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-rw-r--r--examples/graph_squeezenet_v1_1.cpp205
1 files changed, 116 insertions, 89 deletions
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 9cc183fbbd..ed0f692db2 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/graph.h"
+
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
@@ -35,8 +36,7 @@ using namespace arm_compute::graph_utils;
class GraphSqueezenet_v1_1Example : public Example
{
public:
- GraphSqueezenet_v1_1Example()
- : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1.1")
+ GraphSqueezenet_v1_1Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1.1")
{
}
bool do_setup(int argc, char **argv) override
@@ -49,7 +49,7 @@ public:
common_params = consume_common_graph_parameters(common_opts);
// Return when help menu is requested
- if(common_params.help)
+ if (common_params.help)
{
cmd_parser.print_help(argv[0]);
return false;
@@ -62,104 +62,128 @@ public:
std::string data_path = common_params.data_path;
// Create a preprocessor object
- const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+ const std::array<float, 3> mean_rgb{{122.68f, 116.67f, 104.01f}};
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
// Create input descriptor
const auto operation_layout = common_params.data_layout;
- const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
- TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
+ const TensorShape tensor_shape =
+ permute_shape(TensorShape(227U, 227U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
+ TensorDescriptor input_descriptor =
+ TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
// Set weights trained layout
const DataLayout weights_layout = DataLayout::NCHW;
- graph << common_params.target
- << common_params.fast_math_hint
+ graph << common_params.target << common_params.fast_math_hint
<< InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
<< ConvolutionLayer(
- 3U, 3U, 64U,
- 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))
- .set_name("conv1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
+ 3U, 3U, 64U,
+ 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))
+ .set_name("conv1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("relu_conv1")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout,
+ 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))
- .set_name("fire2/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("fire3/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
+ 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))
+ .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, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3")
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout,
+ 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))
- .set_name("fire4/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("fire5/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
+ 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))
+ .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, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5")
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout,
+ 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))
- .set_name("fire6/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("fire7/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("fire8/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("fire9/squeeze1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");
+ 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))
+ .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))
- .set_name("conv10")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
+ 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))
+ .set_name("conv10")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name("relu_conv10")
<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")
- << FlattenLayer().set_name("flatten")
- << SoftmaxLayer().set_name("prob")
+ << FlattenLayer().set_name("flatten") << SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -188,27 +212,30 @@ private:
CommonGraphParams common_params;
Stream graph;
- ConcatLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
- unsigned int expand1_filt, unsigned int expand3_filt)
+ ConcatLayer get_expand_fire_node(const std::string &data_path,
+ std::string &&param_path,
+ DataLayout weights_layout,
+ unsigned int expand1_filt,
+ unsigned int expand3_filt)
{
std::string total_path = "/cnn_data/squeezenet_v1_1_model/" + param_path + "_";
SubStream i_a(graph);
- i_a << ConvolutionLayer(
- 1U, 1U, expand1_filt,
- 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))
- .set_name(param_path + "/expand1x1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");
+ i_a << ConvolutionLayer(1U, 1U, expand1_filt,
+ 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))
+ .set_name(param_path + "/expand1x1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(param_path + "/relu_expand1x1");
SubStream i_b(graph);
- i_b << ConvolutionLayer(
- 3U, 3U, expand3_filt,
- 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))
- .set_name(param_path + "/expand3x3")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");
+ i_b << ConvolutionLayer(3U, 3U, expand3_filt,
+ 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))
+ .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));
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 21:52:14 +0000