def main():
os.environ['CHAINER_SEED'] = str(args.seed)
logging.info('chainer seed = ' + os.environ['CHAINER_SEED'])
_mkdir(args.snapshot_directory)
_mkdir(args.log_directory)
meter_train = Meter()
meter_train.load(args.snapshot_directory)
#==============================================================================
# Dataset
#==============================================================================
def read_npy_files(directory):
filenames = []
files = os.listdir(os.path.join(directory, "images"))
for filename in files:
if filename.endswith(".npy"):
filenames.append(filename)
filenames.sort()
dataset_images = []
dataset_viewpoints = []
for i in range(len(filenames)):
images_npy_path = os.path.join(directory, "images", filenames[i])
viewpoints_npy_path = os.path.join(directory, "viewpoints", filenames[i])
tmp_images = np.load(images_npy_path)
tmp_viewpoints = np.load(viewpoints_npy_path)
assert tmp_images.shape[0] == tmp_viewpoints.shape[0]
dataset_images.extend(tmp_images)
dataset_viewpoints.extend(tmp_viewpoints)
dataset_images = np.array(dataset_images)
dataset_viewpoints = np.array(dataset_viewpoints)
dataset = list()
for i in range(len(dataset_images)):
item = {'image':dataset_images[i],'viewpoint':dataset_viewpoints[i]}
dataset.append(item)
return dataset
def read_files(directory):
filenames = []
files = os.listdir(directory)
for filename in files:
if filename.endswith(".h5"):
filenames.append(filename)
filenames.sort()
dataset_images = []
dataset_viewpoints = []
for i in range(len(filenames)):
F = h5py.File(os.path.join(directory,filenames[i]))
tmp_images = list(F["images"])
tmp_viewpoints = list(F["viewpoints"])
dataset_images.extend(tmp_images)
dataset_viewpoints.extend(tmp_viewpoints)
dataset_images = np.array(dataset_images)
dataset_viewpoints = np.array(dataset_viewpoints)
dataset = list()
for i in range(len(dataset_images)):
item = {'image':dataset_images[i],'viewpoint':dataset_viewpoints[i]}
dataset.append(item)
return dataset
dataset_train = read_files(args.train_dataset_directory)
# ipdb.set_trace()
if args.test_dataset_directory is not None:
dataset_test = read_files(args.test_dataset_directory)
# ipdb.set_trace()
#==============================================================================
# Hyperparameters
#==============================================================================
hyperparams = HyperParameters()
hyperparams.num_layers = args.generation_steps
hyperparams.generator_share_core = args.generator_share_core
hyperparams.inference_share_core = args.inference_share_core
hyperparams.h_channels = args.h_channels
hyperparams.z_channels = args.z_channels
hyperparams.u_channels = args.u_channels
hyperparams.r_channels = args.r_channels
hyperparams.image_size = (args.image_size, args.image_size)
hyperparams.representation_architecture = args.representation_architecture
hyperparams.pixel_sigma_annealing_steps = args.pixel_sigma_annealing_steps
hyperparams.initial_pixel_sigma = args.initial_pixel_sigma
hyperparams.final_pixel_sigma = args.final_pixel_sigma
hyperparams.save(args.snapshot_directory)
print(hyperparams, "\n")
#==============================================================================
# Model
#==============================================================================
model = Model(hyperparams)
model.load(args.snapshot_directory, meter_train.epoch)
#==============================================================================
# Pixel-variance annealing
#==============================================================================
variance_scheduler = PixelVarianceScheduler(
sigma_start=args.initial_pixel_sigma,
sigma_end=args.final_pixel_sigma,
final_num_updates=args.pixel_sigma_annealing_steps)
variance_scheduler.load(args.snapshot_directory)
print(variance_scheduler, "\n")
pixel_log_sigma = np.full(
(args.batch_size, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
#==============================================================================
# Selecting the GPU
#==============================================================================
# xp = np
# gpu_device = args.gpu_device
# using_gpu = gpu_device >= 0
# if using_gpu:
# cuda.get_device(gpu_device).use()
# xp = cp
# devices = tuple([chainer.get_device(f"@cupy:{gpu}") for gpu in args.gpu_devices])
# if any(device.xp is chainerx for device in devices):
# sys.stderr.write("Cannot support ChainerX devices.")
# sys.exit(1)
ngpu = args.ngpu
using_gpu = ngpu > 0
xp=cp
if ngpu == 1:
gpu_id = 0
# Make a specified GPU current
chainer.cuda.get_device_from_id(gpu_id).use()
model.to_gpu() # Copy the model to the GPU
logging.info('single gpu calculation.')
elif ngpu > 1:
gpu_id = 0
devices = {'main': gpu_id}
for gid in six.moves.xrange(1, ngpu):
devices['sub_%d' % gid] = gid
logging.info('multi gpu calculation (#gpus = %d).' % ngpu)
logging.info('batch size is automatically increased (%d -> %d)' % (
args.batch_size, args.batch_size * args.ngpu))
else:
gpu_id = -1
logging.info('cpu calculation')
#==============================================================================
# Logging
#==============================================================================
csv = DataFrame()
csv.load(args.log_directory)
#==============================================================================
# Optimizer
#==============================================================================
initial_training_step=0
# lr = compute_lr_at_step(initial_training_step) # function in GQN AdamOptimizer
optimizer = chainer.optimizers.Adam(beta1=0.9, beta2=0.99, eps=1e-8) #lr is needed originally
optimizer.setup(model)
# optimizer = AdamOptimizer(
# model.parameters,
# initial_lr=args.initial_lr,
# final_lr=args.final_lr,
# initial_training_step=variance_scheduler.training_step)
# )
print(optimizer, "\n")
#==============================================================================
# Training iterations
#==============================================================================
if ngpu>1:
train_iters = [
chainer.iterators.MultiprocessIterator(dataset_train, args.batch_size, n_processes=args.number_processes, order_sampler=chainer.iterators.ShuffleOrderSampler()) for i in chainer.datasets.split_dataset_n_random(dataset_train, len(devices))
]
updater = CustomParallelUpdater(train_iters, optimizer, devices, converter=chainer.dataset.concat_examples, pixel_log_sigma=pixel_log_sigma)
elif ngpu==1:
train_iters = chainer.iterators.SerialIterator(dataset_train,args.batch_size,shuffle=True)
updater = CustomUpdater(train_iters, optimizer, device=0, converter=chainer.dataset.concat_examples, pixel_log_sigma=pixel_log_sigma)
else:
raise NotImplementedError('Implement for single gpu or cpu')
trainer = chainer.training.Trainer(updater,(args.epochs,'epoch'),args.snapshot_directory)
trainer.extend(AnnealLearningRate(
initial_lr=args.initial_lr,
final_lr=args.final_lr,
annealing_steps=args.pixel_sigma_annealing_steps,
optimizer=optimizer),
trigger=(1,'iteration'))
