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main_train_psnr_joint_rtvl_rm_gam.py
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main_train_psnr_joint_rtvl_rm_gam.py
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import os.path
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import torch
from utils import utils_logger
from utils import utils_image as util
from utils import utils_option as option
from utils.utils_dist import get_dist_info, init_dist
from data.select_dataset import define_Dataset
from models.select_model import define_Model
import models.network_image_retrieval as rtvl_module
from bicubic_pytorch.core import imresize
'''
# --------------------------------------------
# training code for MSRResNet
# --------------------------------------------
# Kai Zhang ([email protected])
# github: https://github.com/cszn/KAIR
# --------------------------------------------
# https://github.com/xinntao/BasicSR
# --------------------------------------------
'''
def MultiLableContrastiveLoss(neg_margin=1):
def lossfn(outp, y):
loss = 0
l = (y @ y.T) > 0
dist = torch.cdist(outp, outp)
loss += 0.5 * l * dist
loss += 0.5 * l.logical_not() *\
torch.maximum(neg_margin - dist, torch.zeros_like(dist))
return loss.sum()
return lossfn
def main(json_path='options/train_msrresnet_psnr.json'):
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('--opt', type=str, default=json_path, help='Path to option JSON file.')
parser.add_argument('--launcher', default='pytorch', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', default=False)
opt = option.parse(parser.parse_args().opt, is_train=True)
opt['dist'] = parser.parse_args().dist
# ----------------------------------------
# distributed settings
# ----------------------------------------
if opt['dist']:
init_dist('pytorch')
opt['rank'], opt['world_size'] = get_dist_info()
if opt['rank'] == 0:
util.mkdirs((path for key, path in opt['path'].items() if 'pretrained' not in key))
# ----------------------------------------
# update opt
# ----------------------------------------
# -->-->-->-->-->-->-->-->-->-->-->-->-->-
init_iter_G, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
init_iter_E, init_path_E = option.find_last_checkpoint(opt['path']['models'], net_type='E')
if init_iter_G > 0:
opt['path']['pretrained_netG'] = init_path_G
if init_iter_E > 0:
opt['path']['pretrained_netE'] = init_path_E
init_iter_optimizerG, init_path_optimizerG = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerG')
if init_iter_optimizerG > 0:
opt['path']['pretrained_optimizerG'] = init_path_optimizerG
current_step = max(init_iter_G, init_iter_E, init_iter_optimizerG)
border = opt['scale']
# --<--<--<--<--<--<--<--<--<--<--<--<--<-
# ----------------------------------------
# save opt to a '../option.json' file
# ----------------------------------------
if opt['rank'] == 0:
option.save(opt)
# ----------------------------------------
# return None for missing key
# ----------------------------------------
opt = option.dict_to_nonedict(opt)
# ----------------------------------------
# configure logger
# ----------------------------------------
if opt['rank'] == 0:
logger_name = 'train'
utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log'))
logger = logging.getLogger(logger_name)
logger.info(option.dict2str(opt))
# ----------------------------------------
# seed
# ----------------------------------------
seed = opt['train']['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
print('Random seed: {}'.format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
'''
# ----------------------------------------
# Step--2 (creat dataloader)
# ----------------------------------------
'''
# ----------------------------------------
# 1) create_dataset
# 2) creat_dataloader for train and test
# ----------------------------------------
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
train_set = define_Dataset(dataset_opt)
# train_set, _ = torch.utils.data.random_split(train_set, [5000, len(train_set) - 5000])
train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size']))
train_num_classes = dataset_opt['num_classes']
if opt['rank'] == 0:
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))
if opt['dist']:
train_sampler = DistributedSampler(train_set, shuffle=dataset_opt['dataloader_shuffle'], drop_last=True, seed=seed)
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size']//opt['num_gpu'],
shuffle=False,
num_workers=dataset_opt['dataloader_num_workers']//opt['num_gpu'],
drop_last=True,
pin_memory=True,
sampler=train_sampler)
else:
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size'],
shuffle=dataset_opt['dataloader_shuffle'],
num_workers=dataset_opt['dataloader_num_workers'],
drop_last=True,
pin_memory=True)
elif phase == 'test':
test_set = define_Dataset(dataset_opt)
test_loader = DataLoader(test_set, batch_size=1,
shuffle=False, num_workers=1,
drop_last=False, pin_memory=True)
else:
raise NotImplementedError("Phase [%s] is not recognized." % phase)
