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train_unet_backup.py
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train_unet_backup.py
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import torch
from torch import nn
from unet.unet_transfer import UNet16, UNetResNet
from pathlib import Path
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset, random_split
import torch.nn.functional as F
from torch.autograd import Variable
import shutil
from data_loader import ImgDataSetJoint, ImgDataSet
import os
import argparse
import tqdm
import numpy as np
import scipy.ndimage as ndimage
import albumentations as albu
from albumentations.pytorch import ToTensor
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def create_model(device, type ='vgg16'):
assert type == 'vgg16' or type == 'resnet101'
if type == 'vgg16':
print('create vgg16 model')
model = UNet16(pretrained=True)
elif type == 'resnet101':
encoder_depth = 101
num_classes = 1
print('create resnet101 model')
model = UNetResNet(encoder_depth=encoder_depth, num_classes=num_classes, pretrained=True)
else:
assert False
model.eval()
return model.to(device)
def adjust_learning_rate(optimizer, epoch, lr):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def find_latest_model_path(dir):
model_paths = []
epochs = []
for path in Path(dir).glob('*.pt'):
if 'epoch' not in path.stem:
continue
model_paths.append(path)
parts = path.stem.split('_')
epoch = int(parts[-1])
epochs.append(epoch)
if len(epochs) > 0:
epochs = np.array(epochs)
max_idx = np.argmax(epochs)
return model_paths[max_idx]
else:
return None
def train(train_loader, valid_loader, model, criterion, optimizer, validation, args):
latest_model_path = find_latest_model_path(args.model_dir)
best_model_path = os.path.join(*[args.model_dir, 'model_best.pt'])
if latest_model_path is not None:
state = torch.load(latest_model_path)
epoch = state['epoch']
model.load_state_dict(state['model'])
#if latest model path does exist, best_model_path should exists as well
assert Path(best_model_path).exists() == True, f'best model path {best_model_path} does not exist'
#load the min loss so far
best_state = torch.load(latest_model_path)
min_val_los = best_state['valid_loss']
print(f'Restored model at epoch {epoch}. Min validation loss so far is : {min_val_los}')
epoch += 1
print(f'Started training model from epoch {epoch}')
else:
print('Started training model from epoch 0')
epoch = 0
min_val_los = 9999
valid_losses = []
for epoch in range(epoch, args.n_epoch + 1):
adjust_learning_rate(optimizer, epoch, args.lr)
tq = tqdm.tqdm(total=(len(train_loader) * args.batch_size))
tq.set_description(f'Epoch {epoch}')
losses = AverageMeter()
model.train()
for i, (input, target) in enumerate(train_loader):
input_var = Variable(input).cuda()
target_var = Variable(target).cuda()
masks_pred = model(input_var)
masks_pred = masks_pred.view(-1)
target_var = target_var.view(-1)
loss = criterion(masks_pred, target_var)
losses.update(loss)
tq.set_postfix(loss='{:.5f}'.format(losses.avg))
tq.update(args.batch_size)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
valid_metrics = validation(model, valid_loader, criterion)
valid_loss = valid_metrics['valid_loss']
valid_losses.append(valid_loss)
print(f'\tvalid_loss = {valid_loss:.5f}')
tq.close()
#save the model of the current epoch
epoch_model_path = os.path.join(*[args.model_dir, f'model_epoch_{epoch}.pt'])
torch.save({
'model': model.state_dict(),
'epoch': epoch,
'valid_loss': valid_loss,
'train_loss': losses.avg
}, epoch_model_path)
if valid_loss < min_val_los:
min_val_los = valid_loss
torch.save({
'model': model.state_dict(),
'epoch': epoch,
'valid_loss': valid_loss,
'train_loss': losses.avg
}, best_model_path)
def validate(model, val_loader, criterion):
losses = AverageMeter()
