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train.py
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train.py
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import sys
import torch
import numpy as np
from tqdm import tqdm
from transform import *
from torchvision import transforms
from torch.utils.data import DataLoader
from generator import CustomDataGenerator
from models import trans_unet, swin_unet, unet
INIT_EPOCH = 0
EPOCHS = 100
BATCH_SIZE = 64
IMG_SIZE = 224
CHANNELS = 3
LEARNING_RATE = 0.001
STEPS = 173
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using:', device)
class dice_loss(torch.nn.Module):
def __init__(self):
super(dice_loss, self).__init__()
self.smooth = 1.
def forward(self, logits, labels):
logf = torch.sigmoid(logits).view(-1)
labf = labels.view(-1)
intersection = (logf * labf).sum()
num = 2. * intersection + self.smooth
den = logf.sum() + labf.sum() + self.smooth
return 1 - (num/den)
def get_dataloader():
trainDataset = CustomDataGenerator(image_file='images_train',
mask_file='masks_train',
root_dir='dataset',
transform=transforms.Compose([
Rescale(256),
RandomFlip(0.5),
RandomRotate(0.5),
RandomErase(0.2),
RandomShear(0.2),
RandomCrop(IMG_SIZE),
ToTensor(),
])
)
# no transforms here, just resize the image
valDataset = CustomDataGenerator(image_file='images_val',
mask_file='masks_val',
root_dir='dataset',
transform=transforms.Compose([
Rescale(256),
CenterCrop(IMG_SIZE),
ToTensor(),
])
)
trainLoader = DataLoader(trainDataset, batch_size=BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=4)
valLoader = DataLoader(valDataset, batch_size=BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=4)
return iter(trainLoader), iter(valLoader)
def train(model, criterion, opt, scheduler):
global INIT_EPOCH, EPOCHS, BATCH_SIZE, SAVE_PATH, device
model.train()
for epoch in range(INIT_EPOCH, EPOCHS):
trainLoader, valLoader = get_dataloader()
# when dataloader runs out of batches, it throws an exception
try:
for batch in tqdm(trainLoader):
images = batch['image'].to(device)
labels = batch['mask'].to(device)
opt.zero_grad(set_to_none=True) # clear gradients w.r.t. parameters
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward() # getting gradients
opt.step() # updating parameters
scheduler.step() # to change the learing rate
except StopIteration:
pass
# get model performace on val set
with torch.no_grad():
accuracy = []
try:
for batch in tqdm(valLoader):
images = batch['image'].to(device)
labels = batch['mask'].to(device)
outputs = model(images)
ls = criterion(outputs, labels).item()
accuracy += [1 - ls]
except StopIteration:
pass
print('Epoch: {}/{} - accuracy: {:.4f}'.format(epoch+1, EPOCHS, np.mean(accuracy)))
# save model checkpoint
if (epoch + 1) % 5 == 0:
torch.save({'epoch': epoch + 1,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': opt.state_dict(),
'loss': loss,
}, SAVE_PATH)
print('checkpoint saved.')
def get_model(name):
global IMG_SIZE, BATCH_SIZE, CHANNELS, device
model, path = None, None
if name == 'swin':
model = swin_unet(IMG_SIZE, BATCH_SIZE).to(device)
path = './train_output/model_checkpoint_swinUnet.pt'
elif name == 'trans':
model = trans_unet(IMG_SIZE).to(device)
path = './train_output/model_checkpoint_transUnet.pt'
elif name == 'unet':
model = unet(n_channels=CHANNELS).to(device)
path = './train_output/model_checkpoint_unetours.pt'
return model, path
if __name__ == '__main__':
# plug-in your model here
NAME = sys.argv[1]
model, SAVE_PATH = get_model(NAME)
print(SAVE_PATH)
criterion = dice_loss()
opt = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.OneCycleLR(opt,
max_lr=LEARNING_RATE*10,
steps_per_epoch=STEPS,
pct_start=0.15,
epochs=EPOCHS
)
# start from last checkpoint
if INIT_EPOCH > 0:
checkpoint = torch.load(SAVE_PATH)
model.load_state_dict(checkpoint['model_state_dict'])
opt.load_state_dict(checkpoint['optimizer_state_dict'])
INIT_EPOCH = checkpoint['epoch']
# loss = checkpoint['loss']
train(model, criterion, opt, scheduler)