wehub-resource-sync
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696 lines
21 KiB
Python
696 lines
21 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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from onnx.reference.ops.op_pool_common import (
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get_output_shape_auto_pad,
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get_output_shape_explicit_padding,
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get_pad_shape,
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pool,
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)
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class AveragePool(Base):
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@staticmethod
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def export_averagepool_2d_precomputed_pads() -> None:
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"""input_shape: [1, 1, 5, 5]
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output_shape: [1, 1, 5, 5]
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pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[5, 5],
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pads=[2, 2, 2, 2],
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)
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x = np.array(
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[
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[
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[
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 10],
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[11, 12, 13, 14, 15],
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[16, 17, 18, 19, 20],
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[21, 22, 23, 24, 25],
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]
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]
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]
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).astype(np.float32)
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y = np.array(
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[
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[
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[
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[7, 7.5, 8, 8.5, 9],
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[9.5, 10, 10.5, 11, 11.5],
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[12, 12.5, 13, 13.5, 14],
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[14.5, 15, 15.5, 16, 16.5],
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[17, 17.5, 18, 18.5, 19],
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]
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]
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]
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).astype(np.float32)
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expect(
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node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads"
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)
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@staticmethod
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def export_averagepool_2d_precomputed_pads_count_include_pad() -> None:
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"""input_shape: [1, 1, 5, 5]
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output_shape: [1, 1, 5, 5]
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pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[5, 5],
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pads=[2, 2, 2, 2],
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count_include_pad=1,
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)
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x = np.array(
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[
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[
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[
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 10],
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[11, 12, 13, 14, 15],
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[16, 17, 18, 19, 20],
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[21, 22, 23, 24, 25],
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]
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]
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]
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).astype(np.float32)
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y = np.array(
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[
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[
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[
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[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
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[4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
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[7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
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[6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
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[6.1200, 8.4000, 10.8000, 8.8800, 6.8400],
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]
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]
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]
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).astype(np.float32)
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expect(
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node,
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inputs=[x],
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outputs=[y],
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name="test_averagepool_2d_precomputed_pads_count_include_pad",
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)
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@staticmethod
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def export_averagepool_2d_precomputed_strides() -> None:
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"""input_shape: [1, 1, 5, 5]
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output_shape: [1, 1, 2, 2]
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2, 2],
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strides=[2, 2],
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)
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x = np.array(
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[
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[
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[
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 10],
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[11, 12, 13, 14, 15],
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[16, 17, 18, 19, 20],
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[21, 22, 23, 24, 25],
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]
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]
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]
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).astype(np.float32)
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y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32)
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expect(
