def main(argv):
parser = argparse.ArgumentParser(
description="Train a network to predict primitives")
parser.add_argument("dataset_directory",
help="Path to the directory containing the dataset")
parser.add_argument("output_directory",
help="Save the output files in that directory")
parser.add_argument(
"--tsdf_directory",
default="",
help="Path to the directory containing the precomputed tsdf files")
parser.add_argument(
"--weight_file",
default=None,
help=("The path to a previously trainined model to continue"
" the training from"))
parser.add_argument("--continue_from_epoch",
default=0,
type=int,
help="Continue training from epoch (default=0)")
parser.add_argument("--n_primitives",
type=int,
default=32,
help="Number of primitives")
parser.add_argument(
"--use_deformations",
action="store_true",
help="Use Superquadrics with deformations as the shape configuration")
parser.add_argument("--train_test_splits_file",
default=None,
help="Path to the train-test splits file")
parser.add_argument("--run_on_gpu", action="store_true", help="Use GPU")
parser.add_argument("--probs_only",
action="store_true",
help="Optimize only using the probabilities")
parser.add_argument("--experiment_tag",
default=None,
help="Tag that refers to the current experiment")
parser.add_argument("--cache_size",
type=int,
default=2000,
help="The batch provider cache size")
parser.add_argument("--seed",
type=int,
default=27,
help="Seed for the PRNG")
add_nn_parameters(parser)
add_dataset_parameters(parser)
add_voxelizer_parameters(parser)
add_training_parameters(parser)
add_sq_mesh_sampler_parameters(parser)
add_regularizer_parameters(parser)
add_gaussian_noise_layer_parameters(parser)
# Parameters related to the loss function and the loss weights
add_loss_parameters(parser)
# Parameters related to loss options
add_loss_options_parameters(parser)
args = parser.parse_args(argv)
if args.train_test_splits_file is not None:
train_test_splits = parse_train_test_splits(
args.train_test_splits_file, args.model_tags)
training_tags = np.hstack(
[train_test_splits["train"], train_test_splits["val"]])
else:
training_tags = args.model_tags
#device = torch.device("cuda:0")
if args.run_on_gpu: #and torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print("Running code on {}".format(device))
# Check if output directory exists and if it doesn't create it
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
# Create an experiment directory using the experiment_tag
if args.experiment_tag is None:
experiment_tag = id_generator(9)
else:
experiment_tag = args.experiment_tag
experiment_directory = os.path.join(args.output_directory, experiment_tag)
if not os.path.exists(experiment_directory):
os.makedirs(experiment_directory)
# Store the parameters for the current experiment in a json file
save_experiment_params(args, experiment_tag, experiment_directory)
print("Save experiment statistics in %s" % (experiment_tag, ))
# Create two files to store the training and test evolution
train_stats = os.path.join(experiment_directory, "train.txt")
val_stats = os.path.join(experiment_directory, "val.txt")
if args.weight_file is None:
train_stats_f = open(train_stats, "w")
else:
train_stats_f = open(train_stats, "a+")
train_stats_f.write(
("epoch loss pcl_to_prim_loss prim_to_pcl_loss bernoulli_regularizer "
"entropy_bernoulli_regularizer parsimony_regularizer "
"overlapping_regularizer sparsity_regularizer\n"))
# Set the random seed
np.random.seed(args.seed)
torch.manual_seed(np.random.randint(np.iinfo(np.int32).max))
if torch.cuda.is_available():
torch.cuda.manual_seed_all(np.random.randint(np.iinfo(np.int32).max))
# Create an object that will sample points in equal distances on the
# surface of the primitive
sampler = get_sampler(args.use_cuboids, args.n_points_from_sq_mesh,
args.D_eta, args.D_omega)
# Create a factory that returns the appropriate voxelizer based on the
# input argument
voxelizer_factory = VoxelizerFactory(args.voxelizer_factory,
np.array(voxelizer_shape(args)),
args.save_voxels_to)
# Create a dataset instance to generate the samples for training
training_dataset = get_dataset_type("euclidean_dual_loss")(
(DatasetBuilder().with_dataset(args.dataset_type).lru_cache(
2000).filter_tags(training_tags).build(args.dataset_directory)),
voxelizer_factory,
args.n_points_from_mesh,
transform=compose_transformations(args.voxelizer_factory))
# Create a batchprovider object to start generating batches
train_bp = BatchProvider(training_dataset,
batch_size=args.batch_size,
cache_size=args.cache_size)
train_bp.ready()
network_params = NetworkParameters.from_options(args)
# Build the model to be used for training
model = network_params.network(network_params)
# Move model to the device to be used
model.to(device)
# Check whether there is a weight file provided to continue training from
if args.weight_file is not None:
model.load_state_dict(torch.load(args.weight_file))
model.train()
# Build an optimizer object to compute the gradients of the parameters
optimizer = optimizer_factory(args, model)
# Loop over the dataset multiple times
pcl_to_prim_losses = []
prim_to_pcl_losses = []
losses = []
for i in range(args.epochs):
bar = get_logger("euclidean_dual_loss", i + 1, args.epochs,
args.steps_per_epoch)
for b, sample in zip(range(args.steps_per_epoch),
yield_infinite(train_bp)):
tags, X, y_target = sample
X, y_target = X.to(device), y_target.to(device)
# based on `tag`
part_point_samples = []
P = []
indices_w_parts = []
for idx_batchwide, tag in enumerate(tags):
