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train.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from utils import get_all_data_loaders, prepare_sub_folder, write_loss, get_config, write_2images, Timer
import argparse
from trainer import DGNet_Trainer
import torch.backends.cudnn as cudnn
import torch
import numpy.random as random
try:
from itertools import izip as zip
except ImportError: # will be 3.x series
pass
import os
import sys
import tensorboardX
import shutil
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/latest.yaml', help='Path to the config file.')
parser.add_argument('--output_path', type=str, default='.', help="outputs path")
parser.add_argument('--name', type=str, default='latest_ablation', help="outputs path")
parser.add_argument("--resume", action="store_true")
parser.add_argument('--trainer', type=str, default='DGNet', help="DGNet")
parser.add_argument('--gpu_ids',default='0', type=str,help='gpu_ids: e.g. 0 0,1,2 0,2')
opts = parser.parse_args()
str_ids = opts.gpu_ids.split(',')
gpu_ids = []
for str_id in str_ids:
gpu_ids.append(int(str_id))
num_gpu = len(gpu_ids)
cudnn.benchmark = True
# Load experiment setting
if opts.resume:
config = get_config('./outputs/'+opts.name+'/config.yaml')
else:
config = get_config(opts.config)
max_iter = config['max_iter']
display_size = config['display_size']
config['vgg_model_path'] = opts.output_path
# Setup model and data loader
if opts.trainer == 'DGNet':
trainer = DGNet_Trainer(config, gpu_ids) # from trainer ->DGNet_Trainer
trainer.cuda()
random.seed(7) #fix random result
train_loader_a, train_loader_b, test_loader_a, test_loader_b = get_all_data_loaders(config) # function from utils.py
# 随机选出一些图片，然后根据选出的任意两张id不同的图片，输出一些训练过程中产生的重构图片或合成图片。
train_a_rand = random.permutation(train_loader_a.dataset.img_num)[0:display_size]
train_b_rand = random.permutation(train_loader_b.dataset.img_num)[0:display_size]
test_a_rand = random.permutation(test_loader_a.dataset.img_num)[0:display_size]
test_b_rand = random.permutation(test_loader_b.dataset.img_num)[0:display_size]
train_display_images_a = torch.stack([train_loader_a.dataset[i][0] for i in train_a_rand]).cuda()
train_display_images_ap = torch.stack([train_loader_a.dataset[i][2] for i in train_a_rand]).cuda()
train_display_images_b = torch.stack([train_loader_b.dataset[i][0] for i in train_b_rand]).cuda()
train_display_images_bp = torch.stack([train_loader_b.dataset[i][2] for i in train_b_rand]).cuda()
test_display_images_a = torch.stack([test_loader_a.dataset[i][0] for i in test_a_rand]).cuda()
test_display_images_ap = torch.stack([test_loader_a.dataset[i][2] for i in test_a_rand]).cuda()
test_display_images_b = torch.stack([test_loader_b.dataset[i][0] for i in test_b_rand]).cuda()
test_display_images_bp = torch.stack([test_loader_b.dataset[i][2] for i in test_b_rand]).cuda()
# Setup logger and output folders
if not opts.resume:
model_name = os.path.splitext(os.path.basename(opts.config))[0]
train_writer = tensorboardX.SummaryWriter(os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory, image_directory = prepare_sub_folder(output_directory)
shutil.copyfile(opts.config, os.path.join(output_directory, 'config.yaml')) # copy config file to output folder
shutil.copyfile('trainer.py', os.path.join(output_directory, 'trainer.py')) # copy file to output folder
shutil.copyfile('reIDmodel.py', os.path.join(output_directory, 'reIDmodel.py')) # copy file to output folder
shutil.copyfile('networks.py', os.path.join(output_directory, 'networks.py')) # copy file to output folder
else:
train_writer = tensorboardX.SummaryWriter(os.path.join(opts.output_path + "/logs", opts.name))
output_directory = os.path.join(opts.output_path + "/outputs", opts.name)
checkpoint_directory, image_directory = prepare_sub_folder(output_directory)
# Start training
iterations = trainer.resume(checkpoint_directory, hyperparameters=config) if opts.resume else 0
config['epoch_iteration'] = round( train_loader_a.dataset.img_num / config['batch_size'] )
print('Every epoch need %d iterations'%config['epoch_iteration'])
nepoch = 0
print('Note that dataloader may hang with too much nworkers.')
