mirror of
https://github.com/zebrajr/pytorch.git
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1488c5dd03
Summary: support 0 size in any of the tensor dimensions in mkldnn Pull Request resolved: https://github.com/pytorch/pytorch/pull/15295 Differential Revision: D13573747 Pulled By: yinghai fbshipit-source-id: 5bf7a0b9e2567e80f44981a7823be5407fc94e53
100 lines
3.1 KiB
Python
100 lines
3.1 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 numpy as np
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from random import randint
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class CopyTest(unittest.TestCase):
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def _get_deep_device(self):
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return caffe2_pb2.DeviceOption(device_type=caffe2_pb2.IDEEP)
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def test_copy_to_ideep(self):
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op = core.CreateOperator(
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"CopyCPUToIDEEP",
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["X"],
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["X_ideep"],
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)
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op.device_option.CopyFrom(self._get_deep_device())
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n = randint(1, 128)
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c = randint(1, 64)
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h = randint(1, 128)
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w = randint(1, 128)
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X = np.random.rand(n, c, h, w).astype(np.float32)
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workspace.FeedBlob("X", X)
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workspace.RunOperatorOnce(op)
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X_ideep = workspace.FetchBlob("X_ideep")
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np.testing.assert_allclose(X, X_ideep)
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def test_copy_to_ideep_zero_dim(self):
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op = core.CreateOperator(
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"CopyCPUToIDEEP",
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["X"],
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["X_ideep"],
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)
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op.device_option.CopyFrom(self._get_deep_device())
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n = 0
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c = randint(1, 128)
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X = np.random.rand(n, c).astype(np.float32)
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workspace.FeedBlob("X", X)
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workspace.RunOperatorOnce(op)
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X_ideep = workspace.FetchBlob("X_ideep")
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np.testing.assert_allclose(X, X_ideep)
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def test_copy_from_ideep(self):
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op = core.CreateOperator(
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"CopyIDEEPToCPU",
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["X_ideep"],
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["X"],
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)
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op.device_option.CopyFrom(self._get_deep_device())
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n = randint(1, 128)
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c = randint(1, 64)
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h = randint(1, 128)
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w = randint(1, 128)
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X = np.random.rand(n, c, h, w).astype(np.float32)
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workspace.FeedBlob("X_ideep", X, self._get_deep_device())
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workspace.RunOperatorOnce(op)
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X_ideep = workspace.FetchBlob("X")
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np.testing.assert_allclose(X, X_ideep)
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def test_copy_from_ideep_zero_dim(self):
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op = core.CreateOperator(
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"CopyIDEEPToCPU",
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["X_ideep"],
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["X"],
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)
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op.device_option.CopyFrom(self._get_deep_device())
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n = 0
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c = randint(1, 64)
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X = np.random.rand(n, c).astype(np.float32)
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workspace.FeedBlob("X_ideep", X, self._get_deep_device())
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workspace.RunOperatorOnce(op)
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X_ideep = workspace.FetchBlob("X")
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np.testing.assert_allclose(X, X_ideep)
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def test_copy_from_ideep_fallthrough(self):
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op = core.CreateOperator(
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"CopyIDEEPToCPU",
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["X_ideep"],
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["X"],)
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op.device_option.CopyFrom(self._get_deep_device())
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n = randint(1, 128)
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c = randint(1, 64)
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h = randint(1, 128)
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w = randint(1, 128)
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X = np.random.rand(n, c, h, w).astype(np.float32)
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workspace.FeedBlob("X_ideep", X)
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workspace.RunOperatorOnce(op)
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X_ideep = workspace.FetchBlob("X")
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np.testing.assert_allclose(X, X_ideep)
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if __name__ == "__main__":
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unittest.main()
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