# TODO: does this sample have any part pt samples?
target_part_samples_path = os.path.exists(
os.path.join(RANDINDEX_SAMPLES_DIR, '{}.pkl'.format(tag)))
if os.path.exists(target_part_samples_path):
with open(target_part_samples_path, 'rb') as f:
foo = pickle.load(f)
point_samples = foo['samples']
point_samples = torch.Tensor(point_samples).to(device)
part_point_samples.append(point_samples)
N = point_samples.shape[0]
nonzero_indices = foo['neighbor_pairs']
assert nonzero_indices.shape[0] == 2
val = np.ones(nonzero_indices.shape[1])
curr_P = torch.sparse_coo_tensor(nonzero_indices, val,
(N, N))
curr_P = curr_P.to(device)
P.append(curr_P)
# turn into matrices here?
indices_w_parts.append(idx_batchwide)
P = torch.stack(P, axis=0)
part_point_samples = torch.stack(part_point_samples, axis=0)
import ipdb
ipdb.set_trace()
# Train on batch
batch_loss, metrics, debug_stats = train_on_batch_w_parts(
model,
lr_schedule(optimizer, i, args.lr, args.lr_factor,
args.lr_epochs), euclidean_dual_loss, X, y_target,
get_regularizer_terms(args, i), sampler,
get_loss_options(args), P, part_point_samples, indices_w_parts)
# Get the regularizer terms
reg_values = debug_stats["regularizer_terms"]
sparsity_regularizer = reg_values["sparsity_regularizer"]
overlapping_regularizer = reg_values["overlapping_regularizer"]
parsimony_regularizer = reg_values["parsimony_regularizer"]
entropy_bernoulli_regularizer = reg_values[
"entropy_bernoulli_regularizer"]
bernoulli_regularizer = reg_values["bernoulli_regularizer"]
# The lossess
pcl_to_prim_loss = debug_stats["pcl_to_prim_loss"].item()
prim_to_pcl_loss = debug_stats["prim_to_pcl_loss"].item()
bar.loss = moving_average(bar.loss, batch_loss, b)
bar.pcl_to_prim_loss = \
moving_average(bar.pcl_to_prim_loss, pcl_to_prim_loss, b)
bar.prim_to_pcl_loss = \
moving_average(bar.prim_to_pcl_loss, prim_to_pcl_loss, b)
losses.append(bar.loss)
prim_to_pcl_losses.append(bar.prim_to_pcl_loss)
pcl_to_prim_losses.append(bar.pcl_to_prim_loss)
bar.bernoulli_regularizer =\
(bar.bernoulli_regularizer * b + bernoulli_regularizer) / (b+1)
bar.parsimony_regularizer =\
(bar.parsimony_regularizer * b + parsimony_regularizer) / (b+1)
bar.overlapping_regularizer =\
(bar.overlapping_regularizer * b + overlapping_regularizer) / (b+1)
bar.entropy_bernoulli_regularizer = \
(bar.entropy_bernoulli_regularizer * b +
entropy_bernoulli_regularizer) / (b+1)
bar.sparsity_regularizer =\
(bar.sparsity_regularizer * b + sparsity_regularizer) / (b+1)
bar.exp_n_prims = metrics[0].sum(-1).mean()
# Update the file that keeps track of the statistics
train_stats_f.write(
("%d %.8f %.8f %.8f %.6f %.6f %.6f %.6f %.6f") %
(i, bar.loss, bar.pcl_to_prim_loss, bar.prim_to_pcl_loss,
bar.bernoulli_regularizer, bar.entropy_bernoulli_regularizer,
bar.parsimony_regularizer, bar.overlapping_regularizer,
bar.sparsity_regularizer))
train_stats_f.write("\n")
train_stats_f.flush()
bar.next()
# Finish the progress bar and save the model after every epoch
bar.finish()
# Stop the batch provider
train_bp.stop()
torch.save(
model.state_dict(),
os.path.join(experiment_directory,
"model_%d" % (i + args.continue_from_epoch, )))
print([
sum(losses[args.steps_per_epoch:]) / float(args.steps_per_epoch),
sum(losses[:args.steps_per_epoch]) / float(args.steps_per_epoch),
sum(pcl_to_prim_losses[args.steps_per_epoch:]) /
float(args.steps_per_epoch),
sum(pcl_to_prim_losses[:args.steps_per_epoch]) /
float(args.steps_per_epoch),
sum(prim_to_pcl_losses[args.steps_per_epoch:]) /
float(args.steps_per_epoch),
sum(prim_to_pcl_losses[:args.steps_per_epoch]) /
float(args.steps_per_epoch),
])
def main(argv):
parser = argparse.ArgumentParser(
description="Do the forward pass and estimate a set of primitives"
)
parser.add_argument(
"dataset_directory",
help="Path to the directory containing the dataset"
