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‎backends/test/suite/operators/test_avgpool1d.py‎

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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# pyre-unsafe
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import torch
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from executorch.backends.test.suite.flow import TestFlow
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from executorch.backends.test.suite.operators import (
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dtype_test,
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operator_test,
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OperatorTest,
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)
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class Model(torch.nn.Module):
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def __init__(
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self,
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kernel_size=3,
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stride=None,
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padding=0,
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ceil_mode=False,
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count_include_pad=True,
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):
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super().__init__()
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self.avgpool = torch.nn.AvgPool1d(
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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ceil_mode=ceil_mode,
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count_include_pad=count_include_pad,
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)
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def forward(self, x):
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return self.avgpool(x)
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@operator_test
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class AvgPool1d(OperatorTest):
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@dtype_test
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def test_avgpool1d_dtype(self, flow: TestFlow, dtype) -> None:
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# Input shape: (batch_size, channels, length)
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self._test_op(
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Model().to(dtype),
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((torch.rand(1, 8, 100) * 10).to(dtype),),
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flow,
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)
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def test_avgpool1d_kernel_size(self, flow: TestFlow) -> None:
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# Test with different kernel sizes
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self._test_op(
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Model(kernel_size=1),
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(torch.randn(1, 8, 100),),
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flow,
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)
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self._test_op(
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Model(kernel_size=5),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_avgpool1d_stride(self, flow: TestFlow) -> None:
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# Test with different stride values
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self._test_op(
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Model(stride=2),
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(torch.randn(1, 8, 100),),
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flow,
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)
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self._test_op(
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Model(stride=3),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_avgpool1d_padding(self, flow: TestFlow) -> None:
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# Test with different padding values
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self._test_op(
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Model(padding=1),
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(torch.randn(1, 8, 100),),
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flow,
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)
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self._test_op(
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Model(padding=2),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_avgpool1d_ceil_mode(self, flow: TestFlow) -> None:
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# Test with ceil_mode=True
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self._test_op(
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Model(ceil_mode=True),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_avgpool1d_count_include_pad(self, flow: TestFlow) -> None:
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# Test with count_include_pad=False
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self._test_op(
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Model(padding=1, count_include_pad=False),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_avgpool1d_batch_sizes(self, flow: TestFlow) -> None:
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# Test with batch inputs
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self._test_op(
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Model(),
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(torch.randn(2, 8, 100),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(8, 8, 100),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(16, 8, 100),),
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flow,
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)
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def test_avgpool1d_input_sizes(self, flow: TestFlow) -> None:
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# Test with different input sizes
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self._test_op(
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Model(),
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(torch.randn(1, 4, 100),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(1, 16, 100),),
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flow,
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)
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def test_avgpool1d_combinations(self, flow: TestFlow) -> None:
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# Test with combinations of parameters
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self._test_op(
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Model(kernel_size=2, stride=2, padding=1),
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(torch.randn(1, 8, 100),),
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flow,
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)
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self._test_op(
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Model(kernel_size=3, stride=2, padding=1, ceil_mode=True),
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(torch.randn(1, 8, 100),),
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flow,
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)
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self._test_op(
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Model(kernel_size=2, stride=2, padding=1, count_include_pad=False),
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(torch.randn(1, 8, 100),),
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flow,
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)

‎backends/test/suite/operators/test_avgpool2d.py‎

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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7+
# pyre-unsafe
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9+
10+
import torch
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from executorch.backends.test.suite.flow import TestFlow
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from executorch.backends.test.suite.operators import (
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dtype_test,
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operator_test,
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OperatorTest,
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)
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19+
class Model(torch.nn.Module):
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def __init__(
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self,
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kernel_size=3,
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stride=None,
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padding=0,
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ceil_mode=False,
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count_include_pad=True,
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):
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super().__init__()
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# Create the avgpool layer with the given parameters
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# torch.nn.AvgPool2d accepts both int and tuple types for kernel_size, stride, and padding
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self.avgpool = torch.nn.AvgPool2d(
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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ceil_mode=ceil_mode,
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count_include_pad=count_include_pad,
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)
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def forward(self, x):
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return self.avgpool(x)
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@operator_test
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class AvgPool2d(OperatorTest):
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@dtype_test
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def test_avgpool2d_dtype(self, flow: TestFlow, dtype) -> None:
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# Input shape: (batch_size, channels, height, width)
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self._test_op(
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Model().to(dtype),
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((torch.rand(1, 8, 20, 20) * 10).to(dtype),),
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flow,
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)
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def test_avgpool2d_kernel_size(self, flow: TestFlow) -> None:
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# Test with different kernel sizes
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self._test_op(
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Model(kernel_size=1),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(kernel_size=5),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(kernel_size=(3, 2)),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_stride(self, flow: TestFlow) -> None:
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# Test with different stride values
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self._test_op(
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Model(stride=2),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(stride=(2, 1)),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_padding(self, flow: TestFlow) -> None:
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# Test with different padding values
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self._test_op(
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Model(padding=1),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(padding=(1, 2)),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_ceil_mode(self, flow: TestFlow) -> None:
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# Test with ceil_mode=True
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self._test_op(
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Model(ceil_mode=True),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_count_include_pad(self, flow: TestFlow) -> None:
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# Test with count_include_pad=False
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self._test_op(
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Model(padding=1, count_include_pad=False),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_batch_sizes(self, flow: TestFlow) -> None:
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# Test with batch inputs
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self._test_op(
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Model(),
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(torch.randn(2, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(8, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(16, 8, 20, 20),),
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flow,
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)
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def test_avgpool2d_input_sizes(self, flow: TestFlow) -> None:
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# Test with different input sizes
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self._test_op(
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Model(),
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(torch.randn(1, 4, 20, 20),),
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flow,
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)
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self._test_op(
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Model(),
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(torch.randn(1, 16, 20, 20),),
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flow,
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)
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def test_avgpool2d_combinations(self, flow: TestFlow) -> None:
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# Test with combinations of parameters
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self._test_op(
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Model(kernel_size=2, stride=2, padding=1),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)
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self._test_op(
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Model(kernel_size=3, stride=2, padding=1, ceil_mode=True),
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(torch.randn(1, 8, 21, 21),),
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flow,
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)
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self._test_op(
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Model(kernel_size=(2, 3), stride=(2, 1), padding=(1, 0), count_include_pad=False),
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(torch.randn(1, 8, 20, 20),),
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flow,
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)

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