wehub-resource-sync
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_device, get_places
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import paddle
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class TensorFill_Test(unittest.TestCase):
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def setUp(self):
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self.shape = [32, 32]
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def test_tensor_fill_true(self):
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typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
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for idx, p in enumerate(get_places()):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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np_arr = np.reshape(
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np.array(range(np.prod(self.shape))), self.shape
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)
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for dtype in typelist:
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var = 1.0
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tensor = paddle.to_tensor(np_arr, place=p, dtype=dtype)
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target = tensor.numpy()
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target[...] = var
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tensor.fill_(var) # var type is basic type in typelist
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self.assertEqual((tensor.numpy() == target).all(), True)
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def test_tensor_fill_backward(self):
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typelist = ['float32']
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for idx, p in enumerate(get_places()):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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np_arr = np.reshape(
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np.array(range(np.prod(self.shape))), self.shape
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)
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for dtype in typelist:
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var = 1
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tensor = paddle.to_tensor(np_arr, place=p, dtype=dtype)
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tensor.stop_gradient = False
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y = tensor * 2
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y.retain_grads()
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y.fill_(var)
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loss = y.sum()
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loss.backward()
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self.assertEqual((y.grad.numpy() == 0).all().item(), True)
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def test_errors(self):
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def test_list():
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x = paddle.to_tensor([2, 3, 4])
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x.fill_([1])
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self.assertRaises(TypeError, test_list)
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if __name__ == '__main__':
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unittest.main()
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