onnx--onnx/onnx/backend/test/case/node/conv.py

# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


class Conv(Base):
    @staticmethod
    def export() -> None:
        x = np.array(
            [
                [
                    [
                        [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 1, 5, 5) input tensor
                        [5.0, 6.0, 7.0, 8.0, 9.0],
                        [10.0, 11.0, 12.0, 13.0, 14.0],
                        [15.0, 16.0, 17.0, 18.0, 19.0],
                        [20.0, 21.0, 22.0, 23.0, 24.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        W = np.array(
            [
                [
                    [
                        [1.0, 1.0, 1.0],  # (1, 1, 3, 3) tensor for convolution weights
                        [1.0, 1.0, 1.0],
                        [1.0, 1.0, 1.0],
                    ]
                ]
            ]
        ).astype(np.float32)

        # Convolution with padding
        node_with_padding = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            kernel_shape=[3, 3],
            # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
            pads=[1, 1, 1, 1],
        )
        y_with_padding = np.array(
            [
                [
                    [
                        [12.0, 21.0, 27.0, 33.0, 24.0],  # (1, 1, 5, 5) output tensor
                        [33.0, 54.0, 63.0, 72.0, 51.0],
                        [63.0, 99.0, 108.0, 117.0, 81.0],
                        [93.0, 144.0, 153.0, 162.0, 111.0],
                        [72.0, 111.0, 117.0, 123.0, 84.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        expect(
            node_with_padding,
            inputs=[x, W],
            outputs=[y_with_padding],
            name="test_basic_conv_with_padding",
        )

        # Convolution without padding
        node_without_padding = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            kernel_shape=[3, 3],
            # Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
            pads=[0, 0, 0, 0],
        )
        y_without_padding = np.array(
            [
                [
                    [
                        [54.0, 63.0, 72.0],  # (1, 1, 3, 3) output tensor
                        [99.0, 108.0, 117.0],
                        [144.0, 153.0, 162.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        expect(
            node_without_padding,
            inputs=[x, W],
            outputs=[y_without_padding],
            name="test_basic_conv_without_padding",
        )

    @staticmethod
    def export_conv_with_strides() -> None:
        x = np.array(
            [
                [
                    [
                        [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 1, 7, 5) input tensor
                        [5.0, 6.0, 7.0, 8.0, 9.0],
                        [10.0, 11.0, 12.0, 13.0, 14.0],
                        [15.0, 16.0, 17.0, 18.0, 19.0],
                        [20.0, 21.0, 22.0, 23.0, 24.0],
                        [25.0, 26.0, 27.0, 28.0, 29.0],
                        [30.0, 31.0, 32.0, 33.0, 34.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        W = np.array(
            [
                [
                    [
                        [1.0, 1.0, 1.0],  # (1, 1, 3, 3) tensor for convolution weights
                        [1.0, 1.0, 1.0],
                        [1.0, 1.0, 1.0],
                    ]
                ]
            ]
        ).astype(np.float32)

        # Convolution with strides=2 and padding
        node_with_padding = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            kernel_shape=[3, 3],
            pads=[1, 1, 1, 1],
            strides=[
                2,
                2,
            ],  # Default values for other attributes: dilations=[1, 1], groups=1
        )
        y_with_padding = np.array(
            [
                [
                    [
                        [12.0, 27.0, 24.0],  # (1, 1, 4, 3) output tensor
                        [63.0, 108.0, 81.0],
                        [123.0, 198.0, 141.0],
                        [112.0, 177.0, 124.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        expect(
            node_with_padding,
            inputs=[x, W],
            outputs=[y_with_padding],
            name="test_conv_with_strides_padding",
        )

        # Convolution with strides=2 and no padding
        node_without_padding = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            kernel_shape=[3, 3],
            pads=[0, 0, 0, 0],
            strides=[
                2,
                2,
            ],  # Default values for other attributes: dilations=[1, 1], groups=1
        )
        y_without_padding = np.array(
            [
                [
                    [
                        [54.0, 72.0],  # (1, 1, 3, 2) output tensor
                        [144.0, 162.0],
                        [234.0, 252.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        expect(
            node_without_padding,
            inputs=[x, W],
            outputs=[y_without_padding],
            name="test_conv_with_strides_no_padding",
        )

        # Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
        node_with_asymmetric_padding = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            kernel_shape=[3, 3],
            pads=[1, 0, 1, 0],
            strides=[
                2,
                2,
            ],  # Default values for other attributes: dilations=[1, 1], groups=1
        )
        y_with_asymmetric_padding = np.array(
            [
                [
                    [
                        [21.0, 33.0],  # (1, 1, 4, 2) output tensor
                        [99.0, 117.0],
                        [189.0, 207.0],
                        [171.0, 183.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        expect(
            node_with_asymmetric_padding,
            inputs=[x, W],
            outputs=[y_with_asymmetric_padding],
            name="test_conv_with_strides_and_asymmetric_padding",
        )

    @staticmethod
    def export_conv_with_autopad_same() -> None:
        x = np.array(
            [
                [
                    [
                        [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 1, 5, 5) input tensor
                        [5.0, 6.0, 7.0, 8.0, 9.0],
                        [10.0, 11.0, 12.0, 13.0, 14.0],
                        [15.0, 16.0, 17.0, 18.0, 19.0],
                        [20.0, 21.0, 22.0, 23.0, 24.0],
                    ]
                ]
            ]
        ).astype(np.float32)
        W = np.array(
            [
                [
                    [
                        [1.0, 1.0, 1.0],  # (1, 1, 3, 3) tensor for convolution weights
                        [1.0, 1.0, 1.0],
                        [1.0, 1.0, 1.0],
                    ]
                ]
            ]
        ).astype(np.float32)

        # Convolution with auto_pad='SAME_LOWER' and strides=2
        node = onnx.helper.make_node(
            "Conv",
            inputs=["x", "W"],
            outputs=["y"],
            auto_pad="SAME_LOWER",
            kernel_shape=[3, 3],
            strides=[2, 2],
        )
        y = np.array(
            [[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]]
        ).astype(np.float32)
        expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same")