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main_adaptiveCS_time_benchmark.py
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from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import argparse
import cv2
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision.utils as vutils
import os
from torchvision import datasets, transforms
from torch.autograd import Variable
from numpy.random import randn
from torch.nn import init
import string, copy
import skimage.io as sio
import time
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--model', help='basic | adaptiveCS | adaptiveCS_resnet',
default='adaptiveCS_resnet_wy_ifusion_ufirst') # adaptiveCS_resnet_wy_lfusion_ufirst
parser.add_argument('--dataset', help='lsun | imagenet | mnist | bsd500 | bsd500_patch', default='cifar10')
parser.add_argument('--datapath', help='path to dataset', default='/home/user/kaixu/myGitHub/CSImageNet/data/')
parser.add_argument('--batch-size', type=int, default=1, metavar='N',
help='input batch size for training (default: 1)')
parser.add_argument('--image-size', type=int, default=64, metavar='N',
help='The height / width of the input image to the network')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=2e-4, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--cuda', action='store_true', default=True,
help='enable CUDA training')
parser.add_argument('--ngpu', type=int, default=1,
help='number of GPUs to use')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--layers-gan', type=int, default=3, metavar='N',
help='number of hierarchies in the GAN (default: 64)')
parser.add_argument('--gpu', type=int, default=0, metavar='N',
help='which GPU do you want to use (default: 1)')
parser.add_argument('--outf', default='./results', help='folder to output images and model checkpoints')
parser.add_argument('--w-loss', type=float, default=0.01, metavar='N.',
help='penalty for the mse and bce loss')
parser.add_argument('--stage', type=int, default=1, help='the stage under training')
parser.add_argument('--transfer', action='store_true', default=False, help='transfer weights')
parser.add_argument('--cr', type=int, default=30, help='compression ratio')
parser.add_argument('--Set', type=int, default=0, metavar='N',
help='Set 5 or Set 14')
opt = parser.parse_args()
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: please run with GPU")
print(opt)
if 'woy' in opt.model:
import models.lapgan_adaptiveCS_resnet_woy as lapgan
elif 'lfusion' in opt.model:
import models.lapgan_adaptiveCS_latefusion as lapgan
# elif 'adaptiveCS_resnet' in opt.model:
elif 'mnist' in opt.dataset:
import models.lapgan_adaptiveCS_resnet_mnist as lapgan
elif 'bsd500_patch' in opt.dataset:
import models.lapgan_adaptiveCS_resnet_bsd500 as lapgan
else:
import models.lapgan_adaptiveCS_resnet as lapgan
# else:
# import models.lapgan_adaptiveCS as lapgan
torch.cuda.set_device(opt.gpu)
print('Current gpu device: gpu %d' % (torch.cuda.current_device()))
if opt.seed is None:
opt.seed = np.random.randint(1, 10000)
print('Random seed: ', opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.cuda:
torch.cuda.manual_seed(opt.seed)
criterion_mse = nn.MSELoss()
cudnn.benchmark = True
if not os.path.exists('%s/%s/cr%s/%s/test' % (opt.outf, opt.dataset, opt.cr, opt.model)):
os.makedirs('%s/%s/cr%s/%s/test' % (opt.outf, opt.dataset, opt.cr, opt.model))
def data_loader():
kwopt = {'num_workers': 1, 'pin_memory': True} if opt.cuda else {}
