clang-format-3.6 regenerated files in this PR
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@ -76,11 +76,10 @@ Status FoldBatchNorms(const GraphDef& input_graph_def,
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int64 weights_cols;
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if (conv_node.op() == "Conv2D") {
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weights_cols = weights.shape().dim_size(3);
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}
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else if (conv_node.op() == "DepthwiseConv2dNative") {
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weights_cols = weights.shape().dim_size(2) * weights.shape().dim_size(3);
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}
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else {
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} else if (conv_node.op() == "DepthwiseConv2dNative") {
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weights_cols =
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weights.shape().dim_size(2) * weights.shape().dim_size(3);
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} else {
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weights_cols = weights.shape().dim_size(1);
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}
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if ((mul_values.shape().dims() != 1) ||
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@ -96,7 +95,8 @@ Status FoldBatchNorms(const GraphDef& input_graph_def,
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auto scaled_weights_vector = scaled_weights.flat<float>();
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for (int64 row = 0; row < weights_vector.dimension(0); ++row) {
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scaled_weights_vector(row) =
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weights_vector(row) * mul_values.flat<float>()(row % weights_cols);
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weights_vector(row) *
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mul_values.flat<float>()(row % weights_cols);
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}
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// Construct the new nodes.
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@ -104,8 +104,8 @@ class FoldBatchNormsTest : public ::testing::Test {
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Output weights_op =
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Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op, weights_op,
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{1, 1, 1, 1}, "VALID");
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
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weights_op, {1, 1, 1, 1}, "VALID");
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Tensor mul_values_data(DT_FLOAT, TensorShape({4}));
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test::FillValues<float>(&mul_values_data, {2.0f, 3.0f, 4.0f, 5.0f});
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@ -32,9 +32,9 @@ Status ErrorIfNotVector(const Tensor& input, const string& input_name,
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int expected_width) {
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if ((input.shape().dims() != 1) ||
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(input.shape().dim_size(0) != expected_width)) {
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return errors::InvalidArgument(
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input_name,
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" input to batch norm has bad shape: ", input.shape().DebugString());
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return errors::InvalidArgument(input_name,
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" input to batch norm has bad shape: ",
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input.shape().DebugString());
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}
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return Status::OK();
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}
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@ -119,11 +119,9 @@ Status FuseScaleOffsetToConvWeights(const std::vector<float>& scale_values,
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int64 weights_cols;
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if (conv_node.op() == "Conv2D") {
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weights_cols = weights.shape().dim_size(3);
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}
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else if (conv_node.op() == "DepthwiseConv2dNative") {
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} else if (conv_node.op() == "DepthwiseConv2dNative") {
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weights_cols = weights.shape().dim_size(2) * weights.shape().dim_size(3);
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}
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else {
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} else {
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weights_cols = weights.shape().dim_size(1);
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}
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CHECK_EQ(weights_cols, scale_values.size());
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@ -208,9 +206,8 @@ Status FuseBatchNormWithBatchToSpace(const NodeMatch& match,
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const NodeDef& conv_node = conv_node_match.node;
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string biasadd_name = conv_node.name() + "/biasadd";
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TF_RETURN_IF_ERROR(
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FuseScaleOffsetToConvWeights(scale_values, offset_values, conv_node_match,
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biasadd_name , new_nodes));
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TF_RETURN_IF_ERROR(FuseScaleOffsetToConvWeights(
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scale_values, offset_values, conv_node_match, biasadd_name, new_nodes));
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NodeDef new_batch_to_space_node = batch_to_space_node;
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// reuse batch_norm node name
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@ -138,8 +138,8 @@ class FoldOldBatchNormsTest : public ::testing::Test {
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Output weights_op =
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Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"),
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input_op, weights_op, {1, 1, 1, 1}, "VALID");
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
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weights_op, {1, 1, 1, 1}, "VALID");
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Tensor mean_data(DT_FLOAT, TensorShape({4}));
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test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f});
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@ -164,7 +164,6 @@ class FoldOldBatchNormsTest : public ::testing::Test {
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GraphDef original_graph_def;
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TF_ASSERT_OK(root.ToGraphDef(&original_graph_def));
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NodeDef batch_norm_node;
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batch_norm_node.set_op("BatchNormWithGlobalNormalization");
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batch_norm_node.set_name("output");
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@ -294,8 +293,8 @@ class FoldOldBatchNormsTest : public ::testing::Test {
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Output weights_op =
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Const(root.WithOpName("weights_op"), Input::Initializer(weights_data));
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"),
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input_op, weights_op, {1, 1, 1, 1}, "VALID");
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Output conv_op = DepthwiseConv2dNative(root.WithOpName("conv_op"), input_op,
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weights_op, {1, 1, 1, 1}, "VALID");
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Tensor mean_data(DT_FLOAT, TensorShape({4}));
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test::FillValues<float>(&mean_data, {10.0f, 20.0f, 30.0f, 40.0f});
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@ -477,16 +476,17 @@ void TestFoldFusedBatchNormsWithBatchToSpace() {
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Tensor block_shape_data(DT_INT32, TensorShape({2}));
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test::FillValues<int32>(&block_shape_data, {1, 2});
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Output block_shape_op =
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Const(root.WithOpName("block_shape_op"), Input::Initializer(block_shape_data));
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Output block_shape_op = Const(root.WithOpName("block_shape_op"),
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Input::Initializer(block_shape_data));
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Tensor crops_data(DT_INT32, TensorShape({2, 2}));
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test::FillValues<int32>(&crops_data, {0, 0, 0, 1});
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Output crops_op =
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Const(root.WithOpName("crops_op"), Input::Initializer(crops_data));
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Output batch_to_space_op = BatchToSpaceND(root.WithOpName("batch_to_space_op"),
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conv_op, block_shape_op, crops_data);
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Output batch_to_space_op =
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BatchToSpaceND(root.WithOpName("batch_to_space_op"), conv_op,
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block_shape_op, crops_data);
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Tensor mean_data(DT_FLOAT, TensorShape({2}));
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test::FillValues<float>(&mean_data, {10.0f, 20.0f});
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@ -495,8 +495,8 @@ void TestFoldFusedBatchNormsWithBatchToSpace() {
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Tensor variance_data(DT_FLOAT, TensorShape({2}));
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test::FillValues<float>(&variance_data, {0.25f, 0.5f});
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Output variance_op = Const(root.WithOpName("variance_op"),
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Input::Initializer(variance_data));
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Output variance_op =
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Const(root.WithOpName("variance_op"), Input::Initializer(variance_data));
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Tensor beta_data(DT_FLOAT, TensorShape({2}));
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test::FillValues<float>(&beta_data, {0.1f, 0.6f});
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@ -570,7 +570,8 @@ TEST_F(FoldOldBatchNormsTest, TestFoldOldBatchNormsAfterDepthwiseConv2dNative) {
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TestFoldOldBatchNormsAfterDepthwiseConv2dNative();
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}
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TEST_F(FoldOldBatchNormsTest, TestFoldFusedBatchNormsAfterDepthwiseConv2dNative) {
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TEST_F(FoldOldBatchNormsTest,
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TestFoldFusedBatchNormsAfterDepthwiseConv2dNative) {
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TestFoldFusedBatchNormsAfterDepthwiseConv2dNative();
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}
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