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train_CASENet_edge.py
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###########################################################################
# Created by: Yuan Hu
# Email: huyuan@radi.ac.cn
# Copyright (c) 2019
###########################################################################
import os
import copy
import time
import numpy as np
import torch
from modeling.sync_batchnorm.replicate import patch_replication_callback
from dataloaders.datasets.bsds_hd5_dim1 import Mydataset
from torch.utils.data import DataLoader
import modeling.dff_encoding.utils as utils
from utils.DFF_losses import EdgeDetectionReweightedLossesSingle
from modeling.CASENet_edge import CaseNet
from utils.saver import Saver
from utils.summaries import TensorboardSummary
from my_options.CaseNet_options import Options
from tqdm import tqdm
import scipy.io as sio
class Trainer(object):
def __init__(self, args):
self.args = args
# Define Saver
self.saver = Saver(args)
self.saver.save_experiment_config()
# Define Tensorboard Summary
self.summary = TensorboardSummary(self.saver.experiment_dir)
self.writer = self.summary.create_summary()
print(self.saver.experiment_dir)
# Define Dataloader
self.train_dataset = Mydataset(root_path=self.args.data_path, split='trainval', crop_size=self.args.crop_size)
self.test_dataset = Mydataset(root_path=self.args.data_path, split='test', crop_size=self.args.crop_size)
self.train_loader = DataLoader(self.train_dataset, batch_size=self.args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True)
self.test_loader = DataLoader(self.test_dataset, batch_size=1, shuffle=False,
num_workers=args.workers)
self.class_num = 1
# Define network
model = CaseNet(self.class_num, backbone=self.args.backbone)
# optimizer using different LR
if args.model == 'dff': # dff
params_list = [{'params': model.pretrained.parameters(), 'lr': self.args.lr},
{'params': model.ada_learner.parameters(), 'lr': self.args.lr*10},
{'params': model.side1.parameters(), 'lr': self.args.lr*10},
{'params': model.side2.parameters(), 'lr': self.args.lr*10},
{'params': model.side3.parameters(), 'lr': self.args.lr*10},
{'params': model.side5.parameters(), 'lr': self.args.lr*10},
{'params': model.side5_w.parameters(), 'lr': self.args.lr*10}]
else: # casenet
assert args.model == 'casenet'
params_list = [{'params': model.pretrained.parameters(), 'lr': self.args.lr},
{'params': model.side1.parameters(), 'lr': self.args.lr*10},
{'params': model.side2.parameters(), 'lr': self.args.lr*10},
{'params': model.side3.parameters(), 'lr': self.args.lr*10},
{'params': model.side5.parameters(), 'lr': self.args.lr*10},
{'params': model.fuse.parameters(), 'lr': self.args.lr*10}]
optimizer = torch.optim.SGD(params_list,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
self.criterion = EdgeDetectionReweightedLossesSingle()
self.model, self.optimizer = model, optimizer
# Using cuda
if self.args.cuda:
self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
patch_replication_callback(self.model)
self.model = self.model.cuda()
# finetune from a trained model
if args.ft:
args.start_epoch = 0
checkpoint = torch.load(args.ft_resume)
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'], strict=False)
else:
self.model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(args.ft_resume, checkpoint['epoch']))
# resuming checkpoint
self.best_pred = 0.0
if args.resume:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
# lr scheduler
self.scheduler = utils.LR_Scheduler(self.args.lr_scheduler, self.args.lr, self.args.epochs, len(self.train_loader), lr_step=self.args.lr_step)
def training(self, epoch):
self.model.train()
tbar = tqdm(self.train_loader)
train_loss = 0.
train_loss_all = 0.
for i, (image, target) in enumerate(tbar):
self.scheduler(self.optimizer, i, epoch)
self.optimizer.zero_grad()
image = image.cuda()
target = target.cuda()
target = target.unsqueeze(1)
outputs = self.model(image.float())
loss = self.criterion(outputs, target)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
train_loss_all += loss.item()
tbar.set_description('train-loss: %.4f' % (train_loss_all / (i + 1)))
if i == 0 or (i+1) % 20 == 0:
#train_loss = train_loss / min(20, i + 1)
train_loss = 0.
print('-> Epoch [%d], Train epoch loss: %.3f' % (
epoch + 1, train_loss_all / (i + 1)))
if self.args.no_val:
# save checkpoint every epoch
if (epoch + 1) % 70 == 0:
is_best = False
self.saver.save_checkpoint({
'epoch': epoch + 1,
'state_dict': self.model.module.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
def test(self, epoch):
print('Test epoch: %d' % epoch)
self.output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1), 'mat')
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
self.model.eval()
tbar = tqdm(self.test_loader, desc='\r')
for i, image in enumerate(tbar):
name = self.test_loader.dataset.images_name[i]
if self.args.cuda:
image = image.cuda()
crop_h,crop_w = image.size(2),image.size(3)
image = image[:,:,0:crop_h-1,0:crop_w-1]
with torch.no_grad():
output_list = self.model(image)
pred2 = output_list[-1]
pred2 = torch.sigmoid(pred2)
pred = torch.zeros(1, 1, crop_h, crop_w)
pred[:, :, 0:crop_h - 1, 0:crop_w - 1] = pred2
pred = pred.squeeze()
pred = pred.data.cpu().numpy()
sio.savemat(os.path.join(self.output_dir, '{}.mat'.format(name)), {'result': pred})
if __name__ == "__main__":
args = Options().parse()
#args.cuda = True
args.cuda = not args.no_cuda and torch.cuda.is_available()
print(args.cuda)
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1:
args.sync_bn = True
else:
args.sync_bn = False
torch.manual_seed(args.seed)
args.data_path = 'data/BSDS-RIND/BSDS-RIND-Edge/Augmentation/'
print(args)
trainer = Trainer(args)
print(['Starting Epoch:', str(args.start_epoch)])
print(['Total Epoches:', str(args.epochs)])
for epoch in range(args.start_epoch, args.epochs):
trainer.test(epoch)
trainer.training(epoch)
if (epoch + 1) % 10 == 0:
trainer.test(epoch)