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https://github.com/zebrajr/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
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given, settings
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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 ShapeTest(hu.HypothesisTestCase):
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@given(n=st.integers(1, 128),
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c=st.integers(1, 128),
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h=st.integers(1, 128),
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w=st.integers(1, 128),
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**mu.gcs)
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@settings(max_examples=10, deadline=None)
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def test_shape(self, n, c, h, w, gc, dc):
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op0 = core.CreateOperator(
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"Shape",
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["X0"],
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["Y0"],
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device_option=dc[0]
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)
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op1 = core.CreateOperator(
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"Shape",
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["X1"],
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["Y1"],
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device_option=dc[1]
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)
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X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('X1', X, dc[1])
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workspace.RunOperatorOnce(op0)
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workspace.RunOperatorOnce(op1)
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Y0 = workspace.FetchBlob('Y0')
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Y1 = workspace.FetchBlob('Y1')
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if not np.allclose(Y0, Y1, atol=0, rtol=0):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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@given(n=st.integers(1, 128),
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c=st.integers(1, 128),
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h=st.integers(1, 128),
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w=st.integers(1, 128),
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axes=st.lists(st.integers(0, 3), min_size=1, max_size=3),
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**mu.gcs)
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@settings(max_examples=10, deadline=None)
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def test_shape_with_axes(self, n, c, h, w, axes, gc, dc):
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axes = list(set(axes)).sort()
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op0 = core.CreateOperator(
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"Shape",
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["X0"],
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["Y0"],
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axes = axes,
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device_option=dc[0]
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)
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op1 = core.CreateOperator(
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"Shape",
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["X1"],
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["Y1"],
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axes = axes,
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device_option=dc[1]
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)
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X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('X1', X, dc[1])
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workspace.RunOperatorOnce(op0)
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workspace.RunOperatorOnce(op1)
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Y0 = workspace.FetchBlob('Y0')
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Y1 = workspace.FetchBlob('Y1')
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if not np.allclose(Y0, Y1, atol=0, rtol=0):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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if __name__ == "__main__":
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unittest.main()
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