arm_compute v18.08
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index d1d6ab4..168a506 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -23,12 +23,10 @@
*/
#include "arm_compute/graph.h"
#include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
-#include <cstdlib>
-#include <tuple>
-
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
@@ -36,72 +34,61 @@
/** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
*/
class InceptionV3Example : public Example
{
public:
- void do_setup(int argc, char **argv) override
+ InceptionV3Example()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV3")
{
- std::string data_path; /* Path to the trainable data */
- std::string image; /* Image data */
- std::string label; /* Label data */
+ }
+ bool do_setup(int argc, char **argv) override
+ {
+ // Parse arguments
+ cmd_parser.parse(argc, argv);
+
+ // Consume common parameters
+ common_params = consume_common_graph_parameters(common_opts);
+
+ // Return when help menu is requested
+ if(common_params.help)
+ {
+ cmd_parser.print_help(argv[0]);
+ return false;
+ }
+
+ // Set default layout if needed
+ if(!common_opts.data_layout->is_set() && common_params.target == Target::NEON)
+ {
+ common_params.data_layout = DataLayout::NCHW;
+ }
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+ ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::F16 && common_params.target == Target::NEON, "F16 NEON not supported for this graph");
+
+ // Print parameter values
+ std::cout << common_params << std::endl;
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
// Create a preprocessor object
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
- // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- Target target_hint = set_target_hint(target);
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+ TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
- // Parse arguments
- if(argc < 2)
- {
- // Print help
- std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 2)
- {
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
- }
- else if(argc == 3)
- {
- data_path = argv[2];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
- std::cout << "No image provided: using random values\n\n";
- }
- else if(argc == 4)
- {
- data_path = argv[2];
- image = argv[3];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
- std::cout << "No text file with labels provided: skipping output accessor\n\n";
- }
- else if(argc == 5)
- {
- data_path = argv[2];
- image = argv[3];
- label = argv[4];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
- std::cout << "No fast math info provided: disabling fast math\n\n";
- }
- else
- {
- data_path = argv[2];
- image = argv[3];
- label = argv[4];
- fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
- }
+ // Set weights trained layout
+ const DataLayout weights_layout = DataLayout::NCHW;
- graph << target_hint
- << fast_math_hint
- << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false))
+ graph << common_params.target
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
<< ConvolutionLayer(3U, 3U, 32U,
- get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_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/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
@@ -114,7 +101,7 @@
.set_name("Conv2d_1a_3x3/BatchNorm/batchnorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 32U,
- get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_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/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
@@ -128,7 +115,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
<< ConvolutionLayer(3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_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/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
@@ -144,7 +131,7 @@
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
<< ConvolutionLayer(1U, 1U, 80U,
- get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
.set_name("Conv2d_3b_1x1/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
@@ -158,7 +145,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
<< ConvolutionLayer(3U, 3U, 192U,
- get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
.set_name("Conv2d_4a_3x3/convolution")
<< BatchNormalizationLayer(get_weights_accessor(data_path,
@@ -173,57 +160,62 @@
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
- graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
32U)
.set_name("Mixed_5b/concat");
- graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ graph << get_inception_node_A(data_path, "Mixed_5c", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
64U, true)
.set_name("Mixed_5c/concat");
- graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ graph << get_inception_node_A(data_path, "Mixed_5d", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
64U)
.set_name("Mixed_5d/concat");
- graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat");
+ graph << get_inception_node_B(data_path, "Mixed_6a", weights_layout, 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat");
- graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
+ graph << get_inception_node_C(data_path, "Mixed_6b", weights_layout, 192U, std::make_tuple(128U, 128U, 192U),
std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
.set_name("Mixed_6b/concat");
- graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
+ graph << get_inception_node_C(data_path, "Mixed_6c", weights_layout, 192U, std::make_tuple(160U, 160U, 192U),
std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
.set_name("Mixed_6c/concat");
- graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
+ graph << get_inception_node_C(data_path, "Mixed_6d", weights_layout, 192U, std::make_tuple(160U, 160U, 192U),
std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
.set_name("Mixed_6d/concat");
- graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
+ graph << get_inception_node_C(data_path, "Mixed_6e", weights_layout, 192U, std::make_tuple(192U, 192U, 192U),
std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
.set_name("Mixed_6e/concat");
- graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
+ graph << get_inception_node_D(data_path, "Mixed_7a", weights_layout, std::make_tuple(192U, 320U),
std::make_tuple(192U, 192U, 192U, 192U))
.set_name("Mixed_7a/concat");
- graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
+ graph << get_inception_node_E(data_path, "Mixed_7b", weights_layout, 320U, std::make_tuple(384U, 384U, 384U),
std::make_tuple(448U, 384U, 384U, 384U), 192U)
.set_name("Mixed_7b/concat");
- graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
+ graph << get_inception_node_E(data_path, "Mixed_7c", weights_layout, 320U, std::make_tuple(384U, 384U, 384U),
std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
.set_name("Mixed_7c/concat");
graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
<< ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
- "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"),
+ "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy", weights_layout),
get_weights_accessor(data_path,
"/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name("Logits/Conv2d_1c_1x1/convolution")
<< ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
<< SoftmaxLayer().set_name("Predictions/Softmax")
- << OutputLayer(get_output_accessor(label, 5));
+ << OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
- config.use_tuner = (target == 2);
- graph.finalize(target_hint, config);
+ config.num_threads = common_params.threads;
+ config.use_tuner = common_params.enable_tuner;
+ config.tuner_file = common_params.tuner_file;
+
+ graph.finalize(common_params.target, config);
+
+ return true;
}
void do_run() override
@@ -232,10 +224,13 @@
}
private:
- Stream graph{ 0, "InceptionV3" };
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
private:
- BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path,
+ BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
unsigned int a_filt,
std::tuple<unsigned int, unsigned int> b_filters,
std::tuple<unsigned int, unsigned int, unsigned int> c_filters,
@@ -256,7 +251,7 @@
SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -272,7 +267,7 @@
SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "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" + conv_id0 + "1x1/convolution")
@@ -286,7 +281,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu")
<< ConvolutionLayer(
5U, 5U, std::get<1>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 2, 2))
.set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution")
@@ -302,7 +297,7 @@
SubStream i_c(graph);
i_c << ConvolutionLayer(
1U, 1U, std::get<0>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -316,7 +311,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+ 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/convolution")
@@ -330,7 +325,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<2>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"),
+ 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/convolution")
@@ -347,7 +342,7 @@
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, d_filt,
- get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ 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/convolution")
@@ -363,7 +358,7 @@
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
- BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path,
+ BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
unsigned int a_filt,
std::tuple<unsigned int, unsigned int, unsigned int> b_filters)
{
@@ -371,7 +366,7 @@
SubStream i_a(graph);
i_a << ConvolutionLayer(
3U, 3U, a_filt,
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
.set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution")
@@ -387,7 +382,7 @@
SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -401,7 +396,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"),
+ 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/convolution")
@@ -415,7 +410,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<2>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 0))
.set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution")
@@ -434,7 +429,7 @@
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
}
- BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path,
+ BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
unsigned int a_filt,
std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
@@ -444,7 +439,7 @@
SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -460,7 +455,7 @@
SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -474,7 +469,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<1>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+ 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/convolution")
@@ -488,7 +483,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<2>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+ 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/convolution")
@@ -504,7 +499,7 @@
SubStream i_c(graph);
i_c << ConvolutionLayer(
1U, 1U, std::get<0>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -518,7 +513,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<1>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"),
+ 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/convolution")
@@ -532,7 +527,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<2>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"),
+ 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/convolution")
@@ -546,7 +541,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<3>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"),
+ 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/convolution")
@@ -560,7 +555,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<4>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"),
+ 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/convolution")
@@ -577,7 +572,7 @@
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, d_filt,
- get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ 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/convolution")
@@ -593,15 +588,15 @@
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
}
- BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path,
- std::tuple<unsigned int, unsigned int> a_filters,
+ BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
+ std::tuple<unsigned int, unsigned int> a_filters,
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters)
{
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, std::get<0>(a_filters),
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -615,7 +610,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(a_filters),
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"),
+ 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(param_path + "/Branch_0/Conv2d_1a_3x3/convolution")
@@ -631,7 +626,7 @@
SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -645,7 +640,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
7U, 1U, std::get<1>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+ 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/convolution")
@@ -659,7 +654,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
<< ConvolutionLayer(
1U, 7U, std::get<2>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+ 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/convolution")
@@ -673,7 +668,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<3>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"),
+ 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(param_path + "/Branch_1/Conv2d_1a_3x3/convolution")
@@ -692,7 +687,7 @@
return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
}
- BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path,
+ BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
unsigned int a_filt,
std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
@@ -710,7 +705,7 @@
SubStream i_a(graph);
i_a << ConvolutionLayer(
1U, 1U, a_filt,
- get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -726,7 +721,7 @@
SubStream i_b(graph);
i_b << ConvolutionLayer(
1U, 1U, std::get<0>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -739,10 +734,10 @@
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
- SubStream i_b1(static_cast<IStream &>(i_b));
+ SubStream i_b1(i_b);
i_b1 << ConvolutionLayer(
3U, 1U, std::get<1>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"),
+ 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/convolution")
@@ -755,10 +750,10 @@
.set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
- SubStream i_b2(static_cast<IStream &>(i_b));
+ SubStream i_b2(i_b);
i_b2 << ConvolutionLayer(
1U, 3U, std::get<2>(b_filters),
- get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "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" + conv_id + "3x1/convolution")
@@ -777,7 +772,7 @@
SubStream i_c(graph);
i_c << ConvolutionLayer(
1U, 1U, std::get<0>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ 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/convolution")
@@ -791,7 +786,7 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
<< ConvolutionLayer(
3U, 3U, std::get<1>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+ 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/convolution")
@@ -804,10 +799,10 @@
.set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu");
- SubStream i_c1(static_cast<IStream &>(i_c));
+ SubStream i_c1(i_c);
i_c1 << ConvolutionLayer(
3U, 1U, std::get<2>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"),
+ 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/convolution")
@@ -820,10 +815,10 @@
.set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
- SubStream i_c2(static_cast<IStream &>(i_c));
+ SubStream i_c2(i_c);
i_c2 << ConvolutionLayer(
1U, 3U, std::get<3>(c_filters),
- get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_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_0d_3x1/convolution")
@@ -843,7 +838,7 @@
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, d_filt,
- get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ 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/convolution")
@@ -862,8 +857,10 @@
/** Main program for Inception V3
*
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
*/
int main(int argc, char **argv)
{