# add information per epoch with report?
# add learning rate annealing, snapshot saver, evaluator
trainer.extend(extensions.LogReport())
trainer.extend(extensions.snapshot(filename='snapshot_epoch_{.updater.epoch}',
savefun=chainer.serializers.save_hdf5,
target=optimizer.target),
trigger=(args.report_interval_iters,'epoch'))
trainer.extend(extensions.ProgressBar())
reports = ['epoch', 'main/loss', 'main/bits_per_pixel', 'main/NLL', 'main/MSE']
#Validation
if args.test_dataset_directory is not None:
test_iters = chainer.iterators.SerialIterator(
dataset_test,args.batch_size*6, repeat=False, shuffle=False)
trainer.extend(Validation(
test_iters, chainer.dataset.concat_examples, optimizer.target, variance_scheduler,device=0))
reports.append('validation/main/bits_per_pixel')
reports.append('validation/main/NLL')
reports.append('validation/main/MSE')
reports.append('elapsed_time')
trainer.extend(
extensions.PrintReport(reports), trigger=(args.report_interval_iters, 'iteration'))
# np.random.seed(args.seed)
# cp.random.seed(args.seed)
trainer.run()
def main():
_mkdir(args.snapshot_directory)
_mkdir(args.log_directory)
meter_train = Meter()
meter_train.load(args.snapshot_directory)
#==============================================================================
# Workaround to fix OpenMPI bug
#==============================================================================
multiprocessing.set_start_method("forkserver")
p = multiprocessing.Process(target=print, args=("", ))
p.start()
p.join()
#==============================================================================
# Selecting the GPU
#==============================================================================
comm = chainermn.create_communicator()
device = comm.intra_rank
cuda.get_device(device).use()
def _print(*args):
if comm.rank == 0:
print(*args)
_print("Using {} GPUs".format(comm.size))
#==============================================================================
# Dataset
#==============================================================================
dataset_train = Dataset(args.train_dataset_directory)
dataset_test = None
if args.test_dataset_directory is not None:
dataset_test = Dataset(args.test_dataset_directory)
#==============================================================================
# Hyperparameters
#==============================================================================
hyperparams = HyperParameters()
hyperparams.num_layers = args.generation_steps
hyperparams.generator_share_core = args.generator_share_core
hyperparams.inference_share_core = args.inference_share_core
hyperparams.h_channels = args.h_channels
hyperparams.z_channels = args.z_channels
hyperparams.u_channels = args.u_channels
hyperparams.r_channels = args.r_channels
hyperparams.image_size = (args.image_size, args.image_size)
hyperparams.representation_architecture = args.representation_architecture
hyperparams.pixel_sigma_annealing_steps = args.pixel_sigma_annealing_steps
hyperparams.initial_pixel_sigma = args.initial_pixel_sigma
hyperparams.final_pixel_sigma = args.final_pixel_sigma
_print(hyperparams, "\n")
if comm.rank == 0:
hyperparams.save(args.snapshot_directory)
#==============================================================================
# Model
#==============================================================================
model = Model(hyperparams)
model.load(args.snapshot_directory, meter_train.epoch)
model.to_gpu()
#==============================================================================
# Pixel-variance annealing
#==============================================================================
variance_scheduler = PixelVarianceScheduler(
sigma_start=args.initial_pixel_sigma,
sigma_end=args.final_pixel_sigma,
final_num_updates=args.pixel_sigma_annealing_steps)
variance_scheduler.load(args.snapshot_directory)
_print(variance_scheduler, "\n")
pixel_log_sigma = cp.full(
(args.batch_size, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
#==============================================================================
# Logging
#==============================================================================
csv = DataFrame()
csv.load(args.log_directory)
#==============================================================================
# Optimizer
#==============================================================================
optimizer = AdamOptimizer(
model.parameters,
initial_lr=args.initial_lr,
final_lr=args.final_lr,
initial_training_step=variance_scheduler.training_step)
_print(optimizer, "\n")
#==============================================================================
# Algorithms
#==============================================================================
def encode_scene(images, viewpoints):
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
# Sample number of views
total_views = images.shape[1]
num_views = random.choice(range(1, total_views + 1))
# Sample views
observation_view_indices = list(range(total_views))
random.shuffle(observation_view_indices)
observation_view_indices = observation_view_indices[:num_views]
observation_images = preprocess_images(
images[:, observation_view_indices])
observation_query = viewpoints[:, observation_view_indices]
representation = model.compute_observation_representation(
observation_images, observation_query)
# Sample query view
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 = cuda.to_gpu(query_images)
query_viewpoints = cuda.to_gpu(query_viewpoints)
return representation, query_images, query_viewpoints
def estimate_ELBO(query_images, z_t_param_array, pixel_mean,
pixel_log_sigma):
# KL Diverge, pixel_ln_varnce
kl_divergence = 0
for params_t in z_t_param_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params_t
normal_q = chainer.distributions.Normal(
mean_z_q, log_scale=ln_var_z_q)
normal_p = chainer.distributions.Normal(
mean_z_p, log_scale=ln_var_z_p)
kld_t = chainer.kl_divergence(normal_q, normal_p)
kl_divergence += cf.sum(kld_t)
kl_divergence = kl_divergence / args.batch_size
# Negative log-likelihood of generated image
batch_size = query_images.shape[0]
num_pixels_per_batch = np.prod(query_images.shape[1:])
normal = chainer.distributions.Normal(
query_images, log_scale=pixel_log_sigma)
log_px = cf.sum(normal.log_prob(pixel_mean)) / batch_size
negative_log_likelihood = -log_px
# Empirical ELBO
ELBO = log_px - kl_divergence
# https://arxiv.org/abs/1604.08772 Section.2
# https://www.reddit.com/r/MachineLearning/comments/56m5o2/discussion_calculation_of_bitsdims/
bits_per_pixel = -(ELBO / num_pixels_per_batch - np.log(256)) / np.log(
2)
return ELBO, bits_per_pixel, negative_log_likelihood, kl_divergence
#==============================================================================
# Training iterations
#==============================================================================
dataset_size = len(dataset_train)
random.seed(0)
np.random.seed(0)
cp.random.seed(0)
for epoch in range(args.epochs):
_print("Epoch {}/{}:".format(
epoch + 1,
args.epochs,
))
meter_train.next_epoch()
subset_indices = list(range(len(dataset_train.subset_filenames)))
subset_size_per_gpu = len(subset_indices) // comm.size
if len(subset_indices) % comm.size != 0:
subset_size_per_gpu += 1
for subset_loop in range(subset_size_per_gpu):
random.shuffle(subset_indices)
subset_index = subset_indices[comm.rank]
subset = dataset_train.read(subset_index)
iterator = gqn.data.Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
#------------------------------------------------------------------------------
# Scene encoder
#------------------------------------------------------------------------------
# images.shape: (batch, views, height, width, channels)
images, viewpoints = subset[data_indices]
representation, query_images, query_viewpoints = encode_scene(
images, viewpoints)
#------------------------------------------------------------------------------
# Compute empirical ELBO
#------------------------------------------------------------------------------
# Compute distribution parameterws
(z_t_param_array,
pixel_mean) = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
# Compute ELBO
(ELBO, bits_per_pixel, negative_log_likelihood,
kl_divergence) = estimate_ELBO(query_images, z_t_param_array,
pixel_mean, pixel_log_sigma)
#------------------------------------------------------------------------------
# Update parameters
#------------------------------------------------------------------------------
loss = -ELBO
model.cleargrads()
loss.backward()
optimizer.update(meter_train.num_updates)
#------------------------------------------------------------------------------
# Logging
#------------------------------------------------------------------------------
with chainer.no_backprop_mode():
mean_squared_error = cf.mean_squared_error(
query_images, pixel_mean)
meter_train.update(
