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train_vqvae.py
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train_vqvae.py
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'''
#####################################################################################
Author: neel04
Credits for most of OG code: Rosinality (vq-vae-2)
#####################################################################################
'''
import argparse
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torch.cuda.amp import GradScaler, autocast
from torchvision import datasets, transforms, utils
from torchinfo import summary
import hiddenlayer as hl
import wandb
from tqdm.auto import tqdm
from vqvae import VQVAE
from scheduler import CycleScheduler
def train(loader, val_loader):
device = torch.device("cpu" if args.cpu_run else "cuda")
print(f'\nUsing Device: {device}')
scaler = GradScaler() #init grad scaler
#initializing the model
model = VQVAE(in_channel=3, channel=128, n_res_block=args.res_blocks,
n_res_channel=args.res_channel,
embed_dim=args.embed_dim, n_embed=args.n_embed,
decay=args.decay).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = None
if args.sched == "cycle":
scheduler = CycleScheduler(
optimizer,
args.lr,
n_iter=len(loader) * args.epoch,
momentum=None,
warmup_proportion=0.05,
)
print(summary(model, (args.batch_size, 3, args.size, args.size)))
print(model) #vanilla pytorch summary
hl_graph = hl.build_graph(model, torch.zeros([1, 3, 256, 256]).to(device),
transforms=[hl.transforms.Fold("Relu > Conv3x3 > Relu > Conv1x1", "ResBlock"), hl.transforms.FoldDuplicates()]) #pretty print visualization of model
hl_graph.save("./model_graph", format="pdf")
loader, val_loader = tqdm(loader), tqdm(val_loader)
criterion = nn.MSELoss()
latent_loss_weight = 0.30
latent_loss_beta_list = torch.linspace(0, latent_loss_weight, 5).tolist()
sample_size = 20
mse_sum = 0
mse_n = 0
val_mse_sum, val_mse_n, beta_index = 0, 0, 0 #init beta index
with wandb.init(project=args.wandb_project_name, config=args.__dict__, save_code=True, name=args.run_name, magic=True):
for epoch in range(args.epoch):
#Starting Epoch loops
model.train()
for i, (img, label) in enumerate(loader):
model.zero_grad(set_to_none=True)
img = img.to(device)
with autocast():
out, latent_loss = model(img)
recon_loss = criterion(out, img)
latent_loss = latent_loss.mean()
beta_index = epoch #for consistency
if beta_index > 4: #5-1
beta_index = latent_loss_beta_list[-1]
loss = recon_loss + latent_loss_beta_list[int(beta_index)] * latent_loss
scaler.scale(loss).backward() #added loss to backprop
if scheduler is not None:
scheduler.step()
scaler.unscale_(optimizer) #unscaling grads
torch.nn.utils.clip_grad_norm_(model.parameters(), args.gradclip) #grad clipping
scaler.step(optimizer)
scaler.update() #finally updating scaler
mse_sum += recon_loss.item() * img.shape[0]
mse_n += img.shape[0]
lr = optimizer.param_groups[0]['lr']
wandb.log({"epoch": epoch+1, "mse": recon_loss.item(),
"latent_loss": latent_loss.item(), "avg_mse": (mse_sum / mse_n),
"lr": lr})
if i % 25 == 0:
print({"epoch": epoch+1, "mse": recon_loss.item(),
"latent_loss": latent_loss.item(), "avg_mse": (mse_sum/ mse_n),
"lr": lr})
#Performing Validation and loggign out images
if epoch > 0 and epoch % 9 == 0: #i % 100 == 0
model.eval()
model = model.to(device)
#--------------VALIDATION------------------
for i, (img, label) in enumerate(val_loader):
img = img.to(device)
with torch.no_grad():
out, latent_loss = model(img)
val_recon_loss = criterion(out, img)
val_latent_loss = latent_loss.mean()
val_mse_sum += recon_loss.item() * img.shape[0]
val_mse_n += img.shape[0]
wandb.log({"epoch": epoch+1, "val_mse": val_recon_loss.item(),
"val_latent_loss": val_latent_loss.item(), "val_avg_mse": (val_mse_sum/ val_mse_n),
"lr": lr})
print({"epoch": epoch+1, "val_mse": val_recon_loss.item(),
"val_latent_loss": val_latent_loss.item(), "val_avg_mse": (val_mse_sum/ val_mse_n),
"lr": lr})
model.train()
#Saving the model checkpoints every epoch
if epoch % 1 == 0:
model.eval()
sample = img[:sample_size]
with torch.no_grad():
out, _ = model(sample)
utils.save_image(
torch.cat([sample, out], 0),
f'/kaggle/working/samples/{str(epoch + 1).zfill(5)}_{str(i).zfill(5)}.png',
nrow=sample_size,
normalize=True,
range=(-1, 1),
)
wandb.log({f"{epoch+1}_Samples" : [wandb.Image(img) for img in torch.cat( [sample, out], 0) ]})
torch.save(model.state_dict(), f'./checkpoint/vqvae_{str(epoch + 1).zfill(3)}.pt')
model.train()
print(f'\n\n---EPOCH {epoch} CCOMPLETED---\n\n')
if __name__ == '__main__':
'''
These are the default values:-
n_res_block=2, n_res_channel=32, embed_dim=64, n_embed=512, decay=0.99
'''
parser = argparse.ArgumentParser()
parser.add_argument('--res-blocks', type=int, default=4)
parser.add_argument('--res-channel', type=int, default=32)
parser.add_argument('--embed-dim', type=int, default=64)
parser.add_argument('--n-embed', type=int, default=512)
parser.add_argument('--gradclip', type=float, default=5)
parser.add_argument('--decay', type=float, default=0.99)
parser.add_argument('--size', type=int, default=256)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--epoch', type=int, default=200)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--sched', type=str)
parser.add_argument('--num-workers', type=int)
parser.add_argument('--wandb-project-name', type=str)
parser.add_argument('--run-name', type=str)
parser.add_argument('--cpu-run', type=bool, default=False)
parser.add_argument('training_path', type=str)
parser.add_argument('--validation-path', type=str)
args = parser.parse_args()
print(args)
transform = transforms.Compose(
[
transforms.Resize(args.size),
transforms.CenterCrop(args.size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
dataset = datasets.ImageFolder(args.training_path, transform=transform)
val_dataset = datasets.ImageFolder(args.validation_path, transform=transform)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, #True
num_workers=args.num_workers, pin_memory=True, prefetch_factor=2)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True, prefetch_factor=2)
#Finally starting the training
train(loader, val_loader)