arm_compute v18.11
diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp
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+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph.h"
+#include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement ResNetV2_50 network using the Compute Library's graph API */
+class GraphResNetV2_50Example : public Example
+{
+public:
+ GraphResNetV2_50Example()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV2_50")
+ {
+ }
+ bool do_setup(int argc, char **argv) override
+ {
+ // Parse arguments
+ cmd_parser.parse(argc, argv);
+
+ // Consume common parameters
+ common_params = consume_common_graph_parameters(common_opts);
+
+ // Return when help menu is requested
+ if(common_params.help)
+ {
+ cmd_parser.print_help(argv[0]);
+ return false;
+ }
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+ ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::F16 && common_params.target == Target::NEON, "F16 NEON not supported for this graph");
+
+ // Print parameter values
+ std::cout << common_params << std::endl;
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
+ std::string model_path = "/cnn_data/resnet_v2_50_model/";
+ if(!data_path.empty())
+ {
+ data_path += model_path;
+ }
+
+ // Create a preprocessor object
+ std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+ TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+ // Set weights trained layout
+ const DataLayout weights_layout = DataLayout::NCHW;
+
+ graph << common_params.target
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false /* Do not convert to BGR */))
+ << ConvolutionLayer(
+ 7U, 7U, 64U,
+ get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
+ PadStrideInfo(2, 2, 3, 3))
+ .set_name("conv1/convolution")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
+
+ add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
+ add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
+ add_residual_block(data_path, "block3", weights_layout, 256, 6, 2);
+ add_residual_block(data_path, "block4", weights_layout, 512, 3, 1);
+
+ graph << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "postnorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "postnorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "postnorm_gamma.npy"),
+ get_weights_accessor(data_path, "postnorm_beta.npy"),
+ 0.000009999999747378752f)
+ .set_name("postnorm/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("postnorm/Relu")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
+ << ConvolutionLayer(
+ 1U, 1U, 1001U,
+ get_weights_accessor(data_path, "logits_weights.npy", weights_layout),
+ get_weights_accessor(data_path, "logits_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("logits/convolution")
+ << FlattenLayer().set_name("predictions/Reshape")
+ << SoftmaxLayer().set_name("predictions/Softmax")
+ << OutputLayer(get_output_accessor(common_params, 5));
+
+ // Finalize graph
+ GraphConfig config;
+ config.num_threads = common_params.threads;
+ config.use_tuner = common_params.enable_tuner;
+ graph.finalize(common_params.target, config);
+
+ return true;
+ }
+
+ void do_run() override
+ {
+ // Run graph
+ graph.run();
+ }
+
+private:
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
+
+ void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout,
+ unsigned int base_depth, unsigned int num_units, unsigned int stride)
+ {
+ for(unsigned int i = 0; i < num_units; ++i)
+ {
+ // Generate unit names
+ std::stringstream unit_path_ss;
+ unit_path_ss << name << "_unit_" << (i + 1) << "_bottleneck_v2_";
+ std::stringstream unit_name_ss;
+ unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v2/";
+
+ std::string unit_path = unit_path_ss.str();
+ std::string unit_name = unit_name_ss.str();
+
+ const TensorShape last_shape = graph.graph().node(graph.tail_node())->output(0)->desc().shape;
+ unsigned int depth_in = last_shape[arm_compute::get_data_layout_dimension_index(common_params.data_layout, DataLayoutDimension::CHANNEL)];
+ unsigned int depth_out = base_depth * 4;
+
+ // All units have stride 1 apart from last one
+ unsigned int middle_stride = (i == (num_units - 1)) ? stride : 1;
+
+ // Preact
+ SubStream preact(graph);
+ preact << BatchNormalizationLayer(
+ get_weights_accessor(data_path, unit_path + "preact_moving_mean.npy"),
+ get_weights_accessor(data_path, unit_path + "preact_moving_variance.npy"),
+ get_weights_accessor(data_path, unit_path + "preact_gamma.npy"),
+ get_weights_accessor(data_path, unit_path + "preact_beta.npy"),
+ 0.000009999999747378752f)
+ .set_name(unit_name + "preact/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "preact/Relu");
+
+ // Create bottleneck path
+ SubStream shortcut(graph);
+ if(depth_in == depth_out)
+ {
+ if(middle_stride != 1)
+ {
+ shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
+ }
+ }
+ else
+ {
+ shortcut.forward_tail(preact.tail_node());
+ shortcut << ConvolutionLayer(
+ 1U, 1U, depth_out,
+ get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "shortcut_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "shortcut/convolution");
+ }
+
+ // Create residual path
+ SubStream residual(preact);
+ residual << ConvolutionLayer(
+ 1U, 1U, base_depth,
+ get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv1/convolution")
+ << BatchNormalizationLayer(
+ 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"),
+ 0.000009999999747378752f)
+ .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_path + "conv2_weights.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(middle_stride, middle_stride, 1, 1))
+ .set_name(unit_name + "conv2/convolution")
+ << BatchNormalizationLayer(
+ 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"),
+ 0.000009999999747378752f)
+ .set_name(unit_name + "conv2/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+ << ConvolutionLayer(
+ 1U, 1U, depth_out,
+ get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
+ get_weights_accessor(data_path, unit_path + "conv3_biases.npy", weights_layout),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(unit_name + "conv3/convolution");
+
+ graph << EltwiseLayer(std::move(shortcut), std::move(residual), EltwiseOperation::Add).set_name(unit_name + "add");
+ }
+ }
+};
+
+/** Main program for ResNetV2_50
+ *
+ * Model is based on:
+ * https://arxiv.org/abs/1603.05027
+ * "Identity Mappings in Deep Residual Networks"
+ * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+ *
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, char **argv)
+{
+ return arm_compute::utils::run_example<GraphResNetV2_50Example>(argc, argv);
+}