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authorGeorgios Pinitas <georgios.pinitas@arm.com>2019-01-31 12:53:10 +0000
committerGeorgios Pinitas <georgios.pinitas@arm.com>2019-02-04 11:58:06 +0000
commit62c3639b086d768661edc04b9b7e01a54edf486b (patch)
tree08d50663a66ff6ab9812e98b8756c35d68704275
parent1509e4bfcfd4b613e2f1ad584c51b80b5fb05a8c (diff)
downloadComputeLibrary-62c3639b086d768661edc04b9b7e01a54edf486b.tar.gz
COMPMID-1913: Add names to all graph examples
Change-Id: I90e7bb61a31403fc002cb451752d8260dad0d35e Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com> Reviewed-on: https://review.mlplatform.org/620 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Diffstat
-rw-r--r--examples/graph_googlenet.cpp66
-rw-r--r--examples/graph_inception_v4.cpp257
-rw-r--r--examples/graph_squeezenet.cpp66
-rw-r--r--examples/graph_squeezenet_v1_1.cpp66
-rw-r--r--examples/graph_yolov3.cpp30
5 files changed, 309 insertions, 176 deletions
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 0e9ac20996..583ca2cade 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -82,40 +82,44 @@ public:
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
PadStrideInfo(2, 2, 3, 3))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
- << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+ .set_name("conv1/7x7_s2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/relu_7x7")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2")
+ << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("pool1/norm1")
<< ConvolutionLayer(
1U, 1U, 64U,
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv2/3x3_reduce")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3_reduce")
<< ConvolutionLayer(
3U, 3U, 192U,
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
- graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U);
- graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
- graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U);
- graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U);
- graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U);
- graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U);
- graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
- graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
- graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("conv2/3x3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3")
+ << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("conv2/norm2")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2");
+ graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U).set_name("inception_3a/concat");
+ graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U).set_name("inception_3b/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2");
+ graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U).set_name("inception_4a/concat");
+ graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U).set_name("inception_4b/concat");
+ graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U).set_name("inception_4c/concat");
+ graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U).set_name("inception_4d/concat");
+ graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_4e/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2");
+ graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_5a/concat");
+ graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U).set_name("inception_5b/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1")
<< FullyConnectedLayer(
1000U,
get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
- << SoftmaxLayer()
+ .set_name("loss3/classifier")
+ << SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -153,7 +157,8 @@ private:
get_weights_accessor(data_path, total_path + "1x1_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_1x1");
SubStream i_b(graph);
i_b << ConvolutionLayer(
@@ -161,13 +166,15 @@ private:
get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/3x3_reduce")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3_reduce")
<< ConvolutionLayer(
3U, 3U, std::get<1>(b_filters),
get_weights_accessor(data_path, total_path + "3x3_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "3x3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/3x3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3");
SubStream i_c(graph);
i_c << ConvolutionLayer(
@@ -175,22 +182,25 @@ private:
get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/5x5_reduce")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5_reduce")
<< ConvolutionLayer(
5U, 5U, std::get<1>(c_filters),
get_weights_accessor(data_path, total_path + "5x5_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "5x5_b.npy"),
PadStrideInfo(1, 1, 2, 2))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/5x5")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5");
SubStream i_d(graph);
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool")
<< ConvolutionLayer(
1U, 1U, d_filt,
get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/pool_proj")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_pool_proj");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 3de61dac7d..0b0360acfb 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -80,64 +80,71 @@ public:
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
// Conv2d_2a_3x3
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_2a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Conv2d_2a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
// Conv2d_2b_3x3
<< ConvolutionLayer(3U, 3U, 64U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("Conv2d_2b_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Conv2d_2b_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu");
- graph << get_mixed_3a(data_path, weights_layout);
- graph << get_mixed_4a(data_path, weights_layout);
- graph << get_mixed_5a(data_path, weights_layout);
+ graph << get_mixed_3a(data_path, weights_layout).set_name("Mixed_3a/concat");
+ graph << get_mixed_4a(data_path, weights_layout).set_name("Mixed_4a/concat");
+ graph << get_mixed_5a(data_path, weights_layout).set_name("Mixed_5a/concat");
// 4 inception A blocks
- graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b");
- graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c");
- graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d");
- graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e");
+ graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b").set_name("Mixed_5b/concat");
+ graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c").set_name("Mixed_5c/concat");
+ graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d").set_name("Mixed_5d/concat");
+ graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e").set_name("Mixed_5e/concat");
// reduction A block
- graph << get_reductionA_block(data_path, weights_layout);
+ graph << get_reductionA_block(data_path, weights_layout).set_name("Mixed_6a/concat");
// 7 inception B blocks
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g");
- graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b").set_name("Mixed_6b/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c").set_name("Mixed_6c/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d").set_name("Mixed_6d/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e").set_name("Mixed_6e/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f").set_name("Mixed_6f/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g").set_name("Mixed_6g/concat");
+ graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h").set_name("Mixed_6h/concat");
// reduction B block
- graph << get_reductionB_block(data_path, weights_layout);
+ graph << get_reductionB_block(data_path, weights_layout).set_name("Mixed_7a/concat");