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node,
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inputs=[x],
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outputs=[y],
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name="test_averagepool_2d_precomputed_strides",
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)
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@staticmethod
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def export_averagepool_2d_precomputed_same_upper() -> None:
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"""input_shape: [1, 1, 5, 5]
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output_shape: [1, 1, 3, 3]
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pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[3, 3],
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strides=[2, 2],
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auto_pad="SAME_UPPER",
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)
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x = np.array(
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[
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[
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[
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 10],
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[11, 12, 13, 14, 15],
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[16, 17, 18, 19, 20],
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[21, 22, 23, 24, 25],
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]
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]
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]
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).astype(np.float32)
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y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype(
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np.float32
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)
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expect(
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node,
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inputs=[x],
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outputs=[y],
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name="test_averagepool_2d_precomputed_same_upper",
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)
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@staticmethod
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def export_averagepool_1d_default() -> None:
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"""input_shape: [1, 3, 32]
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output_shape: [1, 3, 31]
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2],
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)
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x = np.random.randn(1, 3, 32).astype(np.float32)
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x_shape = np.shape(x)
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pads = None
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kernel_shape = [2]
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strides = [1]
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out_shape, _ = get_output_shape_explicit_padding(
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pads, x_shape[2:], kernel_shape, strides
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)
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padded = x
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y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default")
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@staticmethod
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def export_averagepool_2d_default() -> None:
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"""input_shape: [1, 3, 32, 32]
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output_shape: [1, 3, 31, 31]
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2, 2],
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)
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x = np.random.randn(1, 3, 32, 32).astype(np.float32)
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x_shape = np.shape(x)
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pads = None
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kernel_shape = (2, 2)
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strides = (1, 1)
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out_shape, _ = get_output_shape_explicit_padding(
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pads, x_shape[2:], kernel_shape, strides
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)
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padded = x
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y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default")
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@staticmethod
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def export_averagepool_3d_default() -> None:
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"""input_shape: [1, 3, 32, 32, 32]
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output_shape: [1, 3, 31, 31, 31]
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2, 2, 2],
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)
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x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
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x_shape = np.shape(x)
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pads = None
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kernel_shape = [2, 2, 2]
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strides = [1, 1, 1]
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out_shape, _ = get_output_shape_explicit_padding(
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pads, x_shape[2:], kernel_shape, strides
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)
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padded = x
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y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default")
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@staticmethod
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def export_averagepool_2d_same_upper() -> None:
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"""input_shape: [1, 3, 32, 32]
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output_shape: [1, 3, 32, 32]
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pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2, 2],
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auto_pad="SAME_UPPER",
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)
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x = np.random.randn(1, 3, 32, 32).astype(np.float32)
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x_shape = np.shape(x)
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kernel_shape = (2, 2)
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strides = (1, 1)
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out_shape = get_output_shape_auto_pad(
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"SAME_UPPER", x_shape[2:], kernel_shape, strides
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)
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pad_shape = get_pad_shape(
