diff options
| -rw-r--r-- | arm_compute/graph/nodes/DeconvolutionLayerNode.h | 10 | ||||
| -rw-r--r-- | arm_compute/graph/nodes/EltwiseLayerNode.h | 12 | ||||
| m--------- | data | 0 | ||||
| -rw-r--r-- | examples/graph_edsr.cpp | 108 | ||||
| -rw-r--r-- | examples/graph_edsr.h | 1281 | ||||
| -rw-r--r-- | src/graph/nodes/DeconvolutionLayerNode.cpp | 12 | ||||
| -rw-r--r-- | src/graph/nodes/EltwiseLayerNode.cpp | 15 |
7 files changed, 1422 insertions, 16 deletions
diff --git a/arm_compute/graph/nodes/DeconvolutionLayerNode.h b/arm_compute/graph/nodes/DeconvolutionLayerNode.h index 09601ab69..563389836 100644 --- a/arm_compute/graph/nodes/DeconvolutionLayerNode.h +++ b/arm_compute/graph/nodes/DeconvolutionLayerNode.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -36,9 +36,10 @@ class DeconvolutionLayerNode final : public INode public: /** Constructor * - * @param[in] info DeConvolution layer attributes + * @param[in] info DeConvolution layer attributes + * @param[in] out_quant_info (Optional) Output quantization infomation */ - DeconvolutionLayerNode(PadStrideInfo info); + DeconvolutionLayerNode(PadStrideInfo info, QuantizationInfo out_quant_info = QuantizationInfo()); /** Deconvolution metadata accessor * * @return Deconvolution information @@ -63,7 +64,8 @@ public: void accept(INodeVisitor &v) override; private: - PadStrideInfo _info; + PadStrideInfo _info; + QuantizationInfo _out_quant_info; }; } // namespace graph } // namespace arm_compute diff --git a/arm_compute/graph/nodes/EltwiseLayerNode.h b/arm_compute/graph/nodes/EltwiseLayerNode.h index 7fb0aff64..1ca8e9c23 100644 --- a/arm_compute/graph/nodes/EltwiseLayerNode.h +++ b/arm_compute/graph/nodes/EltwiseLayerNode.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -36,11 +36,12 @@ class EltwiseLayerNode final : public INode public: /** Constructor * - * @param[in] op Element-wise operation to perform - * @param[in] c_policy (Optional) Convert policy used for the operation - * @param[in] r_policy (Optional) Rounding policy used for the operation + * @param[in] op Element-wise operation to perform + * @param[in] out_quant_info (Optional) Output quantization information + * @param[in] c_policy (Optional) Convert policy used for the operation + * @param[in] r_policy (Optional) Rounding policy used for the operation */ - EltwiseLayerNode(EltwiseOperation op, ConvertPolicy c_policy = ConvertPolicy::SATURATE, RoundingPolicy r_policy = RoundingPolicy::TO_ZERO); + EltwiseLayerNode(EltwiseOperation op, QuantizationInfo out_quant_info = QuantizationInfo(), ConvertPolicy c_policy = ConvertPolicy::SATURATE, RoundingPolicy r_policy = RoundingPolicy::TO_ZERO); /** Eltwise operation accessor * * @return Eltwise operation that is to be performed by the node @@ -67,6 +68,7 @@ public: private: EltwiseOperation _op; + QuantizationInfo _out_quant_info; ConvertPolicy _convert_policy; RoundingPolicy _rounding_policy; }; diff --git a/data b/data -Subproject 528083d746da33998c493551b3d620782071847 +Subproject 4f2e2b36de1b0d4c45ecc8afbfb121f48564ca8 diff --git a/examples/graph_edsr.cpp b/examples/graph_edsr.cpp new file mode 100644 index 000000000..405c355bf --- /dev/null +++ b/examples/graph_edsr.cpp @@ -0,0 +1,108 @@ +/* + * Copyright (c) 2020 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/Utils.h" + +#include "support/ToolchainSupport.h" +#include "utils/CommonGraphOptions.h" +#include "utils/Utils.h" + +#include "graph_edsr.h" + +using namespace arm_compute::graph; +using namespace arm_compute::utils; + +class GraphEdsrExample : public Example +{ +public: + GraphEdsrExample() + : cmd_parser(), common_opts(cmd_parser), common_params() + { + expected_output_filename = cmd_parser.add_option<SimpleOption<std::string>>("expected-output-filename", ""); + expected_output_filename->set_help("Name of npy file containing the expected output to validate the graph output."); + } + + GraphEdsrExample(const GraphEdsrExample &) = delete; + GraphEdsrExample &operator=(const GraphEdsrExample &) = delete; + GraphEdsrExample(GraphEdsrExample &&) = default; + GraphEdsrExample &operator=(GraphEdsrExample &&) = default; + ~GraphEdsrExample() override = default; + + bool do_setup(int argc, char **argv) override + { + // Parse arguments + cmd_parser.parse(argc, argv); + cmd_parser.validate(); + + // 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; + } + + // Print parameter values + std::cout << common_params << std::endl; + + model.setup(common_params, *expected_output_filename); + + GraphConfig config; + config.num_threads = common_params.threads; + config.use_tuner = common_params.enable_tuner; + config.tuner_mode = common_params.tuner_mode; + config.tuner_file = common_params.tuner_file; + + context.set_config(config); + + auto pass_manager = create_default_pass_manager(common_params.target, config); + manager.finalize_graph(model.graph(), context, pass_manager, common_params.target); + + return true; + } + + void do_run() override + { + manager.execute_graph(model.graph()); + } + +private: + CommandLineParser cmd_parser; + CommonGraphOptions common_opts; + CommonGraphParams common_params; + + GraphContext context{}; + GraphManager manager{}; + + SimpleOption<std::string> *expected_output_filename{ nullptr }; + + GraphEdsr model{}; +}; + +int main(int argc, char **argv) +{ + return run_example<GraphEdsrExample>(argc, argv); +} diff --git a/examples/graph_edsr.h b/examples/graph_edsr.h new file mode 100644 index 000000000..8941430c7 --- /dev/null +++ b/examples/graph_edsr.h @@ -0,0 +1,1281 @@ +/* + * Copyright (c) 2020 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. + */ + +#ifndef ARM_COMPUTE_GRAPH_EDSR_H +#define ARM_COMPUTE_GRAPH_EDSR_H + +#include "arm_compute/graph.h" + +#include "utils/GraphUtils.h" + +class GraphEdsr +{ +public: + GraphEdsr() + : _graph(0, "EDSR") + { + } + + bool setup(const arm_compute::utils::CommonGraphParams &common_params, const arm_compute::utils::SimpleOption<std::string> &expected_output_filename) + { + using namespace arm_compute; + using namespace arm_compute::graph; + using namespace arm_compute::utils; + using namespace arm_compute::graph_utils; + + const auto &data_path = common_params.data_path; + const auto &target = common_params.target; + + NodeID id_upscale_net_FakeQuantWithMinMaxVars_transposed = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 12, 2, 2, 3 }, + DataType::QASYMM8, + QuantizationInfo(0.00393533194437623, 1), + DataLayout::NHWC }); + INode *node_upscale_net_FakeQuantWithMinMaxVars_transposed = _graph.node(id_upscale_net_FakeQuantWithMinMaxVars_transposed); + node_upscale_net_FakeQuantWithMinMaxVars_transposed->set_common_node_parameters(NodeParams{ "upscale_net_FakeQuantWithMinMaxVars_transposed", target }); + node_upscale_net_FakeQuantWithMinMaxVars_transposed->output(0)->set_accessor(get_weights_accessor(data_path, + "/cnn_data/edsr_model/upscale_net_FakeQuantWithMinMaxVars_transposed.npy", DataLayout::NHWC)); + + NodeID id_pre_upscale_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 12 }, + DataType::S32, + QuantizationInfo(2.9644968435604824e-06), + DataLayout::NHWC }); + INode *node_pre_upscale_Conv2D_bias = _graph.node(id_pre_upscale_Conv2D_bias); + node_pre_upscale_Conv2D_bias->set_common_node_parameters(NodeParams{ "pre_upscale_Conv2D_bias", target }); + node_pre_upscale_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/pre_upscale_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_pre_upscale_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 12 }, + DataType::QASYMM8, + QuantizationInfo(0.000455576169770211, 128), + DataLayout::NHWC }); + INode *node_pre_upscale_FakeQuantWithMinMaxVars = _graph.node(id_pre_upscale_FakeQuantWithMinMaxVars); + node_pre_upscale_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "pre_upscale_FakeQuantWithMinMaxVars", target }); + node_pre_upscale_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/pre_upscale_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_post_residual_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.2760000345224398e-06), + DataLayout::NHWC }); + INode *node_post_residual_Conv2D_bias = _graph.node(id_post_residual_Conv2D_bias); + node_post_residual_Conv2D_bias->set_common_node_parameters(NodeParams{ "post_residual_Conv2D_bias", target }); + node_post_residual_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/post_residual_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_post_residual_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00036424631252884865, 129), + DataLayout::NHWC }); + INode *node_post_residual_FakeQuantWithMinMaxVars = _graph.node(id_post_residual_FakeQuantWithMinMaxVars); + node_post_residual_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "post_residual_FakeQuantWithMinMaxVars", target }); + node_post_residual_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/post_residual_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + TensorShape scalar_4d_shape{}; + + scalar_4d_shape.set(0, 1, false).set(1, 1, false).set(2, 1, false).set(3, 1, false); + + NodeID id_mul_15_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_15_y = _graph.node(id_mul_15_y); + node_mul_15_y->set_common_node_parameters(NodeParams{ "mul_15_y", target }); + node_mul_15_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_15_y.npy", DataLayout::NHWC)); + + NodeID id_block_15_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.2441644230420934e-06), + DataLayout::NHWC }); + INode *node_block_15_1_Conv2D_bias = _graph.node(id_block_15_1_Conv2D_bias); + node_block_15_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_15_1_Conv2D_bias", target }); + node_block_15_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_15_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_15_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00037038681330159307, 125), + DataLayout::NHWC }); + INode *node_block_15_1_FakeQuantWithMinMaxVars = _graph.node(id_block_15_1_FakeQuantWithMinMaxVars); + node_block_15_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_15_1_FakeQuantWithMinMaxVars", target }); + node_block_15_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_15_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_14_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_14_y = _graph.node(id_mul_14_y); + node_mul_14_y->set_common_node_parameters(NodeParams{ "mul_14_y", target }); + node_mul_14_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_14_y.npy", DataLayout::NHWC)); + + NodeID id_block_14_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.3417260333881131e-06), + DataLayout::NHWC }); + INode *node_block_14_1_Conv2D_bias = _graph.node(id_block_14_1_Conv2D_bias); + node_block_14_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_14_1_Conv2D_bias", target }); + node_block_14_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_14_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_14_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00040307495510205626, 127), + DataLayout::NHWC }); + INode *node_block_14_1_FakeQuantWithMinMaxVars = _graph.node(id_block_14_1_FakeQuantWithMinMaxVars); + node_block_14_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_14_1_FakeQuantWithMinMaxVars", target }); + node_block_14_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_14_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_13_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_13_y = _graph.node(id_mul_13_y); + node_mul_13_y->set_common_node_parameters(NodeParams{ "mul_13_y", target }); + node_mul_13_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_13_y.npy", DataLayout::NHWC)); + + NodeID id_block_13_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.2636977544389083e-06), + DataLayout::NHWC }); + INode *node_block_13_1_Conv2D_bias = _graph.node(id_block_13_1_Conv2D_bias); + node_block_13_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_13_1_Conv2D_bias", target }); + node_block_13_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_13_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_13_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003858553245663643, 131), + DataLayout::NHWC }); + INode *node_block_13_1_FakeQuantWithMinMaxVars = _graph.node(id_block_13_1_FakeQuantWithMinMaxVars); + node_block_13_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_13_1_FakeQuantWithMinMaxVars", target }); + node_block_13_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_13_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_12_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_12_y = _graph.node(id_mul_12_y); + node_mul_12_y->set_common_node_parameters(NodeParams{ "mul_12_y", target }); + node_mul_12_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_12_y.npy", DataLayout::NHWC)); + + NodeID id_block_12_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.3479783547154511e-06), + DataLayout::NHWC }); + INode *node_block_12_1_Conv2D_bias = _graph.node(id_block_12_1_Conv2D_bias); + node_block_12_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_12_1_Conv2D_bias", target }); + node_block_12_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_12_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_12_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00041212860378436744, 130), + DataLayout::NHWC }); + INode *node_block_12_1_FakeQuantWithMinMaxVars = _graph.node(id_block_12_1_FakeQuantWithMinMaxVars); + node_block_12_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_12_1_FakeQuantWithMinMaxVars", target }); + node_block_12_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_12_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_11_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_11_y = _graph.node(id_mul_11_y); + node_mul_11_y->set_common_node_parameters(NodeParams{ "mul_11_y", target }); + node_mul_11_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_11_y.npy", DataLayout::NHWC)); + + NodeID id_block_11_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.2847248171965475e-06), + DataLayout::NHWC }); + INode *node_block_11_1_Conv2D_bias = _graph.node(id_block_11_1_Conv2D_bias); + node_block_11_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_11_1_Conv2D_bias", target }); + node_block_11_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_11_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_11_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00040296532097272575, 131), + DataLayout::NHWC }); + INode *node_block_11_1_FakeQuantWithMinMaxVars = _graph.node(id_block_11_1_FakeQuantWithMinMaxVars); + node_block_11_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_11_1_FakeQuantWithMinMaxVars", target }); + node_block_11_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_11_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_10_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_10_y = _graph.node(id_mul_10_y); + node_mul_10_y->set_common_node_parameters(NodeParams{ "mul_10_y", target }); + node_mul_10_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_10_y.npy", DataLayout::NHWC)); + + NodeID id_block_10_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.1997129831797793e-06), + DataLayout::NHWC }); + INode *node_block_10_1_Conv2D_bias = _graph.node(id_block_10_1_Conv2D_bias); + node_block_10_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_10_1_Conv2D_bias", target }); + node_block_10_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_10_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_10_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00036640543839894235, 129), + DataLayout::NHWC }); + INode *node_block_10_1_FakeQuantWithMinMaxVars = _graph.node(id_block_10_1_FakeQuantWithMinMaxVars); + node_block_10_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_10_1_FakeQuantWithMinMaxVars", target }); + node_block_10_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_10_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_9_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_9_y = _graph.node(id_mul_9_y); + node_mul_9_y->set_common_node_parameters(NodeParams{ "mul_9_y", target }); + node_mul_9_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_9_y.npy", DataLayout::NHWC)); + + NodeID id_block_9_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.1920226370421005e-06), + DataLayout::NHWC }); + INode *node_block_9_1_Conv2D_bias = _graph.node(id_block_9_1_Conv2D_bias); + node_block_9_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_9_1_Conv2D_bias", target }); + node_block_9_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_9_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_9_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003706997958943248, 129), + DataLayout::NHWC }); + INode *node_block_9_1_FakeQuantWithMinMaxVars = _graph.node(id_block_9_1_FakeQuantWithMinMaxVars); + node_block_9_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_9_1_FakeQuantWithMinMaxVars", target }); + node_block_9_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_9_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_8_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_8_y = _graph.node(id_mul_8_y); + node_mul_8_y->set_common_node_parameters(NodeParams{ "mul_8_y", target }); + node_mul_8_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_8_y.npy", DataLayout::NHWC)); + + NodeID id_block_8_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.218903321387188e-06), + DataLayout::NHWC }); + INode *node_block_8_1_Conv2D_bias = _graph.node(id_block_8_1_Conv2D_bias); + node_block_8_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_8_1_Conv2D_bias", target }); + node_block_8_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_8_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_8_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00038377835880964994, 127), + DataLayout::NHWC }); + INode *node_block_8_1_FakeQuantWithMinMaxVars = _graph.node(id_block_8_1_FakeQuantWithMinMaxVars); + node_block_8_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_8_1_FakeQuantWithMinMaxVars", target }); + node_block_8_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_8_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_7_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_7_y = _graph.node(id_mul_7_y); + node_mul_7_y->set_common_node_parameters(NodeParams{ "mul_7_y", target }); + node_mul_7_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_7_y.npy", DataLayout::NHWC)); + + NodeID id_block_7_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.257252392861119e-06), + DataLayout::NHWC }); + INode *node_block_7_1_Conv2D_bias = _graph.node(id_block_7_1_Conv2D_bias); + node_block_7_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_7_1_Conv2D_bias", target }); + node_block_7_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_7_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_7_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00039844686398282647, 129), + DataLayout::NHWC }); + INode *node_block_7_1_FakeQuantWithMinMaxVars = _graph.node(id_block_7_1_FakeQuantWithMinMaxVars); + node_block_7_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_7_1_FakeQuantWithMinMaxVars", target }); + node_block_7_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_7_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_6_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_6_y = _graph.node(id_mul_6_y); + node_mul_6_y->set_common_node_parameters(NodeParams{ "mul_6_y", target }); + node_mul_6_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_6_y.npy", DataLayout::NHWC)); + + NodeID id_block_6_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.244850636794581e-06), + DataLayout::NHWC }); + INode *node_block_6_1_Conv2D_bias = _graph.node(id_block_6_1_Conv2D_bias); + node_block_6_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_6_1_Conv2D_bias", target }); + node_block_6_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_6_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_6_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00040187727427110076, 132), + DataLayout::NHWC }); + INode *node_block_6_1_FakeQuantWithMinMaxVars = _graph.node(id_block_6_1_FakeQuantWithMinMaxVars); + node_block_6_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_6_1_FakeQuantWithMinMaxVars", target }); + node_block_6_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_6_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_5_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_5_y = _graph.node(id_mul_5_y); + node_mul_5_y->set_common_node_parameters(NodeParams{ "mul_5_y", target }); + node_mul_5_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_5_y.npy", DataLayout::NHWC)); + + NodeID id_block_5_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.241092718373693e-06), + DataLayout::NHWC }); + INode *node_block_5_1_Conv2D_bias = _graph.node(id_block_5_1_Conv2D_bias); + node_block_5_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_5_1_Conv2D_bias", target }); + node_block_5_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_5_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_5_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003938926674891263, 129), + DataLayout::NHWC }); + INode *node_block_5_1_FakeQuantWithMinMaxVars = _graph.node(id_block_5_1_FakeQuantWithMinMaxVars); + node_block_5_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_5_1_FakeQuantWithMinMaxVars", target }); + node_block_5_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_5_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_4_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_4_y = _graph.node(id_mul_4_y); + node_mul_4_y->set_common_node_parameters(NodeParams{ "mul_4_y", target }); + node_mul_4_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_4_y.npy", DataLayout::NHWC)); + + NodeID id_block_4_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.1748390988941537e-06), + DataLayout::NHWC }); + INode *node_block_4_1_Conv2D_bias = _graph.node(id_block_4_1_Conv2D_bias); + node_block_4_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_4_1_Conv2D_bias", target }); + node_block_4_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_4_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_4_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003788181929849088, 129), + DataLayout::NHWC }); + INode *node_block_4_1_FakeQuantWithMinMaxVars = _graph.node(id_block_4_1_FakeQuantWithMinMaxVars); + node_block_4_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_4_1_FakeQuantWithMinMaxVars", target }); + node_block_4_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_4_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_3_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_3_y = _graph.node(id_mul_3_y); + node_mul_3_y->set_common_node_parameters(NodeParams{ "mul_3_y", target }); + node_mul_3_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_3_y.npy", DataLayout::NHWC)); + + NodeID id_block_3_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.1937011095142225e-06), + DataLayout::NHWC }); + INode *node_block_3_1_Conv2D_bias = _graph.node(id_block_3_1_Conv2D_bias); + node_block_3_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_3_1_Conv2D_bias", target }); + node_block_3_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_3_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_3_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003944312920793891, 129), + DataLayout::NHWC }); + INode *node_block_3_1_FakeQuantWithMinMaxVars = _graph.node(id_block_3_1_FakeQuantWithMinMaxVars); + node_block_3_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_3_1_FakeQuantWithMinMaxVars", target }); + node_block_3_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_3_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_2_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_2_y = _graph.node(id_mul_2_y); + node_mul_2_y->set_common_node_parameters(NodeParams{ "mul_2_y", target }); + node_mul_2_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_2_y.npy", DataLayout::NHWC)); + + NodeID id_block_2_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.1634580232566805e-06), + DataLayout::NHWC }); + INode *node_block_2_1_Conv2D_bias = _graph.node(id_block_2_1_Conv2D_bias); + node_block_2_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_2_1_Conv2D_bias", target }); + node_block_2_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_2_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_2_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0003789655165746808, 132), + DataLayout::NHWC }); + INode *node_block_2_1_FakeQuantWithMinMaxVars = _graph.node(id_block_2_1_FakeQuantWithMinMaxVars); + node_block_2_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_2_1_FakeQuantWithMinMaxVars", target }); + node_block_2_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_2_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_1_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_1_y = _graph.node(id_mul_1_y); + node_mul_1_y->set_common_node_parameters(NodeParams{ "mul_1_y", target }); + node_mul_1_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_1_y.npy", DataLayout::NHWC)); + + NodeID id_block_1_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.197920255435747e-06), + DataLayout::NHWC }); + INode *node_block_1_1_Conv2D_bias = _graph.node(id_block_1_1_Conv2D_bias); + node_block_1_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_1_1_Conv2D_bias", target }); + node_block_1_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_1_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_1_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00038527738070115447, 132), + DataLayout::NHWC }); + INode *node_block_1_1_FakeQuantWithMinMaxVars = _graph.node(id_block_1_1_FakeQuantWithMinMaxVars); + node_block_1_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_1_1_FakeQuantWithMinMaxVars", target }); + node_block_1_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_1_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_mul_y = _graph.add_node<ConstNode>( + TensorDescriptor + { + scalar_4d_shape, + DataType::QASYMM8, + QuantizationInfo(0.0003921568568330258), + DataLayout::NHWC }); + INode *node_mul_y = _graph.node(id_mul_y); + node_mul_y->set_common_node_parameters(NodeParams{ "mul_y", target }); + node_mul_y->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/mul_y.npy", DataLayout::NHWC)); + + NodeID id_block_0_1_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.315485519626236e-06), + DataLayout::NHWC }); + INode *node_block_0_1_Conv2D_bias = _graph.node(id_block_0_1_Conv2D_bias); + node_block_0_1_Conv2D_bias->set_common_node_parameters(NodeParams{ "block_0_1_Conv2D_bias", target }); + node_block_0_1_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_0_1_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_block_0_1_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.00039420535904355347, 129), + DataLayout::NHWC }); + INode *node_block_0_1_FakeQuantWithMinMaxVars = _graph.node(id_block_0_1_FakeQuantWithMinMaxVars); + node_block_0_1_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "block_0_1_FakeQuantWithMinMaxVars", target }); + node_block_0_1_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/block_0_1_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + NodeID id_pre_residual_Conv2D_bias = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 256 }, + DataType::S32, + QuantizationInfo(1.7214160834555514e-06), + DataLayout::NHWC }); + INode *node_pre_residual_Conv2D_bias = _graph.node(id_pre_residual_Conv2D_bias); + node_pre_residual_Conv2D_bias->set_common_node_parameters(NodeParams{ "pre_residual_Conv2D_bias", target }); + node_pre_residual_Conv2D_bias->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/pre_residual_Conv2D_bias.npy", DataLayout::NHWC)); + + NodeID id_pre_residual_FakeQuantWithMinMaxVars = _graph.add_node<ConstNode>( + TensorDescriptor + { + TensorShape{ 3, 3, 3, 256 }, + DataType::QASYMM8, + QuantizationInfo(0.0004389610840007663, 127), + DataLayout::NHWC }); + INode *node_pre_residual_FakeQuantWithMinMaxVars = _graph.node(id_pre_residual_FakeQuantWithMinMaxVars); + node_pre_residual_FakeQuantWithMinMaxVars->set_common_node_parameters(NodeParams{ "pre_residual_FakeQuantWithMinMaxVars", target }); + node_pre_residual_FakeQuantWithMinMaxVars->output(0)->set_accessor(get_weights_accessor(data_path, "/cnn_data/edsr_model/pre_residual_FakeQuantWithMinMaxVars.npy", + DataLayout::NHWC)); + + TensorShape input_shape{}; + input_shape.set(0, 3, false).set(1, 360, false).set(2, 640, false).set(3, 1, false); + + NodeID id_input = _graph.add_node<InputNode>( + TensorDescriptor + { + input_shape, + DataType::QASYMM8, + QuantizationInfo(0.003921568859368563), + DataLayout::NHWC }); + INode *node_input = _graph.node(id_input); + node_input->set_common_node_parameters(NodeParams{ "input", target }); + node_input->output(0)->set_accessor(get_input_accessor(common_params)); + + NodeID id_pre_residual_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.0033370566088706255, 96)); + INode *node_pre_residual_BiasAdd = _graph.node(id_pre_residual_BiasAdd); + node_pre_residual_BiasAdd->set_common_node_parameters(NodeParams{ "pre_residual_BiasAdd", target }); + _graph.add_connection(id_input, 0, id_pre_residual_BiasAdd, 0); + _graph.add_connection(id_pre_residual_FakeQuantWithMinMaxVars, 0, id_pre_residual_BiasAdd, 1); + _graph.add_connection(id_pre_residual_Conv2D_bias, 0, id_pre_residual_BiasAdd, 2); + + NodeID id_block_0_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.007344874087721109, 185)); + INode *node_block_0_1_BiasAdd = _graph.node(id_block_0_1_BiasAdd); + node_block_0_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_0_1_BiasAdd", target }); + _graph.add_connection(id_pre_residual_BiasAdd, 0, id_block_0_1_BiasAdd, 0); + _graph.add_connection(id_block_0_1_FakeQuantWithMinMaxVars, 0, id_block_0_1_BiasAdd, 1); + _graph.add_connection(id_block_0_1_Conv2D_bias, 0, id_block_0_1_BiasAdd, 2); + + NodeID id_mul = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0006341293919831514, 174 }); + INode *node_mul = _graph.node(id_mul); + node_mul->set_common_node_parameters(NodeParams{ "mul", target }); + _graph.add_connection(id_block_0_1_BiasAdd, 0, id_mul, 0); + _graph.add_connection(id_mul_y, 0, id_mul, 1); + + NodeID id_add = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0031092411372810602, 95 }); + INode *node_add = _graph.node(id_add); + node_add->set_common_node_parameters(NodeParams{ "add", target }); + _graph.add_connection(id_pre_residual_BiasAdd, 0, id_add, 0); + _graph.add_connection(id_mul, 0, id_add, 1); + + NodeID id_block_1_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.005333727691322565, 117)); + INode *node_block_1_1_BiasAdd = _graph.node(id_block_1_1_BiasAdd); + node_block_1_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_1_1_BiasAdd", target }); + _graph.add_connection(id_add, 0, id_block_1_1_BiasAdd, 0); + _graph.add_connection(id_block_1_1_FakeQuantWithMinMaxVars, 0, id_block_1_1_BiasAdd, 1); + _graph.add_connection(id_block_1_1_Conv2D_bias, 0, id_block_1_1_BiasAdd, 2); + + NodeID id_mul_1 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004965941770933568, 122 }); + INode *node_mul_1 = _graph.node(id_mul_1); + node_mul_1->set_common_node_parameters(NodeParams{ "mul_1", target }); + _graph.add_connection(id_block_1_1_BiasAdd, 0, id_mul_1, 0); + _graph.add_connection(id_mul_1_y, 0, id_mul_1, 1); + + NodeID id_add_1 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0030700892675668, 96 }); + INode *node_add_1 = _graph.node(id_add_1); + node_add_1->set_common_node_parameters(NodeParams{ "add_1", target }); + _graph.add_connection(id_add, 0, id_add_1, 0); + _graph.add_connection(id_mul_1, 0, id_add_1, 1); + + NodeID id_block_2_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004199742339551449, 132)); + INode *node_block_2_1_BiasAdd = _graph.node(id_block_2_1_BiasAdd); + node_block_2_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_2_1_BiasAdd", target }); + _graph.add_connection(id_add_1, 0, id_block_2_1_BiasAdd, 0); + _graph.add_connection(id_block_2_1_FakeQuantWithMinMaxVars, 0, id_block_2_1_BiasAdd, 1); + _graph.add_connection(id_block_2_1_Conv2D_bias, 0, id_block_2_1_BiasAdd, 2); + + NodeID id_mul_2 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004133903712499887, 130 }); + INode *node_mul_2 = _graph.node(id_mul_2); + node_mul_2->set_common_node_parameters(NodeParams{ "mul_2", target }); + _graph.add_connection(id_block_2_1_BiasAdd, 0, id_mul_2, 0); + _graph.add_connection(id_mul_2_y, 0, id_mul_2, 1); + + NodeID id_add_2 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.003026385325938463, 94 }); + INode *node_add_2 = _graph.node(id_add_2); + node_add_2->set_common_node_parameters(NodeParams{ "add_2", target }); + _graph.add_connection(id_add_1, 0, id_add_2, 0); + _graph.add_connection(id_mul_2, 0, id_add_2, 1); + + NodeID id_block_3_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.003977528307586908, 142)); + INode *node_block_3_1_BiasAdd = _graph.node(id_block_3_1_BiasAdd); + node_block_3_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_3_1_BiasAdd", target }); + _graph.add_connection(id_add_2, 0, id_block_3_1_BiasAdd, 0); + _graph.add_connection(id_block_3_1_FakeQuantWithMinMaxVars, 0, id_block_3_1_BiasAdd, 1); + _graph.add_connection(id_block_3_1_Conv2D_bias, 0, id_block_3_1_BiasAdd, 