ELBO=float(ELBO.data),
bits_per_pixel=float(bits_per_pixel.data),
negative_log_likelihood=float(
negative_log_likelihood.data),
kl_divergence=float(kl_divergence.data),
mean_squared_error=float(mean_squared_error.data))
#------------------------------------------------------------------------------
# Annealing
#------------------------------------------------------------------------------
variance_scheduler.update(meter_train.num_updates)
pixel_log_sigma[...] = math.log(
variance_scheduler.standard_deviation)
if subset_loop % 100 == 0:
_print(" Subset {}/{}:".format(
subset_loop + 1,
subset_size_per_gpu,
dataset_size,
))
_print(" {}".format(meter_train))
_print(" lr: {} - sigma: {}".format(
optimizer.learning_rate,
variance_scheduler.standard_deviation))
#------------------------------------------------------------------------------
# Validation
#------------------------------------------------------------------------------
meter_test = None
if dataset_test is not None:
meter_test = Meter()
batch_size_test = args.batch_size * 6
subset_indices_test = list(
range(len(dataset_test.subset_filenames)))
pixel_log_sigma_test = cp.full(
(batch_size_test, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
subset_size_per_gpu = len(subset_indices_test) // comm.size
with chainer.no_backprop_mode():
for subset_loop in range(subset_size_per_gpu):
subset_index = subset_indices_test[subset_loop * comm.size
+ comm.rank]
subset = dataset_train.read(subset_index)
iterator = gqn.data.Iterator(
subset, batch_size=batch_size_test)
for data_indices in iterator:
images, viewpoints = subset[data_indices]
# Scene encoder
representation, query_images, query_viewpoints = encode_scene(
images, viewpoints)
# Compute empirical ELBO
(z_t_param_array, pixel_mean
) = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
(ELBO, bits_per_pixel, negative_log_likelihood,
kl_divergence) = estimate_ELBO(
query_images, z_t_param_array, pixel_mean,
pixel_log_sigma_test)
mean_squared_error = cf.mean_squared_error(
query_images, pixel_mean)
# Logging
meter_test.update(
ELBO=float(ELBO.data),
bits_per_pixel=float(bits_per_pixel.data),
negative_log_likelihood=float(
negative_log_likelihood.data),
kl_divergence=float(kl_divergence.data),
mean_squared_error=float(mean_squared_error.data))
meter = meter_test.allreduce(comm)
_print(" Test:")
_print(" {} - done in {:.3f} min".format(
meter,
meter.elapsed_time,
))
model.save(args.snapshot_directory, meter_train.epoch)
variance_scheduler.save(args.snapshot_directory)
meter_train.save(args.snapshot_directory)
csv.save(args.log_directory)
_print("Epoch {} done in {:.3f} min".format(
epoch + 1,
meter_train.epoch_elapsed_time,
))
_print(" {}".format(meter_train))
_print(" lr: {} - sigma: {} - training_steps: {}".format(
optimizer.learning_rate,
variance_scheduler.standard_deviation,
meter_train.num_updates,
))
_print(" Time elapsed: {:.3f} min".format(
meter_train.elapsed_time))
def main():
meter_train = Meter()
assert meter_train.load(args.snapshot_directory)
#==============================================================================
# Selecting the GPU
#==============================================================================
xp = np
gpu_device = args.gpu_device
using_gpu = gpu_device >= 0
if using_gpu:
cuda.get_device(gpu_device).use()
xp = cp
#==============================================================================
# Dataset
#==============================================================================
dataset_test = Dataset(args.test_dataset_directory)
#==============================================================================
# Hyperparameters
#==============================================================================
hyperparams = HyperParameters()
assert hyperparams.load(args.snapshot_directory)
print(hyperparams, "\n")
#==============================================================================
# Model
#==============================================================================
model = Model(hyperparams)
assert model.load(args.snapshot_directory, meter_train.epoch)
if using_gpu:
model.to_gpu()
#==============================================================================
# Pixel-variance annealing
#==============================================================================
variance_scheduler = PixelVarianceScheduler()
assert variance_scheduler.load(args.snapshot_directory)
print(variance_scheduler, "\n")
#==============================================================================
# Algorithms
#==============================================================================
def encode_scene(images, viewpoints):
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
# Sample number of views
total_views = images.shape[1]
num_views = random.choice(range(1, total_views + 1))
# Sample views
observation_view_indices = list(range(total_views))
random.shuffle(observation_view_indices)
observation_view_indices = observation_view_indices[:num_views]
observation_images = preprocess_images(
images[:, observation_view_indices])
observation_query = viewpoints[:, observation_view_indices]
representation = model.compute_observation_representation(
observation_images, observation_query)
# Sample query view
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, gpu_device)
query_viewpoints = to_device(query_viewpoints, gpu_device)
return representation, query_images, query_viewpoints
def estimate_ELBO(query_images, z_t_param_array, pixel_mean,
pixel_log_sigma):
# KL Diverge, pixel_ln_varnce
kl_divergence = 0
for params_t in z_t_param_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params_t
normal_q = chainer.distributions.Normal(mean_z_q,
log_scale=ln_var_z_q)
normal_p = chainer.distributions.Normal(mean_z_p,
log_scale=ln_var_z_p)
kld_t = chainer.kl_divergence(normal_q, normal_p)
kl_divergence += cf.sum(kld_t)
kl_divergence = kl_divergence / args.batch_size
# Negative log-likelihood of generated image
batch_size = query_images.shape[0]
num_pixels_per_batch = np.prod(query_images.shape[1:])
normal = chainer.distributions.Normal(query_images,
log_scale=pixel_log_sigma)
log_px = cf.sum(normal.log_prob(pixel_mean)) / batch_size
negative_log_likelihood = -log_px
# Empirical ELBO
ELBO = log_px - kl_divergence
# https://arxiv.org/abs/1604.08772 Section.2
# https://www.reddit.com/r/MachineLearning/comments/56m5o2/discussion_calculation_of_bitsdims/
bits_per_pixel = -(ELBO / num_pixels_per_batch -
np.log(256)) / np.log(2)
return ELBO, bits_per_pixel, negative_log_likelihood, kl_divergence
#==============================================================================
# Test the model
#==============================================================================
meter = Meter()
pixel_log_sigma = xp.full((args.batch_size, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
with chainer.no_backprop_mode():
for subset_index, subset in enumerate(dataset_test):
iterator = Iterator(subset, batch_size=args.batch_size)
for data_indices in iterator:
images, viewpoints = subset[data_indices]
# Scene encoder
representation, query_images, query_viewpoints = encode_scene(
images, viewpoints)
# Compute empirical ELBO
(z_t_param_array,
pixel_mean) = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
(ELBO, bits_per_pixel, negative_log_likelihood,
kl_divergence) = estimate_ELBO(query_images, z_t_param_array,
pixel_mean, pixel_log_sigma)
mean_squared_error = cf.mean_squared_error(
query_images, pixel_mean)
# Logging
meter.update(ELBO=float(ELBO.data),
bits_per_pixel=float(bits_per_pixel.data),
negative_log_likelihood=float(
negative_log_likelihood.data),
kl_divergence=float(kl_divergence.data),
mean_squared_error=float(mean_squared_error.data))
if subset_index % 100 == 0:
print(" Subset {}/{}:".format(
subset_index + 1,
len(dataset_test),
))
print(" {}".format(meter))
print(" Test:")
print(" {} - done in {:.3f} min".format(
meter,
meter.elapsed_time,
))
def main():
try:
os.makedirs(args.figure_directory)
except:
pass
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cp
dataset = gqn.data.Dataset(args.dataset_directory)
meter = Meter()
assert meter.load(args.snapshot_directory)
hyperparams = HyperParameters()
assert hyperparams.load(args.snapshot_directory)
model = Model(hyperparams)
assert model.load(args.snapshot_directory, meter.epoch)
if using_gpu:
model.to_gpu()
total_observations_per_scene = 4
fps = 30
black_color = -0.5
image_shape = (3, ) + hyperparams.image_size
axis_observations_image = np.zeros(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
dtype=np.float32)
#==============================================================================
# Utilities
#==============================================================================
def to_device(array):
if using_gpu:
array = cuda.to_gpu(array)
return array
def fill_observations_axis(observation_images):