)
parser.add_argument(
"output_directory",
help="Save the output files in that directory"
)
parser.add_argument(
"--tsdf_directory",
default="",
help="Path to the directory containing the precomputed tsdf files"
)
parser.add_argument(
"--weight_file",
default=None,
help="The path to the previously trainined model to be used"
)
parser.add_argument(
"--n_primitives",
type=int,
default=32,
help="Number of primitives"
)
parser.add_argument(
"--use_deformations",
action="store_true",
help="Use Superquadrics with deformations as the shape configuration"
)
parser.add_argument(
"--run_on_gpu",
action="store_true",
help="Use GPU"
)
add_dataset_parameters(parser)
add_nn_parameters(parser)
add_voxelizer_parameters(parser)
add_gaussian_noise_layer_parameters(parser)
add_loss_parameters(parser)
add_loss_options_parameters(parser)
args = parser.parse_args(argv)
# A sampler instance
e = EqualDistanceSamplerSQ(200)
# Check if output directory exists and if it doesn't create it
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if args.run_on_gpu and torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print "Running code on ", device
# Create a factory that returns the appropriate voxelizer based on the
# input argument
voxelizer_factory = VoxelizerFactory(
args.voxelizer_factory,
np.array(voxelizer_shape(args)),
args.save_voxels_to
)
# Create a dataset instance to generate the samples for training
dataset = get_dataset_type("euclidean_dual_loss")(
(DatasetBuilder()
.with_dataset(args.dataset_type)
.filter_tags(args.model_tags)
.build(args.dataset_directory)),
voxelizer_factory,
args.n_points_from_mesh,
n_bbox=args.n_bbox,
n_surface=args.n_surface,
equal=args.equal,
transform=compose_transformations(voxelizer_factory)
)
# TODO: Change batch_size in dataloader
dataloader = DataLoader(dataset, batch_size=1, num_workers=4)
network_params = NetworkParameters.from_options(args)
# Build the model to be used for testing
model = network_params.network(network_params)
# Move model to device to be used
model.to(device)
if args.weight_file is not None:
# Load the model parameters of the previously trained model
model.load_state_dict(
torch.load(args.weight_file, map_location=device)
)
model.eval()
losses = []
pcl_to_prim_losses = []
prim_to_pcl_losses = []
prog = Progbar(len(dataloader))
i = 0
for sample in dataloader:
X, y_target = sample
X, y_target = X.to(device), y_target.to(device)
# Do the forward pass and estimate the primitive parameters
y_hat = model(X)
reg_terms = {
"regularizer_type": [],
"bernoulli_regularizer_weight": 0.0,
"entropy_bernoulli_regularizer_weight": 0.0,
"parsimony_regularizer_weight": 0.0,
"overlapping_regularizer_weight": 0.0,
"sparsity_regularizer_weight": 0.0,
}
loss, debug_stats = euclidean_dual_loss(
y_hat,
y_target,
reg_terms,
e,
get_loss_options(args)
)
if not np.isnan(loss.item()):
losses.append(loss.item())
pcl_to_prim_losses.append(debug_stats["pcl_to_prim_loss"].item())
prim_to_pcl_losses.append(debug_stats["prim_to_pcl_loss"].item())
# Update progress bar
prog.update(i+1)
i += 1
np.savetxt(
os.path.join(args.output_directory, "losses.txt"),
losses
)
np.savetxt(
os.path.join(args.output_directory, "pcl_to_prim_losses.txt"),
pcl_to_prim_losses
)
np.savetxt(
os.path.join(args.output_directory, "prim_to_pcl_losses.txt"),
prim_to_pcl_losses
)
np.savetxt(
os.path.join(args.output_directory, "mean_std_losses.txt"),
[np.mean(losses), np.std(losses),
np.mean(pcl_to_prim_losses), np.std(pcl_to_prim_losses),
np.mean(prim_to_pcl_losses), np.std(prim_to_pcl_losses)]
)
print "loss: %.7f +/- %.7f - pcl_to_prim_loss %.7f +/- %.7f - prim_to_pcl_loss %.7f +/- %.7f" %(
np.mean(losses),
np.std(losses),
np.mean(pcl_to_prim_losses),
np.std(pcl_to_prim_losses),
np.mean(prim_to_pcl_losses),
np.std(prim_to_pcl_losses)
)