def get_datasets(config):
dataset_name = config['name']
if dataset_name == 'shapenet':
train_dataset = ShapeNetDataset(root_dir=config['path'],
classes=config['classes'],
split='train',
is_random_rotated=config['is_rotated'],
num_samples=config['num_samples'],
use_pcn_model_list=True)
val_dataset_dict = ShapeNetDataset.get_validation_datasets(
root_dir=config['path'],
classes=config['classes'],
is_random_rotated=config['is_rotated'],
num_samples=config['num_samples'],
use_pcn_model_list=True)
test_dataset_dict = ShapeNetDataset.get_test_datasets(
root_dir=config['path'],
classes=config['classes'],
is_random_rotated=config['is_rotated'],
num_samples=config['num_samples'],
use_pcn_model_list=True,
is_gen=config['gen_test_set'])
elif dataset_name == 'completion':
train_dataset = ShapeNetCompletion3DDataset(root_dir=config['path'],
split='train',
classes=config['classes'])
val_dataset_dict = ShapeNetCompletion3DDataset.get_validation_datasets(
config['path'], classes=config['classes'])
test_dataset_dict = ShapeNetCompletion3DDataset.get_test_datasets(
config['path'])
elif dataset_name == '3depn':
train_dataset = ShapeNet3DEPNDataset(root_dir=config['path'],
split='train',
classes=config['classes'])
val_dataset_dict = ShapeNet3DEPNDataset.get_validation_datasets(
config['path'], classes=config['classes'])
test_dataset_dict = ShapeNet3DEPNDataset.get_test_datasets(
config['path'], classes=config['classes'])
else:
raise ValueError(
f'Invalid dataset name. Expected `shapenet`, `completion` or `3depn`. Got: `{dataset_name}`'
)
return train_dataset, val_dataset_dict, test_dataset_dict
def prepare_dataset(config, split='train', batch_size=32):
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split=split)
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
return dataset
def prepare_data(config, split='train', batch_size=32):
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split=split)
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
data_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
return data_loader
def calc_jsd_valid(model, config, prior_std=1.0, split='valid'):
model.eval()
device = cuda_setup(config['cuda'], config['gpu'])
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split=split)
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
classes_selected = ('all' if not config['classes'] else ','.join(
config['classes']))
num_samples = len(dataset.point_clouds_names_valid)
data_loader = DataLoader(dataset,
batch_size=num_samples,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
# We take 3 times as many samples as there are in test data in order to
# perform JSD calculation in the same manner as in the reference publication
x, _ = next(iter(data_loader))
x = x.to(device)
# We average JSD computation from 3 independent trials.
js_results = []
for _ in range(3):
noise = prior_std * torch.randn(3 * num_samples, model.zdim)
noise = noise.to(device)
with torch.no_grad():
x_g = model.decode(noise)
if x_g.shape[-2:] == (3, 2048):
x_g.transpose_(1, 2)
jsd = jsd_between_point_cloud_sets(x, x_g, voxels=28)
js_results.append(jsd)
js_result = np.mean(js_results)
return js_result
def main(config):
set_seed(config['seed'])
results_dir = prepare_results_dir(config, 'vae', 'training')
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger('vae')
device = cuda_setup(config['cuda'], config['gpu'])
log.info(f'Device variable: {device}')
if device.type == 'cuda':
log.info(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
metrics_path = join(results_dir, 'metrics')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
elif dataset_name == 'custom':
dataset = TxtDataset(root_dir=config['data_dir'],
classes=config['classes'],
config=config)
elif dataset_name == 'benchmark':
dataset = Benchmark(root_dir=config['data_dir'],
classes=config['classes'],
config=config)
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.info("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset,
batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
drop_last=True,
pin_memory=True,
collate_fn=collate_fn)
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
encoder_pocket = aae.PocketEncoder(config).to(device)
encoder_visible = aae.VisibleEncoder(config).to(device)
hyper_network.apply(weights_init)
encoder_pocket.apply(weights_init)
encoder_visible.apply(weights_init)
if config['reconstruction_loss'].lower() == 'chamfer':
from losses.champfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
# from utils.metrics import earth_mover_distance
# reconstruction_loss = earth_mover_distance
from losses.earth_mover_distance import EMD
reconstruction_loss = EMD().to(device)
else:
raise ValueError(
f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
#
# Optimizers
#
e_hn_optimizer = getattr(optim, config['optimizer']['E_HN']['type'])
e_hn_optimizer = e_hn_optimizer(
chain(encoder_visible.parameters(), encoder_pocket.parameters(),
hyper_network.parameters()),
**config['optimizer']['E_HN']['hyperparams'])
log.info("Starting epoch: %s" % starting_epoch)
if starting_epoch > 1:
log.info("Loading weights...")
hyper_network.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch - 1:05}_G.pth')))
encoder_pocket.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch - 1:05}_EP.pth')))
encoder_visible.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch - 1:05}_EV.pth')))
e_hn_optimizer.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch - 1:05}_EGo.pth')))
log.info("Loading losses...")
losses_e = np.load(join(metrics_path,
f'{starting_epoch - 1:05}_E.npy')).tolist()
losses_kld = np.load(
join(metrics_path, f'{starting_epoch - 1:05}_KLD.npy')).tolist()
losses_eg = np.load(
join(metrics_path, f'{starting_epoch - 1:05}_EG.npy')).tolist()
else:
log.info("First epoch")
losses_e = []
losses_kld = []
losses_eg = []
if config['target_network_input']['normalization']['enable']:
normalization_type = config['target_network_input']['normalization'][
'type']
assert normalization_type == 'progressive', 'Invalid normalization type'
target_network_input = None
for epoch in range(starting_epoch, config['max_epochs'] + 1):
start_epoch_time = datetime.now()
log.debug("Epoch: %s" % epoch)
hyper_network.train()
encoder_visible.train()
encoder_pocket.train()
total_loss_all = 0.0
total_loss_r = 0.0
total_loss_kld = 0.0
for i, point_data in enumerate(points_dataloader, 1):
# get only visible part of point cloud
X = point_data['non-visible']
X = X.to(device, dtype=torch.float)
# get not visible part of point cloud
X_visible = point_data['visible']
X_visible = X_visible.to(device, dtype=torch.float)
# get whole point cloud
X_whole = point_data['cloud']
X_whole = X_whole.to(device, dtype=torch.float)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
X_visible.transpose_(X_visible.dim() - 2, X_visible.dim() - 1)
X_whole.transpose_(X_whole.dim() - 2, X_whole.dim() - 1)
codes, mu, logvar = encoder_pocket(X)
mu_visible = encoder_visible(X_visible)
target_networks_weights = hyper_network(
torch.cat((codes, mu_visible), 1))
X_rec = torch.zeros(X_whole.shape).to(device)
for j, target_network_weights in enumerate(
target_networks_weights):
target_network = aae.TargetNetwork(
config, target_network_weights).to(device)
if not config['target_network_input'][
'constant'] or target_network_input is None:
target_network_input = generate_points(
config=config,
epoch=epoch,
size=(X_whole.shape[2], X_whole.shape[1]))
X_rec[j] = torch.transpose(
target_network(target_network_input.to(device)), 0, 1)
loss_r = torch.mean(config['reconstruction_coef'] *
reconstruction_loss(
X_whole.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
loss_kld = 0.5 * (torch.exp(logvar) + torch.pow(mu, 2) - 1 -
logvar).sum()
loss_all = loss_r + loss_kld
e_hn_optimizer.zero_grad()
encoder_visible.zero_grad()
encoder_pocket.zero_grad()
hyper_network.zero_grad()
loss_all.backward()
e_hn_optimizer.step()
total_loss_r += loss_r.item()
total_loss_kld += loss_kld.item()
total_loss_all += loss_all.item()
log.info(f'[{epoch}/{config["max_epochs"]}] '
f'Loss_ALL: {total_loss_all / i:.4f} '
f'Loss_R: {total_loss_r / i:.4f} '
f'Loss_E: {total_loss_kld / i:.4f} '
f'Time: {datetime.now() - start_epoch_time}')
losses_e.append(total_loss_r)
losses_kld.append(total_loss_kld)
losses_eg.append(total_loss_all)
#
# Save intermediate results
#
X = X.cpu().numpy()
X_whole = X_whole.cpu().numpy()
X_rec = X_rec.detach().cpu().numpy()
if epoch % config['save_frequency'] == 0:
for k in range(min(5, X_rec.shape[0])):
fig = plot_3d_point_cloud(X_rec[k][0],
X_rec[k][1],
X_rec[k][2],
in_u_sphere=True,
show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples',
f'{epoch}_{k}_reconstructed.png'))
plt.close(fig)
fig = plot_3d_point_cloud(X_whole[k][0],
X_whole[k][1],
X_whole[k][2],
in_u_sphere=True,
show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples', f'{epoch}_{k}_real.png'))
plt.close(fig)
fig = plot_3d_point_cloud(X[k][0],
X[k][1],
X[k][2],
in_u_sphere=True,
show=False)
fig.savefig(
join(results_dir, 'samples', f'{epoch}_{k}_visible.png'))
plt.close(fig)
if config['clean_weights_dir']:
log.debug('Cleaning weights path: %s' % weights_path)
shutil.rmtree(weights_path, ignore_errors=True)
os.makedirs(weights_path, exist_ok=True)
if epoch % config['save_frequency'] == 0:
log.debug('Saving data...')
