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train_single_database.py
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train_single_database.py
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import os
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
import torch.backends.cudnn as cudnn
import IQADataset
import models.stairIQA_resnet as stairIQA_resnet
from utils import performance_fit
def parse_args():
"""Parse input arguments. """
parser = argparse.ArgumentParser(description="In the wild Image Quality Assessment")
parser.add_argument('--gpu', help="GPU device id to use [0]", default=0, type=int)
parser.add_argument('--num_epochs', help='Maximum number of training epochs.', default=30, type=int)
parser.add_argument('--batch_size', help='Batch size.', default=40, type=int)
parser.add_argument('--resize', help='resize.', type=int)
parser.add_argument('--crop_size', help='crop_size.',type=int)
parser.add_argument('--lr', type=float, default=0.00001)
parser.add_argument('--decay_ratio', type=float, default=0.9)
parser.add_argument('--decay_interval', type=float, default=10)
parser.add_argument('--snapshot', help='Path of model snapshot.', default='', type=str)
parser.add_argument('--results_path', type=str)
parser.add_argument('--database_dir', type=str)
parser.add_argument('--model', default='ResNet', type=str)
parser.add_argument('--multi_gpu', type=bool, default=False)
parser.add_argument('--print_samples', type=int, default = 50)
parser.add_argument('--database', default='FLIVE', type=str)
parser.add_argument('--test_method', default='five', type=str,
help='use the center crop or five crop to test the image')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
gpu = args.gpu
cudnn.enabled = True
num_epochs = args.num_epochs
batch_size = args.batch_size
lr = args.lr
decay_interval = args.decay_interval
decay_ratio = args.decay_ratio
snapshot = args.snapshot
database = args.database
print_samples = args.print_samples
results_path = args.results_path
database_dir = args.database_dir
resize = args.resize
crop_size = args.crop_size
best_all = np.zeros([10, 4])
for exp_id in range(10):
print('The current exp_id is ' + str(exp_id))
if not os.path.exists(snapshot):
os.makedirs(snapshot)
trained_model_file = os.path.join(snapshot, 'train-ind-{}-{}-exp_id-{}.pkl'.format(database, args.model, exp_id))
print('The save model name is ' + trained_model_file)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu)
if database == 'Koniq10k':
train_filename_list = 'csvfiles/Koniq10k_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/Koniq10k_test_'+str(exp_id)+'.csv'
elif database == 'FLIVE':
train_filename_list = 'csvfiles/FLIVE_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/FLIVE_test_'+str(exp_id)+'.csv'
elif database == 'FLIVE_patch':
train_filename_list = 'csvfiles/FLIVE_patch_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/FLIVE_patch_test_'+str(exp_id)+'.csv'
elif database == 'LIVE_challenge':
train_filename_list = 'csvfiles/LIVE_challenge_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/LIVE_challenge_test_'+str(exp_id)+'.csv'
elif database == 'SPAQ':
train_filename_list = 'csvfiles/SPQA_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/SPQA_test_'+str(exp_id)+'.csv'
elif database == 'BID':
train_filename_list = 'csvfiles/BID_train_'+str(exp_id)+'.csv'
test_filename_list = 'csvfiles/BID_test_'+str(exp_id)+'.csv'
print(train_filename_list)
print(test_filename_list)
# load the network
if args.model == 'stairIQA_resnet':
model = stairIQA_resnet.resnet50(pretrained = True)
transformations_train = transforms.Compose([transforms.Resize(resize),transforms.RandomCrop(crop_size), \
transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
if args.test_method == 'one':
transformations_test = transforms.Compose([transforms.Resize(resize),transforms.CenterCrop(crop_size), \
transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
elif args.test_method == 'five':
transformations_test = transforms.Compose([transforms.Resize(resize),transforms.FiveCrop(crop_size), \
(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), \
(lambda crops: torch.stack([transforms.Normalize(mean=[0.485, 0.456, 0.406], \
std=[0.229, 0.224, 0.225])(crop) for crop in crops]))])
