def decompress(strings, min_v, max_v, shape, model, ckpt_dir):
"""Decompress bitstream to cubes.
Input: compressed bitstream.
Output: cubes with shape [batch size, length, width, height, channel(1)]
"""
print('===== Decompress =====')
# load model.
#model = importlib.import_module(model)
# analysis_transform = model.AnalysisTransform()
synthesis_transform = model.SynthesisTransform()
entropy_bottleneck = EntropyBottleneck()
checkpoint = tf.train.Checkpoint(synthesis_transform=synthesis_transform,
estimator=entropy_bottleneck)
status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))
start = time.time()
ys = entropy_bottleneck.decompress(strings, min_v, max_v, shape, shape[-1])
print("Entropy Decode: {}s".format(round(time.time() - start, 4)))
def loop_synthesis(y):
y = tf.expand_dims(y, 0)
x = synthesis_transform(y)
return tf.squeeze(x, [0])
start = time.time()
xs = tf.map_fn(loop_synthesis,
ys,
dtype=tf.float32,
parallel_iterations=1,
back_prop=False)
print("Synthesis Transform: {}s".format(round(time.time() - start, 4)))
return xs
def decompress_less_mem(y_strings, y_min_vs, y_max_vs, y_shape, z_strings, z_min_v, z_max_v, z_shape, model, ckpt_dir):
"""Decompress bitstream to cubes.
Input: compressed bitstream. latent representations (y) and hyper prior (z).
Output: cubes with shape [batch size, length, width, height, channel(1)]
"""
print('===== Decompress =====')
# load model.
#model = importlib.import_module(model)
synthesis_transform = model.SynthesisTransform()
hyper_encoder = model.HyperEncoder()
hyper_decoder = model.HyperDecoder()
entropy_bottleneck = EntropyBottleneck()
conditional_entropy_model = SymmetricConditional()
checkpoint = tf.train.Checkpoint(synthesis_transform=synthesis_transform,
hyper_encoder=hyper_encoder,
hyper_decoder=hyper_decoder,
estimator=entropy_bottleneck)
status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))
start = time.time()
zs = entropy_bottleneck.decompress(z_strings, z_min_v, z_max_v, z_shape, z_shape[-1])
print("Entropy Decoder (Hyper): {}s".format(round(time.time()-start, 4)))
def loop_hyper_deocder(z):
z = tf.expand_dims(z, 0)
loc, scale = hyper_decoder(z)
return tf.squeeze(loc, [0]), tf.squeeze(scale, [0])
start = time.time()
locs, scales = tf.map_fn(loop_hyper_deocder, zs, dtype=(tf.float32, tf.float32),
parallel_iterations=1, back_prop=False)
lower_bound = 1e-9# TODO
scales = tf.maximum(scales, lower_bound)
print("Hyper Decoder: {}s".format(round(time.time()-start, 4)))
start = time.time()
# ys = conditional_entropy_model.decompress(y_strings, locs, scales, y_min_v, y_max_v, y_shape)
def loop_range_decode(args):
y_string, loc, scale, y_min_v, y_max_v = args
loc = tf.expand_dims(loc, 0)
scale = tf.expand_dims(scale, 0)
y_decoded = conditional_entropy_model.decompress(y_string, loc, scale, y_min_v, y_max_v, y_shape)
return tf.squeeze(y_decoded, 0)
args = (y_strings, locs, scales, y_min_vs, y_max_vs)
ys = tf.map_fn(loop_range_decode, args, dtype=tf.float32, parallel_iterations=1, back_prop=False)
print("Entropy Decoder: {}s".format(round(time.time()-start, 4)))
def loop_synthesis(y):
y = tf.expand_dims(y, 0)
x = synthesis_transform(y)
return tf.squeeze(x, [0])
start = time.time()
xs = tf.map_fn(loop_synthesis, ys, dtype=tf.float32, parallel_iterations=1, back_prop=False)
print("Synthesis Transform: {}s".format(round(time.time()-start, 4)))
return xs
# model = importlib.import_module(args.model)
# Define parameters.
BATCH_SIZE = args.batch_size
DISPLAY_STEP = 1000
SAVE_STEP = 4000
RATIO_EVAL = 10 #
NUM_ITEATION = 3e5
alpha = args.alpha
beta = args.beta
init_ckpt_dir = args.init_ckpt_dir
# Define variables.
analysis_transform, synthesis_transform = model.AnalysisTransform(
), model.SynthesisTransform()
entropy_bottleneck = EntropyBottleneck()
global_step = tf.train.get_or_create_global_step()
lr = 1e-05
main_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
## Define checkpoint.
if args.reset_optimizer == 0:
checkpoint = tf.train.Checkpoint(analysis_transform=analysis_transform,
synthesis_transform=synthesis_transform,
estimator=entropy_bottleneck,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(main_optimizer=main_optimizer,
analysis_transform=analysis_transform,
SAVE_STEP = 5000
RATIO_EVAL = 9 #
# NUM_ITEATION = 2e5
NUM_ITEATION = int(args.num_iteration)
alpha = args.alpha
beta = args.beta
gamma = args.gamma # weight of hyper prior.
