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Graph transform to flatten atrous (dilated) convolutions (i.e., a sequence of SpaceToBatchND-Conv-BatchToSpaceND ops) to a regular Conv op with upsampled filters.

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A. Unique TensorFlower 2017-09-12 11:58:49 -07:00 committed by TensorFlower Gardener
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2
tensorflow/tools/graph_transforms/BUILD
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@ -94,6 +94,7 @@ cc_library(
"add_default_attributes.cc",
"backports.cc",
"fake_quantize_training.cc",
"flatten_atrous.cc",
"fold_batch_norms.cc",
"fold_constants_lib.cc",
"fold_old_batch_norms.cc",
@ -145,6 +146,7 @@ tf_cc_test(
"add_default_attributes_test.cc",
"backports_test.cc",
"fake_quantize_training_test.cc",
"flatten_atrous_test.cc",
"fold_batch_norms_test.cc",
"fold_constants_test.cc",
"fold_old_batch_norms_test.cc",

15
tensorflow/tools/graph_transforms/README.md
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@ -14,6 +14,7 @@
* [Transform Reference](#transform-reference)
* [add_default_attributes](#add_default_attributes)
* [backport_concatv2](#backport_concatv2)
* [flatten_atrous_conv](#flatten_atrous_conv)
* [fold_batch_norms](#fold_batch_norms)
* [fold_constants](#fold_constants)
* [fold_old_batch_norms](#fold_old_batch_norms)
@ -354,6 +355,20 @@ TensorFlow framework and includes ConcatV2, and you want to run it on an older
version that only supports Concat, this transform will take care of converting
those newer ops to the equivalent older form.
### flatten_atrous_conv
Args: None \
Prerequisites: [fold_constants](#fold_constants)
This transform flattens atrous convolution, corresponding to a sequence of
SpaceToBatchND-Conv2D-BatchToSpaceND operations, converting it to a regular
Conv2D op with upsampled filters. This transforms should only be used in order
to run graphs having atrous convolution on platforms that do not yet natively
support SpaceToBatchND and BatchToSpaceND operations. You will need to make
sure you run [fold_constants](#fold_constants) after this transform. If
applicable, you should run this transform before
[fold_batch_norms](#fold_batch_norms).
### fold_batch_norms
Args: None \

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tensorflow/tools/graph_transforms/flatten_atrous.cc Normal file
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@ -0,0 +1,141 @@
/* 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.
==============================================================================*/
#include "tensorflow/core/graph/graph_constructor.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/subgraph.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"
#include "tensorflow/tools/graph_transforms/transform_utils.h"
namespace tensorflow {
namespace graph_transforms {
Status FlattenAtrousConv(const GraphDef& input_graph_def,
const TransformFuncContext& context,
GraphDef* output_graph_def) {
GraphDef replaced_graph_def;
TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes(
input_graph_def, // clang-format off
{"BatchToSpaceND",
{
{"Conv2D|DepthwiseConv2dNative",
{
{"SpaceToBatchND",
{
{"*"}, // Input to the flattened op.
{"*"}, // block_shape
{"*"} // paddings
}
},
{"*"} // filter
}
},
{"*"}, // block_shape
{"*"} // crops
}
}, // clang-format on
[](const NodeMatch& match, const std::set<string>& input_nodes,
const std::set<string>& output_nodes,
std::vector<NodeDef>* new_nodes) {
// Find all the nodes we expect in the subgraph.
const NodeDef& batch_to_space_node = match.node;
const NodeDef& conv_node = match.inputs[0].node;
const NodeDef& filter_node = match.inputs[0].inputs[1].node;
const NodeDef& input_node = match.inputs[0].inputs[0].inputs[0].node;
const NodeDef& space_to_batch_block_shape_node =
match.inputs[0].inputs[0].inputs[1].node;
// The atrous rate value is inferred from the block shape.
Tensor block_shape =
GetNodeTensorAttr(space_to_batch_block_shape_node, "value");
const int32 block_height = block_shape.flat<int32>()(0);
const int32 block_width = block_shape.flat<int32>()(1);
// Compute the upsampled filter.
const Tensor& filter = GetNodeTensorAttr(filter_node, "value");
const int32 filter_height = filter.dim_size(0);
const int32 filter_width = filter.dim_size(1);
const int32 in_channels = filter.dim_size(2);
const int32 out_channels = filter.dim_size(3);
const int32 upsampled_filter_height =
(filter_height - 1) * block_height + 1;
const int32 upsampled_filter_width =
(filter_width - 1) * block_width + 1;
Tensor upsampled_filter(
DT_FLOAT,
TensorShape({upsampled_filter_height, upsampled_filter_width,
in_channels, out_channels}));
auto filter_eigen = filter.tensor<float, 4>();
auto upsampled_filter_eigen = upsampled_filter.tensor<float, 4>();
upsampled_filter_eigen.setZero();
for (int h = 0; h < filter_height; ++h) {
for (int w = 0; w < filter_width; ++w) {
for (int c_in = 0; c_in < in_channels; ++c_in) {
for (int c_out = 0; c_out < out_channels; ++c_out) {
upsampled_filter_eigen(block_height * h, block_width * w, c_in,
c_out) = filter_eigen(h, w, c_in, c_out);
}
}
}
}
NodeDef upsampled_filter_node;
upsampled_filter_node.set_op("Const");
upsampled_filter_node.set_name(filter_node.name());
SetNodeAttr("dtype", DT_FLOAT, &upsampled_filter_node);
SetNodeTensorAttr<float>("value", upsampled_filter,
&upsampled_filter_node);
// Set up the new flattened version of the convolution op.
NodeDef flattened_conv_node;
flattened_conv_node.set_name(batch_to_space_node.name());
flattened_conv_node.set_op(conv_node.op());
flattened_conv_node.set_device(conv_node.device());
AddNodeInput(input_node.name(), &flattened_conv_node);
AddNodeInput(upsampled_filter_node.name(), &flattened_conv_node);
CopyNodeAttr(conv_node, "T", "T", &flattened_conv_node);
CopyNodeAttr(conv_node, "strides", "strides", &flattened_conv_node);
SetNodeAttr("padding", "SAME", &flattened_conv_node);
CopyNodeAttr(conv_node, "data_format", "data_format",
&flattened_conv_node);
if (conv_node.op() == "Conv2D") {
CopyNodeAttr(conv_node, "use_cudnn_on_gpu", "use_cudnn_on_gpu",
&flattened_conv_node);
}
new_nodes->push_back(input_node);
new_nodes->push_back(upsampled_filter_node);
new_nodes->push_back(flattened_conv_node);
return Status::OK();
},
{}, &replaced_graph_def));
*output_graph_def = replaced_graph_def;
return Status::OK();
}
REGISTER_GRAPH_TRANSFORM("flatten_atrous_conv", FlattenAtrousConv);
} // namespace graph_transforms
} // namespace tensorflow

