arm_compute v19.02
Change-Id: I853a3ecf38f206da13c1b03640c8adf73c20477c
diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
index 2bff367..429a3d2 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -61,89 +61,29 @@
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;
- // Create model path
- std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
-
// 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);
- // Create a preprocessor object
- std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
-
- // Get trainable parameters data path
- std::string data_path = common_params.data_path;
-
- // Add model path to data path
- if(!data_path.empty())
- {
- data_path += model_path;
- }
-
- // Create graph
+ // Set graph hints
graph << common_params.target
<< DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
- << 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, "Conv_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL))
- .set_name("Conv")
- << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"),
- 0.0010000000474974513f)
- .set_name("Conv/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
- .set_name("Conv/Relu6");
+ << common_params.fast_math_hint;
- get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
- get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
- get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
- get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
- get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true);
- get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
- get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
- get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true);
-
- graph << ConvolutionLayer(1U, 1U, 1280U,
- get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("Conv_1")
- << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"),
- 0.0010000000474974513f)
- .set_name("Conv_1/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
- .set_name("Conv_1/Relu6")
- << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
- << ConvolutionLayer(1U, 1U, 1001U,
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("Logits/Conv2d_1c_1x1")
- << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
+ // Create core graph
+ if(arm_compute::is_data_type_float(common_params.data_type))
+ {
+ create_graph_float(input_descriptor);
+ }
+ else
+ {
+ create_graph_qasymm8(input_descriptor);
+ }
+ // Create common tail
+ graph << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
<< SoftmaxLayer().set_name("Predictions/Softmax")
<< OutputLayer(get_output_accessor(common_params, 5));
@@ -170,16 +110,102 @@
CommonGraphParams common_params;
Stream graph;
- void get_expanded_conv(const std::string &data_path, std::string &¶m_path,
- unsigned int input_channels, unsigned int output_channels,
- PadStrideInfo dwc_pad_stride_info,
- bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6)
+private:
+ enum class IsResidual
+ {
+ Yes,
+ No
+ };
+
+ enum class HasExpand
+ {
+ Yes,
+ No
+ };
+
+private:
+ void create_graph_float(TensorDescriptor &input_descriptor)
+ {
+ // Create model path
+ const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
+
+ // Create a preprocessor object
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
+
+ // Add model path to data path
+ if(!data_path.empty())
+ {
+ data_path += model_path;
+ }
+
+ graph << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
+ << ConvolutionLayer(3U, 3U, 32U,
+ get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL))
+ .set_name("Conv")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"),
+ 0.0010000000474974513f)
+ .set_name("Conv/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name("Conv/Relu6");
+
+ get_expanded_conv_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
+ get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
+ get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
+
+ graph << ConvolutionLayer(1U, 1U, 1280U,
+ get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv_1")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"),
+ 0.0010000000474974513f)
+ .set_name("Conv_1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name("Conv_1/Relu6")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+ << ConvolutionLayer(1U, 1U, 1001U,