torch.save(hyper_network.state_dict(),
join(weights_path, f'{epoch:05}_G.pth'))
torch.save(encoder_visible.state_dict(),
join(weights_path, f'{epoch:05}_EV.pth'))
torch.save(encoder_pocket.state_dict(),
join(weights_path, f'{epoch:05}_EP.pth'))
torch.save(e_hn_optimizer.state_dict(),
join(weights_path, f'{epoch:05}_EGo.pth'))
np.save(join(metrics_path, f'{epoch:05}_E'), np.array(losses_e))
np.save(join(metrics_path, f'{epoch:05}_KLD'),
np.array(losses_kld))
np.save(join(metrics_path, f'{epoch:05}_EG'), np.array(losses_eg))
def all_metrics(config, weights_path, device, epoch, jsd_value):
from utils.metrics import compute_all_metrics
print('All metrics')
if epoch is None:
print('Finding latest epoch...')
epoch = find_latest_epoch(weights_path)
print(f'Epoch: {epoch}')
if jsd_value is not None:
print(f'Best Epoch selected via mimnimal JSD: {epoch}')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split='test')
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
classes_selected = ('all' if not config['classes'] else ','.join(
config['classes']))
print(
f'Test dataset. Selected {classes_selected} classes. Loaded {len(dataset)} '
f'samples.')
distribution = config['metrics']['distribution']
assert distribution in ['normal', 'beta'
], 'Invalid distribution. Choose normal or beta'
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
hyper_network.eval()
data_loader = DataLoader(dataset,
batch_size=32,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
hyper_network.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_G.pth')))
result = {}
size = 0
start_clock = datetime.now()
for point_data in data_loader:
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
with torch.no_grad():
noise = torch.zeros(X.shape[0], config['z_size']).to(device)
if distribution == 'normal':
noise.normal_(config['metrics']['normal_mu'],
config['metrics']['normal_std'])
elif distribution == 'beta':
noise_np = np.random.beta(config['metrics']['beta_a'],
config['metrics']['beta_b'],
noise.shape)
noise = torch.tensor(noise_np).float().round().to(device)
target_networks_weights = hyper_network(noise)
X_rec = torch.zeros(X.shape).to(device)
for j, target_network_weights in enumerate(
target_networks_weights):
target_network = aae.TargetNetwork(
config, target_network_weights).to(device)
target_network_input = generate_points(config=config,
epoch=epoch,
size=(X.shape[2],
X.shape[1]))
X_rec[j] = torch.transpose(
target_network(target_network_input.to(device)), 0, 1)
for k, v in compute_all_metrics(
torch.transpose(X, 1, 2).contiguous(),
torch.transpose(X_rec, 1, 2).contiguous(),
X.shape[0]).items():
result[k] = (size * result.get(k, 0.0) +
X.shape[0] * v.item()) / (size + X.shape[0])
size += X.shape[0]
result['jsd'] = jsd_value
print(f'Time: {datetime.now() - start_clock}')
print(f'Result:')
for k, v in result.items():
print(f'{k}: {v}')
def jsd(config, weights_path, device):
print(
'Evaluating Jensen-Shannon divergences on validation set on all saved epochs.'
)
# Find all epochs that have saved model weights
e_epochs = _get_epochs_by_regex(weights_path, r'(?P<epoch>\d{5})_E\.pth')
g_epochs = _get_epochs_by_regex(weights_path, r'(?P<epoch>\d{5})_G\.pth')
epochs = sorted(e_epochs.intersection(g_epochs))
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split='valid')
else:
raise ValueError(
f'Invalid dataset name. Expected `shapenet`. Got: `{dataset_name}`'
)
classes_selected = ('all' if not config['classes'] else ','.join(
config['classes']))
print(
f'Valid dataset. Selected {classes_selected} classes. Loaded {len(dataset)} '
f'samples.')
distribution = config['metrics']['distribution']
assert distribution in ['normal', 'beta'
], 'Invalid distribution. Choose normal or beta'
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
hyper_network.eval()
num_samples = len(dataset.point_clouds_names_valid)
data_loader = DataLoader(dataset,
batch_size=num_samples,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
X, _ = next(iter(data_loader))
X = X.to(device)
# We take 3 times as many samples as there are in test data in order to
# perform JSD calculation in the same manner as in the reference publication
noise = torch.zeros(3 * X.shape[0], config['z_size']).to(device)
results = {}
n_last_epochs = config['metrics'].get('jsd_how_many_last_epochs', -1)
epochs = epochs[-n_last_epochs:] if n_last_epochs > 0 else epochs
print(f'Testing epochs: {epochs}')
for epoch in reversed(epochs):
try:
hyper_network.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_G.pth')))
start_clock = datetime.now()
# We average JSD computation from 3 independent trials.
js_results = []
for _ in range(3):
if distribution == 'normal':
noise.normal_(config['metrics']['normal_mu'],
config['metrics']['normal_std'])
elif distribution == 'beta':
noise_np = np.random.beta(config['metrics']['beta_a'],
config['metrics']['beta_b'],
noise.shape)
noise = torch.tensor(noise_np).float().round().to(device)
with torch.no_grad():
target_networks_weights = hyper_network(noise)
X_rec = torch.zeros(3 * X.shape[0], X.shape[1],
X.shape[2]).to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X_rec.size(-1) == 3:
X_rec.transpose_(X_rec.dim() - 2, X_rec.dim() - 1)
for j, target_network_weights in enumerate(
target_networks_weights):
target_network = aae.TargetNetwork(
config, target_network_weights).to(device)
target_network_input = generate_points(
config=config,
epoch=epoch,
size=(X_rec.shape[2], X_rec.shape[1]))
X_rec[j] = torch.transpose(
target_network(target_network_input.to(device)), 0,
1)
jsd = jsd_between_point_cloud_sets(
X.cpu().numpy(),
torch.transpose(X_rec, 1, 2).cpu().numpy())
js_results.append(jsd)
js_result = np.mean(js_results)
print(f'Epoch: {epoch} JSD: {js_result: .6f} '
f'Time: {datetime.now() - start_clock}')
results[epoch] = js_result
except KeyboardInterrupt:
print(f'Interrupted during epoch: {epoch}')
break
results = pd.DataFrame.from_dict(results, orient='index', columns=['jsd'])
print(f"Minimum JSD at epoch {results.idxmin()['jsd']}: "
f"{results.min()['jsd']: .6f}")
return results.idxmin()['jsd'], results.min()['jsd']
def minimum_matching_distance(config, weights_path, device):
from utils.metrics import EMD_CD
print('Minimum Matching Distance (MMD) Test split')
epoch = find_latest_epoch(weights_path)
print(f'Last Epoch: {epoch}')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'],
split='test')
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
classes_selected = ('all' if not config['classes'] else ','.join(
config['classes']))
print(
f'Test dataset. Selected {classes_selected} classes. Loaded {len(dataset)} '
f'samples.')
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
encoder = aae.Encoder(config).to(device)
hyper_network.eval()
encoder.eval()
num_samples = len(dataset.point_clouds_names_test)
data_loader = DataLoader(dataset,
batch_size=num_samples,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
encoder.load_state_dict(torch.load(join(weights_path,
f'{epoch:05}_E.pth')))
hyper_network.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_G.pth')))
result = {}
for point_data in data_loader:
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
with torch.no_grad():
z_a, _, _ = encoder(X)
target_networks_weights = hyper_network(z_a)
X_rec = torch.zeros(X.shape).to(device)
for j, target_network_weights in enumerate(
target_networks_weights):
target_network = aae.TargetNetwork(
config, target_network_weights).to(device)
target_network_input = generate_points(config=config,
epoch=epoch,
size=(X.shape[2],
X.shape[1]))
X_rec[j] = torch.transpose(
target_network(target_network_input.to(device)), 0, 1)
for k, v in EMD_CD(
torch.transpose(X, 1, 2).contiguous(),
torch.transpose(X_rec, 1, 2).contiguous(),
X.shape[0]).items():
result[k] = result.get(k, 0.0) + v.item()
print(result)
def main(config):
set_seed(config['seed'])
results_dir = prepare_results_dir(config, 'aae', 'training')
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger('aae')
device = cuda_setup(config['cuda'], config['gpu'])
log.info(f'Device variable: {device}')
if device.type == 'cuda':
log.info(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
metrics_path = join(results_dir, 'metrics')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.info("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset, batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
pin_memory=True)
pointnet = config.get('pointnet', False)
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
if pointnet:
from models.pointnet import PointNet
encoder = PointNet(config).to(device)
# PointNet initializes it's own weights during instance creation
else:
encoder = aae.Encoder(config).to(device)
encoder.apply(weights_init)
discriminator = aae.Discriminator(config).to(device)
hyper_network.apply(weights_init)
discriminator.apply(weights_init)
if config['reconstruction_loss'].lower() == 'chamfer':
if pointnet:
from utils.metrics import chamfer_distance
reconstruction_loss = chamfer_distance
else:
from losses.champfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
from utils.metrics import earth_mover_distance
reconstruction_loss = earth_mover_distance
else:
raise ValueError(f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
#
# Optimizers
#
e_hn_optimizer = getattr(optim, config['optimizer']['E_HN']['type'])
e_hn_optimizer = e_hn_optimizer(chain(encoder.parameters(), hyper_network.parameters()),
**config['optimizer']['E_HN']['hyperparams'])
discriminator_optimizer = getattr(optim, config['optimizer']['D']['type'])
discriminator_optimizer = discriminator_optimizer(discriminator.parameters(),
**config['optimizer']['D']['hyperparams'])
log.info("Starting epoch: %s" % starting_epoch)
if starting_epoch > 1:
log.info("Loading weights...")
hyper_network.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}_G.pth')))
encoder.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}_E.pth')))
discriminator.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_D.pth')))
e_hn_optimizer.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}_EGo.pth')))
discriminator_optimizer.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_Do.pth')))
log.info("Loading losses...")
