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| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-07-20 13:23:44 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:54:54 +0000 |
| commit | e2220551b7a64b929650ba9a60529c31e70c13c5 (patch) | |
| tree | 5d609887f15b4392cdade7bb388710ceafc62260 /examples/graph_resnext50.cpp | |
| parent | eff8d95991205e874091576e2d225f63246dd0bb (diff) | |
| download | ComputeLibrary-e2220551b7a64b929650ba9a60529c31e70c13c5.tar.gz | |
COMPMID-1367: Enable NHWC in graph examples
Change-Id: Iabc54a3a1bdcd46a9a921cda39c7c85fef672b72
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141449
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'examples/graph_resnext50.cpp')
| -rw-r--r-- | examples/graph_resnext50.cpp | 32 |
1 files changed, 19 insertions, 13 deletions
diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp index c0e9b9f22a..e7ef013f17 100644 --- a/examples/graph_resnext50.cpp +++ b/examples/graph_resnext50.cpp @@ -60,7 +60,6 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; @@ -68,26 +67,32 @@ public: // Get trainable parameters data path std::string data_path = common_params.data_path; + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + + // Set weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params)) + << InputLayer(input_descriptor, get_input_accessor(common_params)) << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) .set_name("bn_data/Scale") << ConvolutionLayer( 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) .set_name("conv0/Convolution") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); - add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); - add_residual_block(data_path, 512, 2, 4, 2); - add_residual_block(data_path, 1024, 3, 6, 2); - add_residual_block(data_path, 2048, 4, 3, 2); + add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); + add_residual_block(data_path, weights_layout, 512, 2, 4, 2); + add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); + add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") << FlattenLayer().set_name("predictions/Reshape") @@ -116,7 +121,8 @@ private: CommonGraphParams common_params; Stream graph; - void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) + void add_residual_block(const std::string &data_path, DataLayout weights_layout, + unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) { for(unsigned int i = 0; i < num_units; ++i) { @@ -137,7 +143,7 @@ private: SubStream right(graph); right << ConvolutionLayer( 1U, 1U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv1/convolution") @@ -145,7 +151,7 @@ private: << ConvolutionLayer( 3U, 3U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), pad_grouped_conv, 32) .set_name(unit_name + "conv2/convolution") @@ -156,7 +162,7 @@ private: << ConvolutionLayer( 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv3/convolution"); @@ -166,7 +172,7 @@ private: { left << ConvolutionLayer( 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "sc_weights.npy"), + get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) .set_name(unit_name + "sc/convolution") |