train_dataset = IQADataset.IQA_dataloader(database_dir, train_filename_list, transformations_train, database)
test_dataset = IQADataset.IQA_dataloader(database_dir, test_filename_list, transformations_test, database)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=8)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, num_workers=8)
if args.multi_gpu:
model = torch.nn.DataParallel(model)
model = model.to(device)
else:
model = model.to(device)
criterion = nn.MSELoss().to(device)
param_num = 0
for param in model.parameters():
param_num += int(np.prod(param.shape))
print('Trainable params: %.2f million' % (param_num / 1e6))
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.0000001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=decay_interval, gamma=decay_ratio)
print("Ready to train network")
best_test_criterion = -1 # SROCC min
best = np.zeros(4)
n_train = len(train_dataset)
n_test = len(test_dataset)
for epoch in range(num_epochs):
# train
model.train()
batch_losses = []
batch_losses_each_disp = []
session_start_time = time.time()
for i, (image, mos) in enumerate(train_loader):
image = image.to(device)
mos = mos[:,np.newaxis]
mos = mos.to(device)
mos_output = model(image)
loss = criterion(mos_output, mos)
batch_losses.append(loss.item())
batch_losses_each_disp.append(loss.item())
optimizer.zero_grad() # clear gradients for next train
torch.autograd.backward(loss)
optimizer.step()
if (i+1) % print_samples == 0:
session_end_time = time.time()
avg_loss_epoch = sum(batch_losses_each_disp) / print_samples
print('Epoch: %d/%d | Step: %d/%d | Training loss: %.4f' % \
(epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, \
avg_loss_epoch))
batch_losses_each_disp = []
print('CostTime: {:.4f}'.format(session_end_time - session_start_time))
session_start_time = time.time()
avg_loss = sum(batch_losses) / (len(train_dataset) // batch_size)
print('Epoch %d averaged training loss: %.4f' % (epoch + 1, avg_loss))
scheduler.step()
lr_current = scheduler.get_last_lr()
print('The current learning rate is {:.06f}'.format(lr_current[0]))
# Test
model.eval()
y_output = np.zeros(n_test)
y_test = np.zeros(n_test)
with torch.no_grad():
for i, (image, mos) in enumerate(test_loader):
if args.test_method == 'one':
image = image.to(device)
y_test[i] = mos.item()
mos = mos.to(device)
outputs = model(image)
y_output[i] = outputs.item()
elif args.test_method == 'five':
bs, ncrops, c, h, w = image.size()
y_test[i] = mos.item()
image = image.to(device)
mos = mos.to(device)
outputs = model(image.view(-1, c, h, w))
outputs_avg = outputs.view(bs, ncrops, -1).mean(1)
y_output[i] = outputs_avg.item()
test_PLCC, test_SRCC, test_KRCC, test_RMSE = performance_fit(y_test, y_output)
print("Test results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(test_SRCC, test_KRCC, test_PLCC, test_RMSE))
if test_SRCC > best_test_criterion:
print("Update best model using best_val_criterion ")
torch.save(model.state_dict(), trained_model_file)
best[0:4] = [test_SRCC, test_KRCC, test_PLCC, test_RMSE]
best_test_criterion = test_SRCC # update best val SROCC
print("The best Test results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(test_SRCC, test_KRCC, test_PLCC, test_RMSE))
print(database)
best_all[exp_id, :] = best
print("The best Val results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(best[0], best[1], best[2], best[3]))
print('*************************************************************************************************************************')
best_median = np.median(best_all, 0)
best_mean = np.mean(best_all, 0)
best_std = np.std(best_all, 0)
print('*************************************************************************************************************************')
print(best_all)
print("The median val results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(best_median[0], best_median[1], best_median[2], best_median[3]))
print("The mean val results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(best_mean[0], best_mean[1], best_mean[2], best_mean[3]))
print("The std val results: SROCC={:.4f}, KROCC={:.4f}, PLCC={:.4f}, RMSE={:.4f}".format(best_std[0], best_std[1], best_std[2], best_std[3]))
print('*************************************************************************************************************************')