Update `paddle.clamp` (#25906)
* Update `paddle.clamp` rename to `paddle.clip` add fast path for dygraph mode remove `out` rename `input` -> `x` update doc sample * Fix leftover `Variable` wording * Indent doc with spaces * Remove `:alias` in docs * Update `enable_imperative` -> `disable_static` * Remove `imperative` also trigger CI * Update tests for better coverage * Rebase to fix `cosine_similarity` * Fix `cosine_similarity` some moretest_feature_precision_test_c
Yang Zhang
5 years ago
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GitHub
parent
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commit
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# Copyright (c) 2020 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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from __future__ import print_function
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import paddle.tensor as tensor
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import paddle.fluid as fluid
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import numpy as np
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import unittest
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class TestClampAPI(unittest.TestCase):
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def test_dygraph_clamp(self):
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in1 = np.array([[1.2, 3.5], [4.5, 6.4]]).astype('float32')
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with fluid.dygraph.guard():
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x1 = fluid.dygraph.to_variable(in1)
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out1 = tensor.clamp(x1, min=3.5, max=5.0)
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out2 = tensor.clamp(x1, min=2.5)
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self.assertTrue(
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np.allclose(
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out1.numpy(), in1.clip(
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min=3.5, max=5.0)))
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self.assertTrue(np.allclose(out2.numpy(), in1.clip(min=2.5)))
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def test_clamp(self):
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data_shape = [1, 9, 9, 4]
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data = np.random.random(data_shape).astype('float32')
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images = fluid.data(name='image', shape=data_shape, dtype='float32')
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min = fluid.data(name='min', shape=[1], dtype='float32')
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max = fluid.data(name='max', shape=[1], dtype='float32')
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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out_1 = tensor.clamp(images, min=min, max=max)
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out_2 = tensor.clamp(images, min=0.2, max=0.9)
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out_3 = tensor.clamp(images, min=0.3)
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out_4 = tensor.clamp(images, max=0.7)
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out_5 = tensor.clamp(images, min=min)
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out_6 = tensor.clamp(images, max=max)
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res1, res2, res3, res4, res5, res6 = exe.run(
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fluid.default_main_program(),
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feed={
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"image": data,
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"min": np.array([0.2]).astype('float32'),
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"max": np.array([0.8]).astype('float32')
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},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6])
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self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
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self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
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self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
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self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
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self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
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self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
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class TestClampError(unittest.TestCase):
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def test_errors(self):
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x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
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x2 = fluid.layers.data(name='x2', shape=[1], dtype="int8")
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self.assertRaises(TypeError, tensor.clamp, x=x1, min=0.2, max=0.8)
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self.assertRaises(TypeError, tensor.clamp, x=x2, min=0.2, max=0.8)
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if __name__ == '__main__':
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unittest.main()
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