losses_e = np.load(join(metrics_path, f'{starting_epoch - 1:05}_E.npy')).tolist()
losses_g = np.load(join(metrics_path, f'{starting_epoch - 1:05}_G.npy')).tolist()
losses_eg = np.load(join(metrics_path, f'{starting_epoch - 1:05}_EG.npy')).tolist()
losses_d = np.load(join(metrics_path, f'{starting_epoch - 1:05}_D.npy')).tolist()
else:
log.info("First epoch")
losses_e = []
losses_g = []
losses_eg = []
losses_d = []
normalize_points = config['target_network_input']['normalization']['enable']
if normalize_points:
normalization_type = config['target_network_input']['normalization']['type']
assert normalization_type == 'progressive', 'Invalid normalization type'
target_network_input = None
for epoch in range(starting_epoch, config['max_epochs'] + 1):
start_epoch_time = datetime.now()
log.debug("Epoch: %s" % epoch)
hyper_network.train()
encoder.train()
discriminator.train()
total_loss_all = 0.0
total_loss_reconstruction = 0.0
total_loss_encoder = 0.0
total_loss_discriminator = 0.0
total_loss_regularization = 0.0
for i, point_data in enumerate(points_dataloader, 1):
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
if pointnet:
_, feature_transform, codes = encoder(X)
else:
codes, _, _ = encoder(X)
# discriminator training
noise = torch.empty(codes.shape[0], config['z_size']).normal_(mean=config['normal_mu'],
std=config['normal_std']).to(device)
synth_logit = discriminator(codes)
real_logit = discriminator(noise)
if config.get('wasserstein', True):
loss_discriminator = torch.mean(synth_logit) - torch.mean(real_logit)
alpha = torch.rand(codes.shape[0], 1).to(device)
differences = codes - noise
interpolates = noise + alpha * differences
disc_interpolates = discriminator(interpolates)
# gradient_penalty_function
gradients = grad(
outputs=disc_interpolates,
inputs=interpolates,
grad_outputs=torch.ones_like(disc_interpolates).to(device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
slopes = torch.sqrt(torch.sum(gradients ** 2, dim=1))
gradient_penalty = ((slopes - 1) ** 2).mean()
loss_gp = config['gradient_penalty_coef'] * gradient_penalty
loss_discriminator += loss_gp
else:
# An alternative is a = -1, b = 1 iff c = 0
a = 0.0
b = 1.0
loss_discriminator = 0.5 * ((real_logit - b)**2 + (synth_logit - a)**2)
discriminator_optimizer.zero_grad()
discriminator.zero_grad()
loss_discriminator.backward(retain_graph=True)
total_loss_discriminator += loss_discriminator.item()
discriminator_optimizer.step()
# hyper network training
target_networks_weights = hyper_network(codes)
X_rec = torch.zeros(X.shape).to(device)
for j, target_network_weights in enumerate(target_networks_weights):
target_network = aae.TargetNetwork(config, target_network_weights).to(device)
if not config['target_network_input']['constant'] or target_network_input is None:
target_network_input = generate_points(config=config, epoch=epoch, size=(X.shape[2], X.shape[1]))
X_rec[j] = torch.transpose(target_network(target_network_input.to(device)), 0, 1)
if pointnet:
loss_reconstruction = config['reconstruction_coef'] * \
reconstruction_loss(torch.transpose(X, 1, 2).contiguous(),
torch.transpose(X_rec, 1, 2).contiguous(),
batch_size=X.shape[0]).mean()
else:
loss_reconstruction = torch.mean(
config['reconstruction_coef'] *
reconstruction_loss(X.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
# encoder training
synth_logit = discriminator(codes)
if config.get('wasserstein', True):
loss_encoder = -torch.mean(synth_logit)
else:
# An alternative is c = 0 iff a = -1, b = 1
c = 1.0
loss_encoder = 0.5 * (synth_logit - c)**2
if pointnet:
regularization_loss = config['feature_regularization_coef'] * \
feature_transform_regularization(feature_transform).mean()
loss_all = loss_reconstruction + loss_encoder + regularization_loss
else:
loss_all = loss_reconstruction + loss_encoder
e_hn_optimizer.zero_grad()
encoder.zero_grad()
hyper_network.zero_grad()
loss_all.backward()
e_hn_optimizer.step()
total_loss_reconstruction += loss_reconstruction.item()
total_loss_encoder += loss_encoder.item()
total_loss_all += loss_all.item()
if pointnet:
total_loss_regularization += regularization_loss.item()
log.info(
f'[{epoch}/{config["max_epochs"]}] '
f'Total_Loss: {total_loss_all / i:.4f} '
f'Loss_R: {total_loss_reconstruction / i:.4f} '
f'Loss_E: {total_loss_encoder / i:.4f} '
f'Loss_D: {total_loss_discriminator / i:.4f} '
f'Time: {datetime.now() - start_epoch_time}'
)
if pointnet:
log.info(f'Loss_Regularization: {total_loss_regularization / i:.4f}')
losses_e.append(total_loss_reconstruction)
losses_g.append(total_loss_encoder)
losses_eg.append(total_loss_all)
losses_d.append(total_loss_discriminator)
#
# Save intermediate results
#
if epoch % config['save_samples_frequency'] == 0:
log.debug('Saving samples...')
X = X.cpu().numpy()
X_rec = X_rec.detach().cpu().numpy()
for k in range(min(5, X_rec.shape[0])):
fig = plot_3d_point_cloud(X_rec[k][0], X_rec[k][1], X_rec[k][2], in_u_sphere=True, show=False,
title=str(epoch))
fig.savefig(join(results_dir, 'samples', f'{epoch}_{k}_reconstructed.png'))
plt.close(fig)
fig = plot_3d_point_cloud(X[k][0], X[k][1], X[k][2], in_u_sphere=True, show=False)
fig.savefig(join(results_dir, 'samples', f'{epoch}_{k}_real.png'))
plt.close(fig)
if config['clean_weights_dir']:
log.debug('Cleaning weights path: %s' % weights_path)
shutil.rmtree(weights_path, ignore_errors=True)
os.makedirs(weights_path, exist_ok=True)
if epoch % config['save_weights_frequency'] == 0:
log.debug('Saving weights and losses...')
torch.save(hyper_network.state_dict(), join(weights_path, f'{epoch:05}_G.pth'))
torch.save(encoder.state_dict(), join(weights_path, f'{epoch:05}_E.pth'))
torch.save(e_hn_optimizer.state_dict(), join(weights_path, f'{epoch:05}_EGo.pth'))
torch.save(discriminator.state_dict(), join(weights_path, f'{epoch:05}_D.pth'))
torch.save(discriminator_optimizer.state_dict(), join(weights_path, f'{epoch:05}_Do.pth'))
np.save(join(metrics_path, f'{epoch:05}_E'), np.array(losses_e))
np.save(join(metrics_path, f'{epoch:05}_G'), np.array(losses_g))
np.save(join(metrics_path, f'{epoch:05}_EG'), np.array(losses_eg))
np.save(join(metrics_path, f'{epoch:05}_D'), np.array(losses_d))
def main(config):
random.seed(config['seed'])
torch.manual_seed(config['seed'])
torch.cuda.manual_seed_all(config['seed'])
results_dir = prepare_results_dir(config)
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger(__name__)
device = cuda_setup(config['cuda'], config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.debug("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset,
batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
drop_last=True,
pin_memory=True)
#
# Models
#
arch = import_module(f"model.architectures.{config['arch']}")
G = arch.Generator(config).to(device)
E = arch.Encoder(config).to(device)
G.apply(weights_init)
E.apply(weights_init)
if config['reconstruction_loss'].lower() == 'chamfer':
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
reconstruction_loss = EMD().to(device)
else:
raise ValueError(
f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
#
# Optimizers
#
EG_optim = getattr(optim, config['optimizer']['EG']['type'])
EG_optim = EG_optim(chain(E.parameters(), G.parameters()),
**config['optimizer']['EG']['hyperparams'])
if starting_epoch > 1:
G.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch-1:05}_G.pth')))
E.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch-1:05}_E.pth')))
EG_optim.load_state_dict(
torch.load(join(weights_path, f'{starting_epoch-1:05}_EGo.pth')))
for epoch in range(starting_epoch, config['max_epochs'] + 1):
start_epoch_time = datetime.now()
G.train()
E.train()
total_loss = 0.0
for i, point_data in enumerate(points_dataloader, 1):
log.debug('-' * 20)
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
X_rec = G(E(X))
loss = torch.mean(config['reconstruction_coef'] *
reconstruction_loss(
X.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
EG_optim.zero_grad()
E.zero_grad()
G.zero_grad()
loss.backward()
total_loss += loss.item()
EG_optim.step()
log.debug(f'[{epoch}: ({i})] '
f'Loss: {loss.item():.4f} '
f'Time: {datetime.now() - start_epoch_time}')
log.debug(f'[{epoch}/{config["max_epochs"]}] '
f'Loss: {total_loss / i:.4f} '
f'Time: {datetime.now() - start_epoch_time}')
#
# Save intermediate results
#
G.eval()
E.eval()
with torch.no_grad():
X_rec = G(E(X)).data.cpu().numpy()
for k in range(5):
fig = plot_3d_point_cloud(X[k][0],
X[k][1],
X[k][2],
in_u_sphere=True,
show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples', f'{epoch:05}_{k}_real.png'))
plt.close(fig)
for k in range(5):
fig = plot_3d_point_cloud(X_rec[k][0],
X_rec[k][1],
X_rec[k][2],
in_u_sphere=True,
show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples',
f'{epoch:05}_{k}_reconstructed.png'))
plt.close(fig)
if epoch % config['save_frequency'] == 0:
torch.save(G.state_dict(), join(weights_path, f'{epoch:05}_G.pth'))
torch.save(E.state_dict(), join(weights_path, f'{epoch:05}_E.pth'))
torch.save(EG_optim.state_dict(),
join(weights_path, f'{epoch:05}_EGo.pth'))
def main(eval_config):
# Load hyperparameters as they were during training
train_results_path = join(eval_config['results_root'], eval_config['arch'],
eval_config['experiment_name'])
with open(join(train_results_path, 'config.json')) as f:
train_config = json.load(f)
random.seed(train_config['seed'])
torch.manual_seed(train_config['seed'])
torch.cuda.manual_seed_all(train_config['seed'])
setup_logging(join(train_results_path, 'results'))
log = logging.getLogger(__name__)
log.debug('Evaluating JensenShannon divergences on validation set on all '
'saved epochs.')
