wehub-resource-sync
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643 lines
19 KiB
Python
643 lines
19 KiB
Python
# Copyright (c) ONNX Project Contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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import onnx
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from onnx.backend.test.case.base import Base
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from onnx.backend.test.case.node import expect
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class GridSample(Base):
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@staticmethod
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def export_gridsample() -> None:
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="linear",
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padding_mode="zeros",
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align_corners=0,
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)
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# X shape, [N, C, H, W] - [1, 1, 4, 4]
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X = np.array(
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[
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[
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[
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[0.0, 1.0, 2.0, 3.0],
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[4.0, 5.0, 6.0, 7.0],
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[8.0, 9.0, 10.0, 11.0],
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[12.0, 13.0, 14.0, 15.0],
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]
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]
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],
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dtype=np.float32,
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)
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# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
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Grid = np.array(
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[
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[
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[
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[-1.0000, -1.0000],
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[-0.6000, -1.0000],
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[-0.2000, -1.0000],
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[0.2000, -1.0000],
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[0.6000, -1.0000],
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[1.0000, -1.0000],
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],
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[
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[-1.0000, -0.6000],
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[-0.6000, -0.6000],
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[-0.2000, -0.6000],
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[0.2000, -0.6000],
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[0.6000, -0.6000],
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[1.0000, -0.6000],
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],
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[
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[-1.0000, -0.2000],
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[-0.6000, -0.2000],
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[-0.2000, -0.2000],
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[0.2000, -0.2000],
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[0.6000, -0.2000],
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[1.0000, -0.2000],
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],
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[
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[-1.0000, 0.2000],
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[-0.6000, 0.2000],
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[-0.2000, 0.2000],
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[0.2000, 0.2000],
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[0.6000, 0.2000],
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[1.0000, 0.2000],
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],
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[
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[-1.0000, 0.6000],
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[-0.6000, 0.6000],
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[-0.2000, 0.6000],
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[0.2000, 0.6000],
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[0.6000, 0.6000],
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[1.0000, 0.6000],
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],
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[
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[-1.0000, 1.0000],
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[-0.6000, 1.0000],
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[-0.2000, 1.0000],
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[0.2000, 1.0000],
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[0.6000, 1.0000],
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[1.0000, 1.0000],
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],
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]
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],
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dtype=np.float32,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
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Y = np.array(
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[
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[
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[
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[0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
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[0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
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[2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
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[3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
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[5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
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[3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500],
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]
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]
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],
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dtype=np.float32,
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)
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expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample")
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@staticmethod
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def export_gridsample_paddingmode() -> None:
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# X shape, [N, C, H, W] - [1, 1, 3, 2]
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X = np.array(
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[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
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dtype=np.float32,
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)
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# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
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Grid = np.array(
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[
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[
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[
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[-10.0000, -10.0000],
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[-5.0000, -5.0000],
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[-0.2000, -0.2000],
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[10.0000, 10.0000],
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],
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[
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[10.0000, 10.0000],
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[-0.2000, -0.2000],
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[5.0000, 5.0000],
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[10.0000, 10.0000],
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],
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]
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],
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dtype=np.float32,
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)
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# setting padding_mode = 'zeros'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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padding_mode="zeros",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_zeros = np.array(
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[[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_zeros],
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name="test_gridsample_zeros_padding",
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)
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# setting padding_mode = 'border'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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padding_mode="border",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_border = np.array(
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[[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_border],
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name="test_gridsample_border_padding",
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)
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# setting padding_mode = 'reflection'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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padding_mode="reflection",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_reflection = np.array(
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[[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_reflection],
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name="test_gridsample_reflection_padding",
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)
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@staticmethod
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def export_gridsample_mode_aligncorners() -> None:
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# X shape, [N, C, H, W] - [1, 1, 3, 2]
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X = np.array(
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[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
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dtype=np.float32,
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)
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# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
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Grid = np.array(
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[
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[
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[
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[-1.0000, -1.0000],
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[-0.5000, -0.5000],
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[-0.2000, -0.2000],
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[0.0000, 0.0000],
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],
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[
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[0.0000, 0.0000],
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[-0.2000, -0.2000],
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[0.5000, 0.5000],
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[1.0000, 1.0000],
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],
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]
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],
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dtype=np.float32,
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)
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# setting mode = 'bilinear', default align_corners = 0
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="linear",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_bilinear = np.array(
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[[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_bilinear],
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name="test_gridsample_bilinear",
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)
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# setting mode = 'bilinear', align_corners = 1
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="linear",
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align_corners=1,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_align_corners = np.array(
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[[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_align_corners],
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name="test_gridsample_aligncorners_true",
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)
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# setting mode = 'nearest'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="nearest",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_nearest = np.array(
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[[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]],
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dtype=np.float32,
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)
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expect(
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node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest"
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)
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# setting mode = 'bicubic'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="cubic",
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_bicubic = np.array(
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[[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]],
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dtype=np.float32,
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)
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expect(
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node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic"
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)
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# ============================================================================
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# Additional tests
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# The reference output tensors were generated using PyTorch 2.0.