if opt.dataset == 'bsd500_patch':
test_dataset = datasets.ImageFolder(root=opt.datapath + 'val_64x64',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
if opt.Set == 5:
test_dataset = datasets.ImageFolder(root=opt.datapath + 'test_set5_64x64',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.Set == 14:
test_dataset = datasets.ImageFolder(root=opt.datapath + 'test_set14_64x64',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'cifar10':
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'mnist':
test_dataset = datasets.MNIST('./data', train=False,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, **kwopt)
return test_loader
def evaluation(testloader):
# Initialize variables
input, _ = testloader.__iter__().__next__()
input = input.numpy()
sz_input = input.shape
cr1 = 8 * opt.cr
cr2 = 4 * opt.cr
cr3 = 2 * opt.cr
cr4 = opt.cr
channels = sz_input[1]
n = sz_input[2] ** 2
m1 = n / cr1
m2 = n / cr2
m3 = n / cr3
m4 = n / cr4
img_size1 = sz_input[3] / 8
img_size2 = sz_input[3] / 4
img_size3 = sz_input[3] / 2
img_size4 = sz_input[3]
sensing_matrix4 = np.load('sensing_matrix_cr' + str(opt.cr) + '.npy')
# sensing_matrix4 = randn(channels, m4, n)
sensing_matrix3 = sensing_matrix4[:, :m3, :]
sensing_matrix2 = sensing_matrix4[:, :m2, :]
sensing_matrix1 = sensing_matrix4[:, :m1, :]
g1_input = torch.FloatTensor(opt.batch_size, channels, m1)
g2_input = torch.FloatTensor(opt.batch_size, channels, m2)
g3_input = torch.FloatTensor(opt.batch_size, channels, m3)
g4_input = torch.FloatTensor(opt.batch_size, channels, m4)
g1_target = torch.FloatTensor(opt.batch_size, channels, img_size1, img_size1)
g2_target = torch.FloatTensor(opt.batch_size, channels, img_size2, img_size2)
g3_target = torch.FloatTensor(opt.batch_size, channels, img_size3, img_size3)
g4_target = torch.FloatTensor(opt.batch_size, channels, img_size4, img_size4)
y2 = torch.FloatTensor(opt.batch_size, channels, m2)
y3 = torch.FloatTensor(opt.batch_size, channels, m3)
y4 = torch.FloatTensor(opt.batch_size, channels, m4)
# Instantiate models
lapnet1_gen = lapgan.LAPGAN_Generator_level1(channels, channels * m1, opt.ngpu)
lapnet2_gen = lapgan.LAPGAN_Generator_level2(channels, channels * m2, opt.ngpu)
lapnet3_gen = lapgan.LAPGAN_Generator_level3(channels, channels * m3, opt.ngpu)
lapnet4_gen = lapgan.LAPGAN_Generator_level4(channels, channels * m4, opt.ngpu)
if opt.cuda:
lapnet1_gen.cuda()
lapnet2_gen.cuda()
lapnet3_gen.cuda()
lapnet4_gen.cuda()
criterion_mse.cuda()
g1_input, g2_input, g3_input, g4_input = g1_input.cuda(), g2_input.cuda(), g3_input.cuda(), g4_input.cuda()
g1_target, g2_target, g3_target, g4_target = g1_target.cuda(), g2_target.cuda(), g3_target.cuda(), g4_target.cuda()
y2, y3, y4 = y2.cuda(), y3.cuda(), y4.cuda()
if opt.dataset == 'bsd500_patch':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 12 # 0.0260
level2_iter = 17 # 0.0153
level3_iter = 10 # 0.0096 modify
level4_iter = 11 # 0.0053 modify
elif opt.cr == 10:
level1_iter = 12 # 0.0350 18 # 14
level2_iter = 12 # 0.0225 16 # 27
level3_iter = 20 # 0.0148 8 # 27
level4_iter = 7 # 0.0093
elif opt.cr == 20:
level1_iter = 6 # 0.0447 3
level2_iter = 9 # 0.0322 9
level3_iter = 16 # 0.0215
level4_iter = 6 # 0.0144
elif opt.cr == 30:
level1_iter = 7 # 0.0550
level2_iter = 7 # 0.0386
level3_iter = 12 # 0.0283
level4_iter = 7 # 0.0190
if opt.dataset == 'mnist':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 80 # 0.0070 18 # 14
level2_iter = 36 # 0.0059 16 # 27
