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https://github.com/zebrajr/pytorch.git
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29c849ff34
Summary: implement transpose operator for MKLDNN 1. upgrade mkldnn-bridge to support ND transpose 2. implement transpose operator in caffe2. Pull Request resolved: https://github.com/pytorch/pytorch/pull/19955 Differential Revision: D15701832 Pulled By: bddppq fbshipit-source-id: e4337cd0ba6f8180a35c8c70cbb6830a0a84182f
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class TransposeTest(hu.HypothesisTestCase):
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@given(
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X=hu.tensor(min_dim=1, max_dim=5, dtype=np.float32), use_axes=st.booleans(), **mu.gcs)
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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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if __name__ == "__main__":
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unittest.main()
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