weights_path = join(train_results_path, 'weights')
# Find all epochs that have saved model weights
e_epochs = _get_epochs_by_regex(weights_path, r'(?P<epoch>\d{5})_E\.pth')
g_epochs = _get_epochs_by_regex(weights_path, r'(?P<epoch>\d{5})_G\.pth')
epochs = sorted(e_epochs.intersection(g_epochs))
log.debug(f'Testing epochs: {epochs}')
device = cuda_setup(eval_config['cuda'], eval_config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
#
# Dataset
#
dataset_name = train_config['dataset'].lower()
if dataset_name == 'shapenet':
dataset = ShapeNetDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='valid')
elif dataset_name == 'faust':
from datasets.dfaust import DFaustDataset
dataset = DFaustDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='valid')
elif dataset_name == 'mcgill':
from datasets.mcgill import McGillDataset
dataset = McGillDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='valid')
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
classes_selected = ('all' if not train_config['classes'] else ','.join(
train_config['classes']))
log.debug(f'Selected {classes_selected} classes. Loaded {len(dataset)} '
f'samples.')
if 'distribution' in train_config:
distribution = train_config['distribution']
elif 'distribution' in eval_config:
distribution = eval_config['distribution']
else:
log.warning(
'No distribution type specified. Assumed normal = N(0, 0.2)')
distribution = 'normal'
#
# Models
#
arch = import_module(f"model.architectures.{train_config['arch']}")
E = arch.Encoder(train_config).to(device)
G = arch.Generator(train_config).to(device)
E.eval()
G.eval()
num_samples = len(dataset.point_clouds_names_valid)
data_loader = DataLoader(dataset,
batch_size=num_samples,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
# We take 3 times as many samples as there are in test data in order to
# perform JSD calculation in the same manner as in the reference publication
noise = torch.FloatTensor(3 * num_samples, train_config['z_size'], 1)
noise = noise.to(device)
X, _ = next(iter(data_loader))
X = X.to(device)
results = {}
for epoch in reversed(epochs):
try:
E.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_E.pth')))
G.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_G.pth')))
start_clock = datetime.now()
# We average JSD computation from 3 independet trials.
js_results = []
for _ in range(3):
if distribution == 'normal':
noise.normal_(0, 0.2)
elif distribution == 'beta':
noise_np = np.random.beta(train_config['z_beta_a'],
train_config['z_beta_b'],
noise.shape)
noise = torch.tensor(noise_np).float().round().to(device)
with torch.no_grad():
X_g = G(noise)
if X_g.shape[-2:] == (3, 2048):
X_g.transpose_(1, 2)
jsd = jsd_between_point_cloud_sets(X, X_g, voxels=28)
js_results.append(jsd)
js_result = np.mean(js_results)
log.debug(f'Epoch: {epoch} JSD: {js_result: .6f} '
f'Time: {datetime.now() - start_clock}')
results[epoch] = js_result
except KeyboardInterrupt:
log.debug(f'Interrupted during epoch: {epoch}')
break
results = pd.DataFrame.from_dict(results, orient='index', columns=['jsd'])
log.debug(f"Minimum JSD at epoch {results.idxmin()['jsd']}: "
f"{results.min()['jsd']: .6f}")
def main(config):
if config['seed'] >= 0:
random.seed(config['seed'])
torch.manual_seed(config['seed'])
torch.cuda.manual_seed(config['seed'])
np.random.seed(config['seed'])
torch.backends.cudnn.deterministic = True
print("random seed: ", config['seed'])
results_dir = prepare_results_dir(config)
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger(__name__)
logging.getLogger('matplotlib.font_manager').disabled = True
device = cuda_setup(config['cuda'], config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
# load dataset
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.debug("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset, batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
drop_last=True, pin_memory=True)
scale = 1 / (3 * config['n_points'])
# hyper-parameters
valid_frequency = config["valid_frequency"]
num_vae = config["num_vae"]
beta_rec = config["beta_rec"]
beta_kl = config["beta_kl"]
beta_neg = config["beta_neg"]
gamma_r = config["gamma_r"]
apply_random_rotation = "rotate" in config["transforms"]
if apply_random_rotation:
print("applying random rotation to input shapes")
# model
model = SoftIntroVAE(config).to(device)
if config['reconstruction_loss'].lower() == 'chamfer':
from losses.chamfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
else:
raise ValueError(f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
prior_std = config["prior_std"]
prior_logvar = np.log(prior_std ** 2)
print(f'prior: N(0, {prior_std ** 2:.3f})')
# optimizers
optimizer_e = getattr(optim, config['optimizer']['E']['type'])
optimizer_e = optimizer_e(model.encoder.parameters(), **config['optimizer']['E']['hyperparams'])
optimizer_d = getattr(optim, config['optimizer']['D']['type'])
optimizer_d = optimizer_d(model.decoder.parameters(), **config['optimizer']['D']['hyperparams'])
scheduler_e = optim.lr_scheduler.MultiStepLR(optimizer_e, milestones=[350, 450, 550], gamma=0.5)
scheduler_d = optim.lr_scheduler.MultiStepLR(optimizer_d, milestones=[350, 450, 550], gamma=0.5)
if starting_epoch > 1:
model.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}.pth')))
optimizer_e.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}_optim_e.pth')))
optimizer_d.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch - 1:05}_optim_d.pth')))
kls_real = []
kls_fake = []
kls_rec = []
rec_errs = []
exp_elbos_f = []
exp_elbos_r = []
diff_kls = []
best_res = {"epoch": 0, "jsd": None}
for epoch in range(starting_epoch, config['max_epochs'] + 1):
start_epoch_time = datetime.now()
model.train()
if epoch < num_vae:
total_loss = 0.0
pbar = tqdm(iterable=points_dataloader)
for i, point_data in enumerate(pbar, 1):
x, _ = point_data
x = x.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if x.size(-1) == 3:
x.transpose_(x.dim() - 2, x.dim() - 1)
x_rec, mu, logvar = model(x)
loss_rec = reconstruction_loss(x.permute(0, 2, 1) + 0.5, x_rec.permute(0, 2, 1) + 0.5)
while len(loss_rec.shape) > 1:
loss_rec = loss_rec.sum(-1)
loss_rec = loss_rec.mean()
loss_kl = calc_kl(logvar, mu, logvar_o=prior_logvar, reduce="mean")
loss = beta_rec * loss_rec + beta_kl * loss_kl
optimizer_e.zero_grad()
optimizer_d.zero_grad()
loss.backward()
total_loss += loss.item()
optimizer_e.step()
optimizer_d.step()
pbar.set_description_str('epoch #{}'.format(epoch))
pbar.set_postfix(r_loss=loss_rec.data.cpu().item(), kl=loss_kl.data.cpu().item())