axis_observations_image = np.full(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
black_color,
dtype=np.float32)
num_current_obs = len(observation_images)
total_obs = total_observations_per_scene
width = image_shape[2]
x_start = width * (total_obs - num_current_obs) // 2
for obs_image in observation_images:
x_end = x_start + width
axis_observations_image[:, :, x_start:x_end] = obs_image
x_start += width
return axis_observations_image
def compute_camera_angle_at_frame(t):
horizontal_angle_rad = 2 * t * math.pi / (fps * 2) + math.pi / 4
y_rad_top = math.pi / 3
y_rad_bottom = -math.pi / 3
y_rad_range = y_rad_bottom - y_rad_top
if t < fps * 1.5:
vertical_angle_rad = y_rad_top
elif fps * 1.5 <= t and t < fps * 2.5:
interp = (t - fps * 1.5) / fps
vertical_angle_rad = y_rad_top + interp * y_rad_range
elif fps * 2.5 <= t and t < fps * 4:
vertical_angle_rad = y_rad_bottom
elif fps * 4.0 <= t and t < fps * 5:
interp = (t - fps * 4.0) / fps
vertical_angle_rad = y_rad_bottom - interp * y_rad_range
else:
vertical_angle_rad = y_rad_top
return horizontal_angle_rad, vertical_angle_rad
def rotate_query_viewpoint(horizontal_angle_rad, vertical_angle_rad):
camera_direction = np.array([
math.sin(horizontal_angle_rad), # x
math.sin(vertical_angle_rad), # y
math.cos(horizontal_angle_rad), # z
])
camera_direction = args.camera_distance * camera_direction / np.linalg.norm(
camera_direction)
yaw, pitch = compute_yaw_and_pitch(camera_direction)
query_viewpoints = xp.array(
(
camera_direction[0],
camera_direction[1],
camera_direction[2],
math.cos(yaw),
math.sin(yaw),
math.cos(pitch),
math.sin(pitch),
),
dtype=np.float32,
)
query_viewpoints = xp.broadcast_to(query_viewpoints,
(1, ) + query_viewpoints.shape)
return query_viewpoints
#==============================================================================
# Visualization
#==============================================================================
plt.style.use("dark_background")
fig = plt.figure(figsize=(6, 7))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
# fig.suptitle("GQN")
axis_observations = fig.add_subplot(2, 1, 1)
axis_observations.axis("off")
axis_observations.set_title("observations")
axis_generation = fig.add_subplot(2, 1, 2)
axis_generation.axis("off")
axis_generation.set_title("neural rendering")
#==============================================================================
# Generating animation
#==============================================================================
file_number = 1
random.seed(0)
np.random.seed(0)
with chainer.no_backprop_mode():
for subset in dataset:
iterator = gqn.data.Iterator(subset, batch_size=1)
for data_indices in iterator:
animation_frame_array = []
observed_image_array = xp.full(
(total_observations_per_scene, ) + image_shape,
black_color,
dtype=np.float32)
observed_viewpoint_array = xp.zeros(
(total_observations_per_scene, 7), dtype=np.float32)
# shape: (batch, views, height, width, channels)
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 = preprocess_images(images)
batch_index = 0
#------------------------------------------------------------------------------
# Generate images with a single observation
#------------------------------------------------------------------------------
observation_index = 0
# Scene encoder
observed_image = images[batch_index, observation_index]
observed_viewpoint = viewpoints[batch_index, observation_index]
observed_image_array[observation_index] = to_device(
observed_image)
observed_viewpoint_array[observation_index] = to_device(
observed_viewpoint)
representation = model.compute_observation_representation(
observed_image_array[None, :observation_index + 1],
observed_viewpoint_array[None, :observation_index + 1])
# Update figure
axis_observations_image = fill_observations_axis(
[observed_image])
# Rotate camera
for t in range(fps, fps * 6):
artist_array = [
axis_observations.imshow(
make_uint8(axis_observations_image),
interpolation="none",
animated=True)
]
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
t)
query_viewpoints = rotate_query_viewpoint(
horizontal_angle_rad, vertical_angle_rad)
generated_images = model.generate_image(
query_viewpoints, representation)[0]
artist_array.append(
axis_generation.imshow(make_uint8(generated_images),
interpolation="none",
animated=True))
animation_frame_array.append(artist_array)
#------------------------------------------------------------------------------
# Add observations
#------------------------------------------------------------------------------
for n in range(total_observations_per_scene):
axis_observations_image = fill_observations_axis(
images[batch_index, :n + 1])
# Scene encoder
representation = model.compute_observation_representation(
observed_image_array[None, :n + 1],
observed_viewpoint_array[None, :n + 1])
for t in range(fps // 2):
artist_array = [
axis_observations.imshow(
make_uint8(axis_observations_image),
interpolation="none",
animated=True)
]
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
0)
query_viewpoints = rotate_query_viewpoint(
horizontal_angle_rad, vertical_angle_rad)
generated_images = model.generate_image(
query_viewpoints, representation)[0]
artist_array.append(
axis_generation.imshow(
make_uint8(generated_images),
interpolation="none",
animated=True))
animation_frame_array.append(artist_array)
#------------------------------------------------------------------------------
# Generate images with all observations
#------------------------------------------------------------------------------
# Scene encoder
representation = model.compute_observation_representation(
observed_image_array[None, :total_observations_per_scene +
1],
observed_viewpoint_array[
None, :total_observations_per_scene + 1])
# Rotate camera
for t in range(0, fps * 6):
artist_array = [
axis_observations.imshow(
make_uint8(axis_observations_image),
interpolation="none",
animated=True)
]
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
t)
query_viewpoints = rotate_query_viewpoint(
horizontal_angle_rad, vertical_angle_rad)
generated_images = model.generate_image(
query_viewpoints, representation)[0]
artist_array.append(
axis_generation.imshow(make_uint8(generated_images),
interpolation="none",
animated=True))
animation_frame_array.append(artist_array)
#------------------------------------------------------------------------------
# Write to file
#------------------------------------------------------------------------------
anim = animation.ArtistAnimation(fig,
animation_frame_array,
interval=1 / fps,
blit=True,
repeat_delay=0)
# anim.save(
# "{}/shepard_matzler_observations_{}.gif".format(
# args.figure_directory, file_number),
# writer="imagemagick",
# fps=fps)
anim.save("{}/shepard_matzler_observations_{}.mp4".format(
args.figure_directory, file_number),
writer="ffmpeg",
fps=fps)
file_number += 1
def main():
try:
os.makedirs(args.figure_directory)
except:
pass
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cp
dataset = gqn.data.Dataset(args.dataset_directory)
meter = Meter()
assert meter.load(args.snapshot_directory)
hyperparams = HyperParameters()
assert hyperparams.load(args.snapshot_directory)
model = Model(hyperparams)
assert model.load(args.snapshot_directory, meter.epoch)
if using_gpu:
model.to_gpu()
#==============================================================================
# Visualization
#==============================================================================
plt.figure(figsize=(12, 16))
axis_observation_1 = plt.subplot2grid((4, 3), (0, 0))
axis_observation_2 = plt.subplot2grid((4, 3), (0, 1))
axis_observation_3 = plt.subplot2grid((4, 3), (0, 2))
axis_predictions = plt.subplot2grid((4, 3), (1, 0), rowspan=3, colspan=3)
axis_observation_1.axis("off")
axis_observation_2.axis("off")
axis_observation_3.axis("off")
axis_predictions.set_xticks([], [])
axis_predictions.set_yticks([], [])
axis_observation_1.set_title("Observation 1", fontsize=22)
axis_observation_2.set_title("Observation 2", fontsize=22)
axis_observation_3.set_title("Observation 3", fontsize=22)
axis_predictions.set_title("Neural Rendering", fontsize=22)
axis_predictions.set_xlabel("Yaw", fontsize=22)
axis_predictions.set_ylabel("Pitch", fontsize=22)
#==============================================================================
# Generating images
#==============================================================================
num_views_per_scene = 3
num_yaw_pitch_steps = 10
image_width, image_height = hyperparams.image_size
prediction_images = make_uint8(
np.full((num_yaw_pitch_steps * image_width,
num_yaw_pitch_steps * image_height, 3), 0))
file_number = 1
random.seed(0)
np.random.seed(0)
with chainer.no_backprop_mode():
for subset in dataset:
iterator = gqn.data.Iterator(subset, batch_size=1)
for data_indices in iterator:
# shape: (batch, views, height, width, channels)
# range: [-1, 1]
images, viewpoints = subset[data_indices]