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"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
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)
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pad_top = pad_shape[0] // 2
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pad_bottom = pad_shape[0] - pad_top
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pad_left = pad_shape[1] // 2
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pad_right = pad_shape[1] - pad_left
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padded = np.pad(
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x,
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((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
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mode="constant",
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constant_values=np.nan,
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)
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pads = (pad_top, pad_left, pad_bottom, pad_right)
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y = pool(
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padded,
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x_shape,
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kernel_shape,
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strides,
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out_shape,
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"AVG",
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pads_required=pads,
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pads=pads,
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)
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")
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@staticmethod
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def export_averagepool_2d_same_lower() -> None:
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"""input_shape: [1, 3, 32, 32]
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output_shape: [1, 3, 32, 32]
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pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[2, 2],
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auto_pad="SAME_LOWER",
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)
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x = np.random.randn(1, 3, 32, 32).astype(np.float32)
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x_shape = np.shape(x)
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kernel_shape = (2, 2)
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strides = (1, 1)
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out_shape = get_output_shape_auto_pad(
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"SAME_LOWER", x_shape[2:], kernel_shape, strides
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)
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pad_shape = get_pad_shape(
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"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
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)
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pad_bottom = pad_shape[0] // 2
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pad_top = pad_shape[0] - pad_bottom
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pad_right = pad_shape[1] // 2
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pad_left = pad_shape[1] - pad_right
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padded = np.pad(
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x,
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((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
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mode="constant",
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constant_values=np.nan,
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)
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pads = (pad_top, pad_left, pad_bottom, pad_right)
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y = pool(
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padded,
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x_shape,
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kernel_shape,
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strides,
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out_shape,
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"AVG",
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pads_required=pads,
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pads=pads,
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)
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")
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@staticmethod
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def export_averagepool_2d_pads() -> None:
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"""input_shape: [1, 3, 28, 28]
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output_shape: [1, 3, 30, 30]
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pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[3, 3],
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pads=[2, 2, 2, 2],
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)
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x = np.random.randn(1, 3, 28, 28).astype(np.float32)
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x_shape = np.shape(x)
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kernel_shape = (3, 3)
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strides = (1, 1)
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pad_bottom = 2
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pad_top = 2
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pad_right = 2
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pad_left = 2
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pads = [pad_top, pad_left, pad_bottom, pad_right]
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out_shape, extra_pads = get_output_shape_explicit_padding(
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pads, x_shape[2:], kernel_shape, strides, ceil_mode=False
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)
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padded = np.pad(
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x,
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(
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(0, 0),
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(0, 0),
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(extra_pads[0], extra_pads[2]),
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(extra_pads[1], extra_pads[3]),
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),
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mode="constant",
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constant_values=np.nan,
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)
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y = pool(
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padded,
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x_shape,
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kernel_shape,
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strides,
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out_shape,
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"AVG",
