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# Copyright 2015 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.
# ============================================================================
"""Tensorflow op performing fused conv2d bias_add and relu."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
_fused_conv2d_bias_activation_op_so = loader.load_op_library(
resource_loader.get_path_to_datafile("_fused_conv2d_bias_activation_op.so"))
# pylint: disable=redefined-builtin
def fused_conv2d_bias_activation(conv_input,
filter,
bias,
strides=None,
padding=None,
conv_input_scale=1.0,
side_input_scale=0.0,
side_input=None,
activation_mode="Relu",
data_format=None,
filter_format=None,
name=None):
"""Fused 2D conv, bias and activation with optional side input.
Computes a fused 2-D convolution scaled by conv_input_scale,
adds an optional side input scaled by side_input_scale, adds biases,
and applies ReLU. As an equation:
output = ReLU(conv_input_scale * Conv(conv_input, filter) +
side_input_scale * side_input + bias)
Note: In int8 mode, The ReLU will clip the output to the range [0..127].
Args:
conv_input: A `Tensor` of the format specified by `data_format`.
filter: A `Tensor` whose format depends on `data_format`:
if `data_format` is "NCHW_VECT_C", filter should be "OIHW_VECT_I"
otherwise, it should be "HWIO" format.
bias: A 1-D `Tensor` of type `float32`, and dimensions equal to the
number of output channels.
strides: A list of 4 `ints` specifying convolution strides.
if `data_format` is "NCHW" or "NCHW_VECT_C", the order should be NCHW.
if `data_format` is "NHWC", the order should be NHWC.
padding: A `string` from: `"SAME", "VALID"`.
conv_input_scale: A scalar `float32` that will be multiplied by conv_input.
This is optional and defaults to 1. However it should be set to
specify the quantization scale when `data_format` is "NCHW_VECT_C".
side_input_scale: A scalar `float32` that will be multiplied by side_input.
This is optional and defaults to 0.
side_input: A `Tensor` of the format specified by `data_format`.
This is useful for implementing ResNet blocks.
activation_mode: (optional) currently must be the default "Relu".
Note that in qint8 mode, it also clips to 127, so acts like ReluX.
data_format: Specifies the data format.
Possible values are:
"NHWC" float [batch, height, width, channels]
"NCHW" float [batch, channels, height, width]
"NCHW_VECT_C" qint8 [batch, channels / 4, height, width, channels % 4]
Defaults to `"NHWC"`.
Performance is worst for `"NHWC"` and best for `"NCHW_VECT_C"`.
filter_format: Specifies the filter format.
Possible values are:
"HWIO" float [kernel_height, kernel_width, input_channels,
output_channels ]
"OIHW" float [output_channels, input_channels, kernel_height,
kernel_width ]
"OIHW_VECT_I" qint8 [ output_channels, input_channels / 4,
kernel_height, kernel_width, input_channels % 4 ]
Defaults to `"HWIO"`.
name: A name for the operation (optional).
Returns:
A `Tensor` of the format specified by `data_format`.
"""
if strides is None:
strides = [1, 1, 1, 1]
if side_input is None:
side_input = []
return gen_fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
conv_input,
filter,
bias,
side_input,
conv_input_scale,
side_input_scale,
padding=padding,
strides=strides,
activation_mode=activation_mode,
data_format=data_format,
filter_format=filter_format,
name=name)
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