camera_distance = np.mean(
np.linalg.norm(viewpoints[:, :, :3], axis=2))
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
images = preprocess_images(images)
batch_index = 0
#------------------------------------------------------------------------------
# Observations
#------------------------------------------------------------------------------
total_views = images.shape[1]
random_observation_view_indices = list(range(total_views))
random.shuffle(random_observation_view_indices)
random_observation_view_indices = random_observation_view_indices[:
num_views_per_scene]
observed_images = images[:, random_observation_view_indices]
observed_viewpoints = viewpoints[:,
random_observation_view_indices]
representation = model.compute_observation_representation(
observed_images, observed_viewpoints)
axis_observation_1.imshow(
make_uint8(observed_images[batch_index, 0]))
axis_observation_2.imshow(
make_uint8(observed_images[batch_index, 1]))
axis_observation_3.imshow(
make_uint8(observed_images[batch_index, 2]))
y_angle_rad = math.pi / 2
for pitch_loop in range(num_yaw_pitch_steps):
camera_y = math.sin(y_angle_rad)
x_angle_rad = math.pi
for yaw_loop in range(num_yaw_pitch_steps):
camera_direction = np.array([
math.sin(x_angle_rad), camera_y,
math.cos(x_angle_rad)
])
camera_direction = camera_distance * camera_direction / np.linalg.norm(
camera_direction)
yaw, pitch = compute_yaw_and_pitch(camera_direction)
query_viewpoints = xp.array(
(
camera_direction[0],
camera_direction[1],
camera_direction[2],
math.cos(yaw),
math.sin(yaw),
math.cos(pitch),
math.sin(pitch),
),
dtype=np.float32,
)
query_viewpoints = xp.broadcast_to(
query_viewpoints, (1, ) + query_viewpoints.shape)
generated_images = model.generate_image(
query_viewpoints, representation)[0]
yi_start = pitch_loop * image_height
yi_end = (pitch_loop + 1) * image_height
xi_start = yaw_loop * image_width
xi_end = (yaw_loop + 1) * image_width
prediction_images[yi_start:yi_end,
xi_start:xi_end] = make_uint8(
generated_images)
x_angle_rad -= 2 * math.pi / num_yaw_pitch_steps
y_angle_rad -= math.pi / num_yaw_pitch_steps
axis_predictions.imshow(prediction_images)
plt.savefig("{}/shepard_metzler_predictions_{}.png".format(
args.figure_directory, file_number))
file_number += 1
def main():
_mkdir(args.snapshot_directory)
_mkdir(args.log_directory)
meter_train = Meter()
meter_train.load(args.snapshot_directory)
#==============================================================================
# Selecting the GPU
#==============================================================================
xp = np
gpu_device = args.gpu_device
using_gpu = gpu_device >= 0
if using_gpu:
cuda.get_device(gpu_device).use()
xp = cp
#==============================================================================
# Dataset
#==============================================================================
dataset_train = Dataset(args.train_dataset_directory)
dataset_test = None
if args.test_dataset_directory is not None:
dataset_test = Dataset(args.test_dataset_directory)
#==============================================================================
# Hyperparameters
#==============================================================================
hyperparams = HyperParameters()
hyperparams.num_layers = args.generation_steps
hyperparams.generator_share_core = args.generator_share_core
hyperparams.inference_share_core = args.inference_share_core
hyperparams.h_channels = args.h_channels
hyperparams.z_channels = args.z_channels
hyperparams.u_channels = args.u_channels
hyperparams.r_channels = args.r_channels
hyperparams.image_size = (args.image_size, args.image_size)
hyperparams.representation_architecture = args.representation_architecture
hyperparams.pixel_sigma_annealing_steps = args.pixel_sigma_annealing_steps
hyperparams.initial_pixel_sigma = args.initial_pixel_sigma
hyperparams.final_pixel_sigma = args.final_pixel_sigma
hyperparams.save(args.snapshot_directory)
print(hyperparams, "\n")
#==============================================================================
# Model
#==============================================================================
model = Model(hyperparams)
model.load(args.snapshot_directory, meter_train.epoch)
if using_gpu:
model.to_gpu()
#==============================================================================
# Pixel-variance annealing
#==============================================================================
variance_scheduler = PixelVarianceScheduler(
sigma_start=args.initial_pixel_sigma,
sigma_end=args.final_pixel_sigma,
final_num_updates=args.pixel_sigma_annealing_steps)
variance_scheduler.load(args.snapshot_directory)
print(variance_scheduler, "\n")
pixel_log_sigma = xp.full(
(args.batch_size, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
#==============================================================================
# Logging
#==============================================================================
csv = DataFrame()
csv.load(args.log_directory)
#==============================================================================
# Optimizer
#==============================================================================
optimizer = AdamOptimizer(
model.parameters,
initial_lr=args.initial_lr,
final_lr=args.final_lr,
initial_training_step=variance_scheduler.training_step)
print(optimizer, "\n")
#==============================================================================
# Visualization
#==============================================================================
fig = plt.figure(figsize=(9, 6))
axes_train = [
fig.add_subplot(2, 3, 1),
fig.add_subplot(2, 3, 2),
fig.add_subplot(2, 3, 3),
]
axes_train[0].set_title("Training Data")
axes_train[0].axis("off")
axes_train[1].set_title("Reconstruction")
axes_train[1].axis("off")
axes_train[2].set_title("Generation")
axes_train[2].axis("off")
axes_test = [
fig.add_subplot(2, 3, 4),
fig.add_subplot(2, 3, 5),
fig.add_subplot(2, 3, 6),
]
axes_test[0].set_title("Validation Data")
axes_test[0].axis("off")
axes_test[1].set_title("Reconstruction")
axes_test[1].axis("off")
axes_test[2].set_title("Generation")
axes_test[2].axis("off")
#==============================================================================
# Algorithms
#==============================================================================
def encode_scene(images, viewpoints):
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
# Sample number of views
total_views = images.shape[1]
num_views = random.choice(range(1, total_views + 1))
# Sample views
observation_view_indices = list(range(total_views))
random.shuffle(observation_view_indices)
observation_view_indices = observation_view_indices[:num_views]
observation_images = preprocess_images(
images[:, observation_view_indices])
observation_query = viewpoints[:, observation_view_indices]
representation = model.compute_observation_representation(
observation_images, observation_query)
# Sample query view
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, gpu_device)
query_viewpoints = to_device(query_viewpoints, gpu_device)
return representation, query_images, query_viewpoints
def estimate_ELBO(query_images, z_t_param_array, pixel_mean,
pixel_log_sigma):
# KL Diverge, pixel_ln_varnce
kl_divergence = 0
for params_t in z_t_param_array:
mean_z_q, ln_var_z_q, mean_z_p, ln_var_z_p = params_t
normal_q = chainer.distributions.Normal(
mean_z_q, log_scale=ln_var_z_q)
normal_p = chainer.distributions.Normal(
mean_z_p, log_scale=ln_var_z_p)
kld_t = chainer.kl_divergence(normal_q, normal_p)
kl_divergence += cf.sum(kld_t)
kl_divergence = kl_divergence / args.batch_size
# Negative log-likelihood of generated image
batch_size = query_images.shape[0]
num_pixels_per_batch = np.prod(query_images.shape[1:])
normal = chainer.distributions.Normal(
query_images, log_scale=pixel_log_sigma)
log_px = cf.sum(normal.log_prob(pixel_mean)) / batch_size
negative_log_likelihood = -log_px
# Empirical ELBO
ELBO = log_px - kl_divergence
# https://arxiv.org/abs/1604.08772 Section.2
# https://www.reddit.com/r/MachineLearning/comments/56m5o2/discussion_calculation_of_bitsdims/
bits_per_pixel = -(ELBO / num_pixels_per_batch - np.log(256)) / np.log(
2)
return ELBO, bits_per_pixel, negative_log_likelihood, kl_divergence
#==============================================================================
# Training iterations
#==============================================================================
dataset_size = len(dataset_train)
np.random.seed(0)
cp.random.seed(0)
start_training = True
for epoch in range(meter_train.epoch, args.epochs):
print("Epoch {}/{}:".format(
epoch + 1,
args.epochs,
))
meter_train.next_epoch()
for subset_index, subset in enumerate(dataset_train):
iterator = Iterator(subset, batch_size=args.batch_size)
for batch_index, data_indices in enumerate(iterator):
