[Backend Tester] Add maxpool tests

backends/test/suite/operators/test_avgpool1d.py

@@ -0,0 +1,155 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+
+import torch
+from executorch.backends.test.suite.flow import TestFlow
+
+from executorch.backends.test.suite.operators import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+
+class Model(torch.nn.Module):
+    def __init__(
+        self,
+        kernel_size=3,
+        stride=None,
+        padding=0,
+        ceil_mode=False,
+        count_include_pad=True,
+    ):
+        super().__init__()
+        self.avgpool = torch.nn.AvgPool1d(
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            ceil_mode=ceil_mode,
+            count_include_pad=count_include_pad,
+        )
+
+    def forward(self, x):
+        return self.avgpool(x)
+
+
+@operator_test
+class AvgPool1d(OperatorTest):
+    @dtype_test
+    def test_avgpool1d_dtype(self, flow: TestFlow, dtype) -> None:
+        # Input shape: (batch_size, channels, length)
+        self._test_op(
+            Model().to(dtype),
+            ((torch.rand(1, 8, 100) * 10).to(dtype),),
+            flow,
+        )
+
+    def test_avgpool1d_kernel_size(self, flow: TestFlow) -> None:
+        # Test with different kernel sizes
+        self._test_op(
+            Model(kernel_size=1),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=5),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_stride(self, flow: TestFlow) -> None:
+        # Test with different stride values
+        self._test_op(
+            Model(stride=2),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(stride=3),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_padding(self, flow: TestFlow) -> None:
+        # Test with different padding values
+        self._test_op(
+            Model(padding=1),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(padding=2),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_ceil_mode(self, flow: TestFlow) -> None:
+        # Test with ceil_mode=True
+        self._test_op(
+            Model(ceil_mode=True),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_count_include_pad(self, flow: TestFlow) -> None:
+        # Test with count_include_pad=False
+        self._test_op(
+            Model(padding=1, count_include_pad=False),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_batch_sizes(self, flow: TestFlow) -> None:
+        # Test with batch inputs
+        self._test_op(
+            Model(),
+            (torch.randn(2, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(8, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(16, 8, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_input_sizes(self, flow: TestFlow) -> None:
+        # Test with different input sizes
+        self._test_op(
+            Model(),
+            (torch.randn(1, 4, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 16, 100),),
+            flow,
+        )
+
+    def test_avgpool1d_combinations(self, flow: TestFlow) -> None:
+        # Test with combinations of parameters
+        self._test_op(
+            Model(kernel_size=2, stride=2, padding=1),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=3, stride=2, padding=1, ceil_mode=True),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=2, stride=2, padding=1, count_include_pad=False),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )

backends/test/suite/operators/test_avgpool2d.py

@@ -0,0 +1,168 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+
+import torch
+from executorch.backends.test.suite.flow import TestFlow
+
+from executorch.backends.test.suite.operators import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+
+class Model(torch.nn.Module):
+    def __init__(
+        self,
+        kernel_size=3,
+        stride=None,
+        padding=0,
+        ceil_mode=False,
+        count_include_pad=True,
+    ):
+        super().__init__()
+
+        # Create the avgpool layer with the given parameters
+        # torch.nn.AvgPool2d accepts both int and tuple types for kernel_size, stride, and padding
+        self.avgpool = torch.nn.AvgPool2d(
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            ceil_mode=ceil_mode,
+            count_include_pad=count_include_pad,
+        )
+
+    def forward(self, x):
+        return self.avgpool(x)
+
+
+@operator_test
+class AvgPool2d(OperatorTest):
+    @dtype_test
+    def test_avgpool2d_dtype(self, flow: TestFlow, dtype) -> None:
+        # Input shape: (batch_size, channels, height, width)
+        self._test_op(
+            Model().to(dtype),
+            ((torch.rand(1, 8, 20, 20) * 10).to(dtype),),
+            flow,
+        )
+
+    def test_avgpool2d_kernel_size(self, flow: TestFlow) -> None:
+        # Test with different kernel sizes
+        self._test_op(
+            Model(kernel_size=1),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=5),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=(3, 2)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_stride(self, flow: TestFlow) -> None:
+        # Test with different stride values
+        self._test_op(
+            Model(stride=2),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(stride=(2, 1)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_padding(self, flow: TestFlow) -> None:
+        # Test with different padding values
+        self._test_op(
+            Model(padding=1),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(padding=(1, 2)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_ceil_mode(self, flow: TestFlow) -> None:
+        # Test with ceil_mode=True
+        self._test_op(
+            Model(ceil_mode=True),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_count_include_pad(self, flow: TestFlow) -> None:
+        # Test with count_include_pad=False
+        self._test_op(
+            Model(padding=1, count_include_pad=False),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_batch_sizes(self, flow: TestFlow) -> None:
+        # Test with batch inputs
+        self._test_op(
+            Model(),
+            (torch.randn(2, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(8, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(16, 8, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_input_sizes(self, flow: TestFlow) -> None:
+        # Test with different input sizes
+        self._test_op(
+            Model(),
+            (torch.randn(1, 4, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 16, 20, 20),),
+            flow,
+        )
+
+    def test_avgpool2d_combinations(self, flow: TestFlow) -> None:
+        # Test with combinations of parameters
+        self._test_op(
+            Model(kernel_size=2, stride=2, padding=1),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(kernel_size=3, stride=2, padding=1, ceil_mode=True),
+            (torch.randn(1, 8, 21, 21),),
+            flow,
+        )
+        self._test_op(
+            Model(
+                kernel_size=(2, 3),
+                stride=(2, 1),
+                padding=(1, 0),
+                count_include_pad=False,
+            ),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )