def main():
try:
os.mkdir(args.snapshot_directory)
except:
pass
np.random.seed(0)
xp = np
device_gpu = args.gpu_device
device_cpu = -1
using_gpu = device_gpu >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cupy
dataset = gqn.data.Dataset(args.dataset_directory)
hyperparams = HyperParameters()
hyperparams.generator_share_core = args.generator_share_core
hyperparams.generator_share_prior = args.generator_share_prior
hyperparams.generator_generation_steps = args.generation_steps
hyperparams.generator_share_upsampler = args.generator_share_upsampler
hyperparams.inference_share_core = args.inference_share_core
hyperparams.inference_share_posterior = args.inference_share_posterior
hyperparams.h_channels = args.h_channels
hyperparams.z_channels = args.z_channels
hyperparams.u_channels = args.u_channels
hyperparams.image_size = (args.image_size, args.image_size)
hyperparams.representation_channels = args.representation_channels
hyperparams.representation_architecture = args.representation_architecture
hyperparams.pixel_n = args.pixel_n
hyperparams.pixel_sigma_i = args.initial_pixel_variance
hyperparams.pixel_sigma_f = args.final_pixel_variance
hyperparams.save(args.snapshot_directory)
print(hyperparams)
model = Model(hyperparams,
snapshot_directory=args.snapshot_directory,
optimized=args.optimized)
if using_gpu:
model.to_gpu()
scheduler = Scheduler(sigma_start=args.initial_pixel_variance,
sigma_end=args.final_pixel_variance,
final_num_updates=args.pixel_n,
snapshot_directory=args.snapshot_directory)
print(scheduler)
optimizer = AdamOptimizer(model.parameters,
mu_i=args.initial_lr,
mu_f=args.final_lr,
initial_training_step=scheduler.num_updates)
print(optimizer)
pixel_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
scheduler.pixel_variance**2,
dtype="float32")
pixel_ln_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
math.log(scheduler.pixel_variance**2),
dtype="float32")
representation_shape = (args.batch_size,
hyperparams.representation_channels,
args.image_size // 4, args.image_size // 4)
fig = plt.figure(figsize=(9, 3))
axis_data = fig.add_subplot(1, 3, 1)
axis_data.set_title("Data")
axis_data.axis("off")
axis_reconstruction = fig.add_subplot(1, 3, 2)
axis_reconstruction.set_title("Reconstruction")
axis_reconstruction.axis("off")
axis_generation = fig.add_subplot(1, 3, 3)
axis_generation.set_title("Generation")
axis_generation.axis("off")
current_training_step = 0
for iteration in range(args.training_iterations):
mean_kld = 0
mean_nll = 0
mean_mse = 0
mean_elbo = 0
total_num_batch = 0
start_time = time.time()
for subset_index, subset in enumerate(dataset):
iterator = gqn.data.Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
# shape: (batch, views, height, width, channels)
# range: [-1, 1]
images, viewpoints = subset[data_indices]
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
total_views = images.shape[1]
# Sample number of views
num_views = random.choice(range(1, total_views + 1))
observation_view_indices = list(range(total_views))
random.shuffle(observation_view_indices)
observation_view_indices = observation_view_indices[:num_views]
query_index = random.choice(range(total_views))
if num_views > 0:
observation_images = preprocess_images(
images[:, observation_view_indices])
observation_query = viewpoints[:, observation_view_indices]
representation = model.compute_observation_representation(
observation_images, observation_query)
else:
representation = xp.zeros(representation_shape,
dtype="float32")
representation = chainer.Variable(representation)
# Sample query
query_index = random.choice(range(total_views))
query_images = preprocess_images(images[:, query_index])
query_viewpoints = viewpoints[:, query_index]
