| 40 | { |
| 41 | } |
| 42 | bool do_setup(int argc, char **argv) override |
| 43 | { |
| 44 | // Parse arguments |
| 45 | cmd_parser.parse(argc, argv); |
| 46 | cmd_parser.validate(); |
| 47 | |
| 48 | // Consume common parameters |
| 49 | common_params = consume_common_graph_parameters(common_opts); |
| 50 | |
| 51 | // Return when help menu is requested |
| 52 | if (common_params.help) |
| 53 | { |
| 54 | cmd_parser.print_help(argv[0]); |
| 55 | return false; |
| 56 | } |
| 57 | |
| 58 | // Checks |
| 59 | ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), |
| 60 | "QASYMM8 not supported for this graph"); |
| 61 | |
| 62 | // Print parameter values |
| 63 | std::cout << common_params << std::endl; |
| 64 | |
| 65 | // Get trainable parameters data path |
| 66 | std::string data_path = common_params.data_path; |
| 67 | |
| 68 | // Create input descriptor |
| 69 | const auto operation_layout = common_params.data_layout; |
| 70 | const TensorShape tensor_shape = |
| 71 | permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); |
| 72 | TensorDescriptor input_descriptor = |
| 73 | TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); |
| 74 | |
| 75 | // Set weights trained layout |
| 76 | const DataLayout weights_layout = DataLayout::NCHW; |
| 77 | |
| 78 | graph << common_params.target << common_params.fast_math_hint |
| 79 | << InputLayer(input_descriptor, get_input_accessor(common_params)) |
| 80 | << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), |
| 81 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) |
| 82 | .set_name("bn_data/Scale") |
| 83 | << ConvolutionLayer( |
| 84 | 7U, 7U, 64U, |
| 85 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), |
| 86 | get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), |
| 87 | PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) |
| 88 | .set_name("conv0/Convolution") |
| 89 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| 90 | .set_name("conv0/Relu") |
| 91 | << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, |
| 92 | PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))) |
| 93 | .set_name("pool0"); |
| 94 | |
| 95 | add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, |
| 96 | /*stride_conv_unit1*/ 1); |
| 97 | add_residual_block(data_path, weights_layout, 512, 2, 4, 2); |
| 98 | add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); |
| 99 | add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); |
nothing calls this directly
no test coverage detected