else:
batch_kls_real = []
batch_kls_fake = []
batch_kls_rec = []
batch_rec_errs = []
batch_exp_elbo_f = []
batch_exp_elbo_r = []
batch_diff_kls = []
pbar = tqdm(iterable=points_dataloader)
for i, point_data in enumerate(pbar, 1):
x, _ = point_data
x = x.to(device)
# random rotation
if apply_random_rotation:
angle = torch.rand(size=(x.shape[0],)) * 180
rotate_transform = RotateAxisAngle(angle, axis="Z", device=device)
x = rotate_transform.transform_points(x)
# change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if x.size(-1) == 3:
x.transpose_(x.dim() - 2, x.dim() - 1)
noise_batch = prior_std * torch.randn(size=(config['batch_size'], model.zdim)).to(device)
# ----- update E ----- #
for param in model.encoder.parameters():
param.requires_grad = True
for param in model.decoder.parameters():
param.requires_grad = False
fake = model.sample(noise_batch)
real_mu, real_logvar = model.encode(x)
z = reparameterize(real_mu, real_logvar)
x_rec = model.decoder(z)
loss_rec = reconstruction_loss(x.permute(0, 2, 1) + 0.5, x_rec.permute(0, 2, 1) + 0.5)
while len(loss_rec.shape) > 1:
loss_rec = loss_rec.sum(-1)
loss_rec = loss_rec.mean()
loss_real_kl = calc_kl(real_logvar, real_mu, logvar_o=prior_logvar, reduce="mean")
rec_rec, rec_mu, rec_logvar = model(x_rec.detach())
rec_fake, fake_mu, fake_logvar = model(fake.detach())
kl_rec = calc_kl(rec_logvar, rec_mu, logvar_o=prior_logvar, reduce="none")
kl_fake = calc_kl(fake_logvar, fake_mu, logvar_o=prior_logvar, reduce="none")
loss_rec_rec_e = reconstruction_loss(x_rec.detach().permute(0, 2, 1) + 0.5,
rec_rec.permute(0, 2, 1) + 0.5)
while len(loss_rec_rec_e.shape) > 1:
loss_rec_rec_e = loss_rec_rec_e.sum(-1)
loss_rec_fake_e = reconstruction_loss(fake.permute(0, 2, 1) + 0.5, rec_fake.permute(0, 2, 1) + 0.5)
while len(loss_rec_fake_e.shape) > 1:
loss_rec_fake_e = loss_rec_fake_e.sum(-1)
expelbo_rec = (-2 * scale * (beta_rec * loss_rec_rec_e + beta_neg * kl_rec)).exp().mean()
expelbo_fake = (-2 * scale * (beta_rec * loss_rec_fake_e + beta_neg * kl_fake)).exp().mean()
loss_margin = scale * beta_kl * loss_real_kl + 0.25 * (expelbo_rec + expelbo_fake)
lossE = scale * beta_rec * loss_rec + loss_margin
optimizer_e.zero_grad()
lossE.backward()
optimizer_e.step()
# ----- update D ----- #
for param in model.encoder.parameters():
param.requires_grad = False
for param in model.decoder.parameters():
param.requires_grad = True
fake = model.sample(noise_batch)
with torch.no_grad():
z = reparameterize(real_mu.detach(), real_logvar.detach())
rec = model.decoder(z.detach())
loss_rec = reconstruction_loss(x.permute(0, 2, 1) + 0.5, rec.permute(0, 2, 1) + 0.5)
while len(loss_rec.shape) > 1:
loss_rec = loss_rec.sum(-1)
loss_rec = loss_rec.mean()
rec_mu, rec_logvar = model.encode(rec)
z_rec = reparameterize(rec_mu, rec_logvar)
fake_mu, fake_logvar = model.encode(fake)
z_fake = reparameterize(fake_mu, fake_logvar)
rec_rec = model.decode(z_rec.detach())
rec_fake = model.decode(z_fake.detach())
loss_rec_rec = reconstruction_loss(rec.detach().permute(0, 2, 1) + 0.5, rec_rec.permute(0, 2, 1) + 0.5)
while len(loss_rec_rec.shape) > 1:
loss_rec_rec = loss_rec.sum(-1)
loss_rec_rec = loss_rec_rec.mean()
loss_fake_rec = reconstruction_loss(fake.detach().permute(0, 2, 1) + 0.5,
rec_fake.permute(0, 2, 1) + 0.5)
while len(loss_fake_rec.shape) > 1:
loss_fake_rec = loss_rec.sum(-1)
loss_fake_rec = loss_fake_rec.mean()
lossD_rec_kl = calc_kl(rec_logvar, rec_mu, logvar_o=prior_logvar, reduce="mean")
lossD_fake_kl = calc_kl(fake_logvar, fake_mu, logvar_o=prior_logvar, reduce="mean")
lossD = scale * (loss_rec * beta_rec + (
lossD_rec_kl + lossD_fake_kl) * 0.5 * beta_kl + gamma_r * 0.5 * beta_rec * (
loss_rec_rec + loss_fake_rec))
optimizer_d.zero_grad()
lossD.backward()
optimizer_d.step()
if torch.isnan(lossD):
raise SystemError("loss is Nan")
diff_kl = -loss_real_kl.data.cpu() + lossD_fake_kl.data.cpu()
batch_diff_kls.append(diff_kl)
batch_kls_real.append(loss_real_kl.data.cpu().item())
batch_kls_fake.append(lossD_fake_kl.cpu().item())
batch_kls_rec.append(lossD_rec_kl.data.cpu().item())
batch_rec_errs.append(loss_rec.data.cpu().item())
batch_exp_elbo_f.append(expelbo_fake.data.cpu())
batch_exp_elbo_r.append(expelbo_rec.data.cpu())
pbar.set_description_str('epoch #{}'.format(epoch))
pbar.set_postfix(r_loss=loss_rec.data.cpu().item(), kl=loss_real_kl.data.cpu().item(),
diff_kl=diff_kl.item(), expelbo_f=expelbo_fake.cpu().item())
pbar.close()
scheduler_e.step()
scheduler_d.step()
if epoch > num_vae - 1:
kls_real.append(np.mean(batch_kls_real))
kls_fake.append(np.mean(batch_kls_fake))
kls_rec.append(np.mean(batch_kls_rec))
rec_errs.append(np.mean(batch_rec_errs))
exp_elbos_f.append(np.mean(batch_exp_elbo_f))
exp_elbos_r.append(np.mean(batch_exp_elbo_r))
diff_kls.append(np.mean(batch_diff_kls))
# epoch summary
print('#' * 50)
print(f'Epoch {epoch} Summary:')
print(f'beta_rec: {beta_rec}, beta_kl: {beta_kl}, beta_neg: {beta_neg}')
print(
f'rec: {rec_errs[-1]:.3f}, kl: {kls_real[-1]:.3f}, kl_fake: {kls_fake[-1]:.3f}, kl_rec: {kls_rec[-1]:.3f}')
print(
f'diff_kl: {diff_kls[-1]:.3f}, exp_elbo_f: {exp_elbos_f[-1]:.4e}, exp_elbo_r: {exp_elbos_r[-1]:.4e}')
if best_res['jsd'] is not None:
print(f'best jsd: {best_res["jsd"]}, epoch: {best_res["epoch"]}')
print(f'time: {datetime.now() - start_epoch_time}')
print('#' * 50)
# save intermediate results
model.eval()
with torch.no_grad():
noise_batch = prior_std * torch.randn(size=(5, model.zdim)).to(device)
fake = model.sample(noise_batch).data.cpu().numpy()
x_rec, _, _ = model(x, deterministic=True)
x_rec = x_rec.data.cpu().numpy()
fig = plt.figure(dpi=350)
for k in range(5):
ax = fig.add_subplot(3, 5, k + 1, projection='3d')
ax = plot_3d_point_cloud(x[k][0].data.cpu().numpy(), x[k][1].data.cpu().numpy(),
x[k][2].data.cpu().numpy(),
in_u_sphere=True, show=False, axis=ax, show_axis=True, s=4, color='dodgerblue')
remove_ticks_from_ax(ax)
for k in range(5):
ax = fig.add_subplot(3, 5, k + 6, projection='3d')
ax = plot_3d_point_cloud(x_rec[k][0],
x_rec[k][1],
x_rec[k][2],
in_u_sphere=True, show=False, axis=ax, show_axis=True, s=4, color='dodgerblue')
remove_ticks_from_ax(ax)
for k in range(5):
ax = fig.add_subplot(3, 5, k + 11, projection='3d')
ax = plot_3d_point_cloud(fake[k][0], fake[k][1], fake[k][2],
in_u_sphere=True, show=False, axis=ax, show_axis=True, s=4, color='dodgerblue')
remove_ticks_from_ax(ax)
fig.savefig(join(results_dir, 'samples', f'figure_{epoch}'))
plt.close(fig)
if epoch % valid_frequency == 0:
print("calculating valid jsd...")
