu, 9 Jun 2016 20:26:45 +0900 Subject: [PATCH 3/5] delete inception-resnet-v2-aux.lua since it is not tested yet --- models/inception-resnet-v2-aux.lua | 399 ----------------------------- 1 file changed, 399 deletions(-) delete mode 100644 models/inception-resnet-v2-aux.lua diff --git a/models/inception-resnet-v2-aux.lua b/models/inception-resnet-v2-aux.lua deleted file mode 100644 index c93ac371f..000000000 --- a/models/inception-resnet-v2-aux.lua +++ /dev/null @@ -1,399 +0,0 @@ - -local nn = require 'nn' -require 'cunn' -local cudnn = require 'cudnn' - -local Convolution = cudnn.SpatialConvolution -local Avg = cudnn.SpatialAveragePooling -local ReLU = cudnn.ReLU -local Max = nn.SpatialMaxPooling -local SBatchNorm = nn.SpatialBatchNormalization -local BatchNorm = nn.BatchNormalization - -local function addConv(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) - assert(not (nInputPlane == nil)) - assert(not (nOutputPlane == nil)) - assert(not (kh == nil)) - assert(not (kw == nil)) - local sh = sh or 1 - local sw = sw or 1 - local ph = ph or 0 - local pw = pw or 0 - - local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) - :add(SBatchNorm(nOutputPlane)) - :add(ReLU(true)) - return layer -end - -local function addConvLinear(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw) -- addConv without ReLU - assert(not (nInputPlane == nil)) - assert(not (nOutputPlane == nil)) - assert(not (kh == nil)) - assert(not (kw == nil)) - local sh = sh or 1 - local sw = sw or 1 - local ph = ph or 0 - local pw = pw or 0 - - local layer = nn.Sequential():add(Convolution(nInputPlane, nOutputPlane, kh, kw, sh, sw, ph, pw)) - :add(SBatchNorm(nOutputPlane)) - return layer -end - -local function Stem(fs_start) -- fig 3 - local fs0 = {32, 32, 64}; fs0[0] = fs_start - local net = nn.Sequential() - ------------------- nInputPlane, nOutputPlane, k, k, s, s, p, p - net:add(addConv(fs0[0], fs0[1], 3, 3, 2, 2, 0, 0)) - net:add(addConv(fs0[1], fs0[2], 3, 3, 1, 1, 0, 0)) - net:add(addConv(fs0[2], fs0[3], 3, 3, 1, 1, 1, 1)) - - local fs1a = {}; fs1a[0] = fs0[#fs0] - local fs1b = {96}; fs1b[0] = fs0[#fs0] - local concat1 = nn.ConcatTable() - concat1:add(Max(3, 3, 2, 2, 0, 0)) - concat1:add(addConv(fs1b[0], fs1b[1], 3, 3, 2, 2, 0, 0)) - - net:add(concat1) - net:add(nn.JoinTable(2, 4)) - - local fs2a = {64, 96}; fs2a[0] = fs1a[#fs1a] + fs1b[#fs1b] - local fs2b = {64, 64, 64, 96}; fs2b[0] = fs1a[#fs1a] + fs1b[#fs1b] - local concat2 = nn.ConcatTable() - concat2:add(nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2a[1], fs2a[2], 3, 3, 1, 1, 0, 0)) - ) - concat2:add(nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2b[1], fs2b[2], 7, 1, 1, 1, 3, 0)) - :add(addConv(fs2b[2], fs2b[3], 1, 7, 1, 1, 0, 3)) - :add(addConv(fs2b[3], fs2b[4], 3, 3, 1, 1, 0, 0)) - ) - net:add(concat2) - net:add(nn.JoinTable(2, 4)) - - local fs3a = {192}; fs3a[0] = fs2a[#fs2a] + fs2b[#fs2b] - local fs3b = {}; fs3b[0] = fs2a[#fs2a] + fs2b[#fs2b] - local concat3 = nn.ConcatTable() - concat3:add(addConv(fs3a[0], fs3a[1], 3, 3, 2, 2, 0, 0)) - :add(Max(3, 3, 2, 2, 0, 0)) - net:add(concat3) - net:add(nn.JoinTable(2, 4)) - - local fs_final = fs3a[#fs3a] + fs3b[#fs3b] - return net, fs_final -end - -local function InceptionResnetA(fs_start) -- fig 16 - local path1 = nn.Identity() - - local fs2a = {32}; fs2a[0] = fs_start - local fs2b = {32, 