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GregoryComerGregoryComer
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[Backend Tester] Add convolution tests (#12847)
Add tests for convolution-family operators.
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‎backends/test/suite/operators/test_conv1d.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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in_channels=3,
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out_channels=6,
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kernel_size=3,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=True,
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padding_mode="zeros",
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):
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super().__init__()
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self.conv = torch.nn.Conv1d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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padding_mode=padding_mode,
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)
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def forward(self, x):
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return self.conv(x)
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@operator_test
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class Conv1d(OperatorTest):
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@dtype_test
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def test_conv1d_dtype(self, flow: TestFlow, dtype) -> None:
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self._test_op(
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Model().to(dtype),
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((torch.rand(4, 3, 50) * 10).to(dtype),),
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flow,
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)
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def test_conv1d_basic(self, flow: TestFlow) -> None:
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self._test_op(
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Model(),
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(torch.randn(4, 3, 50),),
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flow,
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)
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def test_conv1d_kernel_size(self, flow: TestFlow) -> None:
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self._test_op(
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Model(kernel_size=1),
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(torch.randn(4, 3, 50),),
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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(4, 3, 50),),
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flow,
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)
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def test_conv1d_stride(self, flow: TestFlow) -> None:
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self._test_op(
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Model(stride=2),
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(torch.randn(4, 3, 50),),
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flow,
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)
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def test_conv1d_padding(self, flow: TestFlow) -> None:
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self._test_op(
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Model(padding=1),
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(torch.randn(4, 3, 50),),
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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(4, 3, 50),),
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flow,
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)
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def test_conv1d_dilation(self, flow: TestFlow) -> None:
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self._test_op(
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Model(dilation=2),
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(torch.randn(4, 3, 50),),
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flow,
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)
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def test_conv1d_groups(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=6, out_channels=6, groups=3),
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(torch.randn(4, 6, 50),),
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flow,
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)
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def test_conv1d_depthwise(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=8, out_channels=8, groups=8),
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(torch.randn(4, 8, 50),),
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flow,
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)
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def test_conv1d_no_bias(self, flow: TestFlow) -> None:
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self._test_op(
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Model(bias=False),
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(torch.randn(4, 3, 50),),
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flow,
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)
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def test_conv1d_padding_modes(self, flow: TestFlow) -> None:
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for mode in ["zeros", "reflect", "replicate", "circular"]:
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self._test_op(
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Model(padding=1, padding_mode=mode),
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(torch.randn(4, 3, 50),),
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flow,
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)
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def test_conv1d_channels(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=1, out_channels=1),
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(torch.randn(4, 1, 50),),
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flow,
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)
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self._test_op(
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Model(in_channels=5, out_channels=10),
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(torch.randn(4, 5, 50),),
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flow,
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)

‎backends/test/suite/operators/test_conv2d.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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from typing import Tuple, Union
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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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in_channels=3,
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out_channels=6,
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kernel_size: Union[int, Tuple[int, int]] = 3,
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stride: Union[int, Tuple[int, int]] = 1,
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padding: Union[int, Tuple[int, int]] = 0,
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dilation: Union[int, Tuple[int, int]] = 1,
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groups=1,
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bias=True,
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padding_mode="zeros",
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):
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super().__init__()
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self.conv = torch.nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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padding_mode=padding_mode,
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)
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def forward(self, x):
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return self.conv(x)
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@operator_test
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class Conv2d(OperatorTest):
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@dtype_test
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def test_conv2d_dtype(self, flow: TestFlow, dtype) -> None:
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self._test_op(
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Model().to(dtype),
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((torch.rand(4, 3, 16, 16) * 10).to(dtype),),
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flow,
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)
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def test_conv2d_basic(self, flow: TestFlow) -> None:
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self._test_op(
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Model(),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_kernel_size(self, flow: TestFlow) -> None:
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self._test_op(
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Model(kernel_size=1),
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(torch.randn(4, 3, 16, 16),),
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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(4, 3, 16, 16),),
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flow,
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)
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self._test_op(
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Model(kernel_size=(3, 5)),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_stride(self, flow: TestFlow) -> None:
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self._test_op(
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Model(stride=2),
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(torch.randn(4, 3, 16, 16),),
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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(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_padding(self, flow: TestFlow) -> None:
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self._test_op(
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Model(padding=1),
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(torch.randn(4, 3, 16, 16),),
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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(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_dilation(self, flow: TestFlow) -> None:
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self._test_op(
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Model(dilation=2),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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self._test_op(
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Model(dilation=(2, 1)),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_groups(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=6, out_channels=6, groups=3),
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(torch.randn(4, 6, 16, 16),),
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flow,
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)
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def test_conv2d_depthwise(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=8, out_channels=8, groups=8),
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(torch.randn(4, 8, 16, 16),),
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flow,
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)
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def test_conv2d_no_bias(self, flow: TestFlow) -> None:
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self._test_op(
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Model(bias=False),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_padding_modes(self, flow: TestFlow) -> None:
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for mode in ["zeros", "reflect", "replicate", "circular"]:
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self._test_op(
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Model(padding=1, padding_mode=mode),
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(torch.randn(4, 3, 16, 16),),
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flow,
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)
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def test_conv2d_channels(self, flow: TestFlow) -> None:
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self._test_op(
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Model(in_channels=1, out_channels=1),
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(torch.randn(4, 1, 16, 16),),
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flow,
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)
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self._test_op(
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Model(in_channels=5, out_channels=10),
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(torch.randn(4, 5, 16, 16),),
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flow,
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)
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def test_conv2d_different_spatial_dims(self, flow: TestFlow) -> None:
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self._test_op(
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Model(),
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(torch.randn(4, 3, 20, 16),),
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flow,
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)

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