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train.py
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train.py
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import numpy as np
import imageio
import os
import time
import torch
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from data import set_up_data
from utils import get_cpu_stats_over_ranks
from train_helpers import set_up_hyperparams, load_vaes, load_opt, accumulate_stats, save_model, update_ema
def training_step(H, data_input, target, vae, ema_vae, optimizer, iterate):
t0 = time.time()
vae.zero_grad()
stats = vae.forward(data_input, target)
stats['elbo'].backward()
grad_norm = torch.nn.utils.clip_grad_norm_(vae.parameters(), H.grad_clip).item()
distortion_nans = torch.isnan(stats['distortion']).sum()
rate_nans = torch.isnan(stats['rate']).sum()
stats.update(dict(rate_nans=0 if rate_nans == 0 else 1, distortion_nans=0 if distortion_nans == 0 else 1))
stats = get_cpu_stats_over_ranks(stats)
skipped_updates = 1
# only update if no rank has a nan and if the grad norm is below a specific threshold
if stats['distortion_nans'] == 0 and stats['rate_nans'] == 0 and (H.skip_threshold == -1 or grad_norm < H.skip_threshold):
optimizer.step()
skipped_updates = 0
update_ema(vae, ema_vae, H.ema_rate)
t1 = time.time()
stats.update(skipped_updates=skipped_updates, iter_time=t1 - t0, grad_norm=grad_norm)
return stats
def eval_step(data_input, target, ema_vae):
with torch.no_grad():
stats = ema_vae.forward(data_input, target)
stats = get_cpu_stats_over_ranks(stats)
return stats
def get_sample_for_visualization(data, preprocess_fn, num, dataset):
for x in DataLoader(data, batch_size=num):
break
orig_image = (x[0] * 255.0).to(torch.uint8).permute(0, 2, 3, 1) if dataset == 'ffhq_1024' else x[0]
preprocessed = preprocess_fn(x)[0]
return orig_image, preprocessed
def train_loop(H, data_train, data_valid, preprocess_fn, vae, ema_vae, logprint):
optimizer, scheduler, cur_eval_loss, iterate, starting_epoch = load_opt(H, vae, logprint)
train_sampler = DistributedSampler(data_train, num_replicas=H.mpi_size, rank=H.rank)
viz_batch_original, viz_batch_processed = get_sample_for_visualization(data_valid, preprocess_fn, H.num_images_visualize, H.dataset)
early_evals = set([1] + [2 ** exp for exp in range(3, 14)])
stats = []
iters_since_starting = 0
H.ema_rate = torch.as_tensor(H.ema_rate).cuda()
for epoch in range(starting_epoch, H.num_epochs):
train_sampler.set_epoch(epoch)
for x in DataLoader(data_train, batch_size=H.n_batch, drop_last=True, pin_memory=True, sampler=train_sampler):
data_input, target = preprocess_fn(x)
training_stats = training_step(H, data_input, target, vae, ema_vae, optimizer, iterate)
stats.append(training_stats)
scheduler.step()
if iterate % H.iters_per_print == 0 or iters_since_starting in early_evals:
logprint(model=H.desc, type='train_loss', lr=scheduler.get_last_lr()[0], epoch=epoch, step=iterate, **accumulate_stats(stats, H.iters_per_print))
if iterate % H.iters_per_images == 0 or (iters_since_starting in early_evals and H.dataset != 'ffhq_1024') and H.rank == 0:
write_images(H, ema_vae, viz_batch_original, viz_batch_processed, f'{H.save_dir}/samples-{iterate}.png', logprint)
iterate += 1
iters_since_starting += 1
if iterate % H.iters_per_save == 0 and H.rank == 0:
if np.isfinite(stats[-1]['elbo']):
logprint(model=H.desc, type='train_loss', epoch=epoch, step=iterate, **accumulate_stats(stats, H.iters_per_print))
fp = os.path.join(H.save_dir, 'latest')
logprint(f'Saving model@ {iterate} to {fp}')
save_model(fp, vae, ema_vae, optimizer, H)
if iterate % H.iters_per_ckpt == 0 and H.rank == 0:
save_model(os.path.join(H.save_dir, f'iter-{iterate}'), vae, ema_vae, optimizer, H)
if epoch % H.epochs_per_eval == 0:
valid_stats = evaluate(H, ema_vae, data_valid, preprocess_fn)
logprint(model=H.desc, type='eval_loss', epoch=epoch, step=iterate, **valid_stats)
def evaluate(H, ema_vae, data_valid, preprocess_fn):
stats_valid = []
valid_sampler = DistributedSampler(data_valid, num_replicas=H.mpi_size, rank=H.rank)
for x in DataLoader(data_valid, batch_size=H.n_batch, drop_last=True, pin_memory=True, sampler=valid_sampler):
data_input, target = preprocess_fn(x)
stats_valid.append(eval_step(data_input, target, ema_vae))
vals = [a['elbo'] for a in stats_valid]
finites = np.array(vals)[np.isfinite(vals)]
stats = dict(n_batches=len(vals), filtered_elbo=np.mean(finites), **{k: np.mean([a[k] for a in stats_valid]) for k in stats_valid[-1]})
return stats
def write_images(H, ema_vae, viz_batch_original, viz_batch_processed, fname, logprint):
zs = [s['z'].cuda() for s in ema_vae.forward_get_latents(viz_batch_processed)]
batches = [viz_batch_original.numpy()]
mb = viz_batch_processed.shape[0]
lv_points = np.floor(np.linspace(0, 1, H.num_variables_visualize + 2) * len(zs)).astype(int)[1:-1]
for i in lv_points:
batches.append(ema_vae.forward_samples_set_latents(mb, zs[:i], t=0.1))
for t in [1.0, 0.9, 0.8, 0.7][:H.num_temperatures_visualize]:
batches.append(ema_vae.forward_uncond_samples(mb, t=t))
n_rows = len(batches)
im = np.concatenate(batches, axis=0).reshape((n_rows, mb, *viz_batch_processed.shape[1:])).transpose([0, 2, 1, 3, 4]).reshape([n_rows * viz_batch_processed.shape[1], mb * viz_batch_processed.shape[2], 3])
logprint(f'printing samples to {fname}')
imageio.imwrite(fname, im)
def run_test_eval(H, ema_vae, data_test, preprocess_fn, logprint):
print('evaluating')
stats = evaluate(H, ema_vae, data_test, preprocess_fn)
print('test results')
for k in stats:
print(k, stats[k])
logprint(type='test_loss', **stats)
def main():
H, logprint = set_up_hyperparams()
H, data_train, data_valid_or_test, preprocess_fn = set_up_data(H)
vae, ema_vae = load_vaes(H, logprint)
if H.test_eval:
run_test_eval(H, ema_vae, data_valid_or_test, preprocess_fn, logprint)
else:
train_loop(H, data_train, data_valid_or_test, preprocess_fn, vae, ema_vae, logprint)
if __name__ == "__main__":
main()