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("Logits/Conv2d_1c_1x1");
+ }
+
+ void get_expanded_conv_float(const std::string &data_path, std::string &¶m_path,
+ unsigned int input_channels, unsigned int output_channels,
+ PadStrideInfo dwc_pad_stride_info,
+ HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No,
+ unsigned int expansion_size = 6)
{
std::string total_path = param_path + "_";
SubStream left(graph);
// Add expand node
- if(has_expand)
+ if(has_expand == HasExpand::Yes)
{
left << ConvolutionLayer(1U, 1U, input_channels * expansion_size,
get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW),
@@ -222,7 +248,190 @@
0.0010000000474974513)
.set_name(param_path + "/project/BatchNorm");
- if(is_residual)
+ if(is_residual == IsResidual::Yes)
+ {
+ // Add residual node
+ SubStream right(graph);
+ graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add");
+ }
+ else
+ {
+ graph.forward_tail(left.tail_node());
+ }
+ }
+
+ void create_graph_qasymm8(TensorDescriptor &input_descriptor)
+ {
+ // Create model path
+ const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model";
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
+
+ // Add model path to data path
+ if(!data_path.empty())
+ {
+ data_path += model_path;
+ }
+
+ const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
+ const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128);
+
+ const std::vector<QuantizationInfo> conv_weights_quant_info =
+ {
+ QuantizationInfo(0.03396892547607422f, 122), // Conv
+ QuantizationInfo(0.005167067516595125f, 125), // Conv1
+ QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1
+ };
+
+ // Pointwise expand convolution quantization info
+ const std::vector<QuantizationInfo> pwc_q =
+ {
+ QuantizationInfo(0.254282623529f, 129), // expand_0 (Dummy)
+ QuantizationInfo(0.009758507832884789f, 127), // expand_1
+ QuantizationInfo(0.0036556976847350597f, 144), // expand_2
+ QuantizationInfo(0.0029988749884068966f, 104), // expand_3
+ QuantizationInfo(0.0019244228024035692f, 128), // expand_4
+ QuantizationInfo(0.0013649158645421267f, 135), // expand_5
+ QuantizationInfo(0.0019170437008142471f, 127), // expand_6
+ QuantizationInfo(0.0015538912266492844f, 125), // expand_7
+ QuantizationInfo(0.0014702979242429137f, 134), // expand_8
+ QuantizationInfo(0.0013733493397012353f, 127), // expand_9
+ QuantizationInfo(0.0016282502328976989f, 131), // expand_10
+ QuantizationInfo(0.0016309921629726887f, 134), // expand_11
+ QuantizationInfo(0.0018258779309689999f, 138), // expand_12
+ QuantizationInfo(0.0013828007504343987f, 123), // expand_13
+ QuantizationInfo(0.0020222084131091833f, 135), // expand_14
+ QuantizationInfo(0.04281935095787048f, 102), // expand_15
+ QuantizationInfo(0.002046825597062707f, 135) // expand_16
+ };
+ // Depthwise expand convolution quantization info
+ const std::vector<QuantizationInfo> dwc_q =
+ {
+ QuantizationInfo(0.3436955213546753f, 165), // expand_0
+ QuantizationInfo(0.020969120785593987f, 109), // expand_1
+ QuantizationInfo(0.16981913149356842f, 52), // expand_2
+ QuantizationInfo(0.017202870920300484f, 143), // expand_3
+ QuantizationInfo(0.06525065749883652f, 118), // expand_4
+ QuantizationInfo(0.07909784466028214f, 95), // expand_5
+ QuantizationInfo(0.010087885893881321f, 127), // expand_6
+ QuantizationInfo(0.06092711538076401f, 110), // expand_7
+ QuantizationInfo(0.052407849580049515f, 133), // expand_8
+ QuantizationInfo(0.04077887907624245f, 155), // expand_9
+ QuantizationInfo(0.031107846647500992f, 143), // expand_10
+ QuantizationInfo(0.07080810517072678f, 66), // expand_11
+ QuantizationInfo(0.07448793947696686f, 159), // expand_12
+ QuantizationInfo(0.01525793131440878f, 92), // expand_13
+ QuantizationInfo(0.04166752099990845f, 147), // expand_14
+ QuantizationInfo(0.04281935095787048f, 102), // expand_15
+ QuantizationInfo(0.16456253826618195, 201) // expand_16
+ };
+ // Project convolution quantization info
+ const std::vector<QuantizationInfo> prwc_q =
+ {
+ QuantizationInfo(0.03737175464630127f, 140), // expand_0
+ QuantizationInfo(0.0225360207259655f, 156), // expand_1
+ QuantizationInfo(0.02740888111293316f, 122), // expand_2
+ QuantizationInfo(0.016844693571329117f, 111), // expand_3
+ QuantizationInfo(0.019062912091612816f, 146), // expand_4
+ QuantizationInfo(0.018293123692274094f, 128), // expand_5
