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search_mixed_at2stage.py
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# -*- coding: utf-8 -*-
# @Date : 2019-09-25
# @Author : Xinyu Gong (xy_gong@tamu.edu)
# @Link : None
# @Version : 0.0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import cfg
from functions import train_shared_mixed, train_controller, get_topk_arch_hidden, train_shared_mixed_NoCL
from utils.utils import set_log_dir, save_checkpoint, create_logger, RunningStats
# from utils.inception_score import _init_inception
# from utils.fid_score import create_inception_graph, check_or_download_inception
from operation import ImageFolder, InfiniteSamplerWrapper
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms
from models_search.shared_gan_2stage import GeneratorNoAttnOrigin,Discriminator
from models_search.controller import Controller,Controller3,ControllerNoAttn,ControllerSkip
from models import CLHead
import torch
import os
import torch.nn as nn
from tensorboardX import SummaryWriter
from torchvision import utils as vutils
from tqdm import tqdm
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
device = torch.device("cuda:0")
# def create_ctrler_skip(args, weights_init):
# controller = ControllerSkip(args=args).cuda()
# controller.apply(weights_init)
# ctrl_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, controller.parameters()),
# args.ctrl_lr, (args.beta1, args.beta2))
# return controller, ctrl_optimizer
def create_ctrler_attn(args, weights_init):
controller = ControllerNoAttn(args=args).cuda()
controller.apply(weights_init)
ctrl_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, controller.parameters()),
args.ctrl_lr, (args.beta1, args.beta2))
return controller, ctrl_optimizer
def create_shared_gan(args, weights_init, cur_stage=1):
gen_net = GeneratorNoAttnOrigin(ngf=args.gf_dim, nz=args.latent_dim, nc=3, im_size=args.im_size, cur_stage=cur_stage).cuda()
dis_net = Discriminator(ndf=args.df_dim, nc=3, im_size=args.im_size, cur_stage=cur_stage).cuda()
gen_net.apply(weights_init)
dis_net.apply(weights_init)
gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr, (args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()),
args.d_lr, (args.beta1, args.beta2))
return gen_net, dis_net, gen_optimizer, dis_optimizer
# def create_clhead(args, weights_init, cur_stage=1):
#
# clhead_big = CLHead(inplanes=args.gf_dim * 8)
# clhead_big.apply(weights_init)
# clhead_small = CLHead(inplanes=args.gf_dim * 4)
# clhead_small.apply(weights_init)
#
# clhead_big.to(device)
# clhead_small.to(device)
#
# optimizerCL_big = torch.optim.Adam(clhead_big.mlp.parameters(), args.g_lr, (args.beta1, args.beta2))
# optimizerCL_small = torch.optim.Adam(clhead_small.mlp.parameters(), args.g_lr, (args.beta1, args.beta2))
#
# return clhead_big, clhead_small, optimizerCL_big, optimizerCL_small
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# # set tf env
# _init_inception()
# inception_path = check_or_download_inception(None)
# create_inception_graph(inception_path)
# weight init
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
change_step = [i*args.change_epoch for i in range(1, (args.max_search_iter // args.change_epoch) + 1)]
bk_dic = {}
datasets_dic = {0: './data/few-shot-images/100-shot-grumpy_cat',
1: './data/few-shot-images/100-shot-obama/img',
2: './data/few-shot-images/100-shot-panda',
3: './data/few-shot-images/AnimalFace-cat/img',
4: './data/few-shot-images/AnimalFace-dog/img',
5: './data/few-shot-images/anime-face/img',
# 6: './data/few-shot-images/art-painting/img',
# 7: './data/few-shot-images/fauvism-still-life/img',
# 8: './data/few-shot-images/flat-colored/patterns',
# 9: './data/few-shot-images/moongate/img',
# 10: './data/few-shot-images/pokemon/img',
# 11: './data/few-shot-images/shells/img',
# 12: './data/few-shot-images/skulls/img',
}
# data_idex = random.sample(datasets_dic.keys(),k=1)
# if args.data_root:
# data_root = args.data_root
# else:
# data_root = datasets_dic[int(data_idex[0])]
# datasets_dic.pop(data_idex[0])
# initial
start_search_iter = 0
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model', 'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
# set controller && its optimizer
cur_stage = checkpoint['cur_stage']
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(args, weights_init, cur_stage)
# clhead_big, clhead_small, optimizerCL_big, optimizerCL_small = create_clhead(args, weights_init)
# controller_skip, ctrl_skip_optimizer = create_ctrler_skip(args, weights_init)
controller_attn, ctrl_attn_optimizer = create_ctrler_attn(args, weights_init)
start_search_iter = checkpoint['search_iter']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
# clhead_big.load_state_dict(checkpoint['clhead_big'])
# clhead_small.load_state_dict(checkpoint['clhead_samll'])
# controller_skip.load_state_dict(checkpoint['ctrl_skip_state_dict'])
controller_attn.load_state_dict(checkpoint['ctrl_attn_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
# ctrl_skip_optimizer.load_state_dict(checkpoint['ctrl_skip_optimizer'])
ctrl_attn_optimizer.load_state_dict(checkpoint['ctrl_attn_optimizer'])
# optimizerCL_big.load_state_dict(checkpoint['cloptimizer_big'])
# optimizerCL_small.load_state_dict(checkpoint['cloptimizer_small'])
top_skip_archs = checkpoint['top_skip_archs']
data_root = checkpoint['data_root']
datasets_dic = checkpoint['data_dic']
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(f'=> loaded checkpoint {checkpoint_file} (search iteration {start_search_iter})')
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
data_idex = random.sample(datasets_dic.keys(), k=1)
data_root = datasets_dic[int(data_idex[0])]
datasets_dic.pop(data_idex[0])
# top_skip_archs = None
# cur_stage = 1
#
# # set controller && its optimizer
# controller_skip, ctrl_skip_optimizer = create_ctrler_skip(args, weights_init)
# controller_attn, ctrl_attn_optimizer = create_ctrler_attn(args, weights_init)
cur_stage = 2
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(args, weights_init, cur_stage)
# clhead_big, clhead_small, optimizerCL_big, optimizerCL_small = create_clhead(args, weights_init, cur_stage)
# controller_skip, ctrl_skip_optimizer = create_ctrler_skip(args, weights_init)
controller_attn, ctrl_attn_optimizer = create_ctrler_attn(args, weights_init)
top_skip_archs = [torch.tensor([0, 0, 0, 1, 2]).cuda(), torch.tensor([0, 0, 0, 0, 1]).cuda(), torch.tensor([0, 0, 0, 0, 1]).cuda(), torch.tensor([0, 0, 0, 0, 2]).cuda(), torch.tensor([0, 0, 0, 0, 1]).cuda()]
# set up data_loader
# data_root = './data/few-shot-images/moongate/img'
logger.info(f"<Initial Dataset is: {data_root[22:]}>")
transform_list = [
transforms.Resize((int(args.im_size), int(args.im_size))),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]
trans = transforms.Compose(transform_list)
if 'lmdb' in data_root:
from operation import MultiResolutionDataset
dataset = MultiResolutionDataset(data_root, trans, 1024)
else:
dataset = ImageFolder(root=data_root, transform=trans)
dataloader = iter(DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
sampler=InfiniteSamplerWrapper(dataset), num_workers=args.dataloader_workers,
pin_memory=True))
transform_list_dist = [
transforms.Resize((int(args.im_size), int(args.im_size))),
transforms.ToTensor(),
]
trans_a = transforms.Compose(transform_list_dist)
dataset_a = ImageFolder(root=data_root, transform=trans_a)
loader = DataLoader(dataset_a, batch_size=64, num_workers=8)
dist = './fid_buffer/origin/'
os.system('rm -f {}*'.format(dist))
for i, imgs in enumerate(loader):
for j, img in enumerate(imgs):
vutils.save_image(img,
os.path.join(dist,
'%d.png' % (i * 64 + j))) # , normalize=True, range=(-1,1))
# im_nums = min(len(dataset), args.n_sample)
im_nums = args.n_sample
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'controller_steps': start_search_iter * args.ctrl_step
}
g_loss_history = RunningStats(args.dynamic_reset_window)
d_loss_history = RunningStats(args.dynamic_reset_window)
# train loop
for search_iter in tqdm(range(int(start_search_iter), int(args.max_search_iter)), desc='search progress'):