2); + + NodeID id_mul_3 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0003943995980080217, 141 }); + INode *node_mul_3 = _graph.node(id_mul_3); + node_mul_3->set_common_node_parameters(NodeParams{ "mul_3", target }); + _graph.add_connection(id_block_3_1_BiasAdd, 0, id_mul_3, 0); + _graph.add_connection(id_mul_3_y, 0, id_mul_3, 1); + + NodeID id_add_3 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.003101327223703265, 98 }); + INode *node_add_3 = _graph.node(id_add_3); + node_add_3->set_common_node_parameters(NodeParams{ "add_3", target }); + _graph.add_connection(id_add_2, 0, id_add_3, 0); + _graph.add_connection(id_mul_3, 0, id_add_3, 1); + + NodeID id_block_4_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.0045388080179691315, 146)); + INode *node_block_4_1_BiasAdd = _graph.node(id_block_4_1_BiasAdd); + node_block_4_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_4_1_BiasAdd", target }); + _graph.add_connection(id_add_3, 0, id_block_4_1_BiasAdd, 0); + _graph.add_connection(id_block_4_1_FakeQuantWithMinMaxVars, 0, id_block_4_1_BiasAdd, 1); + _graph.add_connection(id_block_4_1_Conv2D_bias, 0, id_block_4_1_BiasAdd, 2); + + NodeID id_mul_4 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00044342130422592163, 143 }); + INode *node_mul_4 = _graph.node(id_mul_4); + node_mul_4->set_common_node_parameters(NodeParams{ "mul_4", target }); + _graph.add_connection(id_block_4_1_BiasAdd, 0, id_mul_4, 0); + _graph.add_connection(id_mul_4_y, 0, id_mul_4, 1); + + NodeID id_add_4 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.003150839824229479, 98 }); + INode *node_add_4 = _graph.node(id_add_4); + node_add_4->set_common_node_parameters(NodeParams{ "add_4", target }); + _graph.add_connection(id_add_3, 0, id_add_4, 0); + _graph.add_connection(id_mul_4, 0, id_add_4, 1); + + NodeID id_block_5_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.00402890844270587, 132)); + INode *node_block_5_1_BiasAdd = _graph.node(id_block_5_1_BiasAdd); + node_block_5_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_5_1_BiasAdd", target }); + _graph.add_connection(id_add_4, 0, id_block_5_1_BiasAdd, 0); + _graph.add_connection(id_block_5_1_FakeQuantWithMinMaxVars, 0, id_block_5_1_BiasAdd, 1); + _graph.add_connection(id_block_5_1_Conv2D_bias, 0, id_block_5_1_BiasAdd, 2); + + NodeID id_mul_5 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004023382789455354, 132 }); + INode *node_mul_5 = _graph.node(id_mul_5); + node_mul_5->set_common_node_parameters(NodeParams{ "mul_5", target }); + _graph.add_connection(id_block_5_1_BiasAdd, 0, id_mul_5, 0); + _graph.add_connection(id_mul_5_y, 0, id_mul_5, 1); + + NodeID id_add_5 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0030975888948887587, 94 }); + INode *node_add_5 = _graph.node(id_add_5); + node_add_5->set_common_node_parameters(NodeParams{ "add_5", target }); + _graph.add_connection(id_add_4, 0, id_add_5, 0); + _graph.add_connection(id_mul_5, 0, id_add_5, 1); + + NodeID id_block_6_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.00421866774559021, 125)); + INode *node_block_6_1_BiasAdd = _graph.node(id_block_6_1_BiasAdd); + node_block_6_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_6_1_BiasAdd", target }); + _graph.add_connection(id_add_5, 0, id_block_6_1_BiasAdd, 0); + _graph.add_connection(id_block_6_1_FakeQuantWithMinMaxVars, 0, id_block_6_1_BiasAdd, 1); + _graph.add_connection(id_block_6_1_Conv2D_bias, 0, id_block_6_1_BiasAdd, 2); + + NodeID id_mul_6 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00041950203012675047, 125 }); + INode *node_mul_6 = _graph.node(id_mul_6); + node_mul_6->set_common_node_parameters(NodeParams{ "mul_6", target }); + _graph.add_connection(id_block_6_1_BiasAdd, 0, id_mul_6, 0); + _graph.add_connection(id_mul_6_y, 0, id_mul_6, 1); + + NodeID id_add_6 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.003155382815748453, 92 }); + INode *node_add_6 = _graph.node(id_add_6); + node_add_6->set_common_node_parameters(NodeParams{ "add_6", target }); + _graph.add_connection(id_add_5, 0, id_add_6, 0); + _graph.add_connection(id_mul_6, 0, id_add_6, 1); + + NodeID id_block_7_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004250136204063892, 143)); + INode *node_block_7_1_BiasAdd = _graph.node(id_block_7_1_BiasAdd); + node_block_7_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_7_1_BiasAdd", target }); + _graph.add_connection(id_add_6, 0, id_block_7_1_BiasAdd, 0); + _graph.add_connection(id_block_7_1_FakeQuantWithMinMaxVars, 0, id_block_7_1_BiasAdd, 1); + _graph.add_connection(id_block_7_1_Conv2D_bias, 0, id_block_7_1_BiasAdd, 2); + + NodeID id_mul_7 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00042401350219734013, 142 }); + INode *node_mul_7 = _graph.node(id_mul_7); + node_mul_7->set_common_node_parameters(NodeParams{ "mul_7", target }); + _graph.add_connection(id_block_7_1_BiasAdd, 0, id_mul_7, 0); + _graph.add_connection(id_mul_7_y, 0, id_mul_7, 1); + + NodeID id_add_7 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0031760605052113533, 86 }); + INode *node_add_7 = _graph.node(id_add_7); + node_add_7->set_common_node_parameters(NodeParams{ "add_7", target }); + _graph.add_connection(id_add_6, 0, id_add_7, 0); + _graph.add_connection(id_mul_7, 0, id_add_7, 1); + + NodeID id_block_8_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004277155734598637, 123)); + INode *node_block_8_1_BiasAdd = _graph.node(id_block_8_1_BiasAdd); + node_block_8_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_8_1_BiasAdd", target }); + _graph.add_connection(id_add_7, 0, id_block_8_1_BiasAdd, 0); + _graph.add_connection(id_block_8_1_FakeQuantWithMinMaxVars, 0, id_block_8_1_BiasAdd, 1); + _graph.add_connection(id_block_8_1_Conv2D_bias, 0, id_block_8_1_BiasAdd, 2); + + NodeID id_mul_8 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00042673019925132394, 123 }); + INode *node_mul_8 = _graph.node(id_mul_8); + node_mul_8->set_common_node_parameters(NodeParams{ "mul_8", target }); + _graph.add_connection(id_block_8_1_BiasAdd, 0, id_mul_8, 0); + _graph.add_connection(id_mul_8_y, 0, id_mul_8, 1); + + NodeID id_add_8 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0032156009692698717, 86 }); + INode *node_add_8 = _graph.node(id_add_8); + node_add_8->set_common_node_parameters(NodeParams{ "add_8", target }); + _graph.add_connection(id_add_7, 0, id_add_8, 0); + _graph.add_connection(id_mul_8, 0, id_add_8, 1); + + NodeID id_block_9_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.00445037754252553, 129)); + INode *node_block_9_1_BiasAdd = _graph.node(id_block_9_1_BiasAdd); + node_block_9_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_9_1_BiasAdd", target }); + _graph.add_connection(id_add_8, 0, id_block_9_1_BiasAdd, 0); + _graph.add_connection(id_block_9_1_FakeQuantWithMinMaxVars, 0, id_block_9_1_BiasAdd, 1); + _graph.add_connection(id_block_9_1_Conv2D_bias, 0, id_block_9_1_BiasAdd, 2); + + NodeID id_mul_9 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004448975087143481, 129 }); + INode *node_mul_9 = _graph.node(id_mul_9); + node_mul_9->set_common_node_parameters(NodeParams{ "mul_9", target }); + _graph.add_connection(id_block_9_1_BiasAdd, 0, id_mul_9, 0); + _graph.add_connection(id_mul_9_y, 0, id_mul_9, 1); + + NodeID id_add_9 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0032742770854383707, 80 }); + INode *node_add_9 = _graph.node(id_add_9); + node_add_9->set_common_node_parameters(NodeParams{ "add_9", target }); + _graph.add_connection(id_add_8, 0, id_add_9, 0); + _graph.add_connection(id_mul_9, 0, id_add_9, 1); + + NodeID id_block_10_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.003614710411056876, 131)); + INode *node_block_10_1_BiasAdd = _graph.node(id_block_10_1_BiasAdd); + node_block_10_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_10_1_BiasAdd", target }); + _graph.add_connection(id_add_9, 0, id_block_10_1_BiasAdd, 0); + _graph.add_connection(id_block_10_1_FakeQuantWithMinMaxVars, 0, id_block_10_1_BiasAdd, 1); + _graph.add_connection(id_block_10_1_Conv2D_bias, 0, id_block_10_1_BiasAdd, 2); + + NodeID id_mul_10 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00036083892337046564, 130 }); + INode *node_mul_10 = _graph.node(id_mul_10); + node_mul_10->set_common_node_parameters(NodeParams{ "mul_10", target }); + _graph.add_connection(id_block_10_1_BiasAdd, 0, id_mul_10, 0); + _graph.add_connection(id_mul_10_y, 0, id_mul_10, 1); + + NodeID id_add_10 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0031881770119071007, 81 }); + INode *node_add_10 = _graph.node(id_add_10); + node_add_10->set_common_node_parameters(NodeParams{ "add_10", target }); + _graph.add_connection(id_add_9, 0, id_add_10, 0); + _graph.add_connection(id_mul_10, 0, id_add_10, 1); + + NodeID id_block_11_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.003969002980738878, 133)); + INode *node_block_11_1_BiasAdd = _graph.node(id_block_11_1_BiasAdd); + node_block_11_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_11_1_BiasAdd", target }); + _graph.add_connection(id_add_10, 0, id_block_11_1_BiasAdd, 0); + _graph.add_connection(id_block_11_1_FakeQuantWithMinMaxVars, 0, id_block_11_1_BiasAdd, 1); + _graph.add_connection(id_block_11_1_Conv2D_bias, 0, id_block_11_1_BiasAdd, 2); + + NodeID id_mul_11 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0003968806122429669, 133 }); + INode *node_mul_11 = _graph.node(id_mul_11); + node_mul_11->set_common_node_parameters(NodeParams{ "mul_11", target }); + _graph.add_connection(id_block_11_1_BiasAdd, 0, id_mul_11, 0); + _graph.add_connection(id_mul_11_y, 0, id_mul_11, 1); + + NodeID id_add_11 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0032707711216062307, 80 }); + INode *node_add_11 = _graph.node(id_add_11); + node_add_11->set_common_node_parameters(NodeParams{ "add_11", target }); + _graph.add_connection(id_add_10, 0, id_add_11, 0); + _graph.add_connection(id_mul_11, 0, id_add_11, 1); + + NodeID id_block_12_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004366801120340824, 110)); + INode *node_block_12_1_BiasAdd = _graph.node(id_block_12_1_BiasAdd); + node_block_12_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_12_1_BiasAdd", target }); + _graph.add_connection(id_add_11, 0, id_block_12_1_BiasAdd, 0); + _graph.add_connection(id_block_12_1_FakeQuantWithMinMaxVars, 0, id_block_12_1_BiasAdd, 1); + _graph.add_connection(id_block_12_1_Conv2D_bias, 0, id_block_12_1_BiasAdd, 2); + + NodeID id_mul_12 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004365936329122633, 110 }); + INode *node_mul_12 = _graph.node(id_mul_12); + node_mul_12->set_common_node_parameters(NodeParams{ "mul_12", target }); + _graph.add_connection(id_block_12_1_BiasAdd, 0, id_mul_12, 0); + _graph.add_connection(id_mul_12_y, 0, id_mul_12, 1); + + NodeID id_add_12 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.003275055903941393, 79 }); + INode *node_add_12 = _graph.node(id_add_12); + node_add_12->set_common_node_parameters(NodeParams{ "add_12", target }); + _graph.add_connection(id_add_11, 0, id_add_12, 0); + _graph.add_connection(id_mul_12, 0, id_add_12, 1); + + NodeID id_block_13_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004386766813695431, 139)); + INode *node_block_13_1_BiasAdd = _graph.node(id_block_13_1_BiasAdd); + node_block_13_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_13_1_BiasAdd", target }); + _graph.add_connection(id_add_12, 0, id_block_13_1_BiasAdd, 0); + _graph.add_connection(id_block_13_1_FakeQuantWithMinMaxVars, 0, id_block_13_1_BiasAdd, 1); + _graph.add_connection(id_block_13_1_Conv2D_bias, 0, id_block_13_1_BiasAdd, 2); + + NodeID id_mul_13 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004385628562886268, 139 }); + INode *node_mul_13 = _graph.node(id_mul_13); + node_mul_13->set_common_node_parameters(NodeParams{ "mul_13", target }); + _graph.add_connection(id_block_13_1_BiasAdd, 0, id_mul_13, 0); + _graph.add_connection(id_mul_13_y, 0, id_mul_13, 1); + + NodeID id_add_13 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0033287261612713337, 78 }); + INode *node_add_13 = _graph.node(id_add_13); + node_add_13->set_common_node_parameters(NodeParams{ "add_13", target }); + _graph.add_connection(id_add_12, 0, id_add_13, 0); + _graph.add_connection(id_mul_13, 0, id_add_13, 1); + + NodeID id_block_14_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.0038069337606430054, 130)); + INode *node_block_14_1_BiasAdd = _graph.node(id_block_14_1_BiasAdd); + node_block_14_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_14_1_BiasAdd", target }); + _graph.add_connection(id_add_13, 0, id_block_14_1_BiasAdd, 0); + _graph.add_connection(id_block_14_1_FakeQuantWithMinMaxVars, 0, id_block_14_1_BiasAdd, 1); + _graph.add_connection(id_block_14_1_Conv2D_bias, 0, id_block_14_1_BiasAdd, 2); + + NodeID id_mul_14 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.00037829321809113026, 130 }); + INode *node_mul_14 = _graph.node(id_mul_14); + node_mul_14->set_common_node_parameters(NodeParams{ "mul_14", target }); + _graph.add_connection(id_block_14_1_BiasAdd, 0, id_mul_14, 0); + _graph.add_connection(id_mul_14_y, 0, id_mul_14, 1); + + NodeID id_add_14 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0033590947277843952, 77 }); + INode *node_add_14 = _graph.node(id_add_14); + node_add_14->set_common_node_parameters(NodeParams{ "add_14", target }); + _graph.add_connection(id_add_13, 0, id_add_14, 0); + _graph.add_connection(id_mul_14, 0, id_add_14, 1); + + NodeID id_block_15_1_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.004009159281849861, 130)); + INode *node_block_15_1_BiasAdd = _graph.node(id_block_15_1_BiasAdd); + node_block_15_1_BiasAdd->set_common_node_parameters(NodeParams{ "block_15_1_BiasAdd", target }); + _graph.add_connection(id_add_14, 0, id_block_15_1_BiasAdd, 0); + _graph.add_connection(id_block_15_1_FakeQuantWithMinMaxVars, 0, id_block_15_1_BiasAdd, 1); + _graph.add_connection(id_block_15_1_Conv2D_bias, 0, id_block_15_1_BiasAdd, 2); + + NodeID id_mul_15 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Mul, QuantizationInfo{ 0.0004008286341559142, 130 }); + INode *node_mul_15 = _graph.node(id_mul_15); + node_mul_15->set_common_node_parameters(NodeParams{ "mul_15", target }); + _graph.add_connection(id_block_15_1_BiasAdd, 0, id_mul_15, 0); + _graph.add_connection(id_mul_15_y, 0, id_mul_15, 1); + + NodeID id_add_15 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0035031239967793226, 78 }); + INode *node_add_15 = _graph.node(id_add_15); + node_add_15->set_common_node_parameters(NodeParams{ "add_15", target }); + _graph.add_connection(id_add_14, 0, id_add_15, 0); + _graph.add_connection(id_mul_15, 0, id_add_15, 1); + + NodeID id_post_residual_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.005167999770492315, 112)); + INode *node_post_residual_BiasAdd = _graph.node(id_post_residual_BiasAdd); + node_post_residual_BiasAdd->set_common_node_parameters(NodeParams{ "post_residual_BiasAdd", target }); + _graph.add_connection(id_add_15, 0, id_post_residual_BiasAdd, 0); + _graph.add_connection(id_post_residual_FakeQuantWithMinMaxVars, 0, id_post_residual_BiasAdd, 1); + _graph.add_connection(id_post_residual_Conv2D_bias, 0, id_post_residual_BiasAdd, 2); + + NodeID id_add_16 = _graph.add_node<EltwiseLayerNode>( + EltwiseOperation::Add, QuantizationInfo{ 0.0065071373246610165, 89 }); + INode *node_add_16 = _graph.node(id_add_16); + node_add_16->set_common_node_parameters(NodeParams{ "add_16", target }); + _graph.add_connection(id_post_residual_BiasAdd, 0, id_add_16, 0); + _graph.add_connection(id_pre_residual_BiasAdd, 0, id_add_16, 1); + + NodeID id_pre_upscale_BiasAdd = _graph.add_node<ConvolutionLayerNode>( + PadStrideInfo + { + 1, 1, + 1, 1, + 1, 1, + DimensionRoundingType::FLOOR }, + 1, + arm_compute::graph::ConvolutionMethod::Default, + FastMathHint::Disabled, + QuantizationInfo(0.005013593938201666, 26)); + INode *node_pre_upscale_BiasAdd = _graph.node(id_pre_upscale_BiasAdd); + node_pre_upscale_BiasAdd->set_common_node_parameters(NodeParams{ "pre_upscale_BiasAdd", target }); + _graph.add_connection(id_add_16, 0, id_pre_upscale_BiasAdd, 0); + _graph.add_connection(id_pre_upscale_FakeQuantWithMinMaxVars, 0, id_pre_upscale_BiasAdd, 1); + _graph.add_connection(id_pre_upscale_Conv2D_bias, 0, id_pre_upscale_BiasAdd, 2); + + NodeID id_upscale_net_FakeQuantWithMinMaxVars_1 = _graph.add_node<DeconvolutionLayerNode>( + PadStrideInfo + { + 2, 2, + 0, 0, + 0, 0, + DimensionRoundingType::FLOOR }, + QuantizationInfo{ 0.004990961868315935, 26 }); + INode *node_upscale_net_FakeQuantWithMinMaxVars_1 = _graph.node(id_upscale_net_FakeQuantWithMinMaxVars_1); + node_upscale_net_FakeQuantWithMinMaxVars_1->set_common_node_parameters(NodeParams{ "upscale_net_FakeQuantWithMinMaxVars_1", target }); + _graph.add_connection(id_pre_upscale_BiasAdd, 0, id_upscale_net_FakeQuantWithMinMaxVars_1, 0); + _graph.add_connection(id_upscale_net_FakeQuantWithMinMaxVars_transposed, 0, id_upscale_net_FakeQuantWithMinMaxVars_1, 1); + TensorShape output_shape; + output_shape.set(0, 3, false).set(1, 720, false).set(2, 1280, false).set(3, 1, false); + + NodeID id_output_140211982446376 = _graph.add_node<OutputNode>(); + INode *node_output_140211982446376 = _graph.node(id_output_140211982446376); + node_output_140211982446376->set_common_node_parameters(NodeParams{ "output_140211982446376", target }); + _graph.add_connection(id_upscale_net_FakeQuantWithMinMaxVars_1, 0, id_output_140211982446376, 0); + node_output_140211982446376->input(0)->set_accessor(get_npy_output_accessor(expected_output_filename.value(), output_shape, common_params.data_type, + common_params.data_layout)); + + return true; + } + + arm_compute::graph::Graph &graph() + { + return _graph; + } + +private: + arm_compute::graph::Graph _graph; +}; + +#endif /* ARM_COMPUTE_GRAPH_EDSR_H */ diff --git a/src/graph/nodes/DeconvolutionLayerNode.cpp b/src/graph/nodes/DeconvolutionLayerNode.cpp index d4a5b769e..a2e4e2b05 100644 --- a/src/graph/nodes/DeconvolutionLayerNode.cpp +++ b/src/graph/nodes/DeconvolutionLayerNode.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -32,8 +32,8 @@ namespace arm_compute { namespace graph { -DeconvolutionLayerNode::DeconvolutionLayerNode(PadStrideInfo info) - : _info(std::move(info)) +DeconvolutionLayerNode::DeconvolutionLayerNode(PadStrideInfo info, QuantizationInfo out_quant_info) + : _info(std::move(info)), _out_quant_info(std::move(out_quant_info)) { _input_edges.resize(3, EmptyEdgeID); _outputs.resize(1, NullTensorID); @@ -88,6 +88,12 @@ TensorDescriptor DeconvolutionLayerNode::configure_output(size_t idx) const ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr); TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info); + + if(!_out_quant_info.empty()) + { + output_info.set_quantization_info(_out_quant_info); + } + return output_info; } diff --git a/src/graph/nodes/EltwiseLayerNode.cpp b/src/graph/nodes/EltwiseLayerNode.cpp index 568b88242..7970bad5f 100644 --- a/src/graph/nodes/EltwiseLayerNode.cpp +++ b/src/graph/nodes/EltwiseLayerNode.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -30,8 +30,8 @@ namespace arm_compute { namespace graph { -EltwiseLayerNode::EltwiseLayerNode(EltwiseOperation op, ConvertPolicy c_policy, RoundingPolicy r_policy) - : _op(op), _convert_policy(c_policy), _rounding_policy(r_policy) +EltwiseLayerNode::EltwiseLayerNode(EltwiseOperation op, QuantizationInfo out_quant_info, ConvertPolicy c_policy, RoundingPolicy r_policy) + : _op(op), _out_quant_info(out_quant_info), _convert_policy(c_policy), _rounding_policy(r_policy) { _input_edges.resize(2, EmptyEdgeID); _outputs.resize(1, NullTensorID); @@ -71,7 +71,14 @@ TensorDescriptor EltwiseLayerNode::configure_output(size_t idx) const const Tensor *src = input(0); ARM_COMPUTE_ERROR_ON(src == nullptr); - return src->desc(); + auto output_info = src->desc(); + + if(!_out_quant_info.empty()) + { + output_info.set_quantization_info(_out_quant_info); + } + + return output_info; } NodeType EltwiseLayerNode::type() const |