model.eval()
with torch.no_grad():
jsd = calc_jsd_valid(model, config, prior_std=prior_std)
print(f'epoch: {epoch}, jsd: {jsd:.4f}')
if best_res['jsd'] is None:
best_res['jsd'] = jsd
best_res['epoch'] = epoch
elif best_res['jsd'] > jsd:
print(f'epoch: {epoch}: best jsd updated: {best_res["jsd"]} -> {jsd}')
best_res['jsd'] = jsd
best_res['epoch'] = epoch
# save
torch.save(model.state_dict(), join(weights_path, f'{epoch:05}_jsd_{jsd:.4f}.pth'))
if epoch % config['save_frequency'] == 0:
torch.save(model.state_dict(), join(weights_path, f'{epoch:05}.pth'))
torch.save(optimizer_e.state_dict(),
join(weights_path, f'{epoch:05}_optim_e.pth'))
torch.save(optimizer_d.state_dict(),
join(weights_path, f'{epoch:05}_optim_d.pth'))
def main(config):
random.seed(config['seed'])
torch.manual_seed(config['seed'])
torch.cuda.manual_seed_all(config['seed'])
results_dir = prepare_results_dir(config)
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger('vae')
device = cuda_setup(config['cuda'], config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.debug("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset, batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
drop_last=True, pin_memory=True)
#
# Models
#
arch = import_module(f"models.{config['arch']}")
G = arch.Generator(config).to(device)
E = arch.Encoder(config).to(device)
G.apply(weights_init)
E.apply(weights_init)
if config['reconstruction_loss'].lower() == 'chamfer':
from losses.champfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
from losses.earth_mover_distance import EMD
reconstruction_loss = EMD().to(device)
elif config['reconstruction_loss'].lower() == 'cramer_wold':
from losses.cramer_wold import CWSample
reconstruction_loss = CWSample().to(device)
else:
raise ValueError(f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
#
# Float Tensors
#
fixed_noise = torch.FloatTensor(config['batch_size'], config['z_size'], 1)
fixed_noise.normal_(mean=0, std=0.2)
std_assumed = torch.tensor(0.2)
fixed_noise = fixed_noise.to(device)
std_assumed = std_assumed.to(device)
#
# Optimizers
#
EG_optim = getattr(optim, config['optimizer']['EG']['type'])
EG_optim = EG_optim(chain(E.parameters(), G.parameters()),
**config['optimizer']['EG']['hyperparams'])
if starting_epoch > 1:
G.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_G.pth')))
E.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_E.pth')))
EG_optim.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_EGo.pth')))
losses = []
with trange(starting_epoch, config['max_epochs'] + 1) as t:
for epoch in t:
start_epoch_time = datetime.now()
G.train()
E.train()
total_loss = 0.0
losses_eg = []
losses_e = []
losses_kld = []
for i, point_data in enumerate(points_dataloader, 1):
# log.debug('-' * 20)
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
codes, mu, logvar = E(X)
X_rec = G(codes)
loss_e = torch.mean(
# config['reconstruction_coef'] *
reconstruction_loss(X.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
loss_kld = config['reconstruction_coef'] * cw_distance(mu)
# loss_kld = -0.5 * torch.mean(
# 1 - 2.0 * torch.log(std_assumed) + logvar -
# (mu.pow(2) + logvar.exp()) / torch.pow(std_assumed, 2))
loss_eg = loss_e + loss_kld
EG_optim.zero_grad()
E.zero_grad()
G.zero_grad()
loss_eg.backward()
total_loss += loss_eg.item()
EG_optim.step()
losses_e.append(loss_e.item())
losses_kld.append(loss_kld.item())
losses_eg.append(loss_eg.item())
# log.debug
t.set_description(
f'[{epoch}: ({i})] '
f'Loss_EG: {loss_eg.item():.4f} '
f'(REC: {loss_e.item(): .4f}'
f' KLD: {loss_kld.item(): .4f})'
f' Time: {datetime.now() - start_epoch_time}'
)
t.set_description(
f'[{epoch}/{config["max_epochs"]}] '
f'Loss_G: {total_loss / i:.4f} '
f'Time: {datetime.now() - start_epoch_time}'
)
losses.append([
np.mean(losses_e),
np.mean(losses_kld),
np.mean(losses_eg)
])
#
# Save intermediate results
#
G.eval()
E.eval()
with torch.no_grad():
fake = G(fixed_noise).data.cpu().numpy()
codes, _, _ = E(X)
X_rec = G(codes).data.cpu().numpy()
X_numpy = X.cpu().numpy()
for k in range(5):
fig = plot_3d_point_cloud(X_numpy[k][0], X_numpy[k][1], X_numpy[k][2],
in_u_sphere=True, show=False)
fig.savefig(
join(results_dir, 'samples', f'{epoch}_{k}_real.png'))
plt.close(fig)
for k in range(5):
fig = plot_3d_point_cloud(fake[k][0], fake[k][1], fake[k][2],
in_u_sphere=True, show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples', 'fixed', f'{epoch:05}_{k}_fixed.png'))
plt.close(fig)
for k in range(5):
fig = plot_3d_point_cloud(X_rec[k][0],
X_rec[k][1],
X_rec[k][2],
in_u_sphere=True, show=False)
fig.savefig(join(results_dir, 'samples',
f'{epoch}_{k}_reconstructed.png'))
plt.close(fig)
if epoch % config['save_frequency'] == 0:
df = pd.DataFrame(losses, columns=['loss_e', 'loss_kld', 'loss_eg'])
df.to_json(join(results_dir, 'losses', 'losses.json'))
fig = df.plot.line().get_figure()
fig.savefig(join(results_dir, 'losses', f'{epoch:05}_{k}.png'))
torch.save(G.state_dict(), join(weights_path, f'{epoch:05}_G.pth'))
torch.save(E.state_dict(), join(weights_path, f'{epoch:05}_E.pth'))
torch.save(EG_optim.state_dict(),
join(weights_path, f'{epoch:05}_EGo.pth'))
def main(config):
random.seed(config['seed'])
torch.manual_seed(config['seed'])
torch.cuda.manual_seed_all(config['seed'])
results_dir = prepare_results_dir(config)
starting_epoch = find_latest_epoch(results_dir) + 1
if not exists(join(results_dir, 'config.json')):
with open(join(results_dir, 'config.json'), mode='w') as f:
json.dump(config, f)
setup_logging(results_dir)
log = logging.getLogger(__name__)
device = cuda_setup(config['cuda'], config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
weights_path = join(results_dir, 'weights')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.debug("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset, batch_size=config['batch_size'],
shuffle=config['shuffle'],
num_workers=config['num_workers'],
drop_last=True, pin_memory=True)
#
# Models
#
arch = import_module(f"models.{config['arch']}")
G = arch.Generator(config).to(device)
E = arch.Encoder(config).to(device)
D = arch.Discriminator(config).to(device)
G.apply(weights_init)
E.apply(weights_init)
D.apply(weights_init)
if config['reconstruction_loss'].lower() == 'chamfer':
from losses.champfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
from losses.earth_mover_distance import EMD
reconstruction_loss = EMD().to(device)
else:
raise ValueError(f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
#
# Float Tensors
#
distribution = config['distribution'].lower()
if distribution == 'bernoulli':
p = torch.tensor(config['p']).to(device)
sampler = Bernoulli(probs=p)
fixed_noise = sampler.sample(torch.Size([config['batch_size'],
config['z_size']]))
elif distribution == 'beta':
fixed_noise_np = np.random.beta(config['z_beta_a'],
config['z_beta_b'],
size=(config['batch_size'],
config['z_size']))
fixed_noise = torch.tensor(fixed_noise_np).float().to(device)
else:
raise ValueError('Invalid distribution for binaray model.')
#
# Optimizers
#
EG_optim = getattr(optim, config['optimizer']['EG']['type'])
EG_optim = EG_optim(chain(E.parameters(), G.parameters()),
**config['optimizer']['EG']['hyperparams'])
D_optim = getattr(optim, config['optimizer']['D']['type'])
D_optim = D_optim(D.parameters(),
**config['optimizer']['D']['hyperparams'])
if starting_epoch > 1:
G.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_G.pth')))
E.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_E.pth')))
D.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_D.pth')))
D_optim.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_Do.pth')))
EG_optim.load_state_dict(torch.load(
join(weights_path, f'{starting_epoch-1:05}_EGo.pth')))
loss_d_tot, loss_gp_tot, loss_e_tot, loss_g_tot = [], [], [], []
for epoch in range(starting_epoch, config['max_epochs'] + 1):
start_epoch_time = datetime.now()
G.train()
E.train()
D.train()
total_loss_eg = 0.0
total_loss_d = 0.0
for i, point_data in enumerate(points_dataloader, 1):
log.debug('-' * 20)
X, _ = point_data
X = X.to(device)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
codes = E(X)
if distribution == 'bernoulli':
noise = sampler.sample(fixed_noise.size())
elif distribution == 'beta':
noise_np = np.random.beta(config['z_beta_a'],
config['z_beta_b'],
size=(config['batch_size'],
config['z_size']))
noise = torch.tensor(noise_np).float().to(device)
synth_logit = D(codes)
real_logit = D(noise)
loss_d = torch.mean(synth_logit) - torch.mean(real_logit)
loss_d_tot.append(loss_d)
# Gradient Penalty
alpha = torch.rand(config['batch_size'], 1).to(device)
differences = codes - noise
interpolates = noise + alpha * differences
disc_interpolates = D(interpolates)
gradients = grad(
outputs=disc_interpolates,
inputs=interpolates,
grad_outputs=torch.ones_like(disc_interpolates).to(device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
slopes = torch.sqrt(torch.sum(gradients ** 2, dim=1))
gradient_penalty = ((slopes - 1) ** 2).mean()
loss_gp = config['gp_lambda'] * gradient_penalty
loss_gp_tot.append(loss_gp)
###
loss_d += loss_gp
D_optim.zero_grad()
D.zero_grad()
loss_d.backward(retain_graph=True)
total_loss_d += loss_d.item()
D_optim.step()
# EG part of training
X_rec = G(codes)
loss_e = torch.mean(
config['reconstruction_coef'] *
reconstruction_loss(X.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
loss_e_tot.append(loss_e)
synth_logit = D(codes)
loss_g = -torch.mean(synth_logit)
loss_g_tot.append(loss_g)
loss_eg = loss_e + loss_g
EG_optim.zero_grad()
E.zero_grad()
G.zero_grad()
loss_eg.backward()
total_loss_eg += loss_eg.item()
EG_optim.step()
log.debug(f'[{epoch}: ({i})] '
f'Loss_D: {loss_d.item():.4f} '
f'(GP: {loss_gp.item(): .4f}) '
f'Loss_EG: {loss_eg.item():.4f} '
f'(REC: {loss_e.item(): .4f}) '
f'Time: {datetime.now() - start_epoch_time}')
log.debug(
f'[{epoch}/{config["max_epochs"]}] '
f'Loss_D: {total_loss_d / i:.4f} '
f'Loss_EG: {total_loss_eg / i:.4f} '
f'Time: {datetime.now() - start_epoch_time}'
)
#
# Save intermediate results
#
G.eval()
E.eval()
D.eval()
with torch.no_grad():
fake = G(fixed_noise).data.cpu().numpy()
X_rec = G(E(X)).data.cpu().numpy()
X = X.data.cpu().numpy()
plt.figure(figsize=(16, 9))
plt.plot(loss_d_tot, 'r-', label="loss_d")
plt.plot(loss_gp_tot, 'g-', label="loss_gp")
plt.plot(loss_e_tot, 'b-', label="loss_e")
plt.plot(loss_g_tot, 'k-', label="loss_g")
plt.legend()
plt.xlabel("Batch number")
plt.xlabel("Loss value")
plt.savefig(
join(results_dir, 'samples', f'loss_plot.png'))
plt.close()
for k in range(5):
fig = plot_3d_point_cloud(X[k][0], X[k][1], X[k][2],
in_u_sphere=True, show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples', f'{epoch:05}_{k}_real.png'))
plt.close(fig)
for k in range(5):
fig = plot_3d_point_cloud(fake[k][0], fake[k][1], fake[k][2],
in_u_sphere=True, show=False,
title=str(epoch))
fig.savefig(
join(results_dir, 'samples', f'{epoch:05}_{k}_fixed.png'))
plt.close(fig)
for k in range(5):
fig = plot_3d_point_cloud(X_rec[k][0], X_rec[k][1], X_rec[k][2],
in_u_sphere=True, show=False,
title=str(epoch))
fig.savefig(join(results_dir, 'samples',
f'{epoch:05}_{k}_reconstructed.png'))
plt.close(fig)
if epoch % config['save_frequency'] == 0:
torch.save(G.state_dict(), join(weights_path, f'{epoch:05}_G.pth'))
torch.save(D.state_dict(), join(weights_path, f'{epoch:05}_D.pth'))
torch.save(E.state_dict(), join(weights_path, f'{epoch:05}_E.pth'))
torch.save(EG_optim.state_dict(),
join(weights_path, f'{epoch:05}_EGo.pth'))
torch.save(D_optim.state_dict(),
join(weights_path, f'{epoch:05}_Do.pth'))
def main(eval_config):
# Load hyperparameters as they were during training
train_results_path = join(eval_config['results_root'], eval_config['arch'],
eval_config['experiment_name'])
with open(join(train_results_path, 'config.json')) as f:
train_config = json.load(f)
random.seed(train_config['seed'])
torch.manual_seed(train_config['seed'])
torch.cuda.manual_seed_all(train_config['seed'])
setup_logging(join(train_results_path, 'results'))
log = logging.getLogger(__name__)
weights_path = join(train_results_path, 'weights')
if eval_config['epoch'] == 0:
epoch = find_latest_epoch(weights_path)
else:
epoch = eval_config['epoch']
log.debug(f'Starting from epoch: {epoch}')
device = cuda_setup(eval_config['cuda'], eval_config['gpu'])
log.debug(f'Device variable: {device}')
if device.type == 'cuda':
log.debug(f'Current CUDA device: {torch.cuda.current_device()}')
#
# Dataset
#
dataset_name = train_config['dataset'].lower()
if dataset_name == 'shapenet':
dataset = ShapeNetDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='test')
elif dataset_name == 'faust':
from datasets.dfaust import DFaustDataset
dataset = DFaustDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='test')
elif dataset_name == 'mcgill':
from datasets.mcgill import McGillDataset
dataset = McGillDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='test')
elif dataset_name == 'custom':
from datasets.customdataset import CustomDataset
dataset = CustomDataset(root_dir=train_config['data_dir'],
classes=train_config['classes'],
split='test')
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
classes_selected = ('all' if not train_config['classes'] else ','.join(
train_config['classes']))
log.debug(f'Selected {classes_selected} classes. Loaded {len(dataset)} '
f'samples.')