// 3 inception C blocks
- graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b");
- graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c");
- graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d");
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
- << FlattenLayer()
+ graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b").set_name("Mixed_7b/concat");
+ graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c").set_name("Mixed_7c/concat");
+ graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d").set_name("Mixed_7d/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a/AvgPool")
+ << FlattenLayer().set_name("Logits/Flatten")
<< FullyConnectedLayer(
1001U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy"))
- << SoftmaxLayer()
+ .set_name("Logits/MatMul")
+ << SoftmaxLayer().set_name("Logits/Predictions")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -168,18 +175,20 @@ private:
std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_";
SubStream i_a(graph);
- i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
+ i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool");
SubStream i_b(graph);
i_b << ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Relu");
return ConcatLayer(std::move(i_a), std::move(i_b));
}
@@ -192,59 +201,71 @@ private:
i_a << ConvolutionLayer(1U, 1U, 64U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(1U, 1U, 64U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(7U, 1U, 64U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(1U, 7U, 64U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Relu")
<< ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Relu");
return ConcatLayer(std::move(i_a), std::move(i_b));
}
@@ -257,15 +278,17 @@ private:
i_a << ConvolutionLayer(3U, 3U, 192U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Relu");
SubStream i_b(graph);
- i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
+ i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool");
return ConcatLayer(std::move(i_a), std::move(i_b));
}
@@ -278,73 +301,87 @@ private:
i_a << ConvolutionLayer(1U, 1U, 96U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(1U, 1U, 64U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu");
SubStream i_c(graph);
i_c << ConvolutionLayer(1U, 1U, 64U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 96U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
SubStream i_d(graph);
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(1U, 1U, 96U,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
@@ -357,44 +394,52 @@ private:
i_a << ConvolutionLayer(3U, 3U, 384U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(1U, 1U, 192U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 224U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 256U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu");
SubStream i_c(graph);
- i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
}
@@ -407,100 +452,120 @@ private:
i_a << ConvolutionLayer(1U, 1U, 384U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(1U, 1U, 192U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(7U, 1U, 224U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(1U, 7U, 256U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu");
SubStream i_c(graph);
i_c << ConvolutionLayer(1U, 1U, 192U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(1U, 7U, 192U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
<< ConvolutionLayer(7U, 1U, 224U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
<< ConvolutionLayer(1U, 7U, 224U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
<< ConvolutionLayer(7U, 1U, 256U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
SubStream i_d(graph);
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(1U, 1U, 128U,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
@@ -513,62 +578,74 @@ private:
i_a << ConvolutionLayer(1U, 1U, 192U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 192U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(1U, 1U, 256U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(7U, 1U, 256U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(1U, 7U, 320U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/Relu")
<< ConvolutionLayer(3U, 3U, 320U,
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu");
SubStream i_c(graph);
- i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
}
@@ -581,12 +658,14 @@ private:
i_a << ConvolutionLayer(1U, 1U, 256U,
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
SubStream i_b(graph);
i_b << ConvolutionLayer(
@@ -594,13 +673,15 @@ private:
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
SubStream i_b1(i_b);
i_b1 << ConvolutionLayer(
@@ -608,13 +689,15 @@ private:
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
SubStream i_b2(i_b);
i_b2 << ConvolutionLayer(
@@ -622,16 +705,18 @@ private:
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_3x1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_1/Conv2d_0c_3x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_3x1/Relu");
// Merge b1 and b2
- i_b << ConcatLayer(std::move(i_b1), std::move(i_b2));
+ i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat");
SubStream i_c(graph);
i_c << ConvolutionLayer(
@@ -639,37 +724,43 @@ private:
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
1U, 3U, 448U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x1/Relu")
<< ConvolutionLayer(
3U, 1U, 512U,
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
SubStream i_c1(i_c);
i_c1 << ConvolutionLayer(
@@ -677,13 +768,15 @@ private:
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_1x3/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_2/Conv2d_0d_1x3/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_1x3/Relu");
SubStream i_c2(i_c);
i_c2 << ConvolutionLayer(
@@ -691,28 +784,32 @@ private:
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0e_3x1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_2/Conv2d_0e_3x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_3x1/Relu");
// Merge i_c1 and i_c2
- i_c << ConcatLayer(std::move(i_c1), std::move(i_c2));
+ i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
SubStream i_d(graph);
- i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
<< ConvolutionLayer(1U, 1U, 256U,
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
get_random_accessor(1.f, 1.f),
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
0.001f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 4bc516a363..f78fe5d506 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -82,75 +82,85 @@ public:
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
PadStrideInfo(2, 2, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("conv1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
<< ConvolutionLayer(
1U, 1U, 16U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U);
+ .set_name("fire2/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U).set_name("fire2/concat");
graph << ConvolutionLayer(
1U, 1U, 16U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U);
+ .set_name("fire3/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");
graph << ConvolutionLayer(
1U, 1U, 32U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("fire4/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")
<< ConvolutionLayer(
1U, 1U, 32U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U);
+ .set_name("fire5/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");
graph << ConvolutionLayer(
1U, 1U, 48U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U);
+ .set_name("fire6/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U).set_name("fire6/concat");
graph << ConvolutionLayer(
