wehub-resource-sync
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
# Copyright (c) 2025 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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import paddle
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class TestFromNumpy(unittest.TestCase):
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def setUp(self):
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self.shape = [3, 4, 5]
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self.dtypes = [
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"bool",
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"float16",
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"float32",
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"float64",
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"int8",
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"int16",
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"int32",
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"int64",
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"uint8",
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"complex64",
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"complex128",
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]
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self.devices = ["cpu", paddle.CPUPlace()]
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if paddle.base.is_compiled_with_cuda():
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self.devices.append("gpu")
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self.devices.append(paddle.CUDAPlace(0))
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self.stop_gradients = [True, False]
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def prepare_data(self, dtype):
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if dtype == "bool":
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return np.random.randint(0, 2, self.shape)
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else:
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return np.random.randn(*self.shape)
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def test_base(self):
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for dtype in self.dtypes:
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np_data = self.prepare_data(dtype)
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for device in self.devices:
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for stop_gradient in self.stop_gradients:
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tensor = paddle.asarray(
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np_data,
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device=device,
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requires_grad=stop_gradient,
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dtype=dtype,
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)
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target_place = device
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if isinstance(target_place, str):
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target_place = (
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paddle.CPUPlace()
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if target_place == "cpu"
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else paddle.CUDAPlace(0)
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)
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self.assertEqual(tensor.stop_gradient, not stop_gradient)
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self.assertEqual(tensor.place, target_place)
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np.testing.assert_allclose(
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tensor.numpy(), np_data.astype(dtype)
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)
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if __name__ == "__main__":
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unittest.main()
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