diff options
Diffstat (limited to 'examples/graph_vgg19.cpp')
-rw-r--r-- | examples/graph_vgg19.cpp | 295 |
1 files changed, 141 insertions, 154 deletions
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp index efc0bcce19..9293544655 100644 --- a/examples/graph_vgg19.cpp +++ b/examples/graph_vgg19.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/graph.h" + #include "support/ToolchainSupport.h" #include "utils/CommonGraphOptions.h" #include "utils/GraphUtils.h" @@ -34,8 +35,7 @@ using namespace arm_compute::graph_utils; class GraphVGG19Example : public Example { public: - GraphVGG19Example() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19") + GraphVGG19Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VGG19") { } bool do_setup(int argc, char **argv) override @@ -48,7 +48,7 @@ public: common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested - if(common_params.help) + if (common_params.help) { cmd_parser.print_help(argv[0]); return false; @@ -61,165 +61,152 @@ public: std::string data_path = common_params.data_path; // Create a preprocessor object - const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } }; + const std::array<float, 3> mean_rgb{{123.68f, 116.779f, 103.939f}}; std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb); // Create input descriptor const auto operation_layout = common_params.data_layout; - const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); + const TensorShape tensor_shape = + permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = + TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; - graph << common_params.target - << common_params.fast_math_hint - << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) - // Layer 1 - << ConvolutionLayer( - 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv1_1") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu") - << ConvolutionLayer( - 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv1_2") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") - // Layer 2 - << ConvolutionLayer( - 3U, 3U, 128U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2_1") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu") - << ConvolutionLayer( - 3U, 3U, 128U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2_2") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") - // Layer 3 - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv3_1") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv3_2") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv3_3") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu") - << ConvolutionLayer( - 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv3_4") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3") - // Layer 4 - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv4_1") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv4_2") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv4_3") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv4_4") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4") - // Layer 5 - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv5_1") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv5_2") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv5_3") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu") - << ConvolutionLayer( - 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - .set_name("conv5_4") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5") - // Layer 6 - << FullyConnectedLayer( - 4096U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy")) - .set_name("fc6") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu") - // Layer 7 - << FullyConnectedLayer( - 4096U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy")) - .set_name("fc7") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1") - // Layer 8 - << FullyConnectedLayer( - 1000U, - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy")) - .set_name("fc8") - // Softmax - << SoftmaxLayer().set_name("prob") - << OutputLayer(get_output_accessor(common_params, 5)); + graph + << common_params.target << common_params.fast_math_hint + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) + // Layer 1 + << ConvolutionLayer( + 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv1_1") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv1_1/Relu") + << ConvolutionLayer( + 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv1_2") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv1_2/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))) + .set_name("pool1") + // Layer 2 + << ConvolutionLayer( + 3U, 3U, 128U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2_1") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv2_1/Relu") + << ConvolutionLayer( + 3U, 3U, 128U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2_2") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv2_2/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))) + .set_name("pool2") + // Layer 3 + << ConvolutionLayer( + 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv3_1") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv3_1/Relu") + << ConvolutionLayer( + 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv3_2") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv3_2/Relu") + << ConvolutionLayer( + 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv3_3") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv3_3/Relu") + << ConvolutionLayer( + 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv3_4") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv3_4/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))) + .set_name("pool3") + // Layer 4 + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv4_1") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv4_1/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv4_2") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv4_2/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv4_3") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv4_3/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv4_4") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv4_4/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))) + .set_name("pool4") + // Layer 5 + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv5_1") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv5_1/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv5_2") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv5_2/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv5_3") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv5_3/Relu") + << ConvolutionLayer( + 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv5_4") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv5_4/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))) + .set_name("pool5") + // Layer 6 + << FullyConnectedLayer(4096U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy")) + .set_name("fc6") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu") + // Layer 7 + << FullyConnectedLayer(4096U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy")) + .set_name("fc7") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1") + // Layer 8 + << FullyConnectedLayer(1000U, + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy")) + .set_name("fc8") + // Softmax + << SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params, 5)); // Finalize graph GraphConfig config; |