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tensorflow/tools/graph_transforms/flatten_atrous_test.cc Normal file
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@ -0,0 +1,121 @@
/* 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.
==============================================================================*/
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/nn_ops.h"
#include "tensorflow/cc/ops/sendrecv_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/tools/graph_transforms/transform_utils.h"
namespace tensorflow {
namespace graph_transforms {
// Declare here, so we don't need a public header.
Status FlattenAtrousConv(const GraphDef& input_graph_def,
const TransformFuncContext& context,
GraphDef* output_graph_def);
class FlattenAtrousConvTest : public ::testing::Test {
protected:
template <class TConvOp>
void TestFlattenAtrousConv() {
auto root = tensorflow::Scope::NewRootScope();
using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
Tensor input_data(DT_FLOAT, TensorShape({1, 3, 3, 2}));
test::FillValues<float>(
&input_data, {.1f, .4f, .2f, .5f, .3f, .6f, -1.0f, -.4f, -.2f, -.5f,
-.3f, -.6f, .1f, .4f, .2f, .5f, .3f, .6f});
Output input_op =
Const(root.WithOpName("input_op"), Input::Initializer(input_data));
Tensor block_shape_data(DT_INT32, TensorShape({2}));
test::FillValues<int>(&block_shape_data, {2, 2});
Output block_shape_op = Const(root.WithOpName("block_shape_op"),
Input::Initializer(block_shape_data));
Tensor paddings_data(DT_INT32, TensorShape({2, 2}));
test::FillValues<int>(&paddings_data, {1, 2, 1, 2});
Output paddings_op = Const(root.WithOpName("paddings_op"),
Input::Initializer(paddings_data));
Output space_to_batch_op =
SpaceToBatchND(root.WithOpName("space_to_batch_op"), input_op,
block_shape_op, paddings_op);
Tensor weights_data(DT_FLOAT, TensorShape({2, 2, 2, 1}));
test::FillValues<float>(&weights_data,
{.1f, .2f, .3f, .4f, .1f, .2f, .3f, .4f});
Output weights_op =
Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
Output conv_op = TConvOp(root.WithOpName("conv_op"), space_to_batch_op,
weights_op, {1, 1, 1, 1}, "VALID");
Tensor crops_data(DT_INT32, TensorShape({2, 2}));
test::FillValues<int>(&crops_data, {0, 1, 0, 1});
Output crops_op =
Const(root.WithOpName("crops_op"), Input::Initializer(crops_data));
Output batch_to_space_op = BatchToSpaceND(
root.WithOpName("output"), conv_op, block_shape_op, crops_op);
GraphDef original_graph_def;
TF_ASSERT_OK(root.ToGraphDef(&original_graph_def));
std::unique_ptr<Session> original_session(NewSession(SessionOptions()));
TF_ASSERT_OK(original_session->Create(original_graph_def));
std::vector<Tensor> original_outputs;
TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs));
GraphDef modified_graph_def;
TF_ASSERT_OK(FlattenAtrousConv(original_graph_def, {{}, {"output"}},
&modified_graph_def));
std::unique_ptr<Session> modified_session(NewSession(SessionOptions()));
TF_ASSERT_OK(modified_session->Create(modified_graph_def));
std::vector<Tensor> modified_outputs;
TF_ASSERT_OK(modified_session->Run({}, {"output"}, {}, &modified_outputs));
EXPECT_EQ(3, modified_graph_def.node_size());
EXPECT_EQ("input_op", modified_graph_def.node(0).name());
EXPECT_EQ("weights_op", modified_graph_def.node(1).name());
EXPECT_EQ("output", modified_graph_def.node(2).name());
EXPECT_EQ("Const", modified_graph_def.node(0).op());
EXPECT_EQ("Const", modified_graph_def.node(1).op());
EXPECT_EQ(conv_op.node()->type_string(), modified_graph_def.node(2).op());
test::ExpectTensorNear<float>(original_outputs[0], modified_outputs[0],
1e-6);
}
};
TEST_F(FlattenAtrousConvTest, TestFlattenAtrousConv2D) {
TestFlattenAtrousConv<::tensorflow::ops::Conv2D>();
}
TEST_F(FlattenAtrousConvTest, TestFlattenAtrousDepthwiseConv2dNative) {
TestFlattenAtrousConv<::tensorflow::ops::DepthwiseConv2dNative>();
}
} // namespace graph_transforms
} // namespace tensorflow