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https://github.com/saymrwulf/pytorch.git
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4ad3a4fc8b
Summary: This reverts commit a7e38f4f925a4cedf530924bd426c3bb08b5aad8 Differential Revision: D4794432 fbshipit-source-id: 05b2b0d101ebd917527e94ef8a74e63ab40942a4
48 lines
1.4 KiB
Python
48 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 numpy as np
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from hypothesis import given
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import hypothesis.strategies as st
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestTile(hu.HypothesisTestCase):
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@given(M=st.integers(min_value=1, max_value=10),
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K=st.integers(min_value=1, max_value=10),
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N=st.integers(min_value=1, max_value=10),
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tiles=st.integers(min_value=1, max_value=3),
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axis=st.integers(min_value=0, max_value=2),
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**hu.gcs)
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def test_tile(self, M, K, N, tiles, axis, gc, dc):
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X = np.random.rand(M, K, N).astype(np.float32)
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op = core.CreateOperator(
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'Tile', ['X'], 'out',
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tiles=tiles,
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axis=axis,
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)
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def tile_ref(X, tiles, axis):
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dims = [1, 1, 1]
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dims[axis] = tiles
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tiled_data = np.tile(X, tuple(dims))
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return (tiled_data,)
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# Check against numpy reference
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self.assertReferenceChecks(gc, op, [X, tiles, axis],
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tile_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X], 0, [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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