'''
# ----------------------------------------
# Step--3 (initialize model)
# ----------------------------------------
'''
model = define_Model(opt)
model.init_train()
# if opt['rank'] == 0:
# logger.info(model.info_network())
# logger.info(model.info_params())
'''
# ----------------------------------------
# Step--4 (initialize model)
# ----------------------------------------
'''
gama, quantization_alpha = 1.0, 0.1
lam = 1.0
mse_alpha = 0.1
n_bit = opt['netG']['n_bit']
norm_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1).to(model.device)
norm_std = torch.tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1).to(model.device)
rtvl_model = rtvl_module.ResNet50(n_bit)
rtvl_model.load_state_dict(torch.load(opt['path']['pretrained_rtvl']))
rtvl_model.to(model.device)
loss_hash = rtvl_module.DPSHLoss(
train_num_classes, n_bit, len(train_set), model.device)
loss_hash2 = rtvl_module.DPSHLoss(
train_num_classes, n_bit, len(train_set), model.device)
loss_quant = rtvl_module.quantization_swdc_loss
loss_mse = torch.nn.MSELoss()
loss_cl = MultiLableContrastiveLoss(neg_margin=3)
rtvl_optimizer = torch.optim.Adam(rtvl_model.parameters(), lr=1e-5)
'''
# ----------------------------------------
# Step--5 (main training)
# ----------------------------------------
'''
for epoch in range(100): # keep running
if opt['dist']:
train_sampler.set_epoch(epoch)
for i, train_data in enumerate(train_loader):
current_step += 1
if current_step > 14000:
return
if i % 2:
model.feed_data(train_data)
L_img, H_img = train_data['L'].detach().to(model.device), train_data['H'].detach().to(model.device)
L_img = (L_img - norm_mean) / norm_std
H_img = (H_img - norm_mean) / norm_std
rtvl_model.eval()
with torch.no_grad():
fv_rtvl = rtvl_model(imresize(L_img, opt['scale']))
model.netG.rep_vec_list.append(fv_rtvl.detach())
rtvl_model.train()
model.test()
rtvl_optimizer.zero_grad()
E_img = model.E.detach()
E_img = (E_img - norm_mean) / norm_std
y = train_data['img_class'].to(model.device)
idx = train_data['index'].to(model.device)
fv_E = rtvl_model(E_img)
fv_L = rtvl_model(imresize(L_img, opt['scale']))
fv_H = rtvl_model(H_img)
loss = 0.0
# loss += gama * loss_hash(fv_E, y, idx)
# loss += quantization_alpha * loss_quant(fv_E, model.device)
loss += lam * loss_hash2(fv_L, y, idx)
loss += quantization_alpha * loss_quant(fv_L, model.device)
loss += mse_alpha * loss_mse(fv_L, fv_H)
loss.backward()
rtvl_optimizer.step()
else:
# -------------------------------
# 1) update learning rate
# -------------------------------
model.update_learning_rate(current_step)
# -------------------------------
# 2) feed patch pairs
# -------------------------------
model.feed_data(train_data)
rtvl_model.eval()
with torch.no_grad():
fv_rtvl = rtvl_model(imresize(model.L.detach(), opt['scale']))
model.netG.rep_vec_list.append(fv_rtvl.detach())
rtvl_model.train()
# -------------------------------
# 3) optimize parameters
# -------------------------------
model.optimize_parameters(current_step)
# -------------------------------
# 4) training information
# -------------------------------
if current_step % opt['train']['checkpoint_print'] == 0 and opt['rank'] == 0:
logs = model.current_log() # such as loss
message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step, model.current_learning_rate())
for k, v in logs.items(): # merge log information into message
message += '{:s}: {:.3e} '.format(k, v)
message += f'rtvl_loss: {loss} '
logger.info(message)
# -------------------------------
# 5) save model
# -------------------------------
if current_step % opt['train']['checkpoint_save'] == 0 and opt['rank'] == 0:
logger.info('Saving the model.')
model.save(current_step)
torch.save(rtvl_model.state_dict(), f'{model.save_dir}/{current_step}_rtvl.pth')
# # -------------------------------
# # 6) testing
# # -------------------------------
# if current_step % opt['train']['checkpoint_test'] == 0 and opt['rank'] == 0:
# avg_psnr = 0.0
# idx = 0
# for test_data in test_loader:
# idx += 1
# image_name_ext = os.path.basename(test_data['L_path'][0])
# img_name, ext = os.path.splitext(image_name_ext)
# img_dir = os.path.join(opt['path']['images'], img_name)
# util.mkdir(img_dir)
# model.feed_data(test_data)
# model.test()
# visuals = model.current_visuals()
# E_img = util.tensor2uint(visuals['E'])
# H_img = util.tensor2uint(visuals['H'])
# # -----------------------
# # save estimated image E
# # -----------------------
# save_img_path = os.path.join(img_dir, '{:s}_{:d}.png'.format(img_name, current_step))
# util.imsave(E_img, save_img_path)
# # -----------------------
# # calculate PSNR
# # -----------------------
# current_psnr = util.calculate_psnr(E_img, H_img, border=border)
# logger.info('{:->4d}--> {:>10s} | {:<4.2f}dB'.format(idx, image_name_ext, current_psnr))
# avg_psnr += current_psnr
# avg_psnr = avg_psnr / idx
# # testing log
# logger.info('<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}dB\n'.format(epoch, current_step, avg_psnr))
if __name__ == '__main__':
main()