32}; fs2b[0] = fs_start - local fs2c = {32, 48, 64}; fs2c[0] = fs_start - local fs2 = {fs_start}; --local fs2 = {384}; - fs2[0] = fs2a[#fs2a] + fs2b[#fs2b] + fs2c[#fs2c] - local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) - local path2b = nn.Sequential():add(addConv(fs2b[0], fs2b[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2b[1], fs2b[2], 3, 3, 1, 1, 1, 1)) - local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2c[1], fs2c[2], 3, 3, 1, 1, 1, 1)) - :add(addConv(fs2c[2], fs2c[3], 3, 3, 1, 1, 1, 1)) - local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) - :add(path2b) - :add(path2c) - ) - :add(nn.JoinTable(2, 4)) - :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) - :add(nn.MulConstant(0.1)) - - local net = nn.Sequential() - net:add(nn.ConcatTable():add(path1) - :add(path2) - ) - net:add(nn.CAddTable(true)) - net:add(ReLU(true)) - - local fs_final = fs2[#fs2] - assert(fs_final == fs_start) - - return net, fs_final -end - -local function ReductionA(fs_start, k, l, m, n) -- fig 7 - local net = nn.Sequential() - - local concat = nn.ConcatTable() - concat:add(Max(3, 3, 2, 2, 0, 0)) -- path1 - concat:add(addConv(fs_start, n, 3, 3, 2, 2, 0, 0)) -- path2 - concat:add(nn.Sequential():add(addConv(fs_start, k, 1, 1, 1, 1, 0, 0)) -- path3 - :add(addConv(k, l, 3, 3, 1, 1, 1, 1)) - :add(addConv(l, m, 3, 3, 2, 2, 0, 0))) - net:add(concat) - net:add(nn.JoinTable(2, 4)) - - local fs_final = fs_start + n + m - - return net, fs_final -end - -local function InceptionResnetB(fs_start) -- fig 17 - local path1 = nn.Identity() - - local fs2a = {192}; fs2a[0] = fs_start - local fs2c = {128, 160, 192}; fs2c[0] = fs_start - local fs2 = {fs_start}; --local fs2 = {1152}; - fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] - local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) - local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2c[1], fs2c[2], 1, 7, 1, 1, 0, 3)) - :add(addConv(fs2c[2], fs2c[3], 7, 1, 1, 1, 3, 0)) - local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) - :add(path2c) - ) - :add(nn.JoinTable(2, 4)) - :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) - :add(nn.MulConstant(0.1)) - - local net = nn.Sequential() - net:add(nn.ConcatTable():add(path1) - :add(path2) - ) - net:add(nn.CAddTable(true)) - net:add(ReLU(true)) - - local fs_final = fs2[#fs2] - assert(fs_final == fs_start) - - return net, fs_final -end - -local function ReductionB(fs_start) -- fig 18 - local path1 = Max(3, 3, 2, 2, 0, 0) - local fs2 = {256, 384}; fs2[0] = fs_start - local path2 = nn.Sequential():add(addConv(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2[1], fs2[2], 3, 3, 2, 2, 0, 0)) - --local fs3 = {256, 288}; fs3[0] = fs_start -- in the paper, but seems to have typo - local fs3 = {256, 256}; fs3[0] = fs_start - local path3 = nn.Sequential():add(addConv(fs3[0], fs3[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs3[1], fs3[2], 3, 3, 2, 2, 0, 0)) - --local fs4 = {256, 288, 320}; fs4[0] = fs_start -- in the paper, but seems to have typo - local fs4 = {256, 256, 256}; fs4[0] = fs_start - local path4 = nn.Sequential():add(addConv(fs4[0], fs4[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs4[1], fs4[2], 3, 3, 1, 1, 1, 1)) - :add(addConv(fs4[2], fs4[3], 3, 3, 2, 2, 0, 0)) - - local concat = nn.ConcatTable() - concat:add(path1) - concat:add(path2) - concat:add(path3) - concat:add(path4) - - local