#------------------------------------------------------------------------------
# Scene encoder
#------------------------------------------------------------------------------
# images.shape: (batch, views, height, width, channels)
images, viewpoints = subset[data_indices]
representation, query_images, query_viewpoints = encode_scene(
images, viewpoints)
#------------------------------------------------------------------------------
# Compute empirical ELBO
#------------------------------------------------------------------------------
# Compute distribution parameterws
(z_t_param_array
) = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
# # Compute ELBO
# (ELBO, bits_per_pixel, negative_log_likelihood,
# kl_divergence) = estimate_ELBO(query_images, z_t_param_array,
# pixel_mean, pixel_log_sigma)
#------------------------------------------------------------------------------
# Update parameters
#------------------------------------------------------------------------------
loss = -ELBO
model.cleargrads()
loss.backward()
# if start_training:
# g = chainer.computational_graph.build_computational_graph(pixel_mean)
# with open(os.path.join(args.snapshot_directory,'cg.dot'), 'w') as o:
# o.write(g.dump())
# start_training = False
# exit()
optimizer.update(meter_train.num_updates)
#------------------------------------------------------------------------------
# Logging
#------------------------------------------------------------------------------
with chainer.no_backprop_mode():
mean_squared_error = cf.mean_squared_error(
query_images, pixel_mean)
meter_train.update(
ELBO=float(ELBO.data),
bits_per_pixel=float(bits_per_pixel.data),
negative_log_likelihood=float(
negative_log_likelihood.data),
kl_divergence=float(kl_divergence.data),
mean_squared_error=float(mean_squared_error.data))
#------------------------------------------------------------------------------
# Annealing
#------------------------------------------------------------------------------
variance_scheduler.update(meter_train.num_updates)
pixel_log_sigma[...] = math.log(
variance_scheduler.standard_deviation)
if subset_index % 100 == 0:
print(" Subset {}/{}:".format(
subset_index + 1,
dataset_size,
))
print(" {}".format(meter_train))
print(" lr: {} - sigma: {}".format(
optimizer.learning_rate,
variance_scheduler.standard_deviation))
#------------------------------------------------------------------------------
# Visualization
#------------------------------------------------------------------------------
if args.visualize:
axes_train[0].imshow(
make_uint8(query_images[0]), interpolation="none")
axes_train[1].imshow(
make_uint8(pixel_mean.data[0]), interpolation="none")
with chainer.no_backprop_mode():
generated_x = model.generate_image(query_viewpoints[None, 0],
representation[None, 0])
axes_train[2].imshow(
make_uint8(generated_x[0]), interpolation="none")
#------------------------------------------------------------------------------
# Validation
#------------------------------------------------------------------------------
meter_test = None
if dataset_test is not None:
meter_test = Meter()
batch_size_test = args.batch_size * 6
pixel_log_sigma_test = xp.full(
(batch_size_test, 3) + hyperparams.image_size,
math.log(variance_scheduler.standard_deviation),
dtype="float32")
with chainer.no_backprop_mode():
for subset in dataset_test:
iterator = Iterator(subset, batch_size=batch_size_test)
for data_indices in iterator:
images, viewpoints = subset[data_indices]
# Scene encoder
representation, query_images, query_viewpoints = encode_scene(
images, viewpoints)
# Compute empirical ELBO
(z_t_param_array, pixel_mean
) = model.sample_z_and_x_params_from_posterior(
query_images, query_viewpoints, representation)
(ELBO, bits_per_pixel, negative_log_likelihood,
kl_divergence) = estimate_ELBO(
query_images, z_t_param_array, pixel_mean,
pixel_log_sigma_test)
mean_squared_error = cf.mean_squared_error(
query_images, pixel_mean)
# Logging
meter_test.update(
ELBO=float(ELBO.data),
bits_per_pixel=float(bits_per_pixel.data),
negative_log_likelihood=float(
negative_log_likelihood.data),
kl_divergence=float(kl_divergence.data),
mean_squared_error=float(mean_squared_error.data))
print(" Test:")
print(" {} - done in {:.3f} min".format(
meter_test,
meter_test.elapsed_time,
))
if args.visualize:
axes_test[0].imshow(
make_uint8(query_images[0]), interpolation="none")
axes_test[1].imshow(
make_uint8(pixel_mean.data[0]), interpolation="none")
with chainer.no_backprop_mode():
generated_x = model.generate_image(
query_viewpoints[None, 0], representation[None, 0])
axes_test[2].imshow(
make_uint8(generated_x[0]), interpolation="none")
if args.visualize:
plt.pause(1e-10)
csv.append(epoch, meter_train, meter_test)
#------------------------------------------------------------------------------
# Snapshot
#------------------------------------------------------------------------------
model.save(args.snapshot_directory, epoch)
variance_scheduler.save(args.snapshot_directory)
meter_train.save(args.snapshot_directory)
csv.save(args.log_directory)
print("Epoch {} done in {:.3f} min".format(
epoch + 1,
meter_train.epoch_elapsed_time,
))
print(" {}".format(meter_train))
print(" lr: {} - sigma: {} - training_steps: {}".format(
optimizer.learning_rate,
variance_scheduler.standard_deviation,
meter_train.num_updates,
))
print(" Time elapsed: {:.3f} min".format(meter_train.elapsed_time))
def main():
start_time = time.time()
writer = SummaryWriter('/GQN/chainer-gqn/tensor-log')
try:
os.makedirs(args.figure_directory)
except:
pass
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cp
dataset = gqn.data.Dataset(args.dataset_directory)
meter = Meter()
assert meter.load(args.snapshot_directory)
hyperparams = HyperParameters()
assert hyperparams.load(args.snapshot_directory)
model = Model(hyperparams)
assert model.load(args.snapshot_directory, meter.epoch)
if using_gpu:
model.to_gpu()
total_observations_per_scene = 4
fps = 30
black_color = -0.5
image_shape = (3, ) + hyperparams.image_size
axis_observations_image = np.zeros(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
dtype=np.float32)
#==============================================================================
# Utilities
#==============================================================================
def to_device(array):
if using_gpu:
array = cuda.to_gpu(array)
return array
def fill_observations_axis(observation_images):
axis_observations_image = np.full(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
black_color,
dtype=np.float32)
num_current_obs = len(observation_images)
total_obs = total_observations_per_scene
width = image_shape[2]
x_start = width * (total_obs - num_current_obs) // 2
for obs_image in observation_images:
x_end = x_start + width
axis_observations_image[:, :, x_start:x_end] = obs_image
x_start += width
return axis_observations_image
def compute_camera_angle_at_frame(t):
horizontal_angle_rad = 2 * t * math.pi / (fps * 2) + math.pi / 4
y_rad_top = math.pi / 3
y_rad_bottom = -math.pi / 3
y_rad_range = y_rad_bottom - y_rad_top
if t < fps * 1.5:
vertical_angle_rad = y_rad_top
elif fps * 1.5 <= t and t < fps * 2.5:
interp = (t - fps * 1.5) / fps
vertical_angle_rad = y_rad_top + interp * y_rad_range
elif fps * 2.5 <= t and t < fps * 4:
vertical_angle_rad = y_rad_bottom
elif fps * 4.0 <= t and t < fps * 5:
interp = (t - fps * 4.0) / fps
vertical_angle_rad = y_rad_bottom - interp * y_rad_range
else:
vertical_angle_rad = y_rad_top
return horizontal_angle_rad, vertical_angle_rad
def compute_vertical_rotation_at_frame(horizontal, vertical, t):
# move horizontal view only
horizontal_angle_rad = horizontal + (t - fps) * (math.pi / 64)
vertical_angle_rad = vertical + 0
return horizontal_angle_rad, vertical_angle_rad
def rotate_query_viewpoint(horizontal_angle_rad, vertical_angle_rad,
camera_distance):
camera_direction = np.array([
math.sin(horizontal_angle_rad), # x
math.sin(vertical_angle_rad), # y
math.cos(horizontal_angle_rad), # z
])
# removed linalg norm for observation purposes
camera_direction = camera_distance * camera_direction
# ipdb.set_trace()
yaw, pitch = compute_yaw_and_pitch(camera_direction)
query_viewpoints = xp.array(
(
camera_direction[0],
camera_direction[1],
camera_direction[2],
math.cos(yaw),
math.sin(yaw),
math.cos(pitch),
math.sin(pitch),
),
dtype=np.float32,
)
query_viewpoints = xp.broadcast_to(query_viewpoints,
(1, ) + query_viewpoints.shape)
return query_viewpoints
def render(representation,
camera_distance,
obs_viewpoint,
start_t,
end_t,
animation_frame_array,
savename=None,
rotate_camera=True):
all_var_bg = []
all_var = []
all_var_z = []
all_q_view = []
all_c = []
all_h = []
all_u = []
for t in range(start_t, end_t):
artist_array = [
axis_observations.imshow(make_uint8(axis_observations_image),
interpolation="none",
animated=True)
]