# Transfer to gpu if necessary
query_images = to_device(query_images, device_gpu)
query_viewpoints = to_device(query_viewpoints, device_gpu)
z_t_param_array, mean_x = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
# Compute loss
## KL Divergence
loss_kld = 0
for params in z_t_param_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params
kld = gqn.functions.gaussian_kl_divergence(
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p)
loss_kld += cf.sum(kld)
## Negative log-likelihood of generated image
loss_nll = cf.sum(
gqn.functions.gaussian_negative_log_likelihood(
query_images, mean_x, pixel_var, pixel_ln_var))
# Calculate the average loss value
loss_nll = loss_nll / args.batch_size
loss_kld = loss_kld / args.batch_size
loss = loss_nll / scheduler.pixel_variance + loss_kld
model.cleargrads()
loss.backward()
optimizer.update(current_training_step)
loss_nll = float(loss_nll.data) + math.log(256.0)
loss_kld = float(loss_kld.data)
elbo = -(loss_nll + loss_kld)
loss_mse = float(
cf.mean_squared_error(query_images, mean_x).data)
printr(
"Iteration {}: Subset {} / {}: Batch {} / {} - elbo: {:.2f} - loss: nll: {:.2f} mse: {:.6e} kld: {:.5f} - lr: {:.4e} - pixel_variance: {:.5f} - step: {} "
.format(iteration + 1,
subset_index + 1, len(dataset), batch_index + 1,
len(iterator), elbo, loss_nll, loss_mse, loss_kld,
optimizer.learning_rate, scheduler.pixel_variance,
current_training_step))
scheduler.step(iteration, current_training_step)
pixel_var[...] = scheduler.pixel_variance**2
pixel_ln_var[...] = math.log(scheduler.pixel_variance**2)
total_num_batch += 1
current_training_step += 1
mean_kld += loss_kld
mean_nll += loss_nll
mean_mse += loss_mse
mean_elbo += elbo
model.serialize(args.snapshot_directory)
# Visualize
if args.with_visualization:
axis_data.imshow(make_uint8(query_images[0]),
interpolation="none")
axis_reconstruction.imshow(make_uint8(mean_x.data[0]),
interpolation="none")
with chainer.no_backprop_mode():
generated_x = model.generate_image(
query_viewpoints[None, 0], representation[None, 0])
axis_generation.imshow(make_uint8(generated_x[0]),
interpolation="none")
plt.pause(1e-8)
elapsed_time = time.time() - start_time
print(
"\033[2KIteration {} - elbo: {:.2f} - loss: nll: {:.2f} mse: {:.6e} kld: {:.5f} - lr: {:.4e} - pixel_variance: {:.5f} - step: {} - time: {:.3f} min"
.format(iteration + 1, mean_elbo / total_num_batch,
mean_nll / total_num_batch, mean_mse / total_num_batch,
mean_kld / total_num_batch, optimizer.learning_rate,
scheduler.pixel_variance, current_training_step,
elapsed_time / 60))
model.serialize(args.snapshot_directory)
def main():
try:
os.mkdir(args.snapshot_path)
except:
pass
comm = chainermn.create_communicator()
device = comm.intra_rank
print("device", device, "/", comm.size)
cuda.get_device(device).use()
xp = cupy
dataset = gqn.data.Dataset(args.dataset_path)
hyperparams = HyperParameters()
hyperparams.generator_share_core = args.generator_share_core
hyperparams.generator_share_prior = args.generator_share_prior
hyperparams.generator_generation_steps = args.generation_steps
hyperparams.inference_share_core = args.inference_share_core
hyperparams.inference_share_posterior = args.inference_share_posterior
hyperparams.channels_chz = args.channels_chz
hyperparams.generator_channels_u = args.channels_u
hyperparams.inference_channels_map_x = args.channels_map_x
hyperparams.pixel_n = args.pixel_n
hyperparams.pixel_sigma_i = args.initial_pixel_sigma
hyperparams.pixel_sigma_f = args.final_pixel_sigma
if comm.rank == 0:
hyperparams.save(args.snapshot_path)
hyperparams.print()
model = Model(hyperparams, snapshot_directory=args.snapshot_path)
model.to_gpu()
optimizer = Optimizer(
model.parameters,
communicator=comm,
mu_i=args.initial_lr,
mu_f=args.final_lr)
if comm.rank == 0:
optimizer.print()
dataset_mean, dataset_std = dataset.load_mean_and_std()
if comm.rank == 0:
np.save(os.path.join(args.snapshot_path, "mean.npy"), dataset_mean)
np.save(os.path.join(args.snapshot_path, "std.npy"), dataset_std)
# avoid division by zero
dataset_std += 1e-12
sigma_t = hyperparams.pixel_sigma_i
pixel_var = xp.full(
(args.batch_size, 3) + hyperparams.image_size,
sigma_t**2,
dtype="float32")
pixel_ln_var = xp.full(
(args.batch_size, 3) + hyperparams.image_size,
math.log(sigma_t**2),
dtype="float32")
random.seed(0)
subset_indices = list(range(len(dataset.subset_filenames)))
current_training_step = 0
for iteration in range(args.training_iterations):
mean_kld = 0
mean_nll = 0
total_batch = 0
subset_size_per_gpu = len(subset_indices) // comm.size
start_time = time.time()
for subset_loop in range(subset_size_per_gpu):
random.shuffle(subset_indices)
subset_index = subset_indices[comm.rank]
subset = dataset.read(subset_index)
iterator = gqn.data.Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
# shape: (batch, views, height, width, channels)
# range: [-1, 1]
images, viewpoints = subset[data_indices]
# preprocessing
images = (images - dataset_mean) / dataset_std
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3))
total_views = images.shape[1]
# sample number of views
num_views = random.choice(range(total_views))
query_index = random.choice(range(total_views))
if current_training_step == 0 and num_views == 0:
num_views = 1 # avoid OpenMPI error
if num_views > 0:
r = model.compute_observation_representation(
images[:, :num_views], viewpoints[:, :num_views])
else:
r = xp.zeros(
(args.batch_size, hyperparams.channels_r) +
hyperparams.chrz_size,
dtype="float32")
r = chainer.Variable(r)
query_images = images[:, query_index]
query_viewpoints = viewpoints[:, query_index]
# transfer to gpu
query_images = to_gpu(query_images)
query_viewpoints = to_gpu(query_viewpoints)
h0_gen, c0_gen, u_0, h0_enc, c0_enc = model.generate_initial_state(
args.batch_size, xp)
loss_kld = 0
hl_enc = h0_enc
cl_enc = c0_enc
hl_gen = h0_gen
cl_gen = c0_gen
ul_enc = u_0
xq = model.inference_downsampler.downsample(query_images)
for l in range(model.generation_steps):
inference_core = model.get_inference_core(l)
inference_posterior = model.get_inference_posterior(l)
generation_core = model.get_generation_core(l)
generation_piror = model.get_generation_prior(l)
h_next_enc, c_next_enc = inference_core.forward_onestep(
hl_gen, hl_enc, cl_enc, xq, query_viewpoints, r)
mean_z_q = inference_posterior.compute_mean_z(hl_enc)
ln_var_z_q = inference_posterior.compute_ln_var_z(hl_enc)
ze_l = cf.gaussian(mean_z_q, ln_var_z_q)
mean_z_p = generation_piror.compute_mean_z(hl_gen)
ln_var_z_p = generation_piror.compute_ln_var_z(hl_gen)
h_next_gen, c_next_gen, u_next_enc = generation_core.forward_onestep(
hl_gen, cl_gen, ul_enc, ze_l, query_viewpoints, r)
kld = gqn.nn.chainer.functions.gaussian_kl_divergence(
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p)
loss_kld += cf.sum(kld)
hl_gen = h_next_gen
cl_gen = c_next_gen
ul_enc = u_next_enc
hl_enc = h_next_enc
cl_enc = c_next_enc
mean_x = model.generation_observation.compute_mean_x(ul_enc)
negative_log_likelihood = gqn.nn.chainer.functions.gaussian_negative_log_likelihood(
query_images, mean_x, pixel_var, pixel_ln_var)
loss_nll = cf.sum(negative_log_likelihood)
loss_nll /= args.batch_size
loss_kld /= args.batch_size
loss = loss_nll + loss_kld
model.cleargrads()
loss.backward()
optimizer.update(current_training_step)
if comm.rank == 0:
printr(
"Iteration {}: Subset {} / {}: Batch {} / {} - loss: nll: {:.3f} kld: {:.3f} - lr: {:.4e} - sigma_t: {:.6f}".