1U, 1U, 48U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U);
+ .set_name("fire7/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U).set_name("fire7/concat");
graph << ConvolutionLayer(
1U, 1U, 64U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("fire8/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")
<< ConvolutionLayer(
1U, 1U, 64U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U);
+ .set_name("fire9/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U).set_name("fire9/concat");
graph << ConvolutionLayer(
1U, 1U, 1000U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
- << FlattenLayer()
- << SoftmaxLayer()
+ .set_name("conv10")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10")
+ << FlattenLayer().set_name("flatten")
+ << SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -185,7 +195,8 @@ private:
get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/expand1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");
SubStream i_b(graph);
i_b << ConvolutionLayer(
@@ -193,7 +204,8 @@ private:
get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/expand3x3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");
return ConcatLayer(std::move(i_a), std::move(i_b));
}
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 745d089fdb..22a15df450 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -82,75 +82,85 @@ public:
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_b.npy"),
PadStrideInfo(2, 2, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("conv1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
<< ConvolutionLayer(
1U, 1U, 16U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U);
+ .set_name("fire2/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U).set_name("fire2/concat");
graph << ConvolutionLayer(
1U, 1U, 16U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("fire3/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3")
<< ConvolutionLayer(
1U, 1U, 32U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U);
+ .set_name("fire4/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");
graph << ConvolutionLayer(
1U, 1U, 32U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+ .set_name("fire5/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5")
<< ConvolutionLayer(
1U, 1U, 48U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U);
+ .set_name("fire6/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U).set_name("fire6/concat");
graph << ConvolutionLayer(
1U, 1U, 48U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U);
+ .set_name("fire7/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U).set_name("fire7/concat");
graph << ConvolutionLayer(
1U, 1U, 64U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U);
+ .set_name("fire8/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");
graph << ConvolutionLayer(
1U, 1U, 64U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
- graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U);
+ .set_name("fire9/squeeze1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");
+ graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U).set_name("fire9/concat");
graph << ConvolutionLayer(
1U, 1U, 1000U,
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
- << FlattenLayer()
- << SoftmaxLayer()
+ .set_name("conv10")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10")
+ << FlattenLayer().set_name("flatten")
+ << SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -185,7 +195,8 @@ private:
get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/expand1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");
SubStream i_b(graph);
i_b << ConvolutionLayer(
@@ -193,7 +204,8 @@ private:
get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout),
get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ .set_name(param_path + "/expand3x3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");
return ConcatLayer(std::move(i_a), std::move(i_b));
}
diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp
index 11d564c778..6d0f67e1f5 100644
--- a/examples/graph_yolov3.cpp
+++ b/examples/graph_yolov3.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -170,7 +170,7 @@ public:
PadStrideInfo(1, 1, 0, 0))
.set_name("conv2d_59")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_59/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo1")
<< OutputLayer(get_output_accessor(common_params, 5));
route_1 << ConvolutionLayer(
1U, 1U, 256U,
@@ -188,7 +188,7 @@ public:
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu")
<< UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_60");
SubStream concat_1(route_1);
- concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second))
+ concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1")
<< ConvolutionLayer(
1U, 1U, 256U,
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout),
@@ -281,7 +281,7 @@ public:
PadStrideInfo(1, 1, 0, 0))
.set_name("conv2d_67")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_67/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo2")
<< OutputLayer(get_output_accessor(common_params, 5));
route_2 << ConvolutionLayer(
1U, 1U, 128U,
@@ -299,7 +299,7 @@ public:
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu")
<< UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_68");
SubStream concat_2(route_2);
- concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first))
+ concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2")
<< ConvolutionLayer(
1U, 1U, 128U,
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout),
@@ -391,7 +391,7 @@ public:
PadStrideInfo(1, 1, 0, 0))
.set_name("conv2d_75")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_75/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80)
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo3")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -423,7 +423,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_1")
+ .set_name("conv2d_1/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
@@ -437,7 +437,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_2")
+ .set_name("conv2d_2/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
@@ -452,7 +452,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_5")
+ .set_name("conv2d_5/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
@@ -468,7 +468,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_10")
+ .set_name("conv2d_10/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
@@ -491,7 +491,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_27")
+ .set_name("conv2d_27/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
@@ -514,7 +514,7 @@ private:
get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_44")
+ .set_name("conv2d_44/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
@@ -543,6 +543,7 @@ private:
get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_" + param_path + "/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
@@ -550,12 +551,13 @@ private:
get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
0.000001f)
.set_name("conv2d_" + param_path + "/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path + "/LeakyRelu")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path + "/LeakyRelu")
<< ConvolutionLayer(
3U, 3U, filter_size * 2,
get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_" + param_path2 + "/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
@@ -565,7 +567,7 @@ private:
.set_name("conv2d_" + param_path2 + "/BatchNorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path2 + "/LeakyRelu");
- graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add);
+ graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2);
}
};
generated by cgit v1.2.1 (git 2.23.0) at 2024-04-26 13:29:52 +0000