# Copyright 2015 Google Inc. 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.

# ==============================================================================

"""Gradients for operators defined in data_flow_ops.py."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from six.moves import xrange # pylint: disable=redefined-builtin

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import constant_op

from tensorflow.python.ops import data_flow_ops

from tensorflow.python.ops import math_ops

@ops.RegisterGradient("DynamicPartition")

def _DynamicPartitionGrads(op, *grads):

"""Gradients for DynamicPartition."""

data = op.inputs[0]

indices = op.inputs[1]

num_partitions = op.get_attr("num_partitions")

prefix_shape = array_ops.shape(indices)

original_indices = array_ops.reshape(

math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)

partitioned_indices = data_flow_ops.dynamic_partition(

original_indices, indices, num_partitions)

reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)

reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))

return [reconstructed, None]

@ops.RegisterGradient("DynamicStitch")

def _DynamicStitchGrads(op, grad):

"""Gradients for DynamicStitch."""

num_values = len(op.inputs) // 2

indices_grad = [None] * num_values

def AsInt32(x):

return (x if op.inputs[0].dtype == dtypes.int32 else

math_ops.cast(x, dtypes.int32))

inputs = [AsInt32(op.inputs[i]) for i in xrange(num_values)]

if isinstance(grad, ops.IndexedSlices):

output_shape = array_ops.shape(op.outputs[0])

output_rows = output_shape[0]

grad = math_ops.unsorted_segment_sum(grad.values, grad.indices, output_rows)

values_grad = [array_ops.gather(grad, inp) for inp in inputs]

return indices_grad + values_grad

ops.NoGradient("Queue")

ops.NoGradient("QueueEnqueue")

ops.NoGradient("QueueEnqueueMany")

ops.NoGradient("QueueDequeue")

ops.NoGradient("QueueDequeueMany")

ops.NoGradient("QueueClose")

ops.NoGradient("QueueSize")

ops.NoGradient("Stack")

ops.NoGradient("StackPush")

ops.NoGradient("StackPop")

ops.NoGradient("StackClose")

ops.NoGradient("GetSessionHandle")

ops.NoGradient("GetSessionTensor")

ops.NoGradient("DeleteSessionTensor")