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Grid = np.array(
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[
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[
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[[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]],
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[[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]],
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]
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],
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dtype=np.float32,
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)
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="nearest",
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align_corners=0,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_nearest = np.array(
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[[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_nearest],
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name="test_gridsample_nearest_align_corners_0_additional_1",
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)
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# setting mode = 'nearest'
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="nearest",
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align_corners=1,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_nearest = np.array(
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[[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_nearest],
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name="test_gridsample_nearest_align_corners_1_additional_1",
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)
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="linear",
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align_corners=0,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_bilinear = np.array(
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[[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_bilinear],
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name="test_gridsample_bilinear_align_corners_0_additional_1",
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)
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="linear",
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align_corners=1,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_bilinear = np.array(
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[[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_bilinear],
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name="test_gridsample_bilinear_align_corners_1_additional_1",
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)
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# These two new bicubic tests produces slightly higher error ~5e-5
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node = onnx.helper.make_node(
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"GridSample",
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inputs=["X", "Grid"],
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outputs=["Y"],
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mode="cubic",
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align_corners=0,
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)
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# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
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Y_bicubic = np.array(
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[
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[
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[
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[-0.173250, 0.284265, 1.923106, 2.568000],
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[5.170375, 2.284414, 4.744844, 1.046875],
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]
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]
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],
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dtype=np.float32,
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)
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expect(
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node,
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inputs=[X, Grid],
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outputs=[Y_bicubic],
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name="test_gridsample_bicubic_align_corners_0_additional_1",
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)