# if search_iter == args.grow_step:
# logger.info(f"<start search stage2>")
# cur_stage = 2
# top_skip_archs = get_topk_arch_hidden(args, controller_skip, gen_net, dist)
# logger.info(f"discovered archs by fid: {top_skip_archs}")
if search_iter in change_step:
data_idex = random.sample(datasets_dic.keys(), 1)
data_root = datasets_dic[int(data_idex[0])]
datasets_dic.pop(data_idex[0])
logger.info(f"< change dataset of selected dataset {data_root[22:]}>")
if 'lmdb' in data_root:
from operation import MultiResolutionDataset
dataset = MultiResolutionDataset(data_root, trans, 1024)
else:
dataset = ImageFolder(root=data_root, transform=trans)
# im_nums = min(len(dataset),args.n_sample)
dataloader = iter(DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
sampler=InfiniteSamplerWrapper(dataset),
num_workers=args.dataloader_workers,
pin_memory=True))
dataset_a = ImageFolder(root=data_root, transform=trans_a)
loader = DataLoader(dataset_a, batch_size=64, num_workers=8)
dist = './fid_buffer/origin/'
os.system('rm -f {}*'.format(dist))
for i, imgs in enumerate(loader):
for j, img in enumerate(imgs):
vutils.save_image(img,
os.path.join(dist,
'%d.png' % (i * 64 + j))) # , normalize=True, range=(-1,1))
del gen_net, dis_net, gen_optimizer, dis_optimizer
g_loss_history.clear()
d_loss_history.clear()
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(args, weights_init,
cur_stage=cur_stage)
# clhead_big, clhead_small, optimizerCL_big, optimizerCL_small = create_clhead(args, weights_init, cur_stage=cur_stage)
logger.info(f"<start search iteration {search_iter}>")
# del gen_net,dis_net,gen_optimizer,dis_optimizer,clhead_big, clhead_small, optimizerCL_big, optimizerCL_small
# g_loss_history.clear()
# d_loss_history.clear()
# gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(args, weights_init, cur_stage=cur_stage)
# clhead_big, clhead_small, optimizerCL_big, optimizerCL_small = create_clhead(args, weights_init)
# if search_iter >= args.grow_step:
dynamic_reset = train_shared_mixed_NoCL(args, gen_net, dis_net, g_loss_history,
d_loss_history, controller_attn, gen_optimizer,
dis_optimizer, dataloader, top_skip_archs=top_skip_archs, cur_stage=cur_stage)
train_controller(args, controller_attn, ctrl_attn_optimizer, gen_net, im_nums, dist, writer_dict, top_skip_archs=top_skip_archs, cur_stage=cur_stage)
# else:
# dynamic_reset = train_shared_mixed(args, gen_net, dis_net, clhead_big, clhead_small, g_loss_history,
# d_loss_history, controller_skip, gen_optimizer,
# dis_optimizer, optimizerCL_big, optimizerCL_small, dataloader, top_skip_archs=top_skip_archs, cur_stage=cur_stage)
# train_controller(args, controller_skip, ctrl_skip_optimizer, gen_net, im_nums, dist, writer_dict, top_skip_archs=top_skip_archs, cur_stage=cur_stage)
if dynamic_reset:
logger.info('re-initialize share GAN')
del gen_net, dis_net, gen_optimizer, dis_optimizer
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(args, weights_init, cur_stage=cur_stage)
# clhead_big, clhead_small, optimizerCL_big, optimizerCL_small = create_clhead(args, weights_init, cur_stage=cur_stage)
save_checkpoint({
'search_iter': search_iter + 1,
'gen_model': args.gen_model,
'dis_model': args.dis_model,
'controller': args.controller,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
# 'ctrl_skip_state_dict': controller_skip.state_dict(),
'ctrl_attn_state_dict': controller_attn.state_dict(),
# 'clhead_big': clhead_big.state_dict(),
# 'clhead_samll': clhead_small.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
# 'cloptimizer_big': optimizerCL_big.state_dict(),
# 'cloptimizer_small': optimizerCL_small.state_dict(),
# 'ctrl_skip_optimizer': ctrl_skip_optimizer.state_dict(),
'ctrl_attn_optimizer': ctrl_attn_optimizer.state_dict(),
'cur_stage': cur_stage,
'top_skip_archs': top_skip_archs,
'data_root': data_root,
'data_dic': datasets_dic,
'path_helper': args.path_helper
}, False, args.path_helper['ckpt_path'])
final_archs_fid = get_topk_arch_hidden(args, controller_attn, gen_net, dist, top_skip_archs=top_skip_archs)
logger.info(f"discovered archs by fid: {final_archs_fid}")
if __name__ == '__main__':
main()