experiments/STT-tensorflow
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卜居 2018-12-06 22:04:17 +08:00
parent 206ebf0a5f
commit c425f34ad4
4 changed files with 33 additions and 35 deletions
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12
tensorflow/tools/graph_transforms/fold_batch_norms.cc
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@ -76,11 +76,10 @@ Status FoldBatchNorms(const GraphDef& input_graph_def,
int64 weights_cols; int64 weights_cols;
if (conv_node.op() == "Conv2D") { if (conv_node.op() == "Conv2D") {
weights_cols = weights.shape().dim_size(3); weights_cols = weights.shape().dim_size(3);
} } else if (conv_node.op() == "DepthwiseConv2dNative") {
else if (conv_node.op() == "DepthwiseConv2dNative") { weights_cols =
weights_cols = weights.shape().dim_size(2) * weights.shape().dim_size(3); weights.shape().dim_size(2) * weights.shape().dim_size(3);
} } else {
else {
weights_cols = weights.shape().dim_size(1); weights_cols = weights.shape().dim_size(1);
} }
if ((mul_values.shape().dims() != 1) || if ((mul_values.shape().dims() != 1) ||
@ -96,7 +95,8 @@ Status FoldBatchNorms(const GraphDef& input_graph_def,
auto scaled_weights_vector = scaled_weights.flat<float>(); auto scaled_weights_vector = scaled_weights.flat<float>();
for (int64 row = 0; row < weights_vector.dimension(0); ++row) { for (int64 row = 0; row < weights_vector.dimension(0); ++row) {
scaled_weights_vector(row) = scaled_weights_vector(row) =
weights_vector(row) * mul_values.flat<float>()(row % weights_cols); weights_vector(row) *
mul_values.flat<float>()(row % weights_cols);
} }
// Construct the new nodes. // Construct the new nodes.

4
tensorflow/tools/graph_transforms/fold_batch_norms_test.cc
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@ -104,8 +104,8 @@ class FoldBatchNormsTest : public ::testing::Test {
Output weights_op = Output weights_op =
Const(root.WithOpName("weights_op"), Input::Initializer(weights_data)); Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op, weights_op, Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
{1, 1, 1, 1}, "VALID"); weights_op, {1, 1, 1, 1}, "VALID");
Tensor mul_values_data(DT_FLOAT, TensorShape({4})); Tensor mul_values_data(DT_FLOAT, TensorShape({4}));
test::FillValues<float>(&mul_values_data, {2.0f, 3.0f, 4.0f, 5.0f}); test::FillValues<float>(&mul_values_data, {2.0f, 3.0f, 4.0f, 5.0f});

21
tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
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@ -32,9 +32,9 @@ Status ErrorIfNotVector(const Tensor& input, const string& input_name,
int expected_width) { int expected_width) {
if ((input.shape().dims() != 1) || if ((input.shape().dims() != 1) ||
(input.shape().dim_size(0) != expected_width)) { (input.shape().dim_size(0) != expected_width)) {
return errors::InvalidArgument( return errors::InvalidArgument(input_name,
input_name, " input to batch norm has bad shape: ",
" input to batch norm has bad shape: ", input.shape().DebugString()); input.shape().DebugString());
} }
return Status::OK(); return Status::OK();
} }
@ -119,11 +119,9 @@ Status FuseScaleOffsetToConvWeights(const std::vector<float>& scale_values,
int64 weights_cols; int64 weights_cols;
if (conv_node.op() == "Conv2D") { if (conv_node.op() == "Conv2D") {
weights_cols = weights.shape().dim_size(3); weights_cols = weights.shape().dim_size(3);
} } else if (conv_node.op() == "DepthwiseConv2dNative") {
else if (conv_node.op() == "DepthwiseConv2dNative") {
weights_cols = weights.shape().dim_size(2) * weights.shape().dim_size(3); weights_cols = weights.shape().dim_size(2) * weights.shape().dim_size(3);
} } else {
else {
weights_cols = weights.shape().dim_size(1); weights_cols = weights.shape().dim_size(1);
} }
CHECK_EQ(weights_cols, scale_values.size()); CHECK_EQ(weights_cols, scale_values.size());
@ -134,7 +132,7 @@ Status FuseScaleOffsetToConvWeights(const std::vector<float>& scale_values,
auto scaled_weights_vector = scaled_weights.flat<float>(); auto scaled_weights_vector = scaled_weights.flat<float>();
for (int64 row = 0; row < weights_vector.dimension(0); ++row) { for (int64 row = 0; row < weights_vector.dimension(0); ++row) {
scaled_weights_vector(row) = scaled_weights_vector(row) =
weights_vector(row) * scale_values[row % weights_cols]; weights_vector(row) * scale_values[row % weights_cols];
} }
// Figure out the remaining bias to add on. // Figure out the remaining bias to add on.
Tensor bias_offset(DT_FLOAT, {weights_cols}); Tensor bias_offset(DT_FLOAT, {weights_cols});
@ -193,7 +191,7 @@ Status FuseBatchNormWithConv(const NodeMatch& match,
} }
Status FuseBatchNormWithBatchToSpace(const NodeMatch& match, Status FuseBatchNormWithBatchToSpace(const NodeMatch& match,
std::vector<NodeDef>* new_nodes) { std::vector<NodeDef>* new_nodes) {
// Calculate the scale and offset values to apply. // Calculate the scale and offset values to apply.
std::vector<float> scale_values; std::vector<float> scale_values;
std::vector<float> offset_values; std::vector<float> offset_values;
@ -208,9 +206,8 @@ Status FuseBatchNormWithBatchToSpace(const NodeMatch& match,
const NodeDef& conv_node = conv_node_match.node; const NodeDef& conv_node = conv_node_match.node;
string biasadd_name = conv_node.name() + "/biasadd"; string biasadd_name = conv_node.name() + "/biasadd";
TF_RETURN_IF_ERROR( TF_RETURN_IF_ERROR(FuseScaleOffsetToConvWeights(
FuseScaleOffsetToConvWeights(scale_values, offset_values, conv_node_match, scale_values, offset_values, conv_node_match, biasadd_name, new_nodes));
biasadd_name , new_nodes));
NodeDef new_batch_to_space_node = batch_to_space_node; NodeDef new_batch_to_space_node = batch_to_space_node;
// reuse batch_norm node name // reuse batch_norm node name