delta = args.delta # weight of latent representation.
init_ckpt_dir = args.init_ckpt_dir
lower_bound = args.lower_bound
print('lower bound of scale:', lower_bound)
reset_optimizer = bool(args.reset_optimizer)
print('reset_optimizer:::', reset_optimizer)
# Define variables
analysis_transform, synthesis_transform = model.AnalysisTransform(), model.SynthesisTransform()
hyper_encoder, hyper_decoder = model.HyperEncoder(), model.HyperDecoder()
entropy_bottleneck = EntropyBottleneck()
conditional_entropy_model = SymmetricConditional()
global_step = tf.train.get_or_create_global_step()
# lr = tf.train.exponential_decay(1e-4, global_step, 20000, 0.75, staircase=True)
# lr = 1e-5
lr = args.lr
main_optimizer = tf.train.AdamOptimizer(learning_rate = lr)
########## Define checkpoint ##########
if args.reset_optimizer == 0:
checkpoint = tf.train.Checkpoint(analysis_transform=analysis_transform,
def compress_hyper(cubes, model, ckpt_dir, decompress=False):
"""Compress cubes to bitstream.
Input: cubes with shape [batch size, length, width, height, channel(1)].
Output: compressed bitstream.
"""
print('===== Compress =====')
# load model.
#model = importlib.import_module(model)
analysis_transform = model.AnalysisTransform()
synthesis_transform = model.SynthesisTransform()
hyper_encoder = model.HyperEncoder()
hyper_decoder = model.HyperDecoder()
entropy_bottleneck = EntropyBottleneck()
conditional_entropy_model = SymmetricConditional()
checkpoint = tf.train.Checkpoint(analysis_transform=analysis_transform,
synthesis_transform=synthesis_transform,
hyper_encoder=hyper_encoder,
hyper_decoder=hyper_decoder,
estimator=entropy_bottleneck)
status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))
x = tf.convert_to_tensor(cubes, "float32")
def loop_analysis(x):
x = tf.expand_dims(x, 0)
y = analysis_transform(x)
return tf.squeeze(y)
start = time.time()
ys = tf.map_fn(loop_analysis,
x,
dtype=tf.float32,
parallel_iterations=1,
back_prop=False)
print("Analysis Transform: {}s".format(round(time.time() - start, 4)))
def loop_hyper_encoder(y):
y = tf.expand_dims(y, 0)
z = hyper_encoder(y)
return tf.squeeze(z)
start = time.time()
zs = tf.map_fn(loop_hyper_encoder,
ys,
dtype=tf.float32,
parallel_iterations=1,
back_prop=False)
print("Hyper Encoder: {}s".format(round(time.time() - start, 4)))
z_hats, _ = entropy_bottleneck(zs, False)
print("Quantize hyperprior.")
