arm_compute v18.08
diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
index f96a02e..8f8e4a9 100644
--- a/examples/graph_resnext50.cpp
+++ b/examples/graph_resnext50.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;
@@ -35,92 +34,80 @@
/** Example demonstrating how to implement ResNeXt50 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] npy_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
*/
class GraphResNeXt50Example : public Example
{
public:
- void do_setup(int argc, char **argv) override
+ GraphResNeXt50Example()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50")
{
- std::string data_path; /* Path to the trainable data */
- std::string npy_in; /* Input npy data */
- std::string npy_out; /* Output npy data */
-
- // 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;
-
+ }
+ bool do_setup(int argc, char **argv) override
+ {
// Parse arguments
- if(argc < 2)
+ 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)
{
- // Print help
- std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_in] [npy_out] [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] [npy_in] [npy_out] [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] << " [npy_in] [npy_out] [fast_math_hint]\n\n";
- std::cout << "No input npy file provided: using random values\n\n";
- }
- else if(argc == 4)
- {
- data_path = argv[2];
- npy_in = argv[3];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n";
- std::cout << "No output npy file provided: skipping output accessor\n\n";
- }
- else if(argc == 5)
- {
- data_path = argv[2];
- npy_in = argv[3];
- npy_out = 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];
- npy_in = argv[3];
- npy_out = argv[4];
- fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
+ cmd_parser.print_help(argv[0]);
+ return false;
}
- graph << target_hint
- << fast_math_hint
- << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
- get_input_accessor(npy_in))
+ // 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 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);
+
+ // Set weights trained layout
+ const DataLayout weights_layout = DataLayout::NCHW;
+
+ graph << common_params.target
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params))
<< ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
.set_name("bn_data/Scale")
<< ConvolutionLayer(
7U, 7U, 64U,
- get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"),
+ get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
.set_name("conv0/Convolution")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
- add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
- add_residual_block(data_path, 512, 2, 4, 2);
- add_residual_block(data_path, 1024, 3, 6, 2);
- add_residual_block(data_path, 2048, 4, 3, 2);
+ add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
+ add_residual_block(data_path, weights_layout, 512, 2, 4, 2);
+ add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);
+ add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);
graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1")
<< FlattenLayer().set_name("predictions/Reshape")
- << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32));
+ << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32));
// 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
@@ -130,9 +117,13 @@
}
private:
- Stream graph{ 0, "ResNeXt50" };
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
- void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
+ void add_residual_block(const std::string &data_path, DataLayout weights_layout,
+ unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
{
for(unsigned int i = 0; i < num_units; ++i)
{
@@ -153,7 +144,7 @@
SubStream right(graph);
right << ConvolutionLayer(
1U, 1U, base_depth / 2,
- get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv1/convolution")
@@ -161,7 +152,7 @@
<< ConvolutionLayer(
3U, 3U, base_depth / 2,
- 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),
pad_grouped_conv, 32)
.set_name(unit_name + "conv2/convolution")
@@ -172,7 +163,7 @@
<< ConvolutionLayer(
1U, 1U, base_depth,
- get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv3/convolution");
@@ -182,7 +173,7 @@
{
left << ConvolutionLayer(
1U, 1U, base_depth,
- get_weights_accessor(data_path, unit_path + "sc_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
.set_name(unit_name + "sc/convolution")
@@ -199,8 +190,10 @@
/** Main program for ResNeXt50
*
+ * @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] npy_in, [optional] npy_out )
+ * @param[in] argv Arguments
*/
int main(int argc, char **argv)
{