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pads_required=extra_pads,
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pads=pads,
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)
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expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")
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@staticmethod
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def export_averagepool_2d_pads_count_include_pad() -> None:
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"""input_shape: [1, 3, 28, 28]
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output_shape: [1, 3, 30, 30]
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pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
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"""
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node = onnx.helper.make_node(
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"AveragePool",
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inputs=["x"],
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outputs=["y"],
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kernel_shape=[3, 3],
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pads=[2, 2, 2, 2],
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count_include_pad=1,
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)
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x = np.random.randn(1, 3, 28, 28).astype(np.float32)
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x_shape = np.shape(x)
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dilations = (1, 1)
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kernel_shape = (3, 3)
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strides = (1, 1)
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pad_bottom = 2
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pad_top = 2
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pad_right = 2
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pad_left = 2
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|
pads = [pad_top, pad_left, pad_bottom, pad_right]
|
|
out_shape, extra_pads = get_output_shape_explicit_padding(
|
|
pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False
|
|
)
|
|
padded = np.pad(
|
|
x,
|
|
(
|
|
(0, 0),
|
|
(0, 0),
|
|
(extra_pads[0], extra_pads[2]),
|
|
(extra_pads[1], extra_pads[3]),
|
|
),
|
|
mode="constant",
|
|
constant_values=0,
|
|
)
|
|
y = pool(
|
|
padded,
|
|
x_shape,
|
|
kernel_shape,
|
|
strides,
|
|
out_shape,
|
|
"AVG",
|
|
pads_required=extra_pads,
|
|
pads=pads,
|
|
count_include_pad=1,
|
|
)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[x],
|
|
outputs=[y],
|
|
name="test_averagepool_2d_pads_count_include_pad",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_averagepool_2d_strides() -> None:
|
|
"""input_shape: [1, 3, 32, 32]
|
|
output_shape: [1, 3, 10, 10]
|
|
"""
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=[5, 5],
|
|
strides=[3, 3],
|
|
)
|
|
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
|
|
x_shape = np.shape(x)
|
|
kernel_shape = (5, 5)
|
|
strides = (3, 3)
|
|
out_shape, pads = get_output_shape_explicit_padding(
|
|
None, x_shape[2:], kernel_shape, strides, ceil_mode=False
|
|
)
|
|
padded = x
|
|
y = pool(
|
|
padded,
|
|
x_shape,
|
|
kernel_shape,
|
|
strides,
|
|
out_shape,
|
|
"AVG",
|
|
pads_required=pads,
|
|
pads=None,
|
|
)
|
|
|
|
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")
|
|
|
|
@staticmethod
|
|
def export_averagepool_2d_ceil() -> None:
|
|
"""input_shape: [1, 1, 4, 4]
|
|
output_shape: [1, 1, 2, 2]
|
|
"""
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=[3, 3],
|
|
strides=[2, 2],
|
|
ceil_mode=True,
|
|
)
|
|
x = np.array(
|
|
[
|
|
[
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
]
|
|
]
|
|
]
|
|
).astype(np.float32)
|
|
y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32)
|
|
|
|
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")
|
|
|
|
@staticmethod
|
|
def export_averagepool_2d_ceil_last_window_starts_on_pad() -> None:
|
|
"""input_shape: [1, 3, 2, 2]
|
|
output_shape: [1, 3, 1, 1]
|
|
"""
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=[3, 3],
|
|
strides=[3, 3],
|
|
pads=[1, 1, 1, 1],
|
|
ceil_mode=True,
|
|
count_include_pad=1,
|
|
)
|
|
x = np.array(
|
|
[
|
|
[
|
|
[[0.8580, 0.0786], [0.2692, 0.1537]],
|
|
[[0.8816, 0.4353], [0.5772, 0.6623]],
|
|
[[0.9067, 0.9483], [0.5970, 0.7630]],
|
|
]
|
|
]
|
|
).astype(np.float32)
|
|
y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32)
|
|
|
|
expect(
|
|
node,
|
|
inputs=[x],
|
|
outputs=[y],
|
|
name="test_averagepool_2d_ceil_last_window_starts_on_pad",
|
|
)
|
|
|
|
@staticmethod
|
|
def export_averagepool_2d_dilations() -> None:
|
|
"""input_shape: [1, 1, 4, 4]
|
|
output_shape: [1, 1, 2, 2]
|
|
"""
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=[2, 2],
|
|
strides=[1, 1],
|
|
dilations=[2, 2],
|
|
ceil_mode=True,
|
|
)
|
|
|
|
# input shape: [1, 1, 4, 4]
|
|
x = np.array(
|
|
[
|
|
[
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
]
|
|
]
|
|
]
|
|
).astype(np.float32)
|
|
|
|
y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32)
|
|
|
|
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations")
|
|
|
|
@staticmethod
|
|
def export_averagepool_3d_dilations() -> None:
|
|
"""input_shape: [1, 1, 4, 4]
|
|
output_shape: [1, 1, 2, 2]
|
|
"""
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=[2, 2, 2],
|
|
strides=[1, 1, 1],
|
|
dilations=[2, 2, 2],
|
|
ceil_mode=True,
|
|
)
|
|
|
|
# input shape: [1, 1, 4, 4, 4]
|
|
x = np.array(
|
|
[
|
|
[
|
|
[
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
],
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
],
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
],
|
|
[
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
],
|
|
]
|
|
]
|
|
]
|
|
).astype(np.float32)
|
|
|
|
y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32)
|
|
|
|
expect(
|
|
node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small"
|
|
)
|
|
|
|
@staticmethod
|
|
def export_averagepool_3d_dilations_large() -> None:
|
|
x_shape = (32, 32, 32)
|
|
dilations = (2, 2, 2)
|
|
kernel_shape = (5, 5, 5)
|
|
strides = (3, 3, 3)
|
|
count_include_pad = 0
|
|
|
|
for count_include_pad in (0, 1):
|
|
for ceil_mode in (True, False):
|
|
node = onnx.helper.make_node(
|
|
"AveragePool",
|
|
inputs=["x"],
|
|
outputs=["y"],
|
|
kernel_shape=kernel_shape,
|
|
strides=strides,
|
|
dilations=dilations,
|
|
count_include_pad=count_include_pad,
|
|
ceil_mode=ceil_mode,
|
|
)
|
|
|
|
x = np.random.randn(1, 1, *x_shape).astype(np.float32)
|
|
out_shape, extra_pads = get_output_shape_explicit_padding(
|
|
None,
|
|
x_shape,
|
|
kernel_shape,
|
|
strides,
|
|
dilations=dilations,
|
|
ceil_mode=ceil_mode,
|
|
)
|
|
padded = np.pad(
|
|
x,
|
|
(
|
|
(0, 0),
|
|
(0, 0),
|
|
(extra_pads[0], extra_pads[3]),
|
|
(extra_pads[1], extra_pads[4]),
|
|
(extra_pads[2], extra_pads[5]),
|
|
),
|
|
mode="constant",
|
|
constant_values=0 if count_include_pad == 1 else np.nan,
|
|
)
|
|
y = pool(
|
|
padded,
|
|
(1, 1, *x_shape),
|
|
kernel_shape,
|
|
strides,
|
|
out_shape,
|
|
"AVG",
|
|
pads_required=extra_pads,
|
|
pads=None,
|
|
dilations=dilations,
|
|
count_include_pad=count_include_pad,
|
|
)
|
|
|
|
test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}"
|
|
expect(node, inputs=[x], outputs=[y], name=test_name)
|