COMPMID-1104 Add fast math hint in the graph API
Change-Id: I83db135fa94c6884e080f0229a9b6430d908c029
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129823
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/graph/GraphBuilder.h b/arm_compute/graph/GraphBuilder.h
index bbbbefb..aea28eb 100644
--- a/arm_compute/graph/GraphBuilder.h
+++ b/arm_compute/graph/GraphBuilder.h
@@ -99,6 +99,8 @@
ITensorAccessorUPtr beta_accessor = nullptr, ITensorAccessorUPtr gamma_accessor = nullptr);
/** Adds a convolution layer node to the graph
*
+ * TODO (COMPMID-1113): Add a graph descriptor for convolution layer node
+ *
* @param[in] g Graph to add the node to
* @param[in] params Common node parameters
* @param[in] input Input to the convolution layer node as a NodeID-Index pair
@@ -107,6 +109,7 @@
* @param[in] conv_info Convolution layer information
* @param[in] num_groups (Optional) Number of groups for a grouped convolution. Defaults to 1
* @param[in] method (Optional) Convolution method to use
+ * @param[in] fast_math_hint (Optional) Fast math hint
* @param[in] weights_accessor (Optional) Accessor of the weights node data
* @param[in] bias_accessor (Optional) Accessor of the bias node data
* @param[in] weights_quant_info (Optional) Weights quantization info
@@ -116,7 +119,7 @@
*/
static NodeID add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input,
Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info,
- unsigned int num_groups = 1, ConvolutionMethod method = ConvolutionMethod::DEFAULT,
+ unsigned int num_groups = 1, ConvolutionMethod method = ConvolutionMethod::DEFAULT, FastMathHint fast_math_hint = FastMathHint::DISABLED,
ITensorAccessorUPtr weights_accessor = nullptr, ITensorAccessorUPtr bias_accessor = nullptr,
const QuantizationInfo weights_quant_info = QuantizationInfo(),
const QuantizationInfo out_quant_info = QuantizationInfo());
diff --git a/arm_compute/graph/TypePrinter.h b/arm_compute/graph/TypePrinter.h
index 0ecd57d..6babd39 100644
--- a/arm_compute/graph/TypePrinter.h
+++ b/arm_compute/graph/TypePrinter.h
@@ -295,6 +295,24 @@
return os;
}
+/** Formatted output of the FastMathHint type. */
+inline ::std::ostream &operator<<(::std::ostream &os, const FastMathHint &hint)
+{
+ switch(hint)
+ {
+ case FastMathHint::ENABLED:
+ os << "ENABLED";
+ break;
+ case FastMathHint::DISABLED:
+ os << "DISABLED";
+ break;
+ default:
+ ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
+ }
+
+ return os;
+}
+
/** Formatted output of the DepthwiseConvolutionMethod type. */
inline ::std::ostream &operator<<(::std::ostream &os, const DepthwiseConvolutionMethod &method)
{
diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h
index b195ed7..a910610 100644
--- a/arm_compute/graph/Types.h
+++ b/arm_compute/graph/Types.h
@@ -116,6 +116,13 @@
OPTIMIZED_3x3, /**< Optimized 3x3 direct depthwise convolution */
};
+/** Enable or disable fast math for Convolution layer */
+enum class FastMathHint
+{
+ ENABLED, /**< Fast math enabled for Convolution layer */
+ DISABLED, /**< Fast math disabled for Convolution layer */
+};
+
/** Supported nodes */
enum class NodeType
{
diff --git a/arm_compute/graph/backends/ValidateHelpers.h b/arm_compute/graph/backends/ValidateHelpers.h
index c203e8c..db3f8ba 100644
--- a/arm_compute/graph/backends/ValidateHelpers.h
+++ b/arm_compute/graph/backends/ValidateHelpers.h
@@ -83,6 +83,7 @@
const PadStrideInfo conv_info = node.convolution_info();
const ConvolutionMethod conv_algorithm = node.convolution_method();
+ //const bool fast_math = node.fast_math_hint() == FastMathHint::ENABLED; // FIXME (COMPMID-1138): uncomment once NEON and GLES support fast_math
// Validate function
Status status{};
@@ -95,7 +96,7 @@
status = GEMMConvolutionLayer::validate(input, weights, biases, output, conv_info);
break;
case ConvolutionMethod::WINOGRAD:
- status = WinogradConvolutionLayer::validate(input, weights, biases, output, conv_info);
+ status = WinogradConvolutionLayer::validate(input, weights, biases, output, conv_info /*, fast_math*/);
break;
case ConvolutionMethod::DEFAULT:
status = ConvolutionLayer::validate(input, weights, biases, output, conv_info);
@@ -107,7 +108,7 @@
// If validation fails try the Default approach
if(!bool(status))
{
- status = ConvolutionLayer::validate(input, weights, biases, output, conv_info);
+ status = ConvolutionLayer::validate(input, weights, biases, output, conv_info /*, fast_math*/);
if(bool(status))
{
ARM_COMPUTE_LOG_GRAPH_INFO("Switched ConvolutionLayer method of node with ID : "
diff --git a/arm_compute/graph/frontend/IStreamOperators.h b/arm_compute/graph/frontend/IStreamOperators.h
index 350d78f..4d680f9 100644
--- a/arm_compute/graph/frontend/IStreamOperators.h
+++ b/arm_compute/graph/frontend/IStreamOperators.h
@@ -96,6 +96,18 @@
s.hints().depthwise_convolution_method_hint = depthwise_convolution_method_hint;
return s;
}
+/** Overloaded stream operator to provide a fast math hint to the graph
+ *
+ * @param[in, out] s Stream to provide the hint to
+ * @param[in] fast_math_hint Convolution method hint to be considered
+ *
