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"""
author: Min Seok Lee and Wooseok Shin
"""
import os
import cv2
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from dataloader import get_train_augmentation, get_test_augmentation, get_loader, gt_to_tensor
from util.utils import AvgMeter
from util.metrics import Evaluation_metrics
from util.losses import Optimizer, Scheduler, Criterion
from model.TRACER import TRACER
class Trainer():
def __init__(self, args, save_path):
super(Trainer, self).__init__()
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.size = args.img_size
self.tr_img_folder = os.path.join(args.data_path, args.dataset, 'Train/images/')
self.tr_gt_folder = os.path.join(args.data_path, args.dataset, 'Train/masks/')
self.tr_edge_folder = os.path.join(args.data_path, args.dataset, 'Train/edges/')
self.train_transform = get_train_augmentation(img_size=args.img_size, ver=args.aug_ver)
self.test_transform = get_test_augmentation(img_size=args.img_size)
self.train_loader = get_loader(self.tr_img_folder, self.tr_gt_folder, self.tr_edge_folder, phase='train',
batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,
transform=self.train_transform, seed=args.seed)
self.val_loader = get_loader(self.tr_img_folder, self.tr_gt_folder, self.tr_edge_folder, phase='val',
batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,
transform=self.test_transform, seed=args.seed)
# Network
self.model = TRACER(args).to(self.device)
if args.multi_gpu:
self.model = nn.DataParallel(self.model).to(self.device)
# Loss and Optimizer
self.criterion = Criterion(args)
self.optimizer = Optimizer(args, self.model)
self.scheduler = Scheduler(args, self.optimizer)
# Train / Validate
min_loss = 1000
early_stopping = 0
t = time.time()
for epoch in range(1, args.epochs + 1):
self.epoch = epoch
train_loss, train_mae = self.training(args)
val_loss, val_mae = self.validate()
if args.scheduler == 'Reduce':
self.scheduler.step(val_loss)
else:
self.scheduler.step()
# Save models
if val_loss < min_loss:
early_stopping = 0
best_epoch = epoch
best_mae = val_mae
min_loss = val_loss
torch.save(self.model.state_dict(), os.path.join(save_path, 'best_model.pth'))
print(f'-----------------SAVE:{best_epoch}epoch----------------')
else:
early_stopping += 1
if early_stopping == args.patience + 5:
break
print(f'\nBest Val Epoch:{best_epoch} | Val Loss:{min_loss:.3f} | Val MAE:{best_mae:.3f} '
f'time: {(time.time() - t) / 60:.3f}M')
# Test time
datasets = ['DUTS', 'DUT-O', 'HKU-IS', 'ECSSD', 'PASCAL-S']
for dataset in datasets:
args.dataset = dataset
test_loss, test_mae, test_maxf, test_avgf, test_s_m = self.test(args, os.path.join(save_path))
print(
f'Test Loss:{test_loss:.3f} | MAX_F:{test_maxf:.3f} | AVG_F:{test_avgf:.3f} | MAE:{test_mae:.3f} '
f'| S_Measure:{test_s_m:.3f}, time: {time.time() - t:.3f}s')
end = time.time()
print(f'Total Process time:{(end - t) / 60:.3f}Minute')
def training(self, args):
self.model.train()
train_loss = AvgMeter()
train_mae = AvgMeter()
for images, masks, edges in tqdm(self.train_loader):
images = torch.tensor(images, device=self.device, dtype=torch.float32)
masks = torch.tensor(masks, device=self.device, dtype=torch.float32)
edges = torch.tensor(edges, device=self.device, dtype=torch.float32)
self.optimizer.zero_grad()
outputs, edge_mask, ds_map = self.model(images)
loss1 = self.criterion(outputs, masks)
loss2 = self.criterion(ds_map[0], masks)
loss3 = self.criterion(ds_map[1], masks)
loss4 = self.criterion(ds_map[2], masks)
loss_mask = self.criterion(edge_mask, edges)
loss = loss1 + loss2 + loss3 + loss4 + loss_mask
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), args.clipping)
self.optimizer.step()
# Metric
mae = torch.mean(torch.abs(outputs - masks))
# log
train_loss.update(loss.item(), n=images.size(0))
train_mae.update(mae.item(), n=images.size(0))
print(f'Epoch:[{self.epoch:03d}/{args.epochs:03d}]')
print(f'Train Loss:{train_loss.avg:.3f} | MAE:{train_mae.avg:.3f}')
return train_loss.avg, train_mae.avg
def validate(self):
self.model.eval()
val_loss = AvgMeter()
val_mae = AvgMeter()
with torch.no_grad():
for images, masks, edges in tqdm(self.val_loader):
images = torch.tensor(images, device=self.device, dtype=torch.float32)
masks = torch.tensor(masks, device=self.device, dtype=torch.float32)
edges = torch.tensor(edges, device=self.device, dtype=torch.float32)
outputs, edge_mask, ds_map = self.model(images)
loss1 = self.criterion(outputs, masks)
loss2 = self.criterion(ds_map[0], masks)
loss3 = self.criterion(ds_map[1], masks)
