mirror of
https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
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from caffe2.python import core, workspace
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from hypothesis import given, settings
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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class TestTransposeOp(serial.SerializedTestCase):
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@given(
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X=hu.tensor(dtype=np.float32), use_axes=st.booleans(), **hu.gcs)
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@settings(deadline=None, max_examples=50)
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def test_transpose(self, X, use_axes, gc, dc):
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ndim = len(X.shape)
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axes = np.arange(ndim)
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np.random.shuffle(axes)
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if (use_axes):
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], axes=axes, device_option=gc)
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else:
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], device_option=gc)
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def transpose_ref(X):
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if use_axes:
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return [np.transpose(X, axes=axes)]
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else:
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return [np.transpose(X)]
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self.assertReferenceChecks(gc, op, [X], transpose_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(M=st.integers(10, 200), N=st.integers(10, 200), **hu.gcs)
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@settings(max_examples=10, deadline=None)
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def test_transpose_large_matrix(self, M, N, gc, dc):
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op = core.CreateOperator("Transpose", ["X"], ["Y"], device_option=gc)
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X = np.random.rand(M, N).astype(np.float32) - 0.5
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def transpose_ref(X):
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return [np.transpose(X)]
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self.assertReferenceChecks(gc, op, [X], transpose_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@unittest.skipIf(not workspace.has_cuda_support, "no cuda support")
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@given(X=hu.tensor(dtype=np.float32), use_axes=st.booleans(),
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**hu.gcs_cuda_only)
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def test_transpose_cudnn(self, X, use_axes, gc, dc):
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ndim = len(X.shape)
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axes = np.arange(ndim)
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np.random.shuffle(axes)
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if (use_axes):
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], axes=axes, engine="CUDNN",
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device_option=hu.cuda_do)
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else:
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op = core.CreateOperator(
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"Transpose", ["X"], ["Y"], engine="CUDNN",
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device_option=hu.cuda_do)
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def transpose_ref(X):
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if use_axes:
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return [np.transpose(X, axes=axes)]
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else:
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return [np.transpose(X)]
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self.assertReferenceChecks(hu.gpu_do, op, [X], transpose_ref)
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self.assertGradientChecks(hu.gpu_do, op, [X], 0, [0])
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if __name__ == "__main__":
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unittest.main()
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