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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Code probability model used for entropy coding."""
import json
from six.moves import xrange
import tensorflow as tf
from entropy_coder.lib import blocks
from entropy_coder.model import entropy_coder_model
from entropy_coder.model import model_factory
# pylint: disable=not-callable
class BrnnPredictor(blocks.BlockBase):
"""BRNN prediction applied on one layer."""
def __init__(self, code_depth, name=None):
super(BrnnPredictor, self).__init__(name)
with self._BlockScope():
hidden_depth = 2 * code_depth
# What is coming from the previous layer/iteration
# is going through a regular Conv2D layer as opposed to the binary codes
# of the current layer/iteration which are going through a masked
# convolution.
self._adaptation0 = blocks.RasterScanConv2D(
hidden_depth, [7, 7], [1, 1], 'SAME',
strict_order=True,
bias=blocks.Bias(0), act=tf.tanh)
self._adaptation1 = blocks.Conv2D(
hidden_depth, [3, 3], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
self._predictor = blocks.CompositionOperator([
blocks.LineOperator(
blocks.RasterScanConv2DLSTM(
depth=hidden_depth,
filter_size=[1, 3],
hidden_filter_size=[1, 3],
strides=[1, 1],
padding='SAME')),
blocks.Conv2D(hidden_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh),
blocks.Conv2D(code_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
])
def _Apply(self, x, s):
# Code estimation using both:
# - the state from the previous iteration/layer,
# - the binary codes that are before in raster scan order.
h = tf.concat(values=[self._adaptation0(x), self._adaptation1(s)], axis=3)
estimated_codes = self._predictor(h)
return estimated_codes
class LayerPrediction(blocks.BlockBase):
"""Binary code prediction for one layer."""
def __init__(self, layer_count, code_depth, name=None):
super(LayerPrediction, self).__init__(name)
self._layer_count = layer_count
# No previous layer.
self._layer_state = None
self._current_layer = 0
with self._BlockScope():
# Layers used to do the conditional code prediction.
self._brnn_predictors = []
for _ in xrange(layer_count):
self._brnn_predictors.append(BrnnPredictor(code_depth))
# Layers used to generate the input of the LSTM operating on the
# iteration/depth domain.
hidden_depth = 2 * code_depth
self._state_blocks = []
for _ in xrange(layer_count):
self._state_blocks.append(blocks.CompositionOperator([
blocks.Conv2D(
hidden_depth, [3, 3], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh),
blocks.Conv2D(
code_depth, [3, 3], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
]))
# Memory of the RNN is equivalent to the size of 2 layers of binary
# codes.
hidden_depth = 2 * code_depth
self._layer_rnn = blocks.CompositionOperator([
blocks.Conv2DLSTM(
depth=hidden_depth,
filter_size=[1, 1],
hidden_filter_size=[1, 1],
strides=[1, 1],
padding='SAME'),
blocks.Conv2D(hidden_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh),
blocks.Conv2D(code_depth, [1, 1], [1, 1], 'SAME',
bias=blocks.Bias(0), act=tf.tanh)
])
def _Apply(self, x):
assert self._current_layer < self._layer_count
# Layer state is set to 0 when there is no previous iteration.
if self._layer_state is None:
self._layer_state = tf.zeros_like(x, dtype=tf.float32)
# Code estimation using both:
# - the state from the previous iteration/layer,
# - the binary codes that are before in raster scan order.
estimated_codes = self._brnn_predictors[self._current_layer](
x, self._layer_state)
# Compute the updated layer state.
h = self._state_blocks[self._current_layer](x)
self._layer_state = self._layer_rnn(h)
self._current_layer += 1
return estimated_codes
class ProgressiveModel(entropy_coder_model.EntropyCoderModel):
"""Progressive BRNN entropy coder model."""
def __init__(self):
super(ProgressiveModel, self).__init__()
def Initialize(self, global_step, optimizer, config_string):
if config_string is None:
raise ValueError('The progressive model requires a configuration.')
config = json.loads(config_string)
if 'coded_layer_count' not in config:
config['coded_layer_count'] = 0
self._config = config
self._optimizer = optimizer
self._global_step = global_step
def BuildGraph(self, input_codes):
"""Build the graph corresponding to the progressive BRNN model."""
layer_depth = self._config['layer_depth']
layer_count = self._config['layer_count']
code_shape = input_codes.get_shape()
code_depth = code_shape[-1].value
if self._config['coded_layer_count'] > 0:
prefix_depth = self._config['coded_layer_count'] * layer_depth
if code_depth < prefix_depth:
raise ValueError('Invalid prefix depth: {} VS {}'.format(
prefix_depth, code_depth))
input_codes = input_codes[:, :, :, :prefix_depth]
code_shape = input_codes.get_shape()
code_depth = code_shape[-1].value
if code_depth % layer_depth != 0:
raise ValueError(
'Code depth must be a multiple of the layer depth: {} vs {}'.format(
code_depth, layer_depth))
code_layer_count = code_depth // layer_depth
if code_layer_count > layer_count:
raise ValueError('Input codes have too many layers: {}, max={}'.format(
code_layer_count, layer_count))
# Block used to estimate binary codes.
layer_prediction = LayerPrediction(layer_count, layer_depth)
# Block used to compute code lengths.
code_length_block = blocks.CodeLength()
# Loop over all the layers.
code_length = []
code_layers = tf.split(
value=input_codes, num_or_size_splits=code_layer_count, axis=3)
for k in xrange(code_layer_count):
x = code_layers[k]
predicted_x = layer_prediction(x)
# Saturate the prediction to avoid infinite code length.
epsilon = 0.001
predicted_x = tf.clip_by_value(
predicted_x, -1 + epsilon, +1 - epsilon)
code_length.append(code_length_block(
blocks.ConvertSignCodeToZeroOneCode(x),
blocks.ConvertSignCodeToZeroOneCode(predicted_x)))
tf.summary.scalar('code_length_layer_{:02d}'.format(k), code_length[-1])
code_length = tf.stack(code_length)
self.loss = tf.reduce_mean(code_length)
tf.summary.scalar('loss', self.loss)
# Loop over all the remaining layers just to make sure they are
# instantiated. Otherwise, loading model params could fail.
dummy_x = tf.zeros_like(code_layers[0])
for _ in xrange(layer_count - code_layer_count):
dummy_predicted_x = layer_prediction(dummy_x)
# Average bitrate over total_line_count.
self.average_code_length = tf.reduce_mean(code_length)
if self._optimizer:
optim_op = self._optimizer.minimize(self.loss,
global_step=self._global_step)
block_updates = blocks.CreateBlockUpdates()
if block_updates:
with tf.get_default_graph().control_dependencies([optim_op]):
self.train_op = tf.group(*block_updates)
else:
self.train_op = optim_op
else:
self.train_op = None
def GetConfigStringForUnitTest(self):
s = '{\n'
s += '"layer_depth": 1,\n'
s += '"layer_count": 8\n'
s += '}\n'
return s
@model_factory.RegisterEntropyCoderModel('progressive')
def CreateProgressiveModel():
return ProgressiveModel()
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