diff options
Diffstat (limited to 'examples/graph_mobilenet.cpp')
| -rw-r--r-- | examples/graph_mobilenet.cpp | 19 |
1 files changed, 13 insertions, 6 deletions
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 6e2921a8a6..870e67daa5 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -132,13 +132,15 @@ public: get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) + .set_name("Conv2d_0") << BatchNormalizationLayer( get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); + .set_name("Conv2d_0/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); @@ -152,14 +154,15 @@ public: graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a") << ConvolutionLayer( 1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) - << ReshapeLayer(TensorShape(1001U)) - << SoftmaxLayer() + .set_name("Logits/Conv2d_1c_1x1") + << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") + << SoftmaxLayer().set_name("Softmax") << OutputLayer(get_output_accessor(label, 5)); // Finalize graph @@ -188,25 +191,29 @@ private: get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), dwc_pad_stride_info) + .set_name(total_path + "depthwise/depthwise") << BatchNormalizationLayer( get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name(total_path + "depthwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") << ConvolutionLayer( 1U, 1U, conv_filt, get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), conv_pad_stride_info) + .set_name(total_path + "pointwise/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), 0.001f) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)); + .set_name(total_path + "pointwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); return BranchLayer(std::move(sg)); } |