if num_gpu>1:
print('Now you are using %d gpus.'%num_gpu)
trainer.dis_a = torch.nn.DataParallel(trainer.dis_a, gpu_ids)
trainer.dis_b = trainer.dis_a
trainer = torch.nn.DataParallel(trainer, gpu_ids)
while True:
for it, ((images_a,labels_a, pos_a), (images_b, labels_b, pos_b)) in enumerate(zip(train_loader_a, train_loader_b)):
if num_gpu>1:
trainer.module.update_learning_rate()
else:
trainer.update_learning_rate()
images_a, images_b = images_a.cuda().detach(), images_b.cuda().detach()
pos_a, pos_b = pos_a.cuda().detach(), pos_b.cuda().detach()
labels_a, labels_b = labels_a.cuda().detach(), labels_b.cuda().detach()
with Timer("Elapsed time in update: %f"):
# Main training code
x_ab, x_ba, s_a, s_b, f_a, f_b, p_a, p_b, pp_a, pp_b, x_a_recon, x_b_recon, x_a_recon_p, x_b_recon_p = \
trainer.forward(images_a, images_b, pos_a, pos_b)
if num_gpu>1:
trainer.module.dis_update(x_ab.clone(), x_ba.clone(), images_a, images_b, config, num_gpu)
trainer.module.gen_update(x_ab, x_ba, s_a, s_b, f_a, f_b, p_a, p_b, pp_a, pp_b, x_a_recon, x_b_recon, x_a_recon_p, x_b_recon_p, images_a, images_b, pos_a, pos_b, labels_a, labels_b, config, iterations, num_gpu)
else:
trainer.dis_update(x_ab.clone(), x_ba.clone(), images_a, images_b, config, num_gpu=1)
trainer.gen_update(x_ab, x_ba, s_a, s_b, f_a, f_b, p_a, p_b, pp_a, pp_b, x_a_recon, x_b_recon, x_a_recon_p, x_b_recon_p, images_a, images_b, pos_a, pos_b, labels_a, labels_b, config, iterations, num_gpu=1)
torch.cuda.synchronize() # 等着GPU把所有工作都做完
# Dump training stats in log file
if (iterations + 1) % config['log_iter'] == 0:
print("\033[1m Epoch: %02d Iteration: %08d/%08d \033[0m" % (nepoch, iterations + 1, max_iter), end=" ")
if num_gpu==1:
write_loss(iterations, trainer, train_writer)
else:
write_loss(iterations, trainer.module, train_writer)
# Write images
if (iterations + 1) % config['image_save_iter'] == 0: # 可以看到在训练中生成器生成的重构图片或者合成图片是什么样子的
with torch.no_grad():
if num_gpu>1:
test_image_outputs = trainer.module.sample(test_display_images_a, test_display_images_b)
# sample : 输入：两张图片x_a,x_b， 输出：x_a, x_a_recon, x_aba, x_ab1, x_b, x_b_recon, x_bab, x_ba1
else:
test_image_outputs = trainer.sample(test_display_images_a, test_display_images_b)
write_2images(test_image_outputs, display_size, image_directory, 'test_%08d' % (iterations + 1))
del test_image_outputs
if (iterations + 1) % config['image_display_iter'] == 0:
with torch.no_grad():
if num_gpu>1:
image_outputs = trainer.module.sample(train_display_images_a, train_display_images_b)
else:
image_outputs = trainer.sample(train_display_images_a, train_display_images_b)
write_2images(image_outputs, display_size, image_directory, 'train_%08d' % (iterations + 1))
del image_outputs
# Save network weights
if (iterations + 1) % config['snapshot_save_iter'] == 0:
if num_gpu>1:
trainer.module.save(checkpoint_directory, iterations)
else:
trainer.save(checkpoint_directory, iterations)
iterations += 1
if iterations >= max_iter:
sys.exit('Finish training')
# Save network weights by epoch number
nepoch = nepoch+1
if(nepoch + 1) % 10 == 0:
if num_gpu>1:
trainer.module.save(checkpoint_directory, iterations)
else:
trainer.save(checkpoint_directory, iterations)