wehub-resource-sync
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447 lines
15 KiB
Python
447 lines
15 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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def get_roi_align_input_values():
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X = np.array(
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[
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[
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[
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[
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0.2764,
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0.7150,
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0.1958,
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0.3416,
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0.4638,
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0.0259,
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0.2963,
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0.6518,
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0.4856,
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0.7250,
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],
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[
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0.9637,
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0.0895,
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0.2919,
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0.6753,
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0.0234,
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0.6132,
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0.8085,
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0.5324,
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0.8992,
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0.4467,
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],
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[
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0.3265,
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0.8479,
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0.9698,
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0.2471,
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0.9336,
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0.1878,
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0.4766,
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0.4308,
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0.3400,
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0.2162,
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],
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[
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0.0206,
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0.1720,
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0.2155,
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0.4394,
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0.0653,
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0.3406,
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0.7724,
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0.3921,
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0.2541,
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0.5799,
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],
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[
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0.4062,
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0.2194,
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0.4473,
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0.4687,
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0.7109,
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0.9327,
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0.9815,
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0.6320,
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0.1728,
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0.6119,
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],
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[
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0.3097,
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0.1283,
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0.4984,
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0.5068,
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0.4279,
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0.0173,
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0.4388,
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0.0430,
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0.4671,
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0.7119,
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],
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[
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0.1011,
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0.8477,
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0.4726,
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0.1777,
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0.9923,
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0.4042,
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0.1869,
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0.7795,
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0.9946,
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0.9689,
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],
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[
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0.1366,
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0.3671,
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0.7011,
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0.6234,
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0.9867,
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0.5585,
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0.6985,
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0.5609,
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0.8788,
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0.9928,
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],
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[
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0.5697,
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0.8511,
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0.6711,
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0.9406,
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0.8751,
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0.7496,
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0.1650,
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0.1049,
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0.1559,
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0.2514,
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],
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[
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0.7012,
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0.4056,
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0.7879,
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0.3461,
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0.0415,
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0.2998,
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0.5094,
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0.3727,
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0.5482,
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0.0502,
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],
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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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batch_indices = np.array([0, 0, 0], dtype=np.int64)
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rois = np.array([[0, 0, 9, 9], [0, 5, 4, 9], [5, 5, 9, 9]], dtype=np.float32)
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return X, batch_indices, rois
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class RoiAlign(Base):
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@staticmethod
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def export_roialign_aligned_false() -> None:
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node = onnx.helper.make_node(
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"RoiAlign",
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inputs=["X", "rois", "batch_indices"],
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outputs=["Y"],
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spatial_scale=1.0,
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output_height=5,
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output_width=5,
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sampling_ratio=2,
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coordinate_transformation_mode="output_half_pixel",
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)
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X, batch_indices, rois = get_roi_align_input_values()
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# (num_rois, C, output_height, output_width)
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Y = np.array(
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[
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[
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[
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[0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
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[0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
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[0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
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[0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
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[0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
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]
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],
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[
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[
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[0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
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[0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
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[0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
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[0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
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[0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
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]
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],
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[
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[
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[0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
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[0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
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[0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
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[0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
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[0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
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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, rois, batch_indices],
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outputs=[Y],
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name="test_roialign_aligned_false",
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)
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@staticmethod
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def export_roialign_aligned_true() -> None:
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node = onnx.helper.make_node(
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"RoiAlign",
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inputs=["X", "rois", "batch_indices"],
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outputs=["Y"],
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spatial_scale=1.0,
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output_height=5,
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output_width=5,
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sampling_ratio=2,
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coordinate_transformation_mode="half_pixel",
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)
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X, batch_indices, rois = get_roi_align_input_values()
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# (num_rois, C, output_height, output_width)
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Y = np.array(
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[
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[
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[
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[0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
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[0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
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[0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
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[0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
