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76 lines
2.6 KiB
76 lines
2.6 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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import paddle.fluid.core as core
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from op_test import OpTest
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import paddle.fluid as fluid
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class TestRandomCropOp(OpTest):
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def setUp(self):
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to_crop = np.array([[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]] *
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5).astype(np.int32)
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self.possible_res = [
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np.array([[1, 2, 3], [5, 6, 7]]).astype(np.int32),
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np.array([[2, 3, 4], [6, 7, 8]]).astype(np.int32),
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np.array([[5, 6, 7], [9, 10, 11]]).astype(np.int32),
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np.array([[6, 7, 8], [10, 11, 12]]).astype(np.int32)
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]
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self.op_type = "random_crop"
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self.inputs = {'X': to_crop, 'Seed': np.array([10]).astype('int64')}
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self.outputs = {'Out': np.array([]), 'SeedOut': np.array([])}
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self.attrs = {'shape': [2, 3]}
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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def verify_output(self, outs):
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out = np.array(outs[1])
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for ins in out[:]:
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is_equal = [(ins == res).all() for res in self.possible_res]
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self.assertIn(True, is_equal)
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class TestRandomCropOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program()):
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def test_x_type():
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input_data = np.random.random(2, 3, 256, 256).astype("float32")
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fluid.layers.random_crop(input_data)
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self.assertRaises(TypeError, test_x_type)
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def test_x_dtype():
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x2 = fluid.layers.data(
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name='x2', shape=[None, 3, 256, 256], dtype='float16')
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fluid.layers.random_crop(x2)
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self.assertRaises(TypeError, test_x_dtype)
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def test_shape_type():
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x3 = fluid.layers.data(
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name='x3', shape=[None, 3, 256, 256], dtype='float32')
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fluid.layers.random_crop(x3, shape=1)
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self.assertRaises(TypeError, test_shape_type)
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if __name__ == "__main__":
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unittest.main()
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