format(iteration + 1, subset_loop * comm.size + 1,
len(dataset), batch_index + 1,
len(subset) // args.batch_size,
float(loss_nll.data), float(loss_kld.data),
optimizer.learning_rate, sigma_t))
sf = hyperparams.pixel_sigma_f
si = hyperparams.pixel_sigma_i
sigma_t = max(
sf + (si - sf) *
(1.0 - current_training_step / hyperparams.pixel_n), sf)
pixel_var[...] = sigma_t**2
pixel_ln_var[...] = math.log(sigma_t**2)
total_batch += 1
current_training_step += comm.size
# current_training_step += 1
mean_kld += float(loss_kld.data)
mean_nll += float(loss_nll.data)
if comm.rank == 0:
model.serialize(args.snapshot_path)
if comm.rank == 0:
elapsed_time = time.time() - start_time
print(
"\033[2KIteration {} - loss: nll: {:.3f} kld: {:.3f} - lr: {:.4e} - sigma_t: {:.6f} - step: {} - elapsed_time: {:.3f} min".
format(iteration + 1, mean_nll / total_batch,
mean_kld / total_batch, optimizer.learning_rate,
sigma_t, current_training_step, elapsed_time / 60))
model.serialize(args.snapshot_path)
def main():
##############################################
# To avoid OpenMPI bug
multiprocessing.set_start_method("forkserver")
p = multiprocessing.Process(target=print, args=("", ))
p.start()
p.join()
##############################################
try:
os.mkdir(args.snapshot_directory)
except:
pass
comm = chainermn.create_communicator()
device = comm.intra_rank
print("device", device, "/", comm.size)
cuda.get_device(device).use()
xp = cupy
dataset = gqn.data.Dataset(args.dataset_directory)
hyperparams = HyperParameters()
hyperparams.generator_share_core = args.generator_share_core
hyperparams.generator_share_prior = args.generator_share_prior
hyperparams.generator_generation_steps = args.generation_steps
hyperparams.generator_u_channels = args.u_channels
hyperparams.generator_share_upsampler = args.generator_share_upsampler
hyperparams.generator_subpixel_convolution_enabled = args.generator_subpixel_convolution_enabled
hyperparams.inference_share_core = args.inference_share_core
hyperparams.inference_share_posterior = args.inference_share_posterior
hyperparams.inference_downsampler_channels = args.inference_downsampler_channels
hyperparams.h_channels = args.h_channels
hyperparams.z_channels = args.z_channels
hyperparams.representation_channels = args.representation_channels
hyperparams.pixel_n = args.pixel_n
hyperparams.pixel_sigma_i = args.initial_pixel_variance
hyperparams.pixel_sigma_f = args.final_pixel_variance
if comm.rank == 0:
hyperparams.save(args.snapshot_directory)
print(hyperparams)
model = Model(hyperparams, snapshot_directory=args.snapshot_directory)
model.to_gpu()
optimizer = AdamOptimizer(model.parameters,
communicator=comm,
mu_i=args.initial_lr,
mu_f=args.final_lr)
if comm.rank == 0:
print(optimizer)
scheduler = Scheduler(sigma_start=args.initial_pixel_variance,
sigma_end=args.final_pixel_variance,
final_num_updates=args.pixel_n)
if comm.rank == 0:
print(scheduler)
pixel_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
scheduler.pixel_variance**2,
dtype="float32")
pixel_ln_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
math.log(scheduler.pixel_variance**2),
dtype="float32")
num_pixels = hyperparams.image_size[0] * hyperparams.image_size[1] * 3
random.seed(0)
subset_indices = list(range(len(dataset.subset_filenames)))
representation_shape = (
args.batch_size,
hyperparams.representation_channels) + hyperparams.chrz_size
current_training_step = 0
for iteration in range(args.training_iterations):
mean_kld = 0
mean_nll = 0
mean_mse = 0
mean_elbo = 0
total_num_batch = 0
subset_size_per_gpu = len(subset_indices) // comm.size
if len(subset_indices) % comm.size != 0:
subset_size_per_gpu += 1
