* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <cstdlib>
using namespace arm_compute::utils;
-using namespace arm_compute::graph;
+using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
-/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API
+/** 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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @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) )
*/
class GraphResNet50Example : public Example
{
false /* Do not convert to BGR */);
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- TargetHint target_hint = set_target_hint(int_target_hint);
+ 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;
// Parse arguments
if(argc < 2)
{
// Print help
- std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+ 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]\n\n";
+ 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]\n\n";
+ 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]\n\n";
+ 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
+ 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;
}
graph << target_hint
- << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */))
+ << 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 */))
<< ConvolutionLayer(
7U, 7U, 64U,
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 3, 3))
+ .set_name("conv1/convolution")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
0.0000100099996416f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
+ .set_name("conv1/BatchNorm")
+ << 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);
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+ 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_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
- << FlattenLayer()
- << SoftmaxLayer()
- << Tensor(get_output_accessor(label, 5));
+ .set_name("logits/convolution")
+ << FlattenLayer().set_name("predictions/Reshape")
+ << SoftmaxLayer().set_name("predictions/Softmax")
+ << OutputLayer(get_output_accessor(label, 5));
- // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
- graph.graph_init(int_target_hint == 2);
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);
}
+
void do_run() override
{
// Run graph
}
private:
- Graph graph{};
+ Stream graph{ 0, "ResNet50" };
void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
{
for(unsigned int i = 0; i < num_units; ++i)
{
- std::stringstream unit;
- unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
- std::string unit_name = unit.str();
+ std::stringstream unit_path_ss;
+ unit_path_ss << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
+ std::stringstream unit_name_ss;
+ unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v1/";
+
+ std::string unit_path = unit_path_ss.str();
+ std::string unit_name = unit_name_ss.str();
unsigned int middle_stride = 1;
middle_stride = stride;
}
- SubGraph right;
+ SubStream right(graph);
right << ConvolutionLayer(
1U, 1U, base_depth,
- get_weights_accessor(data_path, unit_name + "conv1_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv1/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
0.0000100099996416f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(unit_name + "conv1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
<< ConvolutionLayer(
3U, 3U, base_depth,
- get_weights_accessor(data_path, unit_name + "conv2_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(middle_stride, middle_stride, 1, 1))
+ .set_name(unit_name + "conv2/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
0.0000100099996416f)
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .set_name(unit_name + "conv2/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
<< ConvolutionLayer(
1U, 1U, base_depth * 4,
- get_weights_accessor(data_path, unit_name + "conv3_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv3/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"),
- 0.0000100099996416f);
+ get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_beta.npy"),
+ 0.0000100099996416f)
+ .set_name(unit_name + "conv2/BatchNorm");
if(i == 0)
{
- SubGraph left;
+ SubStream left(graph);
left << ConvolutionLayer(
1U, 1U, base_depth * 4,
- get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"),
+ get_weights_accessor(data_path, unit_path + "shortcut_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "shortcut/convolution")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"),
- 0.0000100099996416f);
+ get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_beta.npy"),
+ 0.0000100099996416f)
+ .set_name(unit_name + "shortcut/BatchNorm");
- graph << ResidualLayer(std::move(left), std::move(right));
+ graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
}
else if(middle_stride > 1)
{
- SubGraph left;
- left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+ SubStream left(graph);
+ left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
- graph << ResidualLayer(std::move(left), std::move(right));
+ graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
}
else
{
- graph << ResidualLayer(std::move(right));
+ SubStream left(graph);
+ graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
}
- graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
}
}
};
/** Main program for ResNet50
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @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) )
*/
int main(int argc, char **argv)
{
projects / platform / upstream / armcl.git / blobdiff