Extend ONNX FE with Col2Im operator

src/frontends/onnx/docs/supported_ops.md

@@ -36,7 +36,7 @@ OpenVINO provides support for operations of Default Opset (empty in table below)
 |                        |Celu                                                    |12                      |12                              |                                |
 |                        |CenterCropPad                                           |18                      |18                              |                                |
 |                        |Clip                                                    |11, 1                   |13, 12, 11, 6, 1                |                                |
-|                        |Col2Im                                                  |                        |18                              |                                |
+|                        |Col2Im                                                  |18                      |18                              |                                |
 |                        |Compress                                                |9                       |11, 9                           |                                |
 |                        |Concat                                                  |1                       |13, 11, 4, 1                    |                                |
 |                        |ConcatFromSequence                                      |11                      |11                              |Supported only in certain patterns|

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

@@ -0,0 +1,65 @@
+// Copyright (C) 2018-2026 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/col2im.hpp"
+
+#include "core/operator_set.hpp"
+#include "exceptions.hpp"
+#include "utils/common.hpp"
+
+namespace ov {
+namespace frontend {
+namespace onnx {
+namespace ai_onnx {
+namespace opset_18 {
+ov::OutputVector col2im(const ov::frontend::onnx::Node& node) {
+    // 1. get inputs
+    common::default_op_checks(node, 3);
+    const auto inputs = node.get_ov_inputs();
+    const auto& data = inputs[0];         // input
+    const auto& output_size = inputs[1];  // image_shape
+    const auto& kernel_size = inputs[2];  // block_shape
+
+    // 2. get attributes
+    const size_t spatial_rank = 2;
+
+    std::vector<size_t> default_attr_vals(spatial_rank, 1);
+    auto dilations = node.get_attribute_value<std::vector<size_t>>("dilations", default_attr_vals);
+    auto strides = node.get_attribute_value<std::vector<size_t>>("strides", default_attr_vals);
+
+    std::vector<size_t> default_pads(spatial_rank * 2, 0);
+    auto pads = node.get_attribute_value<std::vector<size_t>>("pads", default_pads);
+    std::vector<size_t> pads_begin;
+    std::vector<size_t> pads_end;
+
+    // ov::op::v15::Col2Im only supports 2D spatial dimensions
+    CHECK_VALID_NODE(node,
+                     dilations.size() == spatial_rank,
+                     "Col2Im 'dilations' attribute must have size [2]. Got: ",
+                     dilations.size());
+    CHECK_VALID_NODE(node,
+                     strides.size() == spatial_rank,
+                     "Col2Im 'strides' attribute must have size [2]. Got: ",
+                     strides.size());
+    CHECK_VALID_NODE(node,
+                     pads.size() == spatial_rank * 2,
+                     "Col2Im 'pads' attribute must have size [4]. Got: ",
+                     pads.size());
+
+    // spatial_rank == pads.size() / 2
+    pads_begin.assign(pads.begin(), pads.begin() + spatial_rank);
+    pads_end.assign(pads.begin() + spatial_rank, pads.end());
+
+    // 3. return Col2Im
+    return {
+        std::make_shared<ov::op::v15::Col2Im>(data, output_size, kernel_size, strides, dilations, pads_begin, pads_end)
+            ->outputs()};
+}
+
+ONNX_OP("Col2Im", OPSET_SINCE(1), ai_onnx::opset_18::col2im);
+}  // namespace opset_18
+}  // namespace ai_onnx
+}  // namespace onnx
+}  // namespace frontend
+}  // namespace ov

src/frontends/onnx/tests/__init__.py

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

@@ -0,0 +1,103 @@
+ir_version: 8
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  node {
+    input: "input"
+    input: "image_shape"
+    input: "block_shape"
+    output: "output"
+    name: "col2im_2D_batch_opset_18"
+    op_type: "Col2Im"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 0
+      ints: 0
+      ints: 0
+      ints: 0
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  name: "col2im_2D_batch_opset_18"
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "image_shape"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "block_shape"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 18
+}

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

@@ -0,0 +1,103 @@
+ir_version: 8
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  node {
+    input: "input"
+    input: "image_shape"
+    input: "block_shape"
+    output: "output"
+    name: "col2im_2D_channel_opset_18"
+    op_type: "Col2Im"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 0
+      ints: 0
+      ints: 0
+      ints: 0
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  name: "col2im_2D_channel_opset_18"
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 12
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "image_shape"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "block_shape"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 18
+}