start_time = time.time()
for subset_loop in range(subset_size_per_gpu):
random.shuffle(subset_indices)
subset_index = subset_indices[comm.rank]
subset = dataset.read(subset_index)
iterator = gqn.data.Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
# shape: (batch, views, height, width, channels)
# range: [-1, 1]
images, viewpoints = subset[data_indices]
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
images = images / 255.0
images += np.random.uniform(
0, 1.0 / 256.0, size=images.shape).astype(np.float32)
total_views = images.shape[1]
# Sample observations
num_views = random.choice(range(total_views + 1))
if current_training_step == 0 and num_views == 0:
num_views = 1 # avoid OpenMPI error
observation_view_indices = list(range(total_views))
random.shuffle(observation_view_indices)
observation_view_indices = observation_view_indices[:num_views]
if num_views > 0:
representation = model.compute_observation_representation(
images[:, observation_view_indices],
viewpoints[:, observation_view_indices])
else:
representation = xp.zeros(representation_shape,
dtype="float32")
representation = chainer.Variable(representation)
# Sample query
query_index = random.choice(range(total_views))
query_images = images[:, query_index]
query_viewpoints = viewpoints[:, query_index]
# Transfer to gpu
query_images = to_gpu(query_images)
query_viewpoints = to_gpu(query_viewpoints)
z_t_param_array, mean_x = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
# Compute loss
## KL Divergence
loss_kld = chainer.Variable(xp.zeros((), dtype=xp.float32))
for params in z_t_param_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params
kld = gqn.functions.gaussian_kl_divergence(
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p)
loss_kld += cf.sum(kld)
##Negative log-likelihood of generated image
loss_nll = cf.sum(
gqn.functions.gaussian_negative_log_likelihood(
query_images, mean_x, pixel_var, pixel_ln_var))
# Calculate the average loss value
loss_nll = loss_nll / args.batch_size
loss_kld = loss_kld / args.batch_size
loss = (loss_nll / scheduler.pixel_variance) + loss_kld
model.cleargrads()
loss.backward()
optimizer.update(current_training_step)
loss_nll = float(loss_nll.data) + math.log(256.0)
loss_kld = float(loss_kld.data)
elbo = -(loss_nll + loss_kld)
loss_mse = float(
cf.mean_squared_error(query_images, mean_x).data)
if comm.rank == 0:
printr(
"Iteration {}: Subset {} / {}: Batch {} / {} - elbo: {:.2f} - loss: nll: {:.2f} mse: {:.5f} kld: {:.5f} - lr: {:.4e} - pixel_variance: {:.5f} - step: {} "
.format(iteration + 1, subset_loop + 1,
subset_size_per_gpu, batch_index + 1,
len(iterator), elbo, loss_nll, loss_mse,
loss_kld, optimizer.learning_rate,
scheduler.pixel_variance,
current_training_step))
total_num_batch += 1
current_training_step += comm.size
mean_kld += loss_kld
mean_nll += loss_nll
mean_mse += loss_mse
mean_elbo += elbo
scheduler.step(current_training_step)
pixel_var[...] = scheduler.pixel_variance**2
pixel_ln_var[...] = math.log(scheduler.pixel_variance**2)
if comm.rank == 0:
model.serialize(args.snapshot_directory)
if comm.rank == 0:
elapsed_time = time.time() - start_time
mean_elbo /= total_num_batch
mean_nll /= total_num_batch
mean_mse /= total_num_batch
mean_kld /= total_num_batch
print(
"\033[2KIteration {} - elbo: {:.2f} - loss: nll: {:.2f} mse: {:.5f} kld: {:.5f} - lr: {:.4e} - pixel_variance: {:.5f} - step: {} - time: {:.3f} min"
.format(iteration + 1, mean_elbo, mean_nll, mean_mse, mean_kld,
optimizer.learning_rate, scheduler.pixel_variance,
current_training_step, elapsed_time / 60))
model.serialize(args.snapshot_directory)
def main():
try:
os.mkdir(args.snapshot_directory)
