c1933fcaf0
* Support default strides for avg and max pooling * Fix code style * Remove changes from other ticket
168 lines
7.9 KiB
Python
168 lines
7.9 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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d2_params = [{'kernel_size': [3, 3], 'stride': 1, 'padding': 0},
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{'kernel_size': [3, 3], 'stride': [1, 1], 'padding': 1},
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{'kernel_size': [3, 3], 'stride': [1, 1], 'padding': [0, 1]},
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{'kernel_size': [3, 3], 'stride': [1, 1], 'padding': [1, 0]},
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{'kernel_size': [3, 3], 'stride': [2, 1], 'padding': 0},
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{'kernel_size': [2, 1], 'stride': [2, 1], 'padding': 0},
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{'kernel_size': [2, 1], 'stride': None, 'padding': 0},
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{'kernel_size': [2, 1], 'stride': [], 'padding': 0},
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]
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d1_params = [{'kernel_size': 3, 'stride': 1, 'padding': 0},
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{'kernel_size': (4,), 'stride': 1, 'padding': 1},
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{'kernel_size': 4, 'stride': (5,), 'padding': 2},
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{'kernel_size': 4, 'stride': None, 'padding': 0},
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]
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d3_params = [{'kernel_size': [3, 3, 3], 'stride': 1, 'padding': 0},
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{'kernel_size': [3, 3, 3], 'stride': [1, 1, 1], 'padding': 1},
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{'kernel_size': [3, 3, 3], 'stride': [
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3, 3, 3], 'padding': [0, 0, 0]},
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{'kernel_size': [3, 2, 1], 'stride': [
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3, 1, 1], 'padding': [0, 0, 0]},
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{'kernel_size': [3, 2, 1], 'stride': None, 'padding': [0, 0, 0]},
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]
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class TestPooling(PytorchLayerTest):
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def _prepare_input(self, ndim=4):
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import numpy as np
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shape = (1, 3, 15, 15, 15)
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return (np.random.randn(*shape[:ndim]).astype(np.float32),)
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def create_model(self, op_type, kernel_size, stride, padding, dilation=1, ceil_mode=True, count_include_pad=True):
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import torch
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class aten_avg_pooling_base(torch.nn.Module):
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def __init__(self):
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super(aten_avg_pooling_base, self).__init__()
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.ceil_mode = ceil_mode
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self.count_include_pad = count_include_pad
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def forward(self, x):
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pass
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class aten_max_pooling_base(torch.nn.Module):
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def __init__(self):
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super(aten_max_pooling_base, self).__init__()
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.ceil_mode = ceil_mode
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def forward(self, x):
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pass
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class aten_avg_pool2d(aten_avg_pooling_base):
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def forward(self, x):
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return torch.nn.functional.avg_pool2d(x, self.kernel_size, self.stride, self.padding, self.ceil_mode,
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self.count_include_pad)
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class aten_avg_pool3d(aten_avg_pooling_base):
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def forward(self, x):
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return torch.nn.functional.avg_pool3d(x, self.kernel_size, self.stride, self.padding, self.ceil_mode,
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self.count_include_pad)
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class aten_avg_pool1d(aten_avg_pooling_base):
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def forward(self, x):
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return torch.nn.functional.avg_pool1d(x, self.kernel_size, self.stride, self.padding, self.ceil_mode,
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self.count_include_pad)
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class aten_max_pool2d(aten_max_pooling_base):
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def forward(self, x):
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return torch.nn.functional.max_pool2d(x, self.kernel_size, self.stride, self.padding, self.dilation,
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self.ceil_mode)
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class aten_max_pool3d(aten_max_pooling_base):
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def forward(self, x):
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return torch.nn.functional.max_pool3d(x, self.kernel_size, self.stride, self.padding, self.dilation,
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self.ceil_mode)
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class aten_max_pool1d(aten_max_pooling_base):
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def forward(self, x):
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return torch.nn.functional.max_pool1d(x, self.kernel_size, self.stride, self.padding, self.dilation,
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self.ceil_mode)
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ops = {
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"max_pool1d": aten_max_pool1d,
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"max_pool2d": aten_max_pool2d,
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"max_pool3d": aten_max_pool3d,
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"avg_pool1d": aten_avg_pool1d,
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"avg_pool2d": aten_avg_pool2d,
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"avg_pool3d": aten_avg_pool3d
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}
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ref_net = None
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aten_pooling = ops[op_type]
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return aten_pooling(), ref_net, f"aten::{op_type}"
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@pytest.mark.parametrize("params", d1_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("count_include_pad", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_avg_pool1d(self, params, ceil_mode, count_include_pad, ie_device, precision, ir_version):
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self._test(*self.create_model("avg_pool1d", **params, ceil_mode=ceil_mode, count_include_pad=count_include_pad),
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ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 3}, trace_model=True,
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dynamic_shapes=False)
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@pytest.mark.parametrize("params", d2_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("count_include_pad", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_avg_pool2d(self, params, ceil_mode, count_include_pad, ie_device, precision, ir_version):
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self._test(*self.create_model("avg_pool2d", **params, ceil_mode=ceil_mode, count_include_pad=count_include_pad),
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ie_device, precision, ir_version, trace_model=True, dynamic_shapes=False)
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@pytest.mark.parametrize("params", d3_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("count_include_pad", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_avg_pool3d(self, params, ceil_mode, count_include_pad, ie_device, precision, ir_version):
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self._test(*self.create_model("avg_pool3d", **params, ceil_mode=ceil_mode, count_include_pad=count_include_pad),
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ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 5}, trace_model=True,
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dynamic_shapes=False)
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@pytest.mark.parametrize("params", d1_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("dilation", [1, 2])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_max_pool1d(self, params, ceil_mode, dilation, ie_device, precision, ir_version):
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self._test(*self.create_model("max_pool1d", **params, ceil_mode=ceil_mode, dilation=dilation),
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ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 3}, dynamic_shapes=False)
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@pytest.mark.parametrize("params", d2_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("dilation", [1, 2])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_max_pool2d(self, params, ceil_mode, dilation, ie_device, precision, ir_version):
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to_trace = False
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if params["stride"] == []:
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to_trace = True
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self._test(*self.create_model("max_pool2d", **params, ceil_mode=ceil_mode, dilation=dilation),
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ie_device, precision, ir_version, dynamic_shapes=False, trace_model=to_trace)
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@pytest.mark.parametrize("params", d3_params)
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@pytest.mark.parametrize("ceil_mode", [True, False])
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@pytest.mark.parametrize("dilation", [1, 2])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_max_pool3d(self, params, ceil_mode, dilation, ie_device, precision, ir_version):
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self._test(*self.create_model("max_pool3d", **params, ceil_mode=ceil_mode, dilation=dilation),
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ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 5}, dynamic_shapes=False)
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