+ * @return Updated stream
+ */
+inline IStream &operator<<(IStream &s, FastMathHint fast_math_hint)
+{
+ s.hints().fast_math_hint = fast_math_hint;
+ return s;
+}
} // namespace frontend
} // namespace graph
} // namespace arm_compute
diff --git a/arm_compute/graph/frontend/Layers.h b/arm_compute/graph/frontend/Layers.h
index 54cf515..d122a7a 100644
--- a/arm_compute/graph/frontend/Layers.h
+++ b/arm_compute/graph/frontend/Layers.h
@@ -197,7 +197,7 @@
NodeParams common_params = { name(), s.hints().target_hint };
return GraphBuilder::add_convolution_node(s.graph(), common_params, input,
Size2D(_conv_width, _conv_height), _ofm, _conv_info, _num_groups,
- s.hints().convolution_method_hint,
+ s.hints().convolution_method_hint, s.hints().fast_math_hint,
std::move(_weights), std::move(_bias), std::move(_weights_quant_info), std::move(_out_quant_info));
}
diff --git a/arm_compute/graph/frontend/Types.h b/arm_compute/graph/frontend/Types.h
index 6cf7460..4789361 100644
--- a/arm_compute/graph/frontend/Types.h
+++ b/arm_compute/graph/frontend/Types.h
@@ -45,6 +45,7 @@
using graph::PoolingType;
using graph::Target;
using graph::ConvolutionMethod;
+using graph::FastMathHint;
using graph::DepthwiseConvolutionMethod;
using graph::TensorDescriptor;
using graph::DimensionRoundingType;
@@ -63,6 +64,7 @@
Target target_hint = { Target::UNSPECIFIED }; /**< Target execution hint */
ConvolutionMethod convolution_method_hint = { ConvolutionMethod::DEFAULT }; /**< Convolution method hint */
DepthwiseConvolutionMethod depthwise_convolution_method_hint = { DepthwiseConvolutionMethod::DEFAULT }; /**< Depthwise Convolution method hint */
+ FastMathHint fast_math_hint = { FastMathHint::DISABLED }; /**< Fast math hint */
};
} // namespace frontend
} // namespace graph
diff --git a/arm_compute/graph/nodes/ConvolutionLayerNode.h b/arm_compute/graph/nodes/ConvolutionLayerNode.h
index d1186a8..aca6028 100644
--- a/arm_compute/graph/nodes/ConvolutionLayerNode.h
+++ b/arm_compute/graph/nodes/ConvolutionLayerNode.h
@@ -38,9 +38,11 @@
*
* @param[in] info Convolution layer attributes
* @param[in] method (Optional) Convolution method to use
+ * @param[in] fast_math_hint (Optional) Fast math hint
* @param[in] out_quant_info (Optional) Output quantization info
*/
- ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method = ConvolutionMethod::DEFAULT, QuantizationInfo out_quant_info = QuantizationInfo());
+ ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method = ConvolutionMethod::DEFAULT, FastMathHint fast_math_hint = FastMathHint::DISABLED,
+ QuantizationInfo out_quant_info = QuantizationInfo());
/** Sets the convolution layer method to use
*
* @param[in] method Method to use for convolution
@@ -51,9 +53,19 @@
* @note This is an indication on which convolution layer implementation to use,
* if it fails to be created the library's heuristic approach will be used
*
- * @return Convolution layer method do be used by the node
+ * @return Convolution layer method to be used by the node
*/
ConvolutionMethod convolution_method() const;
+ /** Sets the fast math fast hint
+ *
+ * @param[in] hint Hint to use for convolution
+ */
+ void set_fast_math_hint(FastMathHint hint);
+ /** Fast math hint accessor
+ *
+ * @return Fast math hint to be used by the node
+ */
+ FastMathHint fast_math_hint() const;
/** Convolution metadata accessor
*
* @return Convolution information
@@ -80,6 +92,7 @@
private:
PadStrideInfo _info;
ConvolutionMethod _method;
+ FastMathHint _fast_math_hint;
QuantizationInfo _out_quant_info;
};
} // namespace graph
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 45c2b56..b97ca54 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -37,7 +37,7 @@
/** Example demonstrating how to implement AlexNet'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 )
+ * @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 GraphAlexnetExample : public Example
{
@@ -60,40 +60,51 @@
const bool is_neon = (target_hint == Target::NEON);
ConvolutionMethod convolution_5x5_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DIRECT;
ConvolutionMethod convolution_3x3_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
// Layer 1
@@ -185,7 +196,7 @@
/** Main program for AlexNet
*
* @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), [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)
{
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index deafe5a..bac3c7c 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -36,7 +36,7 @@
/** Example demonstrating how to implement Googlenet'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphGooglenetExample : public Example
{
@@ -55,40 +55,51 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
<< ConvolutionLayer(
@@ -206,7 +217,7 @@
/** Main program for Googlenet
*
* @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), [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)
{
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 7fa0fc7..8e30fd9 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -36,7 +36,7 @@