except:
pass
images = []
files = os.listdir(args.dataset_path)
for filename in files:
image = np.load(os.path.join(args.dataset_path, filename))
image = image / 255 * 2.0 - 1.0
images.append(image)
images = np.vstack(images)
images = images.transpose((0, 3, 1, 2)).astype(np.float32)
train_dev_split = 0.9
num_images = images.shape[0]
num_train_images = int(num_images * train_dev_split)
num_dev_images = num_images - num_train_images
images_train = images[:args.batch_size]
images_dev = images[args.batch_size:]
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cp
hyperparams = HyperParameters()
hyperparams.generator_share_core = args.generator_share_core
hyperparams.generator_share_prior = args.generator_share_prior
hyperparams.generator_generation_steps = args.generation_steps
hyperparams.inference_share_core = args.inference_share_core
hyperparams.inference_share_posterior = args.inference_share_posterior
hyperparams.layer_normalization_enabled = args.layer_normalization
hyperparams.pixel_n = args.pixel_n
hyperparams.chz_channels = args.chz_channels
hyperparams.inference_channels_downsampler_x = args.channels_downsampler_x
hyperparams.pixel_sigma_i = args.initial_pixel_sigma
hyperparams.pixel_sigma_f = args.final_pixel_sigma
hyperparams.chrz_size = (32, 32)
hyperparams.save(args.snapshot_directory)
hyperparams.print()
model = Model(hyperparams, snapshot_directory=args.snapshot_directory)
if using_gpu:
model.to_gpu()
optimizer = AdamOptimizer(model.parameters,
lr_i=args.initial_lr,
lr_f=args.final_lr)
optimizer.print()
sigma_t = hyperparams.pixel_sigma_i
pixel_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
sigma_t**2,
dtype="float32")
pixel_ln_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
math.log(sigma_t**2),
dtype="float32")
num_pixels = images.shape[1] * images.shape[2] * images.shape[3]
figure = plt.figure(figsize=(20, 4))
axis_1 = figure.add_subplot(1, 5, 1)
axis_2 = figure.add_subplot(1, 5, 2)
axis_3 = figure.add_subplot(1, 5, 3)
axis_4 = figure.add_subplot(1, 5, 4)
axis_5 = figure.add_subplot(1, 5, 5)
for iteration in range(args.training_steps):
x = to_gpu(images_train)
loss_kld = 0
z_t_params_array, r_final = model.generate_z_params_and_x_from_posterior(
x)
for params in z_t_params_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params
kld = draw.nn.functions.gaussian_kl_divergence(
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p)
loss_kld += cf.sum(kld)
mean_x_enc = r_final
negative_log_likelihood = draw.nn.functions.gaussian_negative_log_likelihood(
x, mean_x_enc, pixel_var, pixel_ln_var)
loss_nll = cf.sum(negative_log_likelihood)
loss_mse = cf.mean_squared_error(mean_x_enc, x)
loss_nll /= args.batch_size
loss_kld /= args.batch_size
loss = loss_nll + loss_kld
loss = loss_nll
model.cleargrads()
loss.backward()
optimizer.update(iteration)
sf = hyperparams.pixel_sigma_f
si = hyperparams.pixel_sigma_i
sigma_t = max(sf + (si - sf) * (1.0 - iteration / hyperparams.pixel_n),
sf)
pixel_var[...] = sigma_t**2
pixel_ln_var[...] = math.log(sigma_t**2)
model.serialize(args.snapshot_directory)
print(
"\033[2KIteration {} - loss: nll_per_pixel: {:.6f} - mse: {:.6f} - kld: {:.6f} - lr: {:.4e} - sigma_t: {:.6f}"
.format(iteration + 1,
float(loss_nll.data) / num_pixels, float(loss_mse.data),
float(loss_kld.data), optimizer.learning_rate, sigma_t))
if iteration % 10 == 0:
axis_1.imshow(make_uint8(x[0]))
axis_2.imshow(make_uint8(mean_x_enc.data[0]))
x_dev = images_dev[random.choice(range(num_dev_images))]
axis_3.imshow(make_uint8(x_dev))
with chainer.using_config("train", False), chainer.using_config(
"enable_backprop", False):
x_dev = to_gpu(x_dev)[None, ...]