model.eval()
with torch.no_grad():
for i, (input, target) in enumerate(val_loader):
input_var = Variable(input).cuda()
target_var = Variable(target).cuda()
output = model(input_var)
output = output.view(-1)
target_var = target_var.view(-1)
loss = criterion(output, target_var)
losses.update(loss.item(), input_var.size(0))
return {'valid_loss': losses.avg}
def save_check_point(state, is_best, file_name = 'checkpoint.pth.tar'):
torch.save(state, file_name)
if is_best:
shutil.copy(file_name, 'model_best.pth.tar')
def calc_crack_pixel_weight(mask_dir):
avg_w = 0.0
n_files = 0
for path in Path(mask_dir).glob('*.*'):
n_files += 1
m = ndimage.imread(path)
ncrack = np.sum((m > 0)[:])
w = float(ncrack)/(m.shape[0]*m.shape[1])
avg_w = avg_w + (1-w)
avg_w /= float(n_files)
return avg_w / (1.0 - avg_w)
def create_loader(dir, args):
img_dir = os.path.join(*[dir, 'images'])
mask_dir = os.path.join(*[dir, 'masks'])
img_names = sorted([path.name for path in Path(img_dir).glob('*.jpg')])
mask_names = sorted([path.name for path in Path(mask_dir).glob('*.jpg')])
#sanity checking'
assert len(img_names) == len(mask_names), 'mismatched number of image and masks'
for img_name, mask_name in zip(img_names, mask_names):
assert img_name == mask_name, 'mismatched image name vs mask name'
#join_tfms = albu.Compose([albu.VerticalFlip(), albu.HorizontalFlip(), albu.ShiftScaleRotate()])
join_tfms = albu.Compose([albu.VerticalFlip(), albu.HorizontalFlip()])
#img_tfms = albu.Compose([albu.RandomBrightnessContrast(), albu.RandomGamma(), albu.Normalize(), ToTensor()])
img_tfms = albu.Compose([albu.Normalize(), ToTensor()])
mask_tfms = albu.Compose([ToTensor()])
#dataset = ImgDataSetJoint(img_dir=img_dir, img_fnames=img_names, mask_dir=mask_dir, mask_fnames=mask_names, joint_transform=join_tfms, img_transform=img_tfms, mask_transform=mask_tfms)
dataset = ImgDataSet(img_dir=img_dir, img_fnames=img_names, mask_dir=mask_dir, mask_fnames=mask_names, img_transform=img_tfms, mask_transform=mask_tfms)
train_loader = DataLoader(dataset, args.batch_size, shuffle=True, pin_memory=torch.cuda.is_available(), num_workers=args.num_workers)
return train_loader
def main():
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('-data_dir', type=str, help='input dataset directory')
parser.add_argument('-model_dir', type=str, help='output dataset directory')
parser.add_argument('-model_type', type=str, required=False, default='resnet101', help='vgg16 or resnet101')
parser.add_argument('-n_epoch', default=10, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('-lr', default=0.001, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('-momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('-print_freq', default=20, type=int, metavar='N', help='print frequency (default: 10)')
parser.add_argument('-weight_decay', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('-batch_size', default=4, type=int, help='weight decay (default: 1e-4)')
parser.add_argument('-num_workers', default=4, type=int, help='output dataset directory')
args = parser.parse_args()
os.makedirs(args.model_dir, exist_ok=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = create_model(device, args.model_type)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# crack_weight = 0.4*calc_crack_pixel_weight(DIR_MASK)
# print(f'positive weight: {crack_weight}')
# criterion = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([crack_weight]).to('cuda'))
criterion = nn.BCEWithLogitsLoss().to('cuda')
train_dir = os.path.join(*[args.data_dir, 'train'])
valid_dir = os.path.join(*[args.data_dir, 'valid'])
train_loader = create_loader(train_dir, args)
valid_loader = create_loader(valid_dir, args)
model.cuda()
train(train_loader, valid_loader, model, criterion, optimizer, validate, args)
if __name__ == '__main__':
main()