# convert x,y into radians??
# try reversing the camera direction calculation in rotate query viewpoint (impossible to reverse the linalg norm...)
horizontal_angle_rad = np.arctan2(obs_viewpoint[0],
obs_viewpoint[2])
vertical_angle_rad = np.arcsin(obs_viewpoint[1] / camera_distance)
# xz_diagonal = np.sqrt(np.square(obs_viewpoint[0])+np.square(obs_viewpoint[2]))
# vertical_angle_rad = np.arctan2(obs_viewpoint[1],xz_diagonal)
# vertical_angle_rad = np.arcsin(obs_viewpoint[1]/camera_distance)
# horizontal_angle_rad, vertical_angle_rad = 0,0
# ipdb.set_trace()
horizontal_angle_rad, vertical_angle_rad = compute_vertical_rotation_at_frame(
horizontal_angle_rad, vertical_angle_rad, t)
if rotate_camera == False:
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
0)
query_viewpoints = rotate_query_viewpoint(horizontal_angle_rad,
vertical_angle_rad,
camera_distance)
# obtain generated images, as well as mean and variance before gaussian
generated_images, var_bg, latent_z, ct = model.generate_multi_image(
query_viewpoints, representation, 100)
logging.info("retrieved variables, time elapsed: " +
str(time.time() - start_time))
# cpu_generated_images = chainer.backends.cuda.to_cpu(generated_images)
generated_images = np.squeeze(generated_images)
latent_z = np.squeeze(latent_z)
# ipdb.set_trace()
ct = np.squeeze(ct)
# ht = np.squeeze(np.asarray(ht))
# ut = np.squeeze(np.asarray(ut))
# obtain data from Chainer Variable and obtain mean
var_bg = cp.mean(var_bg, axis=0)
logging.info("variance of bg, time elapsed: " +
str(time.time() - start_time))
var_z = cp.var(latent_z, axis=0)
logging.info("variance of z, time elapsed: " +
str(time.time() - start_time))
# ipdb.set_trace()
# print(ct.shape())
var_c = cp.var(ct, axis=0)
logging.info("variance of c, time elapsed: " +
str(time.time() - start_time))
# var_h = cp.var(ht,axis=0)
# var_u = cp.var(ut,axis=0)
# write viewpoint and image variance to file
gen_img_var = np.var(generated_images, axis=0)
logging.info("calculated variance of gen images, time elapsed: " +
str(time.time() - start_time))
all_var_bg.append((var_bg)[None])
all_var.append((gen_img_var)[None])
all_var_z.append((var_z)[None])
all_q_view.append(
chainer.backends.cuda.to_cpu(horizontal_angle_rad)[None] *
180 / math.pi)
all_c.append((var_c)[None])
logging.info("appending, time elapsed: " +
str(time.time() - start_time))
# all_h.append(chainer.backends.cuda.to_cpu(var_h)[None])
# all_u.append(chainer.backends.cuda.to_cpu(var_u)[None])
# sample = generated_images[0]
pred_mean = cp.mean(generated_images, axis=0)
# artist_array.append(
# axis_generation.imshow(
# make_uint8(pred_mean),
# interpolation="none",
# animated=True))
# animation_frame_array.append(artist_array)
all_var_bg = np.concatenate(chainer.backends.cuda.to_cpu(all_var_bg),
axis=0)
all_var = np.concatenate(chainer.backends.cuda.to_cpu(all_var), axis=0)
all_var_z = np.concatenate(chainer.backends.cuda.to_cpu(all_var_z),
axis=0)
all_c = np.concatenate(chainer.backends.cuda.to_cpu(all_c), axis=0)
# all_h = np.concatenate(all_h,axis=0)
# all_u = np.concatenate(all_u,axis=0)
logging.info("concatenating, time elapsed: " +
str(time.time() - start_time))
with h5py.File(savename, "a") as f:
f.create_dataset("variance_all_viewpoints", data=all_var)
f.create_dataset("query_viewpoints",
data=np.squeeze(np.asarray(all_q_view)))
f.create_dataset("variance_b4_gaussian", data=all_var_bg)
f.create_dataset("variance_of_z", data=all_var_z)
f.create_dataset("c", data=all_c)
# f.create_dataset("h",data=all_h)
# f.create_dataset("u",data=all_u)
logging.info("saving, time elapsed: " + str(time.time() - start_time))
#==============================================================================
# Visualization
#==============================================================================
plt.style.use("dark_background")
fig = plt.figure(figsize=(6, 7))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
# fig.suptitle("GQN")
axis_observations = fig.add_subplot(2, 1, 1)
axis_observations.axis("off")
axis_observations.set_title("observations")
axis_generation = fig.add_subplot(2, 1, 2)
axis_generation.axis("off")
axis_generation.set_title("neural rendering")
#==============================================================================
# Generating animation
#==============================================================================
file_number = 1
random.seed(0)
np.random.seed(0)
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s'
)
with chainer.no_backprop_mode():
for subset in dataset:
iterator = gqn.data.Iterator(subset, batch_size=1)
for data_indices in iterator:
animation_frame_array = []
# shape: (batch, views, height, width, channels)
images, viewpoints = subset[data_indices]
camera_distance = np.mean(
np.linalg.norm(viewpoints[:, :, :3], axis=2))
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
images = preprocess_images(images)
logging.info('preprocess ' + str(time.time() - start_time))
batch_index = 0
total_views = images.shape[1]
random_observation_view_indices = list(range(total_views))
random.shuffle(random_observation_view_indices)
random_observation_view_indices = random_observation_view_indices[:
total_observations_per_scene]
#------------------------------------------------------------------------------
# Observations
#------------------------------------------------------------------------------
observed_images = images[batch_index,
random_observation_view_indices]
observed_viewpoints = viewpoints[
batch_index, random_observation_view_indices]
observed_images = to_device(observed_images)
observed_viewpoints = to_device(observed_viewpoints)
#------------------------------------------------------------------------------
# Generate images with a single observation
#------------------------------------------------------------------------------
# Scene encoder
representation = model.compute_observation_representation(
observed_images[None, :1], observed_viewpoints[None, :1])
# Update figure
observation_index = random_observation_view_indices[0]
observed_image = images[batch_index, observation_index]
axis_observations_image = fill_observations_axis(
[observed_image])
# save observed viewpoint
filename = "{}/variance_{}.hdf5".format(
args.figure_directory, file_number)
if os.path.exists(filename):
os.remove(filename)
with h5py.File(filename, "a") as f:
f.create_dataset("observed_viewpoint",
data=chainer.backends.cuda.to_cpu(
observed_viewpoints[0]))
f.create_dataset(
"obs_viewpoint_horizontal_angle",
data=np.arcsin(
chainer.backends.cuda.to_cpu(
observed_viewpoints[0][0]) / camera_distance) *
180 / math.pi)
logging.info('write 2 variables to hdf5 file, time elapsed: ' +
str(time.time() - start_time))
obs_viewpoint = np.squeeze(observed_viewpoints[0])
# Neural rendering
render(representation,
camera_distance,
observed_viewpoints[0],
fps,
fps * 6,
animation_frame_array,
savename=filename)
logging.info(
'write 4 other variables to hdf5 file, time elapsed: ' +
str(time.time() - start_time))
#------------------------------------------------------------------------------
# Write to file
#------------------------------------------------------------------------------
# anim = animation.ArtistAnimation(
# fig,
# animation_frame_array,
# interval=1 / fps,
# blit=True,
# repeat_delay=0)
# anim.save(
# "{}/shepard_metzler_observations_{}.gif".format(
# args.figure_directory, file_number),
# writer="imagemagick",
# fps=fps)
# anim.save(
# "{}/shepard_metzler_observations_{}.mp4".format(
# args.figure_directory, file_number),
# writer="ffmpeg",
# fps=2)
if file_number == 20:
break
else:
file_number += 1
def main():
try:
os.makedirs(args.figure_directory)
except:
pass
xp = np
using_gpu = args.gpu_device >= 0
if using_gpu:
cuda.get_device(args.gpu_device).use()
xp = cp
dataset = gqn.data.Dataset(args.dataset_directory,
# use_ground_truth=True
)
meter = Meter()
assert meter.load(args.snapshot_directory)
hyperparams = HyperParameters()
assert hyperparams.load(args.snapshot_directory)
model = Model(hyperparams)
assert model.load(args.snapshot_directory, meter.epoch)
if using_gpu:
model.to_gpu()
total_observations_per_scene = 4
fps = 30
black_color = -0.5
image_shape = (3, ) + hyperparams.image_size
axis_observations_image = np.zeros(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
dtype=np.float32)
#==============================================================================
# Utilities
#==============================================================================
def to_device(array):
if using_gpu:
array = cuda.to_gpu(array)
return array
def fill_observations_axis(observation_images):
axis_observations_image = np.full(
(3, image_shape[1], total_observations_per_scene * image_shape[2]),
black_color,
dtype=np.float32)
num_current_obs = len(observation_images)
total_obs = total_observations_per_scene
width = image_shape[2]
x_start = width * (total_obs - num_current_obs) // 2
for obs_image in observation_images:
x_end = x_start + width
axis_observations_image[:, :, x_start:x_end] = obs_image
x_start += width
return axis_observations_image
def compute_camera_angle_at_frame(t):
horizontal_angle_rad = 2 * t * math.pi / (fps * 2) + math.pi / 4
y_rad_top = math.pi / 3
y_rad_bottom = -math.pi / 3
y_rad_range = y_rad_bottom - y_rad_top
if t < fps * 1.5:
vertical_angle_rad = y_rad_top
elif fps * 1.5 <= t and t < fps * 2.5:
interp = (t - fps * 1.5) / fps
vertical_angle_rad = y_rad_top + interp * y_rad_range
elif fps * 2.5 <= t and t < fps * 4:
vertical_angle_rad = y_rad_bottom
elif fps * 4.0 <= t and t < fps * 5:
interp = (t - fps * 4.0) / fps
vertical_angle_rad = y_rad_bottom - interp * y_rad_range
else:
vertical_angle_rad = y_rad_top
return horizontal_angle_rad, vertical_angle_rad
def rotate_query_viewpoint(horizontal_angle_rad, vertical_angle_rad,
camera_distance):
camera_direction = np.array([
math.sin(horizontal_angle_rad), # x
math.sin(vertical_angle_rad), # y
math.cos(horizontal_angle_rad), # z
])
camera_direction = camera_distance * camera_direction / np.linalg.norm(
camera_direction)
yaw, pitch = compute_yaw_and_pitch(camera_direction)
query_viewpoints = xp.array(
(
camera_direction[0],
camera_direction[1],
camera_direction[2],
math.cos(yaw),
math.sin(yaw),
math.cos(pitch),
math.sin(pitch),
),
dtype=np.float32,
)
query_viewpoints = xp.broadcast_to(query_viewpoints,
(1, ) + query_viewpoints.shape)
return query_viewpoints
# added/modified
def compute_horizontal_rotation_at_frame(t):
'''This rotates the scene horizontally.'''