level3_iter = 97 # 0.0025 8 # 27
level4_iter = 6 # 0.0022
elif opt.cr == 10:
level1_iter = 53 # 0.0206 18 # 14
level2_iter = 43 # 0.0100 16 # 27
level3_iter = 95 # 0.0113 8 # 27
level4_iter = 10 # 0.0027
elif opt.cr == 20:
level1_iter = 38 # 0.0475 18 # 14
level2_iter = 43 # 0.0219 16 # 27
level3_iter = 23 # 0.0148 8 # 27
level4_iter = 21 # 0.0043
elif opt.cr == 30:
level1_iter = 34 # 0.0740 18 # 14
level2_iter = 55 # 0.0365 16 # 27
level3_iter = 98 # 0.0242 8 # 27
level4_iter = 28 # 0.0061
if opt.dataset == 'cifar10':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 28 # 0.0126
level2_iter = 81 # 0.0039
level3_iter = 21 # 0.0025
level4_iter = 26 # 0.0008
elif opt.cr == 10:
level1_iter = 24 # 0.0242
level2_iter = 33 # 0.0102
level3_iter = 58 # 0.0045
level4_iter = 21 # 0.0017
elif opt.cr == 20:
level1_iter = 19 # 0.0420
level2_iter = 30 # 0.0221
level3_iter = 44 # 0.0111
level4_iter = 21 # 0.0043
elif opt.cr == 30:
level1_iter = 16 # 0.0532
level2_iter = 34 # 0.0317
level3_iter = 84 # 0.0174
level4_iter = 16 # 0.0078
elif 'wy' in opt.model and 'lfusion' in opt.model:
if opt.cr == 10:
level1_iter = 18
level2_iter = 16
level3_iter = 22
level4_iter = 99
elif 'woy' in opt.model:
if opt.cr == 10:
level1_iter = 18
level2_iter = 7
level3_iter = 5
level4_iter = 1
if opt.stage == 5:
stage1_path = '%s/%s/cr%s/%s/stage5/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, level1_iter)
stage2_path = '%s/%s/cr%s/%s/stage5/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, level2_iter)
stage3_path = '%s/%s/cr%s/%s/stage5/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, level3_iter)
stage4_path = '%s/%s/cr%s/%s/stage5/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, level4_iter)
else:
stage1_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 1, level1_iter)
stage2_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 2, level2_iter)
stage3_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 3, level3_iter)
stage4_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 4, level4_iter)
lapnet1_gen.load_state_dict(torch.load(stage1_path))
lapnet2_gen.load_state_dict(torch.load(stage2_path))
lapnet3_gen.load_state_dict(torch.load(stage3_path))
lapnet4_gen.load_state_dict(torch.load(stage4_path))
lapnet1_gen.eval()
lapnet2_gen.eval()
lapnet3_gen.eval()
lapnet4_gen.eval()
errD_fake_mse_total = 0
elapsed_time = 0
for idx, (data, _) in enumerate(testloader, 0):
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 1x64x64
g4_target[i] = torch.from_numpy(g4_target_temp) # 3x64x64
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2[j, :, :].dot(data_array[i, j].flatten()))
y3[i, j, :] = torch.from_numpy(sensing_matrix3[j, :, :].dot(data_array[i, j].flatten()))
y4[i, j, :] = torch.from_numpy(sensing_matrix4[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input, volatile=True)
y2_var = Variable(y2)
y3_var = Variable(y3)
y4_var = Variable(y4)
torch.cuda.synchronize()
torch.cuda.synchronize()
start = time.time()
g2_input = lapnet1_gen(g1_input_var)
g3_input = lapnet2_gen(g2_input, y2_var)
g4_input = lapnet3_gen(g3_input, y3_var)
g4_output = lapnet4_gen(g4_input, y4_var)
torch.cuda.synchronize()
end = time.time()
elapsed_time += end - start
print('Time cost for one batch: {:.02e}s'.format(end - start))
print('Average time cost for one batch: {:.02e}s'.format(elapsed_time / len(testloader)))
def main():
test_loader = data_loader()
evaluation(test_loader)
if __name__ == '__main__':
main()