def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
if not hasattr(args, 'ada_groups'):
logging.info('old model, no ada groups was found.')
args.ada_groups = False
if not hasattr(args, 'min_groups_per_scale'):
logging.info('old model, no min_groups_per_scale was found.')
args.min_groups_per_scale = 1
if not hasattr(args, 'num_mixture_dec'):
logging.info('old model, no num_mixture_dec was found.')
args.num_mixture_dec = 10
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
# Loading is not strict because of self.weight_normalized in Conv2D class in neural_operations. This variable
# is only used for computing the spectral normalization and it is safe not to load it. Some of our earlier models
# did not have this variable.
model.load_state_dict(checkpoint['state_dict'], strict=False)
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
# get number of bits
num_output = utils.num_output(args.dataset)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(
valid_queue,
model,
num_samples=eval_args.num_iw_samples,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f',
valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model,
bn_eval_mode,
num_samples=36,
t=eval_args.temp,
iter=500)
for ind in range(10): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
output_tiled = utils.tile_image(output_img,
n).cpu().numpy().transpose(
1, 2, 0)
logging.info('sampling time per batch: %0.3f sec',
(end - start))
output_tiled = np.asarray(output_tiled * 255, dtype=np.uint8)
output_tiled = np.squeeze(output_tiled)
plt.imshow(output_tiled)
plt.show()
def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
if not hasattr(args, 'ada_groups'):
logging.info('old model, no ada groups was found.')
args.ada_groups = False
if not hasattr(args, 'min_groups_per_scale'):
logging.info('old model, no min_groups_per_scale was found.')
args.min_groups_per_scale = 1
if not hasattr(args, 'num_mixture_dec'):
logging.info('old model, no num_mixture_dec was found.')
args.num_mixture_dec = 10
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
# Loading is not strict because of self.weight_normalized in Conv2D class in neural_operations. This variable
# is only used for computing the spectral normalization and it is safe not to load it. Some of our earlier models
# did not have this variable.
model.load_state_dict(checkpoint['state_dict'], strict=False)
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
# get number of bits
num_output = utils.num_output(args.dataset)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(
valid_queue,
model,
num_samples=eval_args.num_iw_samples,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f',
valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model,
bn_eval_mode,
num_samples=36,
t=eval_args.temp,
iter=500)
for ind in range(10): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
# save images to 'results/eval-x/images/epochn' where x is exp id and n is epoch muber
# print("tensor shape: {}".format(output_img.shape))
# try saving the images one my one
path_to_images = '/content/gdrive/MyDrive/pipeline_results/NVAE/results/eval-1/images'
if not os.path.exists(path_to_images):
os.makedirs(path_to_images)
for i in range(output_img.size(0)):
vutils.save_image(output_img[i, :, :, :],
'%s/sample_batch%03d_img%03d.png' %
(path_to_images, ind + 1, i + 1),
normalize=True)
def main(args):
# ensures that weight initializations are all the same
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
logging = utils.Logger(args.global_rank, args.save)
writer = utils.Writer(args.global_rank, args.save)
# Get data loaders.
train_queue, valid_queue, num_classes, _ = datasets.get_loaders(args)
args.num_total_iter = len(train_queue) * args.epochs
warmup_iters = len(train_queue) * args.warmup_epochs
swa_start = len(train_queue) * (args.epochs - 1)
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, writer, arch_instance)
model = model.cuda()
logging.info('args = %s', args)
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
logging.info('groups per scale: %s, total_groups: %d',
model.groups_per_scale, sum(model.groups_per_scale))
if args.fast_adamax:
# Fast adamax has the same functionality as torch.optim.Adamax, except it is faster.
cnn_optimizer = Adamax(model.parameters(),
args.learning_rate,
weight_decay=args.weight_decay,
eps=1e-3)
else:
cnn_optimizer = torch.optim.Adamax(model.parameters(),
args.learning_rate,
weight_decay=args.weight_decay,
eps=1e-3)
cnn_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
cnn_optimizer,
float(args.epochs - args.warmup_epochs - 1),
eta_min=args.learning_rate_min)
grad_scalar = GradScaler(2**10)
num_output = utils.num_output(args.dataset, args)
bpd_coeff = 1. / np.log(2.) / num_output
# if load
checkpoint_file = os.path.join(args.save, 'checkpoint.pt')
if args.cont_training:
logging.info('loading the model.')