net = nn.Sequential() - net:add(concat) - net:add(nn.JoinTable(2,4)) - - local fs_final = fs_start + fs2[#fs2] + fs3[#fs3] + fs4[#fs4] - - return net, fs_final -end - -local function InceptionResnetC(fs_start) -- fig 19 - local path1 = nn.Identity() - - local fs2a = {192}; fs2a[0] = fs_start - local fs2c = {192, 224, 256}; fs2c[0] = fs_start - local fs2 = {fs_start}; --local fs2 = {2048}; - fs2[0] = fs2a[#fs2a] + fs2c[#fs2c] - local path2a = nn.Sequential():add(addConv(fs2a[0], fs2a[1], 1, 1, 1, 1, 0, 0)) - local path2c = nn.Sequential():add(addConv(fs2c[0], fs2c[1], 1, 1, 1, 1, 0, 0)) - :add(addConv(fs2c[1], fs2c[2], 1, 3, 1, 1, 0, 1)) - :add(addConv(fs2c[2], fs2c[3], 3, 1, 1, 1, 1, 0)) - local path2 = nn.Sequential():add(nn.ConcatTable():add(path2a) - :add(path2c) - ) - :add(nn.JoinTable(2, 4)) - :add(addConvLinear(fs2[0], fs2[1], 1, 1, 1, 1, 0, 0)) - :add(nn.MulConstant(0.1)) - - local net = nn.Sequential() - net:add(nn.ConcatTable():add(path1) - :add(path2) - ) - net:add(nn.CAddTable(true)) - net:add(ReLU(true)) - - local fs_final = fs2[#fs2] - assert(fs_final == fs_start) - - return net, fs_final -end - -local function AuxClassifier1(fs_start, nClasses) - fs = {128, 128*4*4, 1024}; fs[0] = fs_start - - local model = nn.Sequential() - model:add(Avg(5,5,4,4):ceil()) - model:add(addConv(fs[0], fs[1], 1, 1, 1, 1, 0, 0)) - model:add(nn.View(fs[2]):setNumInputDims(3)) - - local lin = nn.Linear(fs[2], fs[3]) - if cudnn.version >= 4000 then - lin.bias = nil - lin.gradBias = nil - else - lin.bias:zero() - end - model:add(lin) - model:add(BatchNorm(fs[3])) - model:add(nn.ReLU()) - model:add(nn.Linear(fs[3], nClasses)) - return model -end - -local function AuxClassifier2(fs_start, nClasses) - fs = {256, 256*2*2, 1024}; fs[0] = fs_start - - local model = nn.Sequential() - model:add(Avg(5,5,3,3):ceil()) - model:add(addConv(fs[0], fs[1], 1, 1, 1, 1, 0, 0)) - model:add(nn.View(fs[2]):setNumInputDims(3)) - - local lin = nn.Linear(fs[2], fs[3]) - if cudnn.version >= 4000 then - lin.bias = nil - lin.gradBias = nil - else - lin.bias:zero() - end - model:add(lin) - model:add(BatchNorm(fs[3])) - model:add(nn.ReLU()) - model:add(nn.Linear(fs[3], nClasses)) - return model -end - -local function Classifier(fs_start, nClasses) - local nFeatures = fs_start -- set final channels - local model = nn.Sequential() - - -- Add Average Pooling - model:add(Avg(8, 8, 1, 1)) - model:add(nn.View(nFeatures):setNumInputDims(3)) - - -- Add Dropout (keep 0.8) - model:add(nn.Dropout(0.2)) - - -- Add Classifier - model:add(nn.Linear(nFeatures, nClasses)) - - return model -end - -local function createModel(opt) - local nClasses = 1000 - - local feature = nn.Sequential() - -- Add Stem module - local stem, fs = Stem(3) - feature:add(stem) - - -- Add Inception-resnet-A modules (x5) - for i = 1, 5 do - local irA, fs = InceptionResnetA(fs) - feature:add(irA) - end - - -- Add Reduction-A module - local rA, fs = ReductionA(fs, 256, 256, 384, 384) - feature:add(rA) - - -- Add auxiliary classifier - local auxcls1 = AuxClassifier1(fs, nClasses) - - local path2 = nn.Sequential() - -- Add Inception-resnet-B modules (x10) - for i = 1, 10 do - local irB, fs = InceptionResnetB(fs) - path2:add(irB) - end - - -- Add Reduction-B module - local rB, fs = ReductionB(fs) - path2:add(rB) - - -- Add