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[0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
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]
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],
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[
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[
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[0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
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[0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
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[0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
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[0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
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[0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
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]
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],
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[
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[
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[0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
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[0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
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[0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
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[0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
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[0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
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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, rois, batch_indices],
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outputs=[Y],
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name="test_roialign_aligned_true",
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)
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@staticmethod
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def export_roialign_mode_max() -> None:
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X = np.array(
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[
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[
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[
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[
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0.2764,
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0.715,
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0.1958,
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0.3416,
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0.4638,
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0.0259,
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0.2963,
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0.6518,
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0.4856,
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0.725,
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],
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[
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0.9637,
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0.0895,
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0.2919,
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0.6753,
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0.0234,
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0.6132,
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0.8085,
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0.5324,
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0.8992,
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0.4467,
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],
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[
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0.3265,
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0.8479,
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0.9698,
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0.2471,
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0.9336,
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0.1878,
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0.4766,
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0.4308,
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0.34,
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0.2162,
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],
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[
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0.0206,
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0.172,
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0.2155,
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0.4394,
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0.0653,
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0.3406,
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0.7724,
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0.3921,
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0.2541,
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0.5799,
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],
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[
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0.4062,
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0.2194,
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0.4473,
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0.4687,
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0.7109,
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0.9327,
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0.9815,
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0.632,
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0.1728,
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0.6119,
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],
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[
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0.3097,
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0.1283,
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0.4984,
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0.5068,
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0.4279,
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0.0173,
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0.4388,
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0.043,
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0.4671,
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0.7119,
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],
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[
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0.1011,
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0.8477,
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0.4726,
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0.1777,
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0.9923,
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0.4042,
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0.1869,
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0.7795,
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0.9946,
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0.9689,
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],
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[
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0.1366,
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0.3671,
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0.7011,
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0.6234,
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0.9867,
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0.5585,
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0.6985,
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0.5609,
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0.8788,
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0.9928,
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],
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[
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0.5697,
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0.8511,
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0.6711,
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0.9406,
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0.8751,
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0.7496,
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0.165,
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0.1049,
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0.1559,
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0.2514,
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],
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[
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0.7012,
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0.4056,
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0.7879,
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0.3461,
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0.0415,
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0.2998,
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0.5094,
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0.3727,
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0.5482,
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0.0502,
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],
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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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rois = np.array(
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[[0.0, 0.0, 9.0, 9.0], [0.0, 5.0, 4.0, 9.0], [5.0, 5.0, 9.0, 9.0]],
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dtype=np.float32,
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)
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batch_indices = np.array([0, 0, 0], dtype=np.int64)
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Y = np.array(
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[
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[
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[
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[0.3445228, 0.37310338, 0.37865096, 0.446696, 0.37991184],
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[0.4133513, 0.5455125, 0.6651902, 0.55805874, 0.27110294],
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[0.21223956, 0.40924096, 0.8417618, 0.792561, 0.37196714],
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[0.46835402, 0.39741728, 0.8012819, 0.4969306, 0.5495158],
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[0.3595896, 0.5196813, 0.5403741, 0.23814403, 0.19992709],
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]
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],
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[
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[
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[0.30517197, 0.5086199, 0.3189761, 0.4054401, 0.47630402],
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[0.50862, 0.8477, 0.37808004, 0.24936005, 0.79384017],
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[0.17620805, 0.29368007, 0.44870415, 0.4987201, 0.63148826],
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[0.51066005, 0.8511, 0.5368801, 0.9406, 0.70008016],
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[0.4487681, 0.51066035, 0.5042561, 0.5643603, 0.42004836],
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]
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],
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[
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[
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[0.21062402, 0.3510401, 0.37416005, 0.5967599, 0.46507207],
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[0.32336006, 0.31180006, 0.6236001, 0.9946, 0.7751202],
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[0.35744014, 0.5588001, 0.35897616, 0.7030401, 0.6353923],
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[0.5996801, 0.27940005, 0.17948808, 0.35152006, 0.31769615],
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[0.3598083, 0.40752012, 0.2385281, 0.43856013, 0.26313624],
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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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node = onnx.helper.make_node(
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"RoiAlign",
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inputs=["X", "rois", "batch_indices"],
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mode="max",
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outputs=["Y"],
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spatial_scale=1.0,
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output_height=5,
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output_width=5,
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sampling_ratio=2,
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coordinate_transformation_mode="output_half_pixel",
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)
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expect(
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node,
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inputs=[X, rois, batch_indices],
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outputs=[Y],
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name="test_roialign_mode_max",
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)
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