+ ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
+ ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT;
+
+ // Parse arguments
+ if(argc < 2)
+ {
+ // Print help
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 2)
+ {
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 3)
+ {
+ data_path = argv[2];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No image provided: using random values\n\n";
+ }
+ else if(argc == 4)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
+ std::cout << "No text file with labels provided: skipping output accessor\n\n";
+ }
+ else if(argc == 5)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+ std::cout << "No fast math info provided: disabling fast math\n\n";
+ }
+ else
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+ }
+
+ graph << target_hint
+ << first_convolution3x3_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
+ get_input_accessor(image, std::move(preprocessor)))
+ // Layer 1
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
+ 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")
+ << convolution3x3_hint
+ << ConvolutionLayer(
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
+ 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, 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, 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"),
+ 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"),
+ 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"),
+ 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"),
+ 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, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+ // Layer 6
+ << FullyConnectedLayer(
+ 4096U,
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
+ 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"),
+ 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"),
+ get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
+ .set_name("fc8")
+ // Softmax
+ << SoftmaxLayer().set_name("prob")
+ << OutputLayer(get_output_accessor(label, 5));
+
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);