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-rw-r--r--examples/graph_vgg16.cpp58
1 files changed, 37 insertions, 21 deletions
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 9e8e69411f..9c2763f649 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -100,7 +100,8 @@ public:
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv1_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu")
<< convolution3x3_hint
// Layer 2
<< ConvolutionLayer(
@@ -108,108 +109,123 @@ public:
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("conv1_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
// Layer 3
<< ConvolutionLayer(
3U, 3U, 128U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv2_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu")
// Layer 4
<< ConvolutionLayer(
3U, 3U, 128U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("conv2_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
// Layer 5
<< ConvolutionLayer(
3U, 3U, 256U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu")
// Layer 6
<< ConvolutionLayer(
3U, 3U, 256U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv3_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu")
// Layer 7
<< ConvolutionLayer(
3U, 3U, 256U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("conv3_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
// Layer 8
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu")
// Layer 9
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv4_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu")
// Layer 10
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("conv4_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
// Layer 11
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu")
// Layer 12
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("conv5_2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu")
// Layer 13
<< ConvolutionLayer(
3U, 3U, 512U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("conv5_3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
// Layer 14
<< FullyConnectedLayer(
4096U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("fc6")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
// Layer 15
<< FullyConnectedLayer(
4096U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("fc7")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
// Layer 16
<< FullyConnectedLayer(
1000U,
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
+ .set_name("fc8")
// Softmax
- << SoftmaxLayer()
+ << SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(label, 5));
// Finalize graph
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 02:41:21 +0000