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import os
import glob
import numpy as np
import wandb
import copy
import argparse
import matplotlib.pyplot as plt
import torch
import torchvision.transforms as transforms
from torchinfo import summary
from utils import StyleContentDataset, DataStore, denorm_img
from loss import Loss
from model import Model
config = {
"lr": 1e-4,
"max_iter": 80000,
"logging_interval": 100,
"preview_interval": 1000,
"batch_size": 4,
"activations": "ReLU",
"optimizer": "Adam",
"lambda": 7
}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using {device} device")
def prepare_data(style_dir, content_dir, preview_dir):
norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# Training images
transform = transforms.Compose([transforms.Resize(512), transforms.RandomCrop(256)])
style_imgs = glob.glob(os.path.join(style_dir, '*.jpg'))
content_imgs = glob.glob(os.path.join(content_dir, '*.jpg'))
train_dataset = StyleContentDataset(style_imgs, content_imgs, transform=transform, normalize=norm)
datastore = DataStore(train_dataset, batch_size=config['batch_size'], shuffle=True)
# Preview images
transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(256)])
preview_style_imgs = glob.glob(os.path.join(preview_dir, 'style/*.jpg'))
preview_content_imgs = glob.glob(os.path.join(preview_dir, 'content/*.jpg'))
# preview_dataset = StyleContentDataset(preview_style_imgs, preview_content_imgs, transform=transform, normalize=norm)
preview_dataset = StyleContentDataset(preview_style_imgs, [preview_content_imgs[8]] * len(preview_style_imgs), transform=transform, normalize=norm)
preview_datastore = DataStore(preview_dataset, batch_size=len(preview_dataset), shuffle=False)
return datastore, preview_datastore
def preview(model: Model, datastore: DataStore, iteration, save=False, use_wandb=False):
model.eval()
with torch.no_grad():
# np.random.shuffle(datastore.dataset.style_imgs)
# np.random.shuffle(datastore.dataset.content_imgs)
style, content = datastore.get()
style, content = style.to(device), content.to(device)
out = model(content, style)
fig, axs = plt.subplots(8, 6, figsize=(20, 26))
axs = axs.flatten()
i = 0
for (s, c, o) in zip(style, content, out): # style, content, out
axs[i].imshow(denorm_img(s.cpu()).permute(1, 2, 0))
axs[i].axis('off')
axs[i].set_title('style')
axs[i+1].imshow(denorm_img(c.cpu()).permute(1, 2, 0))
axs[i+1].axis('off')
axs[i+1].set_title('content')
axs[i+2].imshow(denorm_img(o.cpu()).permute(1, 2, 0))
axs[i+2].axis('off')
axs[i+2].set_title('output')
i += 3
if save:
fig.savefig(f'outputs/{iteration}_preview.png')
plt.close(fig)
if use_wandb:
wandb.log({'preview': wandb.Image(f'outputs/{iteration}_preview.png')}, step=iteration)
def train_one_iter(datastore: DataStore, model: Model, optimizer: torch.optim.Adam, loss_fn: Loss):
model.train()
style, content = datastore.get()
style, content = style.to(device), content.to(device)
optimizer.zero_grad()
# Forward
out = model(content, style)
# Save activations
style_activations = copy.deepcopy(model.activations)
enc_out = model.encoder(out)
out_activations = model.activations
# Compute loss
loss = loss_fn(enc_out, model.t, out_activations, style_activations)
# Update parameters
loss.backward()
optimizer.step()
return loss.item(), loss_fn.loss_c.item(), loss_fn.loss_s.item()
def train(datastore, preview_datastore, model: Model, optimizer: torch.optim.Adam, use_wandb=False):
train_history = {'style_loss': [], 'content_loss': [], 'loss': []}
# optimizer = torch.optim.Adam(model.decoder.parameters(), lr=config['lr'])
loss_fn = Loss(lamb=config['lambda'])
for i in range(config['max_iter']):
loss, content_loss, style_loss = train_one_iter(datastore, model, optimizer, loss_fn)
train_history['loss'].append(loss)
train_history['style_loss'].append(style_loss)
train_history['content_loss'].append(content_loss)
if i%config['logging_interval'] == 0:
print(f'iter: {i}')
print(f'loss: {loss:>5f}, style loss: {style_loss:>5f}, content loss: {content_loss:>5f}')
print('-------------------------------')
if use_wandb:
wandb.log({
'iter': i, 'loss': loss, 'style_loss': style_loss, 'content_loss': content_loss
})
if i%config['preview_interval'] == 0:
torch.save({
'iter': i, 'model_state': model.state_dict(), 'optimizer_state': optimizer.state_dict()
}, 'outputs/checkpoint.pt')
preview(model, preview_datastore, i, save=True, use_wandb=use_wandb)
return train_history
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--content_path', type=str, help='path to content dataset')
parser.add_argument('--style_path', type=str, help='path to content dataset')
parser.add_argument('--preview_path', type=str, help='path to preview dataset')
parser.add_argument('--wandb', type=str, help='wandb id')
parser.add_argument('--model_path', type=str, help='path to model')
args = parser.parse_args()
use_wandb = False
wandb_key = args.wandb
if wandb_key:
wandb.login(key=wandb_key)
wandb.init(project="assignment-3", name="", reinit=True, config=config)
use_wandb = True
if args.content_path and args.style_path and args.preview_path:
content_dir = args.content_path
style_dir = args.style_path
preview_dir = args.preview_path
else:
print('You didnt specify the data path >:(')
return
if not os.path.isdir('outputs'):
os.mkdir('outputs')
datastore, preview_datastore = prepare_data(style_dir, content_dir, preview_dir)
model = Model()
optimizer = torch.optim.Adam(model.decoder.parameters(), lr=config['lr'])
if args.model_path:
# From checkpoint
checkpoint = torch.load('outputs/checkpoint.pt')
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
config['max_iter'] -= checkpoint['iter']
# From final model
# model.load_state_dict(torch.load(args.model_path, map_location=torch.device(device)))
# print(summary(model))
model.to(device)
train(datastore, preview_datastore, model, optimizer, use_wandb)
torch.save(model.state_dict(), 'outputs/model.pt')
if use_wandb:
artifact = wandb.Artifact('model', type='model')
artifact.add_file('outputs/model.pt')
wandb.log_artifact(artifact)
wandb.finish()
if __name__ == '__main__':
main() |