if 'distribution' in train_config:
distribution = train_config['distribution']
elif 'distribution' in eval_config:
distribution = eval_config['distribution']
else:
log.warning(
'No distribution type specified. Assumed normal = N(0, 0.2)')
distribution = 'normal'
#
# Models
#
arch = import_module(f"models.{eval_config['arch']}")
E = arch.Encoder(train_config).to(device)
G = arch.Generator(train_config).to(device)
#
# Load saved state
#
E.load_state_dict(torch.load(join(weights_path, f'{epoch:05}_E.pth')))
G.load_state_dict(torch.load(join(weights_path, f'{epoch:05}_G.pth')))
E.eval()
G.eval()
num_samples = len(dataset.point_clouds_names_test)
data_loader = DataLoader(dataset,
batch_size=num_samples,
shuffle=False,
num_workers=4,
drop_last=False,
pin_memory=True)
# We take 3 times as many samples as there are in test data in order to
# perform JSD calculation in the same manner as in the reference publication
noise = torch.FloatTensor(3 * num_samples, train_config['z_size'], 1)
noise = noise.to(device)
X, _ = next(iter(data_loader))
X = X.to(device)
X_ = X.data.cpu().numpy()
np.save(join(train_results_path, 'results', f'{epoch:05}_X'), X_)
for i in range(3):
if distribution == 'normal':
noise.normal_(0, 0.2)
else:
noise_np = np.random.beta(train_config['z_beta_a'],
train_config['z_beta_b'], noise.shape)
noise = torch.tensor(noise_np).float().round().to(device)
with torch.no_grad():
X_g = G(noise)
if X_g.shape[-2:] == (3, 2048):
X_g.transpose_(1, 2)
X_g_ = X_g.data.cpu().numpy()
np.save(join(train_results_path, 'results', f'{epoch:05}_Xg_{i}'),
X_g_)
with torch.no_grad():
z_e = E(X.transpose(1, 2))
if isinstance(z_e, tuple):
z_e = z_e[0]
X_rec = G(z_e)
if X_rec.shape[-2:] == (3, 2048):
X_rec.transpose_(1, 2)
X_rec_ = X_rec.data.cpu().numpy()
np.save(join(train_results_path, 'results', f'{epoch:05}_Xrec'), X_rec_)
def main(config):
set_seed(config['seed'])
results_dir = prepare_results_dir(config,
config['arch'],
'experiments',
dirs_to_create=[
'interpolations', 'sphere',
'points_interpolation',
'different_number_points', 'fixed',
'reconstruction', 'sphere_triangles',
'sphere_triangles_interpolation'
])
weights_path = get_weights_dir(config)
epoch = find_latest_epoch(weights_path)
if not epoch:
print("Invalid 'weights_path' in configuration")
exit(1)
setup_logging(results_dir)
global log
log = logging.getLogger('aae')
if not exists(join(results_dir, 'experiment_config.json')):
with open(join(results_dir, 'experiment_config.json'), mode='w') as f:
json.dump(config, f)
device = cuda_setup(config['cuda'], config['gpu'])
log.info(f'Device variable: {device}')
if device.type == 'cuda':
log.info(f'Current CUDA device: {torch.cuda.current_device()}')
#
# Dataset
#
dataset_name = config['dataset'].lower()
if dataset_name == 'shapenet':
from datasets.shapenet import ShapeNetDataset
dataset = ShapeNetDataset(root_dir=config['data_dir'],
classes=config['classes'])
elif dataset_name == 'custom':
dataset = TxtDataset(root_dir=config['data_dir'],
classes=config['classes'],
config=config)
elif dataset_name == 'benchmark':
dataset = Benchmark(root_dir=config['data_dir'],
classes=config['classes'],
config=config)
else:
raise ValueError(f'Invalid dataset name. Expected `shapenet` or '
f'`faust`. Got: `{dataset_name}`')
log.info("Selected {} classes. Loaded {} samples.".format(
'all' if not config['classes'] else ','.join(config['classes']),
len(dataset)))
points_dataloader = DataLoader(dataset,
batch_size=64,
shuffle=True,
num_workers=8,
drop_last=True,
pin_memory=True,
collate_fn=collate_fn)
#
# Models
#
hyper_network = aae.HyperNetwork(config, device).to(device)
encoder_visible = aae.VisibleEncoder(config).to(device)
encoder_pocket = aae.PocketEncoder(config).to(device)
if config['reconstruction_loss'].lower() == 'chamfer':
from losses.champfer_loss import ChamferLoss
reconstruction_loss = ChamferLoss().to(device)
elif config['reconstruction_loss'].lower() == 'earth_mover':
# from utils.metrics import earth_mover_distance
# reconstruction_loss = earth_mover_distance
from losses.earth_mover_distance import EMD
reconstruction_loss = EMD().to(device)
else:
raise ValueError(
f'Invalid reconstruction loss. Accepted `chamfer` or '
f'`earth_mover`, got: {config["reconstruction_loss"]}')
log.info("Weights for epoch: %s" % epoch)
log.info("Loading weights...")
