From 6800117df3be825f0ec5c6cc71c4377322f51b99 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Fri, 6 Mar 2020 16:32:01 +0000 Subject: COMPMID-3221: (DATA_UPDATE) add graph example for EDSR Change-Id: Id74190e2af444da8dab4813fd65016104f3882a9 Signed-off-by: Sang-Hoon Park Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2862 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas Comments-Addressed: Arm Jenkins --- examples/graph_edsr.h | 1281 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1281 insertions(+) create mode 100644 examples/graph_edsr.h (limited to 'examples/graph_edsr.h') diff --git a/examples/graph_edsr.h b/examples/graph_edsr.h new file mode 100644 index 0000000000..8941430c76 --- /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 &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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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( + 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(); + 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 */ -- cgit v1.2.1