checkpoint = torch.load(checkpoint_file, map_location='cpu')
init_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
model = model.cuda()
cnn_optimizer.load_state_dict(checkpoint['optimizer'])
grad_scalar.load_state_dict(checkpoint['grad_scalar'])
cnn_scheduler.load_state_dict(checkpoint['scheduler'])
global_step = checkpoint['global_step']
else:
global_step, init_epoch = 0, 0
for epoch in range(init_epoch, args.epochs):
# update lrs.
if args.distributed:
train_queue.sampler.set_epoch(global_step + args.seed)
valid_queue.sampler.set_epoch(0)
if epoch > args.warmup_epochs:
cnn_scheduler.step()
# Logging.
logging.info('epoch %d', epoch)
# Training.
train_nelbo, global_step = train(train_queue, model, cnn_optimizer,
grad_scalar, global_step,
warmup_iters, writer, logging)
logging.info('train_nelbo %f', train_nelbo)
writer.add_scalar('train/nelbo', train_nelbo, global_step)
model.eval()
# generate samples less frequently
eval_freq = 1 if args.epochs <= 50 else 20
if epoch % eval_freq == 0 or epoch == (args.epochs - 1):
with torch.no_grad():
num_samples = 16
n = int(np.floor(np.sqrt(num_samples)))
for t in [0.7, 0.8, 0.9, 1.0]:
logits = model.sample(num_samples, t)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(
output, torch.distributions.bernoulli.Bernoulli
) else output.sample(t)
output_tiled = utils.tile_image(output_img, n)
writer.add_image('generated_%0.1f' % t, output_tiled,
global_step)
valid_neg_log_p, valid_nelbo = test(valid_queue,
model,
num_samples=10,
args=args,
logging=logging)
logging.info('valid_nelbo %f', valid_nelbo)
logging.info('valid neg log p %f', valid_neg_log_p)
logging.info('valid bpd elbo %f', valid_nelbo * bpd_coeff)
logging.info('valid bpd log p %f', valid_neg_log_p * bpd_coeff)
writer.add_scalar('val/neg_log_p', valid_neg_log_p, epoch)
writer.add_scalar('val/nelbo', valid_nelbo, epoch)
writer.add_scalar('val/bpd_log_p', valid_neg_log_p * bpd_coeff,
epoch)
writer.add_scalar('val/bpd_elbo', valid_nelbo * bpd_coeff, epoch)
save_freq = int(np.ceil(args.epochs / 100))
if epoch % save_freq == 0 or epoch == (args.epochs - 1):
if args.global_rank == 0:
logging.info('saving the model.')
torch.save(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': cnn_optimizer.state_dict(),
'global_step': global_step,
'args': args,
'arch_instance': arch_instance,
'scheduler': cnn_scheduler.state_dict(),
'grad_scalar': grad_scalar.state_dict()
}, checkpoint_file)
# Final validation
valid_neg_log_p, valid_nelbo = test(valid_queue,
model,
num_samples=1000,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
writer.add_scalar('val/neg_log_p', valid_neg_log_p, epoch + 1)
writer.add_scalar('val/nelbo', valid_nelbo, epoch + 1)
writer.add_scalar('val/bpd_log_p', valid_neg_log_p * bpd_coeff, epoch + 1)
writer.add_scalar('val/bpd_elbo', valid_nelbo * bpd_coeff, epoch + 1)
writer.close()
def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
model.load_state_dict(checkpoint['state_dict'])
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes, test_queue = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
if eval_args.eval_on_test:
logging.info('Using the test data for eval.')
valid_queue = test_queue
# get number of bits
num_output = utils.num_output(args.dataset, args)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(valid_queue, model, num_samples=eval_args.num_iw_samples, args=args, logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f', valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model, bn_eval_mode, num_samples=36, t=eval_args.temp, iter=500)
for ind in range(eval_args.repetition): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
# save to file
total_name = "{}/data_to_save_{}_{}.pickle".format(eval_args.save, eval_args.name_to_save, ind)
with open(total_name, 'wb') as handle:
pickle.dump(output_img.deatach().numpy(), handle, protocol=pickle.HIGHEST_PROTOCOL)
output_tiled = utils.tile_image(output_img, n).cpu().numpy().transpose(1, 2, 0)
logging.info('sampling time per batch: %0.3f sec', (end - start))
output_tiled = np.asarray(output_tiled * 255, dtype=np.uint8)
output_tiled = np.squeeze(output_tiled)
plt.imshow(output_tiled)
plt.savefig("{}/generation_{}_{}".format(eval_args.save, eval_args.name_to_save, ind))