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node = onnx.helper.make_node(
|
|
"GridSample",
|
|
inputs=["X", "Grid"],
|
|
outputs=["Y"],
|
|
mode="cubic",
|
|
align_corners=1,
|
|
)
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
|
|
Y_bicubic = np.array(
|
|
[
|
|
[
|
|
[
|
|
[0.304001, 1.128750, 2.266270, 3.144844],
|
|
[4.531500, 2.455360, 4.599819, 4.000000],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[X, Grid],
|
|
outputs=[Y_bicubic],
|
|
name="test_gridsample_bicubic_align_corners_1_additional_1",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_volumeetric_gridsample_mode_aligncorners() -> None:
|
|
X = np.array(
|
|
[
|
|
[
|
|
[
|
|
[[1.0, 2.0], [3.0, 4.0]],
|
|
[[5.0, 6.0], [7.0, 8.0]],
|
|
[[9.0, 10.0], [11.0, 12.0]],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
Grid = np.array(
|
|
[
|
|
[
|
|
[
|
|
[[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]],
|
|
[[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]],
|
|
[[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]],
|
|
[[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]],
|
|
],
|
|
[
|
|
[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]],
|
|
[[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]],
|
|
[[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]],
|
|
[[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]],
|
|
],
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"GridSample",
|
|
inputs=["X", "Grid"],
|
|
outputs=["Y"],
|
|
mode="nearest",
|
|
align_corners=0,
|
|
)
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
|
|
Y_nearest = np.array(
|
|
[
|
|
[
|
|
[
|
|
[[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]],
|
|
[[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[X, Grid],
|
|
outputs=[Y_nearest],
|
|
name="test_gridsample_volumetric_nearest_align_corners_0",
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"GridSample",
|
|
inputs=["X", "Grid"],
|
|
outputs=["Y"],
|
|
mode="nearest",
|
|
align_corners=1,
|
|
)
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
|
|
Y_nearest = np.array(
|
|
[
|
|
[
|
|
[
|
|
[[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]],
|
|
[[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[X, Grid],
|
|
outputs=[Y_nearest],
|
|
name="test_gridsample_volumetric_nearest_align_corners_1",
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"GridSample",
|
|
inputs=["X", "Grid"],
|
|
outputs=["Y"],
|
|
mode="linear",
|
|
align_corners=0,
|
|
)
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
|
|
Y_bilinear = np.array(
|
|
[
|
|
[
|
|
[
|
|
[
|
|
[0.1250, 3.4000],
|
|
[2.0000, 0.4500],
|
|
[4.7000, 10.9000],
|
|
[6.5000, 3.0000],
|
|
],
|
|
[
|
|
[6.5000, 1.7500],
|
|
[4.7000, 3.3000],
|
|
[11.0000, 2.5200],
|
|
[1.5000, 5.4900],
|
|
],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[X, Grid],
|
|
outputs=[Y_bilinear],
|
|
name="test_gridsample_volumetric_bilinear_align_corners_0",
|
|
)
|
|
|
|
node = onnx.helper.make_node(
|
|
"GridSample",
|
|
inputs=["X", "Grid"],
|
|
outputs=["Y"],
|
|
mode="linear",
|
|
align_corners=1,
|
|
)
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
|
|
Y_bilinear = np.array(
|
|
[
|
|
[
|
|
[
|
|
[
|
|
[1.0000, 6.7000],
|
|
[3.7500, 2.4000],
|
|
[5.4000, 9.3000],
|
|
[6.5000, 6.0000],
|
|
],
|
|
[
|
|
[6.5000, 7.0000],
|
|
[5.4000, 6.6000],
|
|
[9.2500, 8.4000],
|
|
[12.0000, 6.1000],
|
|
],
|
|
]
|
|
]
|
|
],
|
|
dtype=np.float32,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[X, Grid],
|
|
outputs=[Y_bilinear],
|
|
name="test_gridsample_volumetric_bilinear_align_corners_1",
|
|
)
|
|
|
|
"""
|
|
For someone who want to test by script. Comment it cause github ONNX CI
|
|
do not have the torch python package.
|
|
@staticmethod
|
|
def export_gridsample_torch(): # type: () -> None
|
|
node = onnx.helper.make_node(
|
|
'GridSample',
|
|
inputs=['X', 'Grid'],
|
|
outputs=['Y'],
|
|
mode='bilinear',
|
|
padding_mode='zeros',
|
|
align_corners=0,
|
|
)
|
|
|
|
# X shape, [N, C, H, W] - [1, 1, 4, 4]
|
|
# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
|
|
# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
|
|
import torch
|
|
X = torch.arange(3 * 3).view(1, 1, 3, 3).float()
|
|
d = torch.linspace(-1, 1, 6)
|
|
meshx, meshy = torch.meshgrid((d, d))
|
|
grid = torch.stack((meshy, meshx), 2)
|
|
Grid = grid.unsqueeze(0)
|
|
Y = torch.nn.functional.grid_sample(X, Grid, mode='bilinear',
|
|
padding_mode='zeros', align_corners=False)
|
|
expect(node, inputs=[X.numpy(), Grid.numpy()], outputs=[Y.numpy()],
|
|
name='test_gridsample_torch')
|
|
"""
|