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Feat: Extend ONNX FE with Com2Im operator (#30144)
1 parent 9fe6071 commit 0dfd2f0

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+817
-9
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‎src/frontends/onnx/docs/supported_ops.md‎

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@@ -35,7 +35,7 @@ OpenVINO provides support for operations of Default Opset (empty in table below)
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| |Celu |12 |12 | |
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| |CenterCropPad |18 |18 | |
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| |Clip |11, 1 |13, 12, 11, 6, 1 | |
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| |Col2Im | |18 | |
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| |Col2Im |18 |18 | |
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| |Compress |9 |11, 9 | |
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| |Concat |1 |13, 11, 4, 1 | |
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| |ConcatFromSequence |11 |11 |Supported only in certain patterns|

‎src/frontends/onnx/frontend/src/op/col2im.cpp‎

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// Copyright (C) 2018-2026 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "openvino/op/col2im.hpp"
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#include "core/operator_set.hpp"
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#include "exceptions.hpp"
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#include "utils/common.hpp"
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namespace ov {
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namespace frontend {
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namespace onnx {
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namespace ai_onnx {
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namespace opset_18 {
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ov::OutputVector col2im(const ov::frontend::onnx::Node& node) {
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// 1. get inputs
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common::default_op_checks(node, 3);
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const auto inputs = node.get_ov_inputs();
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const auto& data = inputs[0]; // input
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const auto& output_size = inputs[1]; // image_shape
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const auto& kernel_size = inputs[2]; // block_shape
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// 2. get attributes
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const size_t spatial_rank = 2;
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std::vector<size_t> default_attr_vals(spatial_rank, 1);
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auto dilations = node.get_attribute_value<std::vector<size_t>>("dilations", default_attr_vals);
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auto strides = node.get_attribute_value<std::vector<size_t>>("strides", default_attr_vals);
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std::vector<size_t> default_pads(spatial_rank * 2, 0);
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auto pads = node.get_attribute_value<std::vector<size_t>>("pads", default_pads);
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std::vector<size_t> pads_begin;
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std::vector<size_t> pads_end;
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// ov::op::v15::Col2Im only supports 2D spatial dimensions
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CHECK_VALID_NODE(node,
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dilations.size() == spatial_rank,
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"Col2Im 'dilations' attribute must have size [2]. Got: ",
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dilations.size());
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CHECK_VALID_NODE(node,
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strides.size() == spatial_rank,
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"Col2Im 'strides' attribute must have size [2]. Got: ",
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strides.size());
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CHECK_VALID_NODE(node,
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pads.size() == spatial_rank * 2,
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"Col2Im 'pads' attribute must have size [4]. Got: ",
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pads.size());
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// spatial_rank == pads.size() / 2
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pads_begin.assign(pads.begin(), pads.begin() + spatial_rank);
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pads_end.assign(pads.begin() + spatial_rank, pads.end());
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// 3. return Col2Im
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return {
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std::make_shared<ov::op::v15::Col2Im>(data, output_size, kernel_size, strides, dilations, pads_begin, pads_end)
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->outputs()};
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}
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ONNX_OP("Col2Im", OPSET_SINCE(1), ai_onnx::opset_18::col2im);
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} // namespace opset_18
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} // namespace ai_onnx
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} // namespace onnx
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} // namespace frontend
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} // namespace ov

‎src/frontends/onnx/tests/__init__.py‎

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@@ -1,6 +1,6 @@
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# -*- coding: utf-8 -*-
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# Copyright (C) 2018-2026 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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# -*- coding: utf-8 -*-
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import pytest
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@@ -52,7 +52,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
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"Axes input must be constant")
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skip_bitwise_ui64 = pytest.mark.skip(reason="AssertionError: Not equal to tolerance rtol=0.001, atol=1e-07")
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xfail_issue_99950 = xfail_test(reason="CenterCropPad func is not supported")
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xfail_issue_99952 = xfail_test(reason="Col2Im operator is not supported")
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xfail_issue_99954 = xfail_test(reason="Constant Pad - RuntimeError: Shape inference of Reference node with name y failed")
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xfail_issue_99955 = xfail_test(reason="GroupNorm is not supported")
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xfail_issue_99957 = xfail_test(reason="LayerNorm - RuntimeError: While validating node '<Node(Reshape): Mean>'")

‎src/frontends/onnx/tests/models/col2im_2D_batch_opset_18.prototxt‎

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ir_version: 8
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producer_name: "OpenVINO ONNX Frontend"
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graph {
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node {
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input: "input"
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input: "image_shape"
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input: "block_shape"
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output: "output"
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name: "col2im_2D_batch_opset_18"
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op_type: "Col2Im"
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attribute {
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name: "dilations"
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ints: 1
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ints: 1
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type: INTS
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}
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attribute {
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name: "pads"
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ints: 0
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ints: 0
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ints: 0
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ints: 0
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type: INTS
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}
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attribute {
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name: "strides"
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ints: 1
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ints: 1
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type: INTS
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}
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}
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name: "col2im_2D_batch_opset_18"
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input {
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name: "input"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 2
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}
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dim {
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dim_value: 4
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}
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dim {
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dim_value: 4
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}
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}
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}
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}
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}
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input {
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name: "image_shape"
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type {
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tensor_type {
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elem_type: 7
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shape {
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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input {
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name: "block_shape"
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type {
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tensor_type {
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elem_type: 7
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shape {
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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output {
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name: "output"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 2
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}
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 3
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}
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dim {
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dim_value: 3
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}
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}
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}
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}
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}
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}
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opset_import {
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version: 18
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}

‎src/frontends/onnx/tests/models/col2im_2D_channel_opset_18.prototxt‎

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ir_version: 8
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producer_name: "OpenVINO ONNX Frontend"
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graph {
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node {
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input: "input"
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input: "image_shape"
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input: "block_shape"
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output: "output"
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name: "col2im_2D_channel_opset_18"
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op_type: "Col2Im"
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attribute {
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name: "dilations"
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ints: 1
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ints: 1
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type: INTS
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}
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attribute {
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name: "pads"
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ints: 0
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ints: 0
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ints: 0
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ints: 0
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type: INTS
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}
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attribute {
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name: "strides"
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ints: 1
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ints: 1
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type: INTS
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}
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}
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name: "col2im_2D_channel_opset_18"
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input {
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name: "input"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 12
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}
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dim {
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dim_value: 4
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}
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}
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}
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}
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}
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input {
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name: "image_shape"
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type {
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tensor_type {
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elem_type: 7
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shape {
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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input {
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name: "block_shape"
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type {
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tensor_type {
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elem_type: 7
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shape {
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dim {
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dim_value: 2
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}
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}
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}
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}
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}
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output {
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name: "output"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 3
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}
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dim {
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dim_value: 3
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}
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dim {
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dim_value: 3
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}
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}
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}
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}
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}
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}
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opset_import {
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version: 18
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}

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