_, r_final = model.generate_z_params_and_x_from_posterior(
x_dev)
mean_x_enc = r_final
axis_4.imshow(make_uint8(mean_x_enc.data[0]))
mean_x_d = model.generate_image(batch_size=1, xp=xp)
axis_5.imshow(make_uint8(mean_x_d[0]))
plt.pause(0.01)
def main():
try:
os.mkdir(args.snapshot_path)
except:
pass
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cupy
dataset = gqn.data.Dataset(args.dataset_path)
hyperparams = HyperParameters()
hyperparams.generator_share_core = args.generator_share_core
hyperparams.generator_share_prior = args.generator_share_prior
hyperparams.generator_generation_steps = args.generation_steps
hyperparams.inference_share_core = args.inference_share_core
hyperparams.inference_share_posterior = args.inference_share_posterior
hyperparams.pixel_n = args.pixel_n
hyperparams.pixel_sigma_i = args.initial_pixel_sigma
hyperparams.pixel_sigma_f = args.final_pixel_sigma
hyperparams.save(args.snapshot_path)
hyperparams.print()
model = Model(hyperparams, snapshot_directory=args.snapshot_path)
if using_gpu:
model.to_gpu()
optimizer = Optimizer(model.parameters,
mu_i=args.initial_lr,
mu_f=args.final_lr)
optimizer.print()
if args.with_visualization:
figure = gqn.imgplot.figure()
axis1 = gqn.imgplot.image()
axis2 = gqn.imgplot.image()
axis3 = gqn.imgplot.image()
figure.add(axis1, 0, 0, 1 / 3, 1)
figure.add(axis2, 1 / 3, 0, 1 / 3, 1)
figure.add(axis3, 2 / 3, 0, 1 / 3, 1)
plot = gqn.imgplot.window(
figure, (500 * 3, 500),
"Query image / Reconstructed image / Generated image")
plot.show()
sigma_t = hyperparams.pixel_sigma_i
pixel_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
sigma_t**2,
dtype="float32")
pixel_ln_var = xp.full((args.batch_size, 3) + hyperparams.image_size,
math.log(sigma_t**2),
dtype="float32")
dataset_mean, dataset_std = dataset.load_mean_and_std()
np.save(os.path.join(args.snapshot_path, "mean.npy"), dataset_mean)
np.save(os.path.join(args.snapshot_path, "std.npy"), dataset_std)
# avoid division by zero
dataset_std += 1e-12
current_training_step = 0
for iteration in range(args.training_iterations):
mean_kld = 0
mean_nll = 0
total_batch = 0
for subset_index, subset in enumerate(dataset):
iterator = gqn.data.Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
# shape: (batch, views, height, width, channels)
# range: [-1, 1]
images, viewpoints = subset[data_indices]
# preprocessing
images = (images - dataset_mean) / dataset_std
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3))
total_views = images.shape[1]
# sample number of views
num_views = random.choice(range(total_views))
query_index = random.choice(range(total_views))
if num_views > 0:
r = model.compute_observation_representation(
images[:, :num_views], viewpoints[:, :num_views])
else:
r = xp.zeros((args.batch_size, hyperparams.channels_r) +
hyperparams.chrz_size,
dtype="float32")
r = chainer.Variable(r)
query_images = images[:, query_index]
query_viewpoints = viewpoints[:, query_index]