25
tensorflow/tools/graph_transforms/fold_old_batch_norms_test.cc
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@ -138,8 +138,8 @@ class FoldOldBatchNormsTest : public ::testing::Test {
Output weights_op = Output weights_op =
Const(root.WithOpName("weights_op"), Input::Initializer(weights_data)); Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
input_op, weights_op, {1, 1, 1, 1}, "VALID"); weights_op, {1, 1, 1, 1}, "VALID");
Tensor mean_data(DT_FLOAT, TensorShape({4})); Tensor mean_data(DT_FLOAT, TensorShape({4}));
test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f}); test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f});
@ -164,7 +164,6 @@ class FoldOldBatchNormsTest : public ::testing::Test {
GraphDef original_graph_def; GraphDef original_graph_def;
TF_ASSERT_OK(root.ToGraphDef(&original_graph_def)); TF_ASSERT_OK(root.ToGraphDef(&original_graph_def));
NodeDef batch_norm_node; NodeDef batch_norm_node;
batch_norm_node.set_op("BatchNormWithGlobalNormalization"); batch_norm_node.set_op("BatchNormWithGlobalNormalization");
batch_norm_node.set_name("output"); batch_norm_node.set_name("output");
@ -294,8 +293,8 @@ class FoldOldBatchNormsTest : public ::testing::Test {
Output weights_op = Output weights_op =
Const(root.WithOpName("weights_op"), Input::Initializer(weights_data)); Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
input_op, weights_op, {1, 1, 1, 1}, "VALID"); weights_op, {1, 1, 1, 1}, "VALID");
Tensor mean_data(DT_FLOAT, TensorShape({4})); Tensor mean_data(DT_FLOAT, TensorShape({4}));
test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f}); test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f});
@ -477,16 +476,17 @@ void TestFoldFusedBatchNormsWithBatchToSpace() {
Tensor block_shape_data(DT_INT32, TensorShape({2})); Tensor block_shape_data(DT_INT32, TensorShape({2}));
test::FillValues<int32>(&block_shape_data, {1, 2}); test::FillValues<int32>(&block_shape_data, {1, 2});
Output block_shape_op = Output block_shape_op = Const(root.WithOpName("block_shape_op"),
Const(root.WithOpName("block_shape_op"), Input::Initializer(block_shape_data)); Input::Initializer(block_shape_data));
Tensor crops_data(DT_INT32, TensorShape({2, 2})); Tensor crops_data(DT_INT32, TensorShape({2, 2}));
test::FillValues<int32>(&crops_data, {0, 0, 0, 1}); test::FillValues<int32>(&crops_data, {0, 0, 0, 1});
Output crops_op = Output crops_op =
Const(root.WithOpName("crops_op"), Input::Initializer(crops_data)); Const(root.WithOpName("crops_op"), Input::Initializer(crops_data));
Output batch_to_space_op = BatchToSpaceND(root.WithOpName("batch_to_space_op"), Output batch_to_space_op =
conv_op, block_shape_op, crops_data); BatchToSpaceND(root.WithOpName("batch_to_space_op"), conv_op,
block_shape_op, crops_data);
Tensor mean_data(DT_FLOAT, TensorShape({2})); Tensor mean_data(DT_FLOAT, TensorShape({2}));
test::FillValues<float>(&mean_data, {10.0f, 20.0f}); test::FillValues<float>(&mean_data, {10.0f, 20.0f});
@ -495,8 +495,8 @@ void TestFoldFusedBatchNormsWithBatchToSpace() {
Tensor variance_data(DT_FLOAT, TensorShape({2})); Tensor variance_data(DT_FLOAT, TensorShape({2}));
test::FillValues<float>(&variance_data, {0.25f, 0.5f}); test::FillValues<float>(&variance_data, {0.25f, 0.5f});
Output variance_op = Const(root.WithOpName("variance_op"), Output variance_op =
Input::Initializer(variance_data)); Const(root.WithOpName("variance_op"), Input::Initializer(variance_data));
Tensor beta_data(DT_FLOAT, TensorShape({2})); Tensor beta_data(DT_FLOAT, TensorShape({2}));
test::FillValues<float>(&beta_data, {0.1f, 0.6f}); test::FillValues<float>(&beta_data, {0.1f, 0.6f});
@ -570,7 +570,8 @@ TEST_F(FoldOldBatchNormsTest, TestFoldOldBatchNormsAfterDepthwiseConv2dNative) {
TestFoldOldBatchNormsAfterDepthwiseConv2dNative(); TestFoldOldBatchNormsAfterDepthwiseConv2dNative();
} }
TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsAfterDepthwiseConv2dNative) { TEST_F(FoldOldBatchNormsTest,
TestFoldFusedBatchNormsAfterDepthwiseConv2dNative) {
TestFoldFusedBatchNormsAfterDepthwiseConv2dNative(); TestFoldFusedBatchNormsAfterDepthwiseConv2dNative();
} }