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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.
# ==============================================================================
"""Benchmark for fused conv2d bias and activation op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from tensorflow.contrib.fused_conv.python.ops import fused_conv2d_bias_activation_op
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
def build_conv_bias_relu_graph(device, input_shape, filter_shape, strides,
padding, num_iters, data_format):
"""builds a graph containing a sequence of conv2d operations.
Args:
device: String, the device to run on.
input_shape: Shape of the input tensor.
filter_shape: Shape of the filter tensor.
strides: A list of ints. 1-D of length 4. The stride of sliding
window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding
algorithm to use.
num_iters: number of iterations to run conv2d.
data_format: data format string of input, 'NHWC' and 'NCHW' are
supported.
Returns:
An array of tensors to run()
"""
if data_format == "NCHW":
input_shape = [
input_shape[0], input_shape[3], input_shape[1], input_shape[2]
]
with ops.device("/%s:0" % device):
inp = variables.Variable(random_ops.truncated_normal(input_shape))
filt = variables.Variable(random_ops.truncated_normal(filter_shape))
bias_shape = [filter_shape[-1]]
bias = variables.Variable(random_ops.truncated_normal(bias_shape))
outputs = []
conv2d_out = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format)
relu_out = nn_ops.relu(bias_out)
outputs.append(relu_out)
for _ in range(1, num_iters):
with ops.control_dependencies([relu_out]):
conv2d_out = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format)
relu_out = nn_ops.relu(bias_out)
outputs.append(relu_out)
return control_flow_ops.group(*outputs)
def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides,
padding, num_iters, data_format):
"""builds a graph containing a sequence of conv2d operations.
Args:
device: String, the device to run on.
input_shape: Shape of the input tensor.
filter_shape: Shape of the filter tensor.
strides: A list of ints. 1-D of length 4. The stride of sliding
window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding
algorithm to use.
num_iters: number of iterations to run conv2d.
data_format: data format string of input, 'NHWC' and 'NCHW' are
supported.
Returns:
An array of tensors to run()
"""
if data_format == "NCHW":
input_shape = [
input_shape[0], input_shape[3], input_shape[1], input_shape[2]
]
with ops.device("/%s:0" % device):
inp = variables.Variable(random_ops.truncated_normal(input_shape))
filt = variables.Variable(random_ops.truncated_normal(filter_shape))
bias_shape = [filter_shape[-1]]
bias = variables.Variable(random_ops.truncated_normal(bias_shape))
outputs = []
fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
inp,
filt,
bias,
strides,
padding,
data_format=data_format,
activation_mode="Relu")
outputs.append(fused_out)
for _ in range(1, num_iters):
with ops.control_dependencies([fused_out]):
# pylint: disable=g-line-too-long
fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
inp,
filt,
bias,
strides,
padding,
data_format=data_format,
activation_mode="Relu")
outputs.append(fused_out)
return control_flow_ops.group(*outputs)
class FusedConv2DBiasActivationBenchmark(test.Benchmark):
"""Benchmark conv2d!"""
def _run_graph(self, device, input_shape, filter_shape, strides, padding,
num_iters, data_format):
"""runs the graph and print its execution time.
Args:
device: String, the device to run on.
input_shape: Shape of the input tensor.
filter_shape: Shape of the filter tensor.
strides: A list of ints. 1-D of length 4. The stride of sliding
window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding
algorithm to use. num_iters: Number of iterations to run the
benchmark.
num_iters: number of iterations to run conv2d.
data_format: data format string of input, 'NHWC' and 'NCHW' are
supported.
Returns:
The duration of the run in seconds.
"""
graph = ops.Graph()
with graph.as_default():
outputs = build_fused_conv_bias_relu_graph(device, input_shape,
filter_shape, strides, padding,
num_iters, data_format)
with session_lib.Session(graph=graph) as session:
variables.global_variables_initializer().run()
# warmup runs
session.run(outputs)
start_time = time.time()
session.run(outputs)
duration = (time.time() - start_time) / num_iters
print("%s inputshape:%s filtershape:%s strides:%s padding:%s "
"%d iters: %.8f sec" %
(device, str(input_shape).replace(" ", ""),
str(filter_shape).replace(" ", ""),
str(strides).replace(" ", ""), padding, num_iters, duration))
name_template = (
"conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_"
"strides_{strides}_padding_{padding}")
self.report_benchmark(
name=name_template.format(
device=device,
inputshape=str(input_shape).replace(" ", ""),
filtershape=str(filter_shape).replace(" ", ""),
strides=str(strides).replace(" ", ""),
padding=padding).replace(" ", ""),
iters=num_iters,
wall_time=duration)
return duration
def benchmark_fused_conv2d_bias_activation(self):
stride = [1, 1, 1, 1]
paddings = ["VALID", "SAME"]
data_formats = ["NHWC", "NCHW"]
resnet50_input_shapes = [[64, 14, 14, 256], [64, 14, 14, 256], [
64, 14, 14, 1024
], [64, 55, 55, 64], [64, 28, 28, 128], [64, 28, 28, 128], [64, 55, 55, 64],
[64, 7, 7, 512], [64, 7, 7, 512],
[64, 28, 28, 512], [64, 55, 55,
256], [64, 7, 7, 2048]]
resnet50_filter_shapes = [[1, 1, 256, 1024], [3, 3, 256, 256], [
1, 1, 1024, 256
], [1, 1, 64, 256], [1, 1, 128, 512], [3, 3, 128, 128], [3, 3, 64, 64], [
3, 3, 512, 512
], [1, 1, 512, 2048], [1, 1, 512, 128], [1, 1, 256, 64], [1, 1, 2048, 512]]
inception3_input_shapes = [[64, 17, 17, 768], [64, 35, 35, 96], [
64, 35, 35, 288
], [64, 8, 8, 384], [64, 8, 8, 384], [64, 17, 17, 192], [64, 35, 35, 64], [
64, 17, 17, 192
], [64, 17, 17, 160], [64, 17, 17, 160], [64, 17, 17, 768], [
64, 35, 35, 256
], [64, 35, 35, 48], [64, 35, 35, 192], [64, 17, 17, 128], [
64, 17, 17, 160
], [64, 8, 8, 448], [64, 17, 17, 128], [64, 17, 17, 768], [64, 17, 17, 160]]
inception3_filter_shapes = [[1, 1, 768, 192], [3, 3, 96, 96], [
1, 1, 288, 64
], [1, 3, 384, 384], [3, 1, 384, 384], [7, 1, 192, 192], [3, 3, 64, 96], [
1, 7, 192, 192
], [7, 1, 160, 160], [1, 7, 160, 160], [1, 1, 768, 160], [1, 1, 256, 64], [
5, 5, 48, 64
], [1, 1, 192, 64], [1, 7, 128, 128], [1, 7, 160, 192], [3, 3, 448, 384],
[7, 1, 128, 128], [1, 1, 768,
128], [7, 1, 160, 192]]
print("fused conv2d bias activation benchmark using resnet50's shapes:")
for ishape, fshape in zip(resnet50_input_shapes, resnet50_filter_shapes):
for padding in paddings:
for data_format in data_formats:
self._run_graph("gpu", ishape, fshape, stride, padding, 80,
data_format)
print("fused conv2d bias activation benchmark using inception3's shapes:")
for ishape, fshape in zip(inception3_input_shapes,
inception3_filter_shapes):
for padding in paddings:
for data_format in data_formats:
self._run_graph("gpu", ishape, fshape, stride, padding, 80,
data_format)
if __name__ == "__main__":
test.main()
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