/** 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] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class InceptionV3Example : public Example
{
@@ -54,41 +54,53 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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 << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false))
+ graph << target_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+ get_input_accessor(image, std::move(preprocessor), false))
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
@@ -759,7 +771,7 @@
/** Main program for Inception V3
*
* @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 )
+ * @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)
{
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 4217c78..827370e 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -36,7 +36,7 @@
/** Example demonstrating how to implement InceptionV4'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 )
+ * @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 InceptionV4Example final : public Example
{
@@ -57,41 +57,53 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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 << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
- get_input_accessor(image, std::move(preprocessor), false))
+ graph << target_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+ get_input_accessor(image, std::move(preprocessor), false))
// Conv2d_1a_3x3
<< ConvolutionLayer(3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"),
@@ -737,7 +749,7 @@
/** Main program for Inception V4
*
* @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 )
+ * @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)
{
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index ea0916b..92be2d4 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -36,7 +36,7 @@
/** Example demonstrating how to implement LeNet'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), [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphLenetExample : public Example
{
@@ -50,34 +50,45 @@
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] [batches]\n\n";
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches] [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] [batches]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [batches] [fast_math_hint]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 3)
{
//Do something with argv[1]
data_path = argv[2];
- std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+ std::cout << "Usage: " << argv[0] << " [path_to_data] [batches] [fast_math_hint]\n\n";
std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
}
+ else if(argc == 4)
+ {
+ data_path = argv[2];
+ batches = std::strtol(argv[3], nullptr, 0);
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [fast_math_hint]\n\n";
+ std::cout << "No fast math info provided: disabling fast math\n\n";
+ }
else
{
//Do something with argv[1] and argv[2]
- data_path = argv[2];
- batches = std::strtol(argv[3], nullptr, 0);
+ data_path = argv[2];
+ batches = std::strtol(argv[3], nullptr, 0);
+ fast_math_hint = (std::strtol(argv[4], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
}
//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
graph << target_hint
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor(""))
<< ConvolutionLayer(
5U, 5U, 20U,
@@ -125,7 +136,7 @@
/** Main program for LeNet
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
int main(int argc, char **argv)
{
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 813c0bf..7bfc680 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -35,7 +35,7 @@
/** Example demonstrating how to implement MobileNet'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 )
+ * @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] data layout, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphMobilenetExample : public Example
{
@@ -54,6 +54,7 @@
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = ConvolutionMethod::GEMM;
DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3;
+ FastMathHint fast_math_hint = FastMathHint::DISABLED;
// Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
@@ -72,33 +73,33 @@
if(argc < 2)
{
// Print help
- std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
std::cout << "No data layout provided: using NCHW\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 2)
{
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
std::cout << "No data layout provided: using NCHW\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 3)
{
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
std::cout << "No data layout provided: using NCHW\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 4)
{
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 5)
{
data_path = argv[4];
- std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n";
std::cout << "No image provided: using random values\n\n";
std::cout << "No text file with labels provided: skipping output accessor\n\n";
}
@@ -106,14 +107,23 @@
{
data_path = argv[4];
image = argv[5];
- 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 == 7)
{