+ QuantizationInfo(0.014601286500692368f, 147), // expand_6
+ QuantizationInfo(0.016782939434051514f, 124), // expand_7
+ QuantizationInfo(0.012898261658847332f, 125), // expand_8
+ QuantizationInfo(0.019561484456062317f, 144), // expand_9
+ QuantizationInfo(0.007436311338096857f, 129), // expand_10
+ QuantizationInfo(0.00838223285973072f, 136), // expand_11
+ QuantizationInfo(0.023982593789696693f, 154), // expand_12
+ QuantizationInfo(0.009447949007153511f, 140), // expand_13
+ QuantizationInfo(0.00789870135486126f, 139), // expand_14
+ QuantizationInfo(0.03697410225868225f, 131), // expand_15
+ QuantizationInfo(0.008009289391338825f, 111) // expand_16
+ };
+
+ graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
+ get_weights_accessor(data_path, common_params.image))
+ << ConvolutionLayer(
+ 3U, 3U, 32U,
+ get_weights_accessor(data_path, "Conv_weights.npy"),
+ get_weights_accessor(data_path, "Conv_bias.npy"),
+ PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+ 1, conv_weights_quant_info.at(0), mid_quant_info)
+ .set_name("Conv")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6")
+ << DepthwiseConvolutionLayer(3U, 3U,
+ get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"),
+ get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"),
+ PadStrideInfo(1, 1, 1, 1), 1, dwc_q.at(0))
+ .set_name("expanded_conv/depthwise/depthwise")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6")
+ << ConvolutionLayer(1U, 1U, 16U,
+ get_weights_accessor(data_path, "expanded_conv_project_weights.npy"),
+ get_weights_accessor(data_path, "expanded_conv_project_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0))
+ .set_name("expanded_conv/project/Conv2D");
+
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ pwc_q.at(1), dwc_q.at(1), prwc_q.at(1));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ pwc_q.at(3), dwc_q.at(3), prwc_q.at(3));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ pwc_q.at(6), dwc_q.at(6), prwc_q.at(6));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ pwc_q.at(13), dwc_q.at(13), prwc_q.at(13));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15));
+ get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16));
+
+ graph << ConvolutionLayer(1U, 1U, 1280U,
+ get_weights_accessor(data_path, "Conv_1_weights.npy"),
+ get_weights_accessor(data_path, "Conv_1_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1))
+ .set_name("Conv_1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+ << ConvolutionLayer(1U, 1U, 1001U,
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(2))
+ .set_name("Logits/Conv2d_1c_1x1");
+ }
+
+ void get_expanded_conv_qasymm8(const std::string &data_path, std::string &¶m_path, IsResidual is_residual,
+ unsigned int input_channels, unsigned int output_channels,
+ PadStrideInfo dwc_pad_stride_info,
+ const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji)
+ {
+ std::string total_path = param_path + "_";
+
+ SubStream left(graph);
+ left << ConvolutionLayer(1U, 1U, input_channels,
+ get_weights_accessor(data_path, total_path + "project_weights.npy"),
+ get_weights_accessor(data_path, total_path + "project_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0), 1, pwi)
+ .set_name(param_path + "/Conv2D")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6")
+ << DepthwiseConvolutionLayer(3U, 3U,
+ get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
+ get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"),
+ dwc_pad_stride_info, 1, dwi)
+ .set_name(param_path + "/depthwise/depthwise")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6")
+ << ConvolutionLayer(1U, 1U, output_channels,
+ get_weights_accessor(data_path, total_path + "project_weights.npy"),
+ get_weights_accessor(data_path, total_path + "project_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0), 1, pji)
+ .set_name(param_path + "/project/Conv2D");
+
+ if(is_residual == IsResidual::Yes)
{
// Add residual node
SubStream right(graph);
@@ -242,6 +451,8 @@
* "MobileNetV2: Inverted Residuals and Linear Bottlenecks"
* Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
*
+ * Provenance: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz
+ *
* @note To list all the possible arguments execute the binary appended with the --help option
*
* @param[in] argc Number of arguments