[Backend Tester] Add adaptive maxpool tests

backends/test/suite/operators/test_adaptive_avgpool1d.py

@@ -0,0 +1,102 @@
+# 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,
+        output_size=5,
+    ):
+        super().__init__()
+        self.adaptive_avgpool = torch.nn.AdaptiveAvgPool1d(
+            output_size=output_size,
+        )
+
+    def forward(self, x):
+        return self.adaptive_avgpool(x)
+
+
+@operator_test
+class AdaptiveAvgPool1d(OperatorTest):
+    @dtype_test
+    def test_adaptive_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_adaptive_avgpool1d_output_size(self, flow: TestFlow) -> None:
+        # Test with different output sizes
+        self._test_op(
+            Model(output_size=1),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=10),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=50),
+            (torch.randn(1, 8, 100),),
+            flow,
+        )
+
+    def test_adaptive_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_adaptive_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,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 50),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 200),),
+            flow,
+        )

backends/test/suite/operators/test_adaptive_avgpool2d.py

@@ -0,0 +1,112 @@
+# 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,
+        output_size=(5, 5),
+    ):
+        super().__init__()
+        self.adaptive_avgpool = torch.nn.AdaptiveAvgPool2d(
+            output_size=output_size,
+        )
+
+    def forward(self, x):
+        return self.adaptive_avgpool(x)
+
+
+@operator_test
+class AdaptiveAvgPool2d(OperatorTest):
+    @dtype_test
+    def test_adaptive_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_adaptive_avgpool2d_output_size(self, flow: TestFlow) -> None:
+        # Test with different output sizes
+        self._test_op(
+            Model(output_size=1),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(1, 1)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(10, 10)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(5, 10)),
+            (torch.randn(1, 8, 20, 20),),
+            flow,
+        )
+
+    def test_adaptive_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_adaptive_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,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 10, 10),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 30, 30),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 15, 25),),
+            flow,
+        )

backends/test/suite/operators/test_adaptive_avgpool3d.py

@@ -0,0 +1,112 @@
+# 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,
+        output_size=(4, 4, 4),
+    ):
+        super().__init__()
+        self.adaptive_avgpool = torch.nn.AdaptiveAvgPool3d(
+            output_size=output_size,
+        )
+
+    def forward(self, x):
+        return self.adaptive_avgpool(x)
+
+
+@operator_test
+class AdaptiveAvgPool3d(OperatorTest):
+    @dtype_test
+    def test_adaptive_avgpool3d_dtype(self, flow: TestFlow, dtype) -> None:
+        # Input shape: (batch_size, channels, depth, height, width)
+        self._test_op(
+            Model().to(dtype),
+            ((torch.rand(1, 4, 8, 8, 8) * 10).to(dtype),),
+            flow,
+        )
+
+    def test_adaptive_avgpool3d_output_size(self, flow: TestFlow) -> None:
+        # Test with different output sizes
+        self._test_op(
+            Model(output_size=1),
+            (torch.randn(1, 4, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(1, 1, 1)),
+            (torch.randn(1, 4, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(6, 6, 6)),
+            (torch.randn(1, 4, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(output_size=(2, 4, 6)),
+            (torch.randn(1, 4, 8, 8, 8),),
+            flow,
+        )
+
+    def test_adaptive_avgpool3d_batch_sizes(self, flow: TestFlow) -> None:
+        # Test with batch inputs
+        self._test_op(
+            Model(),
+            (torch.randn(2, 4, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(8, 4, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(16, 4, 8, 8, 8),),
+            flow,
+        )
+
+    def test_adaptive_avgpool3d_input_sizes(self, flow: TestFlow) -> None:
+        # Test with different input sizes
+        self._test_op(
+            Model(),
+            (torch.randn(1, 2, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 8, 8, 8, 8),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 4, 6, 6, 6),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 4, 10, 10, 10),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(1, 4, 7, 9, 11),),
+            flow,
+        )