data_path = argv[4];
image = argv[5];
label = argv[6];
+ 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[4];
+ image = argv[5];
+ label = argv[6];
+ fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
}
// Add model path to data path
@@ -125,6 +135,7 @@
graph << target_hint
<< convolution_hint
<< depthwise_convolution_hint
+ << fast_math_hint
<< InputLayer(input_descriptor,
get_input_accessor(image, std::move(preprocessor), false))
<< ConvolutionLayer(
@@ -225,7 +236,9 @@
* [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
* [optional] Path to the weights folder,
* [optional] image,
- * [optional] labels )
+ * [optional] labels,
+ * [optional] data layout,
+ * [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
int main(int argc, char **argv)
{
diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
index 7edd182..2801209 100644
--- a/examples/graph_mobilenet_qasymm8.cpp
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -36,7 +36,7 @@
/** Example demonstrating how to implement QASYMM8 MobileNet'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] npy_input, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphMobileNetQASYMM8Example : public Example
{
@@ -92,37 +92,48 @@
};
// 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);
+ 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] [npy_input] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [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] [npy_input] [labels]\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n";
std::cout << "No input provided: using random values\n\n";
}
else if(argc == 4)
{
data_path = argv[2];
input = 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];
input = 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];
+ input = argv[3];
+ label = argv[4];
+ fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;
}
graph << target_hint
<< DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info),
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
<< ConvolutionLayer(
@@ -220,7 +231,7 @@
/** Main program for MobileNetQASYMM8
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
int main(int argc, char **argv)
{
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 18a028d..6fac372 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -35,7 +35,7 @@
/** Example demonstrating how to implement Microsoft's 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), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphResNet50Example : public Example
{
@@ -55,40 +55,51 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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
+ << 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(
@@ -243,7 +254,7 @@
/** 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), [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)
{
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 8ed43f7..2627c96 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -37,7 +37,7 @@
/** Example demonstrating how to implement Squeezenet'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphSqueezenetExample : public Example
{
@@ -56,40 +56,51 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
<< ConvolutionLayer(
@@ -209,7 +220,7 @@
/** Main program for Squeezenet v1.0
*
* @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), [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)
{
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 529f4fe..c4a5433 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -40,7 +40,7 @@
/** Example demonstrating how to implement Squeezenet's v1.1 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), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphSqueezenet_v1_1Example : public Example
{
@@ -59,40 +59,51 @@
const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
Target target_hint = set_target_hint(target);
ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT;
+ 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
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
<< ConvolutionMethod::DIRECT
@@ -214,7 +225,7 @@
/** Main program for Squeezenet v1.1
*
* @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), [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)
{
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 44b4c4c..6db4e38 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -35,7 +35,7 @@
/** Example demonstrating how to implement VGG16'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphVGG16Example : public Example
{
@@ -57,40 +57,51 @@
ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT;
+ 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
+ << fast_math_hint
<< first_convolution3x3_hint
<< InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
@@ -246,7 +257,7 @@
/** Main program for VGG16
*
* @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), [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)
{
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 229112b..5a281ea 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -35,7 +35,7 @@
/** Example demonstrating how to implement VGG19'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
*/
class GraphVGG19Example : public Example
{
@@ -51,9 +51,10 @@
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// 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);
- const bool is_opencl = target_hint == Target::CL;
+ 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;
+ const bool is_opencl = target_hint == Target::CL;
ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM;
ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT;
@@ -62,36 +63,46 @@
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
<< first_convolution3x3_hint
+ << fast_math_hint
<< InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32),
get_input_accessor(image, std::move(preprocessor)))
// Layer 1
@@ -259,7 +270,7 @@
/** Main program for VGG19
*
* @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), [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)
{
diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
index 56b31c7..df94d0b 100644
--- a/src/graph/GraphBuilder.cpp
+++ b/src/graph/GraphBuilder.cpp
@@ -81,7 +81,7 @@
}
NodeID create_grouped_convolution(Graph &g, NodeParams ¶ms, NodeIdxPair input, NodeID weights, NodeID bias,
- PadStrideInfo conv_info, ConvolutionMethod method, unsigned int num_groups)
+ PadStrideInfo conv_info, ConvolutionMethod method, FastMathHint fast_math_hint, unsigned int num_groups)
{
bool has_bias = (bias != EmptyNodeID);
@@ -102,7 +102,7 @@
std::vector<NodeIdxPair> convolution_outputs;
for(unsigned int i = 0; i < num_groups; ++i)
{
- NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method);
+ NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, fast_math_hint);
g.add_connection(input_split, i, conv_nid, 0);
g.add_connection(weights_split, i, conv_nid, 1);
if(has_bias)
@@ -205,7 +205,7 @@
NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input,
Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info,
- unsigned int num_groups, ConvolutionMethod method,
+ unsigned int num_groups, ConvolutionMethod method, FastMathHint fast_math_hint,
ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor,
const QuantizationInfo weights_quant_info,
const QuantizationInfo out_quant_info)
@@ -245,7 +245,7 @@
if(num_groups == 1)
{
// Create convolution node and connect
- NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, out_quant_info);
+ NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, fast_math_hint, out_quant_info);
g.add_connection(input.node_id, input.index, conv_nid, 0);
g.add_connection(w_nid, 0, conv_nid, 1);
if(has_bias)
@@ -258,7 +258,7 @@
}
else
{
- return create_grouped_convolution(g, params, input, w_nid, b_nid, conv_info, method, num_groups);
+ return create_grouped_convolution(g, params, input, w_nid, b_nid, conv_info, method, fast_math_hint, num_groups);
}
}
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index ece6364..4626cb5 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -166,6 +166,7 @@
const PadStrideInfo conv_info = node.convolution_info();
const ConvolutionMethod conv_algorithm = node.convolution_method();
+ const bool fast_math = node.fast_math_hint() == FastMathHint::ENABLED;
// Create and configure function (we assume that functions have been validated before creation)
std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::CL);
@@ -175,7 +176,7 @@
if(conv_algorithm == ConvolutionMethod::WINOGRAD)
{
std::tie(func, func_name) = create_named_memory_managed_function<CLWinogradConvolutionLayer>(
- std::string("CLWinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+ std::string("CLWinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, ActivationLayerInfo(), fast_math);
}
else if(conv_algorithm == ConvolutionMethod::DIRECT)
{
@@ -190,7 +191,7 @@
else
{
std::tie(func, func_name) = create_named_memory_managed_function<CLConvolutionLayer>(std::string("CLConvolutionLayer"), mm,
- input, weights, biases, output, conv_info);
+ input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1U, 1U), ActivationLayerInfo(), fast_math);
}
// Log info
diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp
index eaf1f7f..6c31a6b 100644
--- a/src/graph/nodes/ConvolutionLayerNode.cpp
+++ b/src/graph/nodes/ConvolutionLayerNode.cpp
@@ -32,8 +32,8 @@
{
namespace graph
{
-ConvolutionLayerNode::ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method, QuantizationInfo out_quant_info)
- : _info(std::move(info)), _method(method), _out_quant_info(out_quant_info)
+ConvolutionLayerNode::ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method, FastMathHint fast_math_hint, QuantizationInfo out_quant_info)
+ : _info(std::move(info)), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(out_quant_info)
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -49,6 +49,16 @@
return _method;
}
+void ConvolutionLayerNode::set_fast_math_hint(FastMathHint hint)
+{
+ _fast_math_hint = hint;
+}
+
+FastMathHint ConvolutionLayerNode::fast_math_hint() const
+{
+ return _fast_math_hint;
+}
+
PadStrideInfo ConvolutionLayerNode::convolution_info() const
{
return _info;