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https://github.com/saymrwulf/pytorch.git
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4778afb8bb
Summary: zrphercule https://pytorch.org/docs/stable/tensors.html#torch.Tensor.expand Pull Request resolved: https://github.com/pytorch/pytorch/pull/10174 Differential Revision: D9136467 Pulled By: bddppq fbshipit-source-id: 825c489899097acda8d43706964d78a104cdf583
63 lines
2.1 KiB
Python
63 lines
2.1 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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from caffe2.python import core, workspace
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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class TestExpandOp(hu.HypothesisTestCase):
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def _rand_shape(self, X_shape, max_length):
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length = np.random.randint(max_length)
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shape = np.ones(length, dtype=np.int64)
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i = len(X_shape) - 1
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for j in reversed(range(length)):
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if i >= 0:
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k = np.random.choice([1, X_shape[i]])
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i -= 1
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else:
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k = np.random.randint(3) + 1
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shape[j] = k
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return shape
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def _run_expand_op_test(self, X, shape, gc, dc):
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shape = np.array(shape)
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op = core.CreateOperator(
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'Expand',
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["X", "shape"],
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["Y"],
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)
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def ref(X, shape):
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return (X * np.ones(abs(shape)),)
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self.assertReferenceChecks(gc, op, [X, shape], ref)
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self.assertDeviceChecks(dc, op, [X, shape], [0])
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self.assertGradientChecks(gc, op, [X, shape], 0, [0])
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@given(X=hu.tensor(max_dim=5, dtype=np.float32),
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**hu.gcs)
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def test_expand_rand_shape(self, X, gc, dc):
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shape = self._rand_shape(X.shape, 5)
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self._run_expand_op_test(X, shape, gc, dc)
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@given(X=st.sampled_from([np.ones([1, 3, 1]),
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np.ones([3, 1, 3]),
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np.ones([1, 3])]),
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**hu.gcs)
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def test_expand_nonrand_shape1(self, X, gc, dc):
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self._run_expand_op_test(X, [3, 1, 3], gc, dc)
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self._run_expand_op_test(X, [3, -1, 3], gc, dc)
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@given(X=st.sampled_from([np.ones([4, 4, 2, 1]),
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np.ones([1, 4, 1, 2]),
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np.ones([4, 1, 2])]),
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**hu.gcs)
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def test_expand_nonrand_shape2(self, X, gc, dc):
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self._run_expand_op_test(X, [4, 1, 2, 2], gc, dc)
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self._run_expand_op_test(X, [4, -1, 2, 2], gc, dc)
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