arm_compute v18.08
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index bafa9a5..e909955 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -23,11 +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>
-
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
@@ -35,75 +34,58 @@
/** Example demonstrating how to implement ResNet50 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 GraphResNet50Example : public Example
{
public:
- void do_setup(int argc, char **argv) override
+ GraphResNet50Example()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNet50")
{
- 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;
+ }
+
+ // 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
const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,
false /* Do not convert to BGR */);
- // 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(224U, 224U, 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(224U, 224U, 3U, 1U), DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */))
+ graph << common_params.target
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */))
<< ConvolutionLayer(
7U, 7U, 64U,
- get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 3, 3))
.set_name("conv1/convolution")
@@ -117,26 +99,29 @@
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
- add_residual_block(data_path, "block1", 64, 3, 2);
- add_residual_block(data_path, "block2", 128, 4, 2);
- add_residual_block(data_path, "block3", 256, 6, 2);
- add_residual_block(data_path, "block4", 512, 3, 1);
+ add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
+ add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
+ add_residual_block(data_path, "block3", weights_layout, 256, 6, 2);
+ add_residual_block(data_path, "block4", weights_layout, 512, 3, 1);
graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
<< ConvolutionLayer(
1U, 1U, 1000U,
- get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name("logits/convolution")
<< FlattenLayer().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;
+ graph.finalize(common_params.target, config);
+
+ return true;
}
void do_run() override
@@ -146,9 +131,13 @@
}
private:
- Stream graph{ 0, "ResNet50" };
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
- void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
+ void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout,
+ unsigned int base_depth, unsigned int num_units, unsigned int stride)
{
for(unsigned int i = 0; i < num_units; ++i)
{
@@ -170,7 +159,7 @@
SubStream right(graph);
right << ConvolutionLayer(
1U, 1U, base_depth,
- get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv1/convolution")
@@ -185,7 +174,7 @@
<< ConvolutionLayer(
3U, 3U, base_depth,
- get_weights_accessor(data_path, unit_path + "conv2_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(middle_stride, middle_stride, 1, 1))
.set_name(unit_name + "conv2/convolution")
@@ -200,7 +189,7 @@
<< ConvolutionLayer(
1U, 1U, base_depth * 4,
- get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv3/convolution")
@@ -217,7 +206,7 @@
SubStream left(graph);
left << ConvolutionLayer(
1U, 1U, base_depth * 4,
- get_weights_accessor(data_path, unit_path + "shortcut_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "shortcut/convolution")
@@ -251,8 +240,10 @@
/** Main program for ResNet50
*
+ * @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)
{