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train_ddp.py
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train_ddp.py
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import torch
import torch.nn as nn
import os
import argparse
import src.utils as utils
import yaml
import os
from src.dataset import create_dataset, gaussian_dequantize
from src.model_utils import flow, get_loss
from src.models import get_model
from src.ema import EMAHelper
from torchvision.utils import make_grid, save_image
import wandb
import time
def main():
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--config', type=str, help='Path to config file.')
group.add_argument('--ckpt', type=str, help='Path to checkpoint file.')
args = parser.parse_args()
if args.ckpt:
ckpt = torch.load(args.ckpt, map_location='cpu')
config = ckpt['config']
else:
config = utils.get_config_and_setup_dirs(args.config)
parser = utils.add_config_to_argparser(config=config, parser=parser)
args = parser.parse_args()
# Update config from command line args, if any.
config.update(vars(args))
log_dir = config['log_dir']
if config['use_wandb'] == True:
wandb.init(project="ddp", config=config, notes=config['wandb_notes'],
id=config['wandb_id'], resume='allow')
config['run_name'] = wandb.run.name
if not args.ckpt:
with open(os.path.join(log_dir, 'config.yaml'), 'w') as fp:
yaml.dump(config, fp)
# gpu information
device = config['device']
if isinstance(device, list):
multi_gpu = True
device_ids = device
device = torch.device("cuda:" + str(device_ids[0])) # if multi-GPUs, set default device to the first gpu
else:
multi_gpu = False
# initialize data
real_dataloader = create_dataset(config)
data_iterator = iter(real_dataloader)
# create model/optimizer/lr scheduler
model = get_model(config)
if multi_gpu:
model = nn.DataParallel(model, device_ids=device_ids)
model = model.to(device)
optim = torch.optim.Adam(model.parameters(), lr=config['learning_rate'], betas=[0.9, 0.999])
scheduler = torch.optim.lr_scheduler.ExponentialLR(optim, config['scheduler_gamma'])
# create ema helper
if config['ema']:
ema_helper = EMAHelper(mu=config['ema_rate'])
ema_helper.register(model)
# load config information
n_epochs = config['n_epochs']
scheduler_steps = config['scheduler_steps'].copy()
loss_func = config['loss_function']
f_divergence = config['f_divergence']
eta = config['eta']
noise_factor = config['noise_factor']
n_flow_steps = config['n_flow_steps']
# if loading from ckpt, load ckpt
if args.ckpt:
model.load_state_dict(ckpt['model'])
optim.load_state_dict(ckpt['optim'])
ema_helper.load_state_dict(ckpt['ema'])
ema_helper.to(device) # state_dict from ckpt defaults to cpu
scheduler.load_state_dict(ckpt['scheduler'])
start_step = ckpt['step'] + 1
while len(scheduler_steps)!=0 and scheduler_steps[0] <= start_step:
scheduler_steps.pop(0)
else:
start_step = 0
# load learned prior
ddp = torch.load(str(config['dataset']) + "_ddp.pt")
# create test noise for tracking progress of training
test_noise = ddp.sample([config['test_batch_size']]).reshape(-1,3,
config['img_size'], config['img_size']).clamp(-1.,1.).to(device)
time_start = time.time()
next_scheduler_step = scheduler_steps.pop(0) if len(scheduler_steps) != 0 else None
for i in range(start_step, n_epochs):
try:
x_de = next(data_iterator)[0].to(device)
except StopIteration:
data_iterator = iter(real_dataloader)
x_de = next(data_iterator)[0].to(device)
x_de = gaussian_dequantize(x_de, sigma=3e-2)
x_nu = ddp.sample([x_de.size(0)]).reshape(-1,3,
config['img_size'], config['img_size']).clamp(-1.,1.).to(device)
x_nu = flow(x_nu, model, n_flow_steps, eta, noise_factor, f_divergence)
model_x_nu = model(x_nu)
model_x_de = model(x_de)
loss, r_x_nu, r_x_de, first, second = get_loss(loss_func, model_x_nu, model_x_de)
optim.zero_grad()
loss.backward()
optim.step()
if config['ema']:
ema_helper.update(model)
if next_scheduler_step is not None and (i+1) == next_scheduler_step:
scheduler.step()
next_scheduler_step = scheduler_steps.pop(0) if len(scheduler_steps) != 0 else None
if config['use_wandb'] == True:
wandb.log({"first_term": first,
"second_term": second,
"loss": loss.item(),
"r_x_nu": r_x_nu,
"r_x_de": r_x_de
})
if (i+1) % config['print_steps'] == 0 or (i+1) == n_epochs:
print(f"Loss for step {i} is {loss}")
print(f"R_x_nu is {r_x_nu}, r_x_de is {r_x_de}")
time_end = time.time()
print(f"Time taken for steps {i+1-config['print_steps']}-{i} is \
{time_end-time_start} seconds\n")
time_start = time.time()
if (i+1) % config['log_steps'] == 0 or (i+1) == n_epochs:
# generate test samples
test_model = ema_helper.ema_copy(model) if config['ema'] else model
test_x = test_noise.clone().detach().to(device)
test_x = flow(test_x, test_model, n_flow_steps, eta, noise_factor, f_divergence).cpu()
img_path = os.path.join(log_dir, "step_" + str(i) + ".png")
grid = make_grid(test_x.clamp(-1.,1.), nrow=8, normalize=True)
save_image(grid, img_path)
if config['use_wandb'] == True:
images = wandb.Image(grid, caption=f"Step {i}")
wandb.log({"generated images": images})
if (i+1) % config['save_steps'] == 0 or (i+1) == n_epochs:
torch.save({'step': i,
'model': model.state_dict(),
'optim': optim.state_dict(),
'ema': ema_helper.state_dict(),
'scheduler': scheduler.state_dict(),
'config': config
},
os.path.join(config['ckpt_dir'], config['dataset'] + '_' + config['loss_function'] + \
'_' + config['f_divergence'] + '.pt'))
if __name__ == "__main__":
main()