loss4 = self.criterion(ds_map[2], masks)
loss_mask = self.criterion(edge_mask, edges)
loss = loss1 + loss2 + loss3 + loss4 + loss_mask
# Metric
mae = torch.mean(torch.abs(outputs - masks))
# log
val_loss.update(loss.item(), n=images.size(0))
val_mae.update(mae.item(), n=images.size(0))
print(f'Valid Loss:{val_loss.avg:.3f} | MAE:{val_mae.avg:.3f}')
return val_loss.avg, val_mae.avg
def test(self, args, save_path):
path = os.path.join(save_path, 'best_model.pth')
self.model.load_state_dict(torch.load(path))
print('###### pre-trained Model restored #####')
te_img_folder = os.path.join(args.data_path, args.dataset, 'Test/images/')
te_gt_folder = os.path.join(args.data_path, args.dataset, 'Test/masks/')
test_loader = get_loader(te_img_folder, te_gt_folder, edge_folder=None, phase='test',
batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, transform=self.test_transform)
self.model.eval()
test_loss = AvgMeter()
test_mae = AvgMeter()
test_maxf = AvgMeter()
test_avgf = AvgMeter()
test_s_m = AvgMeter()
Eval_tool = Evaluation_metrics(args.dataset, self.device)
with torch.no_grad():
for i, (images, masks, original_size, image_name) in enumerate(tqdm(test_loader)):
images = torch.tensor(images, device=self.device, dtype=torch.float32)
outputs, edge_mask, ds_map = self.model(images)
H, W = original_size
for i in range(images.size(0)):
mask = gt_to_tensor(masks[i])
h, w = H[i].item(), W[i].item()
output = F.interpolate(outputs[i].unsqueeze(0), size=(h, w), mode='bilinear')
loss = self.criterion(output, mask)
# Metric
mae, max_f, avg_f, s_score = Eval_tool.cal_total_metrics(output, mask)
# log
test_loss.update(loss.item(), n=1)
test_mae.update(mae, n=1)
test_maxf.update(max_f, n=1)
test_avgf.update(avg_f, n=1)
test_s_m.update(s_score, n=1)
test_loss = test_loss.avg
test_mae = test_mae.avg
test_maxf = test_maxf.avg
test_avgf = test_avgf.avg
test_s_m = test_s_m.avg
return test_loss, test_mae, test_maxf, test_avgf, test_s_m
class Tester():
def __init__(self, args, save_path):
super(Tester, self).__init__()
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.test_transform = get_test_augmentation(img_size=args.img_size)
self.args = args
self.save_path = save_path
# Network
self.model = TRACER(args).to(self.device)
if args.multi_gpu:
self.model = nn.DataParallel(self.model).to(self.device)
path = os.path.join(save_path, 'best_model.pth')
self.model.load_state_dict(torch.load(path))
print('###### pre-trained Model restored #####')
self.criterion = Criterion(args)
te_img_folder = os.path.join(args.data_path, args.dataset, 'Test/images/')
te_gt_folder = os.path.join(args.data_path, args.dataset, 'Test/masks/')
self.test_loader = get_loader(te_img_folder, te_gt_folder, edge_folder=None, phase='test',
batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, transform=self.test_transform)
if args.save_map is not None:
os.makedirs(os.path.join('mask', 'exp'+str(self.args.exp_num), self.args.dataset), exist_ok=True)
def test(self):
self.model.eval()
test_loss = AvgMeter()
test_mae = AvgMeter()
test_maxf = AvgMeter()
test_avgf = AvgMeter()
test_s_m = AvgMeter()
t = time.time()
Eval_tool = Evaluation_metrics(self.args.dataset, self.device)
with torch.no_grad():
for i, (images, masks, original_size, image_name) in enumerate(tqdm(self.test_loader)):
images = torch.tensor(images, device=self.device, dtype=torch.float32)
outputs, edge_mask, ds_map = self.model(images)
H, W = original_size
for i in range(images.size(0)):
mask = gt_to_tensor(masks[i])
h, w = H[i].item(), W[i].item()
output = F.interpolate(outputs[i].unsqueeze(0), size=(h, w), mode='bilinear')
loss = self.criterion(output, mask)
# Metric
mae, max_f, avg_f, s_score = Eval_tool.cal_total_metrics(output, mask)
# Save prediction map
if self.args.save_map is not None:
output = (output.squeeze().detach().cpu().numpy()*255.0).astype(np.uint8) # convert uint8 type
cv2.imwrite(os.path.join('mask', 'exp'+str(self.args.exp_num), self.args.dataset, image_name[i]+'.png'), output)
# log
test_loss.update(loss.item(), n=1)
test_mae.update(mae, n=1)
test_maxf.update(max_f, n=1)
test_avgf.update(avg_f, n=1)
test_s_m.update(s_score, n=1)
test_loss = test_loss.avg
test_mae = test_mae.avg
test_maxf = test_maxf.avg
test_avgf = test_avgf.avg
test_s_m = test_s_m.avg
print(f'Test Loss:{test_loss:.4f} | MAX_F:{test_maxf:.4f} | MAE:{test_mae:.4f} '
f'| S_Measure:{test_s_m:.4f}, time: {time.time() - t:.3f}s')
return test_loss, test_mae, test_maxf, test_avgf, test_s_m
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