def loop_hyper_deocder(z):
z = tf.expand_dims(z, 0)
loc, scale = hyper_decoder(z)
return tf.squeeze(loc, [0]), tf.squeeze(scale, [0])
start = time.time()
locs, scales = tf.map_fn(loop_hyper_deocder,
z_hats,
dtype=(tf.float32, tf.float32),
parallel_iterations=1,
back_prop=False)
lower_bound = 1e-9 # TODO
scales = tf.maximum(scales, lower_bound)
print("Hyper Decoder: {}s".format(round(time.time() - start, 4)))
start = time.time()
z_strings, z_min_v, z_max_v = entropy_bottleneck.compress(zs)
z_shape = tf.shape(zs)[:]
print("Entropy Encode (Hyper): {}s".format(round(time.time() - start, 4)))
start = time.time()
# y_strings, y_min_v, y_max_v = conditional_entropy_model.compress(ys, locs, scales)
# y_shape = tf.shape(ys)[:]
def loop_range_encode(args):
y, loc, scale = args
y = tf.expand_dims(y, 0)
loc = tf.expand_dims(loc, 0)
scale = tf.expand_dims(scale, 0)
y_string, y_min_v, y_max_v = conditional_entropy_model.compress(
y, loc, scale)
return y_string, y_min_v, y_max_v
args = (ys, locs, scales)
y_strings, y_min_vs, y_max_vs = tf.map_fn(loop_range_encode,
args,
dtype=(tf.string, tf.int32,
tf.int32),
parallel_iterations=1,
back_prop=False)
y_shape = tf.convert_to_tensor(np.insert(tf.shape(ys)[1:].numpy(), 0, 1))
print("Entropy Encode: {}s".format(round(time.time() - start, 4)))
if decompress:
start = time.time()
def loop_range_decode(args):
y_string, loc, scale, y_min_v, y_max_v = args
loc = tf.expand_dims(loc, 0)
scale = tf.expand_dims(scale, 0)
y_decoded = conditional_entropy_model.decompress(
y_string, loc, scale, y_min_v, y_max_v, y_shape)
return tf.squeeze(y_decoded, 0)
args = (y_strings, locs, scales, y_min_vs, y_max_vs)
y_decodeds = tf.map_fn(loop_range_decode,
args,
dtype=tf.float32,
parallel_iterations=1,
back_prop=False)
print("Entropy Decode: {}s".format(round(time.time() - start, 4)))
def loop_synthesis(y):
y = tf.expand_dims(y, 0)
x = synthesis_transform(y)
return tf.squeeze(x, [0])
start = time.time()
x_decodeds = tf.map_fn(loop_synthesis,
y_decodeds,
dtype=tf.float32,
parallel_iterations=1,
back_prop=False)
print("Synthesis Transform: {}s".format(round(time.time() - start, 4)))
return y_strings, y_min_vs, y_max_vs, y_shape, z_strings, z_min_v, z_max_v, z_shape, x_decodeds
return y_strings, y_min_vs, y_max_vs, y_shape, z_strings, z_min_v, z_max_v, z_shape
def compress_more_less_mem(cubes, model, ckpt_dir, decompress=False):
"""Compress cubes to bitstream.
Input: cubes with shape [batch size, length, width, height, channel(1)].
Output: compressed bitstream.
"""
print('===== Compress =====')
# load model.
#model = importlib.import_module(model)
analysis_transform = model.AnalysisTransform()
synthesis_transform = model.SynthesisTransform()
hyper_encoder = model.HyperEncoder()
hyper_decoder = model.HyperDecoder()
entropy_bottleneck = EntropyBottleneck()
conditional_entropy_model = SymmetricConditional()
checkpoint = tf.train.Checkpoint(analysis_transform=analysis_transform,
synthesis_transform=synthesis_transform,
hyper_encoder=hyper_encoder,
hyper_decoder=hyper_decoder,
estimator=entropy_bottleneck)
status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))
start = time.time()
y_strings = []
y_min_vs = []
y_max_vs = []
x_decodeds = []
zs = []
for idx, cube in enumerate(cubes):
if idx % 1000 == 0:
print(idx)
x = tf.convert_to_tensor(cube, "float32")
x = tf.expand_dims(x, 0)
y = analysis_transform(x)
z = hyper_encoder(y)
zs.append(z)
z_hat, _ = entropy_bottleneck(z, False)
loc, scale = hyper_decoder(z_hat)
lower_bound = 1e-9# TODO
scale = tf.maximum(scale, lower_bound)
y_string, y_min_v, y_max_v = conditional_entropy_model.compress(y, loc, scale)
y_strings.append(y_string)
y_min_vs.append(y_min_v)
y_max_vs.append(y_max_v)
y_shape = tf.shape(y)
y_dec = conditional_entropy_model.decompress(y_string, loc, scale, y_min_v, y_max_v, y_shape)
x_dec = synthesis_transform(y_dec)
x_dec = x_dec.numpy()
x_decodeds.append(x_dec)
y_strings = tf.convert_to_tensor(y_strings, dtype='string')
y_min_vs = tf.convert_to_tensor(y_min_vs)
y_max_vs = tf.convert_to_tensor(y_max_vs)
zs = tf.concat(zs, axis=0)
x_decodeds = np.concatenate(x_decodeds, 0)
x_decodeds = tf.convert_to_tensor(x_decodeds, 'float32')
z_strings, z_min_v, z_max_v = entropy_bottleneck.compress(zs)
z_shape = tf.shape(zs)[:]
return y_strings, y_min_vs, y_max_vs, y_shape, z_strings, z_min_v, z_max_v, z_shape, x_decodeds