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main.py
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import time
import torch
from torch import nn
from torchvision import transforms
from config_reader import ConfigReader
from grayscalefolder import GrayscaleImageFolder
from model import GrayscaleToColorModel, Trainer
def main():
"""
Loads the training and validation data and trains the model.
"""
# a = torch.cuda.FloatTensor()
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# print(device)
# print(torch.cuda.is_available())
# print(torch.version.cuda)
use_gpu = torch.cuda.is_available()
config = ConfigReader.read()
model = GrayscaleToColorModel(kernel_size=3, activation=nn.ReLU())
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
if use_gpu:
criterion = criterion.cuda()
model = model.cuda()
train_transforms = transforms.Compose(
[transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip()])
train_imagefolder = GrayscaleImageFolder(
f'{config["train_val_split_dir"]}/train', train_transforms, False)
train_loader = torch.utils.data.DataLoader(
train_imagefolder, batch_size=64, shuffle=True)
validation_transforms = transforms.Compose(
[transforms.Resize(256), transforms.CenterCrop(224)])
validation_imagefolder = GrayscaleImageFolder(
f'{config["train_val_split_dir"]}/val', validation_transforms, False)
validation_loader = torch.utils.data.DataLoader(
validation_imagefolder, batch_size=64, shuffle=False)
save_images = True
max_losses = 1e10
epochs = 100
path_to_save = {'grayscale': 'outputs/gray/',
'color': 'outputs/color/'}
lr = 0.01
for lr_count in range(3):
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
lr += 0.05
for epoch in range(epochs):
start = time.time()
Trainer.train_model(train_loader, model,
criterion, optimizer, epoch)
end = time.time()
print(f'Epoch {epoch + 1} took {end - start} seconds')
with torch.no_grad():
losses = Trainer.validate_model(
validation_loader, model, criterion, save_images, path_to_save, epoch)
if losses < max_losses:
max_losses = losses
torch.save(model.state_dict(),
f'ckpt/model-epoch-{epoch + 1}-losses-{losses}')
# image = Image.open(image_file)
#
# # random resize crop and save image
# randresizecrop = RandomResizedCrop(128)
# image = randresizecrop(image)
# lab_image = convertToLAB(image)
# plot_LAB(image, lab_image)
if __name__ == '__main__':
main()