# transfer to gpu
query_images = to_gpu(query_images)
query_viewpoints = to_gpu(query_viewpoints)
h0_gen, c0_gen, u_0, h0_enc, c0_enc = model.generate_initial_state(
args.batch_size, xp)
loss_kld = 0
hl_enc = h0_enc
cl_enc = c0_enc
hl_gen = h0_gen
cl_gen = c0_gen
ul_enc = u_0
xq = model.inference_downsampler.downsample(query_images)
for l in range(model.generation_steps):
inference_core = model.get_inference_core(l)
inference_posterior = model.get_inference_posterior(l)
generation_core = model.get_generation_core(l)
generation_piror = model.get_generation_prior(l)
h_next_enc, c_next_enc = inference_core.forward_onestep(
hl_gen, hl_enc, cl_enc, xq, query_viewpoints, r)
mean_z_q = inference_posterior.compute_mean_z(hl_enc)
ln_var_z_q = inference_posterior.compute_ln_var_z(hl_enc)
ze_l = cf.gaussian(mean_z_q, ln_var_z_q)
mean_z_p = generation_piror.compute_mean_z(hl_gen)
ln_var_z_p = generation_piror.compute_ln_var_z(hl_gen)
h_next_gen, c_next_gen, u_next_enc = generation_core.forward_onestep(
hl_gen, cl_gen, ul_enc, ze_l, query_viewpoints, r)
kld = gqn.nn.chainer.functions.gaussian_kl_divergence(
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p)
loss_kld += cf.sum(kld)
hl_gen = h_next_gen
cl_gen = c_next_gen
ul_enc = u_next_enc
hl_enc = h_next_enc
cl_enc = c_next_enc
mean_x = model.generation_observation.compute_mean_x(ul_enc)
negative_log_likelihood = gqn.nn.chainer.functions.gaussian_negative_log_likelihood(
query_images, mean_x, pixel_var, pixel_ln_var)
loss_nll = cf.sum(negative_log_likelihood)
loss_nll /= args.batch_size
loss_kld /= args.batch_size
loss = loss_nll + loss_kld
model.cleargrads()
loss.backward()
optimizer.update(current_training_step)
if args.with_visualization and plot.closed() is False:
axis1.update(
make_uint8(query_images[0], dataset_mean, dataset_std))
axis2.update(
make_uint8(mean_x.data[0], dataset_mean, dataset_std))
with chainer.no_backprop_mode():
generated_x = model.generate_image(
query_viewpoints[None, 0], r[None, 0], xp)
axis3.update(
make_uint8(generated_x[0], dataset_mean,
dataset_std))
printr(
"Iteration {}: Subset {} / {}: Batch {} / {} - loss: nll: {:.3f} kld: {:.3f} - lr: {:.4e} - sigma_t: {:.6f}"
.format(iteration + 1,
subset_index + 1, len(dataset), batch_index + 1,
len(iterator), float(loss_nll.data),
float(loss_kld.data), optimizer.learning_rate,
sigma_t))
sf = hyperparams.pixel_sigma_f
si = hyperparams.pixel_sigma_i
sigma_t = max(
sf + (si - sf) *
(1.0 - current_training_step / hyperparams.pixel_n), sf)
pixel_var[...] = sigma_t**2
pixel_ln_var[...] = math.log(sigma_t**2)
total_batch += 1
current_training_step += 1
mean_kld += float(loss_kld.data)
mean_nll += float(loss_nll.data)
model.serialize(args.snapshot_path)
print(
"\033[2KIteration {} - loss: nll: {:.3f} kld: {:.3f} - lr: {:.4e} - sigma_t: {:.6f} - step: {}"
.format(iteration + 1, mean_nll / total_batch,
mean_kld / total_batch, optimizer.learning_rate, sigma_t,
current_training_step))