horizontal_angle_rad = 2 * t * math.pi / (fps * 2) + math.pi / 4
vertical_angle_rad = 0
return horizontal_angle_rad, vertical_angle_rad
def get_mse_image(ground_truth, predicted):
'''Calculates MSE between ground truth and predicted observation, and returns an image.'''
assert ground_truth.shape == predicted.shape
mse_image = np.square(ground_truth - predicted) * 0.5
mse_image = np.concatenate(mse_image).astype(np.float32)
mse_image = np.reshape(mse_image, (3, 64, 64))
return mse_image.transpose(1, 2, 0)
def render(representation,
camera_distance,
start_t,
end_t,
gt_images,
gt_viewpoints,
animation_frame_array,
rotate_camera=True):
gt_images = np.squeeze(gt_images)
gt_viewpoints = cp.reshape(cp.asarray(gt_viewpoints), (15, 1, 7))
idx = cp.argsort(cp.squeeze(gt_viewpoints)[:, 0])
gt_images = [
i
for i, v in sorted(zip(gt_images, idx), key=operator.itemgetter(1))
]
gt_viewpoints = [
i for i, v in sorted(zip(gt_viewpoints, idx),
key=operator.itemgetter(1))
]
count = 0
'''shows variance and mean images of 100 samples from the Gaussian.'''
for t in range(start_t, end_t):
artist_array = [
axis_observations.imshow(make_uint8(axis_observations_image),
interpolation="none",
animated=True)
]
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
t)
if rotate_camera == False:
horizontal_angle_rad, vertical_angle_rad = compute_camera_angle_at_frame(
0)
query_viewpoints = rotate_query_viewpoint(horizontal_angle_rad,
vertical_angle_rad,
camera_distance)
# shape 100x1x3x64x64, when Model is from model_testing.py
generated_images = model.generate_image(query_viewpoints,
representation, 100)
# generate predicted from ground truth viewpoints
predicted_images = model.generate_image(gt_viewpoints[count],
representation, 1)
# predicted_images = model.generate_image(query_viewpoints, representation,1)
predicted_images = np.squeeze(predicted_images)
image_mse = get_mse_image(gt_images[count], predicted_images)
# when sampling with 100
cpu_generated_images = chainer.backends.cuda.to_cpu(
generated_images)
generated_images = np.squeeze(cpu_generated_images)
# # cpu calculation
# cpu_image_mean = np.mean(cpu_generated_images,axis=0)
# cpu_image_std = np.std(cpu_generated_images,axis=0)
# cpu_image_var = np.var(cpu_generated_images,axis=0)
# image_mean = np.squeeze(chainer.backends.cuda.to_gpu(cpu_image_mean))
# image_std = chainer.backends.cuda.to_gpu(cpu_image_std)
# image_var = np.squeeze(chainer.backends.cuda.to_gpu(cpu_image_var))
image_mean = cp.mean(cp.squeeze(generated_images), axis=0)
image_var = cp.var(cp.squeeze(generated_images), axis=0)
# convert to black and white.
# grayscale
r, g, b = image_var
gray_image_var = 0.2989 * r + 0.5870 * g + 0.1140 * b
# thresholding Otsu's method
thresh = threshold_otsu(gray_image_var)
var_binary = gray_image_var > thresh
sample_image = np.squeeze(generated_images[0])
if count == 14:
count = 0
elif (t - fps) % 10 == 0:
count += 1
print("computed an image. Count =", count)
artist_array.append(
axis_generation_variance.imshow(var_binary,
cmap=plt.cm.gray,
interpolation="none",
animated=True))
artist_array.append(
axis_generation_mean.imshow(make_uint8(image_mean),
interpolation="none",
animated=True))
artist_array.append(
axis_generation_sample.imshow(make_uint8(sample_image),
interpolation="none",
animated=True))
artist_array.append(
axis_generation_mse.imshow(make_uint8(image_mse),
cmap='gray',
interpolation="none",
animated=True))
animation_frame_array.append(artist_array)
#==============================================================================
# Visualization
#==============================================================================
plt.style.use("dark_background")
fig = plt.figure(figsize=(6, 7))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
# fig.suptitle("GQN")
axis_observations = fig.add_subplot(3, 1, 1)
axis_observations.axis("off")
axis_observations.set_title("observations")
axis_generation_mse = fig.add_subplot(3, 2, 3)
axis_generation_mse.axis("off")
axis_generation_mse.set_title("MSE")
axis_generation_variance = fig.add_subplot(3, 2, 4)
axis_generation_variance.axis("off")
axis_generation_variance.set_title("Variance")
axis_generation_mean = fig.add_subplot(3, 2, 5)
axis_generation_mean.axis("off")
axis_generation_mean.set_title("Mean")
axis_generation_sample = fig.add_subplot(3, 2, 6)
axis_generation_sample.axis("off")
axis_generation_sample.set_title("Normal Rendering")
#==============================================================================
# Generating animation
#==============================================================================
file_number = 1
random.seed(0)
np.random.seed(0)
with chainer.no_backprop_mode():
for subset in dataset:
iterator = gqn.data.Iterator(subset, batch_size=1)
for data_indices in iterator:
animation_frame_array = []
# shape: (batch, views, height, width, channels)
images, viewpoints = subset[data_indices]
# images, viewpoints, original images = subset[data_indices]
camera_distance = np.mean(
np.linalg.norm(viewpoints[:, :, :3], axis=2))
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
images = images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
images = preprocess_images(images)
# (batch, views, height, width, channels) -> (batch, views, channels, height, width)
# original_images = original_images.transpose((0, 1, 4, 2, 3)).astype(np.float32)
# original_images = preprocess_images(original_images)
batch_index = 0
total_views = images.shape[1]
random_observation_view_indices = list(range(total_views))
random.shuffle(random_observation_view_indices)
random_viewed_observation_indices = random_observation_view_indices[:
total_observations_per_scene]
#------------------------------------------------------------------------------
# Ground Truth
#------------------------------------------------------------------------------
gt_images = images
gt_viewpoints = viewpoints
# gt_images = original_images
#------------------------------------------------------------------------------
# Observations
#------------------------------------------------------------------------------
observed_images = images[batch_index,
random_viewed_observation_indices]
observed_viewpoints = viewpoints[
batch_index, random_viewed_observation_indices]
observed_images = to_device(observed_images)
observed_viewpoints = to_device(observed_viewpoints)
#------------------------------------------------------------------------------
# Generate images with a single observation
#------------------------------------------------------------------------------
# Scene encoder
representation = model.compute_observation_representation(
observed_images[None, :1], observed_viewpoints[None, :1])
# Update figure
observation_index = random_viewed_observation_indices[0]
observed_image = images[batch_index, observation_index]
axis_observations_image = fill_observations_axis(
[observed_image])
# Neural rendering
render(representation, camera_distance, fps, fps * 6,
gt_images, gt_viewpoints, animation_frame_array)
#------------------------------------------------------------------------------
# Add observations
#------------------------------------------------------------------------------
for n in range(1, total_observations_per_scene):
observation_indices = random_viewed_observation_indices[:
n +
1]
axis_observations_image = fill_observations_axis(
images[batch_index, observation_indices])
# Scene encoder
representation = model.compute_observation_representation(
observed_images[None, :n + 1],
observed_viewpoints[None, :n + 1])
# Neural rendering
render(representation,
camera_distance,
0,
fps // 2,
gt_images,
gt_viewpoints,
animation_frame_array,
rotate_camera=False)
#------------------------------------------------------------------------------
# Generate images with all observations
#------------------------------------------------------------------------------
# Scene encoder
representation = model.compute_observation_representation(
observed_images[None, :total_observations_per_scene + 1],
observed_viewpoints[None, :total_observations_per_scene +
1])
# Neural rendering
render(representation, camera_distance, 0, fps * 6, gt_images,
gt_viewpoints, animation_frame_array)
#------------------------------------------------------------------------------
# Write to file
#------------------------------------------------------------------------------
anim = animation.ArtistAnimation(fig,
animation_frame_array,
interval=1 / fps,
blit=True,
repeat_delay=0)
# anim.save(
# "{}/shepard_metzler_observations_{}.gif".format(
# args.figure_directory, file_number),
# writer="imagemagick",
# fps=fps)
anim.save("{}/shepard_metzler_observations_{}.mp4".format(
args.figure_directory, file_number),
writer="ffmpeg",
fps=fps)
print("video saved")
file_number += 1