auxiliary classifier - local auxcls2 = AuxClassifier2(fs, nClasses) - - local path3 = nn.Sequential() - -- Add Inception-resnet-C modules (x5) - for i = 1, 5 do - local irC, fs = InceptionResnetC(fs) - path3:add(irC) - end - - -- Add final classifier - local cls = Classifier(fs, nClasses) - path3:add(cls) - - -- first branch - local concat1 = nn.ConcatTable() - local concat2 = nn.ConcatTable() - - concat2:add(path3) - :add(auxcls2) - path2:add(concat2) - concat1:add(path2) - :add(auxcls1) - - local model = nn.Sequential() - model:add(feature) - :add(concat1) - :add(nn.FlattenTable()) - - -- Init - local function ConvInit(name) - for k,v in pairs(model:findModules(name)) do - local n = v.kW*v.kH*v.nOutputPlane - v.weight:normal(0,math.sqrt(2/n)) - if cudnn.version >= 4000 then - v.bias = nil - v.gradBias = nil - else - v.bias:zero() - end - end - end - local function BNInit(name) - for k,v in pairs(model:findModules(name)) do - v.weight:fill(1) - v.bias:zero() - end - end - - ConvInit('cudnn.SpatialConvolution') - ConvInit('nn.SpatialConvolution') - BNInit('fbnn.SpatialBatchNormalization') - BNInit('cudnn.SpatialBatchNormalization') - BNInit('nn.SpatialBatchNormalization') - BNInit('nn.BatchNormalization') - for k,v in pairs(model:findModules('nn.Linear')) do - if v.bias then - v.bias:zero() - end - end - - -- Convert to cuda - model:cuda() - - if opt.cudnn == 'deterministic' then - model:apply(function(m) - if m.setMode then m:setMode(1,1,1) end - end) - end - - model:get(1).gradInput = nil - - return model -end - -return createModel From 2b448e7f36708f02766f546cad3ad0d3f5d01bbe Mon Sep 17 00:00:00 2001 From: Jaehyun Lim Date: Thu, 4 Aug 2016 14:38:37 +0900 Subject: [PATCH 4/5] minor updates on opts.lua --- opts.lua | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/opts.lua b/opts.lua index c7ba12e5f..2e9e2df47 100644 --- a/opts.lua +++ b/opts.lua @@ -58,7 +58,7 @@ function M.parse(arg) opt.resetClassifier = opt.resetClassifier ~= 'false' if opt.netType == 'inception-resnet-v2' or opt.netType == 'inception-resnet-v2-aux' then - opt.momentum = 0.4737 + --opt.momentum = 0.4737 opt.LR = 0.045 --45 opt.step = 12800 end From e1e4a1f7174e5d58840d588df997cbbd198ffdf4 Mon Sep 17 00:00:00 2001 From: Jaehyun Lim Date: Thu, 4 Aug 2016 14:42:06 +0900 Subject: [PATCH 5/5] fixed a critical bug, changing from CAddTable(true) to CAddTable() in all layers in inception-resnet-v2 --- models/inception-resnet-v2.lua | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/models/inception-resnet-v2.lua b/models/inception-resnet-v2.lua index 80b1958cf..6c83402b6 100644 --- a/models/inception-resnet-v2.lua +++ b/models/inception-resnet-v2.lua @@ -109,7 +109,7 @@ local function InceptionResnetA(fs_start) -- fig 16 net:add(nn.ConcatTable():add(path1) :add(path2) ) - net:add(nn.CAddTable(true)) + net:add(nn.CAddTable()) net:add(ReLU(true)) local fs_final = fs2[#fs2] @@ -157,7 +157,7 @@ local function InceptionResnetB(fs_start) -- fig 17 net:add(nn.ConcatTable():add(path1) :add(path2) ) - net:add(nn.CAddTable(true)) + net:add(nn.CAddTable()) net:add(ReLU(true)) local fs_final = fs2[#fs2] @@ -218,7 +218,7 @@ local function InceptionResnetC(fs_start) -- fig 19 net:add(nn.ConcatTable():add(path1) :add(path2) ) - net:add(nn.CAddTable(true)) + net:add(nn.CAddTable()) net:add(ReLU(true)) local fs_final = fs2[#fs2]