hyper_network.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_G.pth')))
encoder_pocket.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_EP.pth')))
encoder_visible.load_state_dict(
torch.load(join(weights_path, f'{epoch:05}_EV.pth')))
hyper_network.eval()
encoder_visible.eval()
encoder_pocket.eval()
total_loss_eg = 0.0
total_loss_e = 0.0
total_loss_kld = 0.0
x = []
with torch.no_grad():
for i, point_data in enumerate(points_dataloader, 1):
X = point_data['non-visible']
X = X.to(device, dtype=torch.float)
# get whole point cloud
X_whole = point_data['cloud']
X_whole = X_whole.to(device, dtype=torch.float)
# get visible point cloud
X_visible = point_data['visible']
X_visible = X_visible.to(device, dtype=torch.float)
# Change dim [BATCH, N_POINTS, N_DIM] -> [BATCH, N_DIM, N_POINTS]
if X.size(-1) == 3:
X.transpose_(X.dim() - 2, X.dim() - 1)
X_whole.transpose_(X_whole.dim() - 2, X_whole.dim() - 1)
X_visible.transpose_(X_visible.dim() - 2, X_visible.dim() - 1)
x.append(X)
codes, mu, logvar = encoder_pocket(X)
mu_visible = encoder_visible(X_visible)
target_networks_weights = hyper_network(
torch.cat((codes, mu_visible), 1))
X_rec = torch.zeros(X_whole.shape).to(device)
for j, target_network_weights in enumerate(
target_networks_weights):
target_network = aae.TargetNetwork(
config, target_network_weights).to(device)
target_network_input = generate_points(config=config,
epoch=epoch,
size=(X_whole.shape[2],
X_whole.shape[1]))
X_rec[j] = torch.transpose(
target_network(target_network_input.to(device)), 0, 1)
loss_e = torch.mean(config['reconstruction_coef'] *
reconstruction_loss(
X_whole.permute(0, 2, 1) + 0.5,
X_rec.permute(0, 2, 1) + 0.5))
loss_kld = 0.5 * (torch.exp(logvar) + torch.pow(mu, 2) - 1 -
logvar).sum()
loss_eg = loss_e + loss_kld
total_loss_e += loss_e.item()
total_loss_kld += loss_kld.item()
total_loss_eg += loss_eg.item()
log.info(f'Loss_ALL: {total_loss_eg / i:.4f} '
f'Loss_R: {total_loss_e / i:.4f} '
f'Loss_E: {total_loss_kld / i:.4f} ')
# take the lowest possible first dim
min_dim = min(x, key=lambda X: X.shape[2]).shape[2]
x = [X[:, :, :min_dim] for X in x]
x = torch.cat(x)
if config['experiments']['interpolation']['execute']:
interpolation(
x, encoder_pocket, hyper_network, device, results_dir, epoch,
config['experiments']['interpolation']['amount'],
config['experiments']['interpolation']['transitions'])
if config['experiments']['interpolation_between_two_points'][
'execute']:
interpolation_between_two_points(
encoder_pocket, hyper_network, device, x, results_dir, epoch,
config['experiments']['interpolation_between_two_points']
['amount'], config['experiments']
['interpolation_between_two_points']['image_points'],
config['experiments']['interpolation_between_two_points']
['transitions'])
if config['experiments']['reconstruction']['execute']:
reconstruction(encoder_pocket, hyper_network, device, x,
results_dir, epoch,
config['experiments']['reconstruction']['amount'])
if config['experiments']['sphere']['execute']:
sphere(encoder_pocket, hyper_network, device, x, results_dir,
epoch, config['experiments']['sphere']['amount'],
config['experiments']['sphere']['image_points'],
config['experiments']['sphere']['start'],
config['experiments']['sphere']['end'],
config['experiments']['sphere']['transitions'])
if config['experiments']['sphere_triangles']['execute']:
sphere_triangles(
encoder_pocket, hyper_network, device, x, results_dir,
config['experiments']['sphere_triangles']['amount'],
config['experiments']['sphere_triangles']['method'],
config['experiments']['sphere_triangles']['depth'],
config['experiments']['sphere_triangles']['start'],
config['experiments']['sphere_triangles']['end'],
config['experiments']['sphere_triangles']['transitions'])
if config['experiments']['sphere_triangles_interpolation']['execute']:
sphere_triangles_interpolation(
encoder_pocket, hyper_network, device, x, results_dir,
config['experiments']['sphere_triangles_interpolation']
['amount'], config['experiments']
['sphere_triangles_interpolation']['method'],
config['experiments']['sphere_triangles_interpolation']
['depth'], config['experiments']
['sphere_triangles_interpolation']['coefficient'],
config['experiments']['sphere_triangles_interpolation']
['transitions'])
if config['experiments']['different_number_of_points']['execute']:
different_number_of_points(
encoder_pocket, hyper_network, x, device, results_dir, epoch,
config['experiments']['different_number_of_points']['amount'],
config['experiments']['different_number_of_points']
['image_points'])
if config['experiments']['fixed']['execute']:
# get visible element from loader (probably should be done using given object for example using
# parser
points_dataloader = DataLoader(dataset,
batch_size=10,
shuffle=True,
num_workers=8,
drop_last=True,
pin_memory=True,
collate_fn=collate_fn)
X_visible = next(iter(points_dataloader))['visible'].to(
device, dtype=torch.float)
X_visible.transpose_(X_visible.dim() - 2, X_visible.dim() - 1)
fixed(hyper_network, encoder_visible, X_visible, device,
results_dir, epoch, config['experiments']['fixed']['amount'],
config['z_size'] // 2,
config['experiments']['fixed']['mean'],
config['experiments']['fixed']['std'], (3, 2048),
config['experiments']['fixed']['triangulation']['execute'],
config['experiments']['fixed']['triangulation']['method'],
config['experiments']['fixed']['triangulation']['depth'])
def make_tsne_reduction(full_model, device, dataset_dict, results_dir, epoch):
'''
this experiment requires changing model.full_model.py:FullModel#forward method return to
reconstruction, latent, target_networks_weights
'''
from datasets.shapenet import ShapeNetDataset
cat_name = 'car'
amount = 100
train_dataset_dict = ShapeNetDataset._get_datasets_for_classes(
'D:\\UJ\\bachelors\\3d-point-clouds-autocomplete\\data\\shapenet',
'train',
use_pcn_model_list=True,
is_random_rotated=False,
num_samples=1,
# classes=['04530566', '02933112']
)
is_compute = False
with torch.no_grad():
latents = {}
tnws = {}
if is_compute:
dataloaders_dict = {
cat_name: DataLoader(cat_ds,
pin_memory=True,
batch_size=1,
num_workers=0)
for cat_name, cat_ds in train_dataset_dict.items()
}
for cat_name, dl in dataloaders_dict.items():
if cat_name != 'car':
continue
cat_latent = []
cat_tnw = []
for data in tqdm(dl, total=len(dl)):
existing, missing, gt, _ = data
existing = existing.to(device)
missing = missing.to(device)
rec, latent, tnw = full_model(existing, missing,
list(gt.shape), epoch,
device)
cat_latent.append(latent.detach().cpu())
cat_tnw.append(tnw.detach().cpu())
latents[cat_name] = torch.cat(cat_latent).numpy()
tnws[cat_name] = torch.cat(cat_tnw).numpy()
latents['all'] = np.concatenate([v for v in latents.values()])
tnws['all'] = np.concatenate([v for v in tnws.values()])
for cat_name in latents.keys():
np.save(join(results_dir, 'temp_exp', f'{cat_name}_latent1'),
latents[cat_name])
np.save(join(results_dir, 'temp_exp', f'{cat_name}_tnw1'),
tnws[cat_name])
else:
for cat_name in train_dataset_dict.keys():
if cat_name != 'car':
continue
latents[cat_name] = np.load(
join(results_dir, 'temp_exp', f'{cat_name}_latent1.npy'))
tnws[cat_name] = np.load(
join(results_dir, 'temp_exp', f'{cat_name}_tnw1.npy'))
for cat_name, ds in dataset_dict.items():
if cat_name != 'car':
continue
cat_ids = np.random.choice(len(ds), amount, replace=False)
cat_latent = []
cat_tnw = []
for i in range(amount):
_, _, gt, _ = ds[cat_ids[i]]
np.save(
join(results_dir, 'temp_exp', 'gts', f'{cat_name}_{i}'),
gt)
existing_x = gt[gt.T[0].argsort()[1024:]]
missing_x = gt[gt.T[0].argsort()[:1024]]
existing_y = gt[gt.T[1].argsort()[1024:]]
missing_y = gt[gt.T[1].argsort()[:1024]]
gt_shape = list(torch.from_numpy(gt).unsqueeze(0).shape)
existing = torch.from_numpy(existing_x).unsqueeze(0).to(device)
missing = torch.from_numpy(missing_x).unsqueeze(0).to(device)
_, latent, tnw = full_model(existing, missing, gt_shape, epoch,
device)
cat_latent.append(latent.cpu())
cat_tnw.append(tnw.cpu())
existing = torch.from_numpy(existing_y).unsqueeze(0).to(device)
missing = torch.from_numpy(missing_y).unsqueeze(0).to(device)
_, latent, tnw = full_model(existing, missing, gt_shape, epoch,
device)
cat_latent.append(latent.cpu())
cat_tnw.append(tnw.cpu())
cat_latent = torch.cat(cat_latent).numpy()
cat_tnw = torch.cat(cat_tnw).numpy()
cc_latent = np.concatenate([latents[cat_name], cat_latent])
cc_tnw = np.concatenate([tnws[cat_name], cat_tnw])
start_time = datetime.now()
print(start_time)
latent_tsne = manifold.TSNE(n_components=2,
init='pca').fit_transform(cc_latent)
print(datetime.now() - start_time)
cat_test_tsne = latent_tsne[-(2 * amount):]
plt.plot(latent_tsne.T[0], latent_tsne.T[1], 'o',
cat_test_tsne.T[0], cat_test_tsne.T[1], 'o')
plt.title('latent')
plt.show()
np.save(join(results_dir, 'temp_exp', f'{cat_name}_latent_tsne'),
latent_tsne)
start_time = datetime.now()
print(start_time)
tnw_tsne = manifold.TSNE(n_components=2,
init='pca').fit_transform(cc_tnw)
print(datetime.now() - start_time)
cat_test_tnw = tnw_tsne[-(2 * amount):]
plt.plot(tnw_tsne.T[0], tnw_tsne.T[1], 'o', cat_test_tnw.T[0],
cat_test_tnw.T[1], 'o')
plt.title('tnw')
plt.show()
np.save(join(results_dir, 'temp_exp', f'{cat_name}_tnw_tsne'),
tnw_tsne)
latent_tsne = np.load(
join(results_dir, 'temp_exp', f'{cat_name}_latent_tsne.npy'))
tnw_tsne = np.load(
join(results_dir, 'temp_exp', f'{cat_name}_tnw_tsne.npy'))
cat_test_tsne = latent_tsne[-(2 * amount):]
cat_test_tnw = tnw_tsne[-(2 * amount):]
latent_dist = np.zeros(amount)
tnw_dist = np.zeros(amount)
for i in range(amount):
latent_dist[i] = np.linalg.norm(cat_test_tsne[2 * i] -
cat_test_tsne[2 * i + 1])
tnw_dist[i] = np.linalg.norm(cat_test_tnw[2 * i] -
cat_test_tnw[2 * i + 1])
plt.plot(latent_tsne.T[0], latent_tsne.T[1], 'o', cat_test_tsne.T[0],
cat_test_tsne.T[1], 'o')
plt.title('latent')
plt.show()
plt.plot(tnw_tsne.T[0], tnw_tsne.T[1], 'o', cat_test_tnw.T[0][0],
cat_test_tnw.T[1][0], 'o', cat_test_tnw.T[0][1],
cat_test_tnw.T[1][1], 'o')
plt.title('tnw')
plt.show()