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‎backends/test/suite/operators/test_adaptive_maxpool1d.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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output_size=5,
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return_indices=False,
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):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool1d(
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output_size=output_size,
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return_indices=return_indices,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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@operator_test
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class AdaptiveMaxPool1d(OperatorTest):
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@dtype_test
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def test_adaptive_maxpool1d_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_adaptive_maxpool1d_output_size(self, flow: TestFlow) -> None:
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# Test with different output sizes
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self._test_op(
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Model(output_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(output_size=10),
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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(output_size=50),
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(torch.randn(1, 8, 100),),
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flow,
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)
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def test_adaptive_maxpool1d_return_indices(self, flow: TestFlow) -> None:
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# Test with return_indices=True
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class ModelWithIndices(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool1d(
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output_size=5,
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return_indices=True,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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input_tensor = torch.randn(1, 8, 100)
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self._test_op(
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ModelWithIndices(),
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(input_tensor,),
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flow,
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)
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def test_adaptive_maxpool1d_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_adaptive_maxpool1d_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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self._test_op(
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Model(),
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(torch.randn(1, 8, 50),),
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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, 8, 200),),
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flow,
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)

‎backends/test/suite/operators/test_adaptive_maxpool2d.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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9+
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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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output_size=(5, 5),
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return_indices=False,
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):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool2d(
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output_size=output_size,
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return_indices=return_indices,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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@operator_test
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class AdaptiveMaxPool2d(OperatorTest):
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@dtype_test
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def test_adaptive_maxpool2d_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_adaptive_maxpool2d_output_size(self, flow: TestFlow) -> None:
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# Test with different output sizes
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self._test_op(
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Model(output_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(output_size=(1, 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(output_size=(10, 10)),
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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(output_size=(5, 10)),
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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_adaptive_maxpool2d_return_indices(self, flow: TestFlow) -> None:
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# Test with return_indices=True
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class ModelWithIndices(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool2d(
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output_size=(5, 5),
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return_indices=True,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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input_tensor = torch.randn(1, 8, 20, 20)
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self._test_op(
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ModelWithIndices(),
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(input_tensor,),
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flow,
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)
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def test_adaptive_maxpool2d_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_adaptive_maxpool2d_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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self._test_op(
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Model(),
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(torch.randn(1, 8, 10, 10),),
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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, 8, 30, 30),),
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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, 8, 15, 25),),
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flow,
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)

‎backends/test/suite/operators/test_adaptive_maxpool3d.py‎

Lines changed: 133 additions & 0 deletions
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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
5+
# LICENSE file in the root directory of this source tree.
6+
7+
# pyre-unsafe
8+
9+
10+
import torch
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from executorch.backends.test.suite.flow import TestFlow
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13+
from executorch.backends.test.suite.operators import (
14+
dtype_test,
15+
operator_test,
16+
OperatorTest,
17+
)
18+
19+
class Model(torch.nn.Module):
20+
def __init__(
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self,
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output_size=(4, 4, 4),
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return_indices=False,
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):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool3d(
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output_size=output_size,
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return_indices=return_indices,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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@operator_test
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class AdaptiveMaxPool3d(OperatorTest):
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@dtype_test
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def test_adaptive_maxpool3d_dtype(self, flow: TestFlow, dtype) -> None:
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# Input shape: (batch_size, channels, depth, height, width)
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self._test_op(
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Model().to(dtype),
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((torch.rand(1, 4, 8, 8, 8) * 10).to(dtype),),
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flow,
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)
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def test_adaptive_maxpool3d_output_size(self, flow: TestFlow) -> None:
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# Test with different output sizes
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self._test_op(
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Model(output_size=1),
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(torch.randn(1, 4, 8, 8, 8),),
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flow,
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)
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self._test_op(
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Model(output_size=(1, 1, 1)),
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(torch.randn(1, 4, 8, 8, 8),),
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flow,
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)
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self._test_op(
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Model(output_size=(6, 6, 6)),
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(torch.randn(1, 4, 8, 8, 8),),
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flow,
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)
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self._test_op(
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Model(output_size=(2, 4, 6)),
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(torch.randn(1, 4, 8, 8, 8),),
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flow,
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)
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def test_adaptive_maxpool3d_return_indices(self, flow: TestFlow) -> None:
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# Test with return_indices=True
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class ModelWithIndices(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.adaptive_maxpool = torch.nn.AdaptiveMaxPool3d(
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output_size=(4, 4, 4),
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return_indices=True,
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)
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def forward(self, x):
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return self.adaptive_maxpool(x)
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input_tensor = torch.randn(1, 4, 8, 8, 8)
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self._test_op(
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ModelWithIndices(),
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(input_tensor,),
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flow,
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)
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def test_adaptive_maxpool3d_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, 4, 8, 8, 8),),
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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, 4, 8, 8, 8),),
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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, 4, 8, 8, 8),),
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flow,
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)
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def test_adaptive_maxpool3d_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, 2, 8, 8, 8),),
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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, 8, 8, 8, 8),),
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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, 4, 6, 6, 6),),
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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, 4, 10, 10, 10),),
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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, 4, 7, 9, 11),),
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flow,
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)

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