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104 lines
3.3 KiB
104 lines
3.3 KiB
# Copyright (c) 2018 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 unittest
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import numpy as np
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from op_test import OpTest, check_out_dtype
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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class TestPadConstantLikeOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = "pad_constant_like"
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float64"),
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'Y': np.random.random(self.y_shape).astype("float64")
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}
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self.attrs = {}
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self.attrs['pad_value'] = self.pad_value
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self.outputs = {
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'Out': np.pad(self.inputs['Y'],
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self.paddings,
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mode='constant',
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constant_values=self.pad_value)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['Y'], 'Out')
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def initTestCase(self):
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self.x_shape = (16, 40)
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self.y_shape = (3, 40)
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self.pad_value = 0.1
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self.paddings = [(0, 13), (0, 0)]
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class TestCase1(TestPadConstantLikeOp):
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def initTestCase(self):
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self.x_shape = (4, 3, 4, 5)
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self.y_shape = (2, 3, 4, 5)
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self.paddings = [(0, 2), (0, 0), (0, 0), (0, 0)]
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self.pad_value = 0.5
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class TestCase2(TestPadConstantLikeOp):
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def initTestCase(self):
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self.x_shape = (4, 3, 4, 10)
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self.y_shape = (2, 3, 2, 10)
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self.paddings = [(0, 2), (0, 0), (0, 2), (0, 0)]
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self.pad_value = 0.5
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class TestPadConstantLikeOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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x_data = np.random.random((2, 2, 2, 2)).astype("float32")
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y_data = np.random.random((2, 2, 2, 2)).astype("float32")
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def test_Variable_x():
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var_y = fluid.data(
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name="data_y", shape=[2, 2, 2, 2], dtype="float32")
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fluid.layers.pad_constant_like(x=x_data, y=var_y)
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self.assertRaises(TypeError, test_Variable_x)
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def test_Variable_y():
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var_x = fluid.data(
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name="data_x", shape=[2, 2, 2, 2], dtype="float32")
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fluid.layers.pad_constant_like(x=var_x, y=y_data)
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self.assertRaises(TypeError, test_Variable_y)
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class TestOutDtype(unittest.TestCase):
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def test_dtype(self):
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api_fn = fluid.layers.pad_constant_like
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check_out_dtype(
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api_fn,
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in_specs=[([2, 3, 2, 3], 'float64'), ([1, 3, 1, 3], )],
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expect_dtypes=['float32', 'float64', 'int32', 'int64'],
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target_index=1,
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pad_value=0.)
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if __name__ == '__main__':
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unittest.main()
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