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139 lines
5.8 KiB
139 lines
5.8 KiB
# 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 unittest
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import numpy as np
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from op_test import OpTest
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import numpy as np
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from paddle.fluid import Program, program_guard
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from paddle import fluid
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import paddle
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class TestChunkOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The type of axis in chunk_op should be int or Variable.
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def test_axis_type():
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x1 = paddle.data(shape=[4], dtype='float16', name='x3')
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paddle.chunk(x=x1, chunks=2, axis=3.2)
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self.assertRaises(TypeError, test_axis_type)
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# The type of axis in chunk op should be int or Variable.
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def test_axis_variable_type():
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x2 = paddle.data(shape=[4], dtype='float16', name='x9')
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x3 = paddle.data(shape=[1], dtype='float16', name='x10')
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paddle.chunk(input=x2, chunks=2, axis=x3)
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self.assertRaises(TypeError, test_axis_variable_type)
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# The type of num_or_sections in chunk_op should be int, tuple or list.
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def test_chunks_type():
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x4 = paddle.data(shape=[4], dtype='float16', name='x4')
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paddle.chunk(input=x4, chunks=2.1, axis=3)
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self.assertRaises(TypeError, test_chunks_type)
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def test_axis_type_tensor():
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x5 = paddle.data(shape=[4], dtype='float16', name='x6')
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paddle.chunk(input=x5, chunks=2, axis=3.2)
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self.assertRaises(TypeError, test_axis_type_tensor)
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class API_TestChunk(unittest.TestCase):
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def test_out(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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data1 = paddle.data('data1', shape=[4, 6, 6], dtype='float64')
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data2 = paddle.data('data2', shape=[1], dtype='int32')
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x0, x1, x2 = paddle.chunk(data1, chunks=3, axis=data2)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([4, 6, 6]).astype('float64')
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input2 = np.array([2]).astype('int32')
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r0, r1, r2, = exe.run(feed={"data1": input1,
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"data2": input2},
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fetch_list=[x0, x1, x2])
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ex_x0, ex_x1, ex_x2 = np.array_split(input1, 3, axis=2)
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self.assertTrue(np.allclose(ex_x0, r0))
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self.assertTrue(np.allclose(ex_x1, r1))
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self.assertTrue(np.allclose(ex_x2, r2))
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class API_TestChunk1(unittest.TestCase):
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def test_out(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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data1 = paddle.data('data1', shape=[4, 6, 6], dtype='float64')
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x0, x1, x2 = paddle.chunk(data1, chunks=3, axis=2)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([4, 6, 6]).astype('float64')
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r0, r1, r2, = exe.run(feed={"data1": input1},
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fetch_list=[x0, x1, x2])
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ex_x0, ex_x1, ex_x2 = np.array_split(input1, 3, axis=2)
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self.assertTrue(np.allclose(ex_x0, r0))
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self.assertTrue(np.allclose(ex_x1, r1))
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self.assertTrue(np.allclose(ex_x2, r2))
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class API_TestDygraphChunk(unittest.TestCase):
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def test_out1(self):
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with fluid.dygraph.guard():
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input_1 = np.random.random([4, 6, 6]).astype("int32")
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# input is a variable which shape is [4, 6, 6]
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input = fluid.dygraph.to_variable(input_1)
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x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1)
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x0_out = x0.numpy()
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x1_out = x1.numpy()
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x2_out = x2.numpy()
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ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1)
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self.assertTrue(np.allclose(ex_x0, x0_out))
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self.assertTrue(np.allclose(ex_x1, x1_out))
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self.assertTrue(np.allclose(ex_x2, x2_out))
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def test_out2(self):
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with fluid.dygraph.guard():
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input_1 = np.random.random([4, 6, 6]).astype("bool")
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# input is a variable which shape is [4, 6, 6]
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input = fluid.dygraph.to_variable(input_1)
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x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1)
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x0_out = x0.numpy()
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x1_out = x1.numpy()
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x2_out = x2.numpy()
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ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1)
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self.assertTrue(np.allclose(ex_x0, x0_out))
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self.assertTrue(np.allclose(ex_x1, x1_out))
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self.assertTrue(np.allclose(ex_x2, x2_out))
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def test_axis_tensor_input(self):
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with fluid.dygraph.guard():
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input_1 = np.random.random([4, 6, 6]).astype("int32")
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# input is a variable which shape is [4, 6, 6]
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input = fluid.dygraph.to_variable(input_1)
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num1 = paddle.full(shape=[1], fill_value=1, dtype='int32')
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x0, x1, x2 = paddle.chunk(input, chunks=3, axis=num1)
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x0_out = x0.numpy()
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x1_out = x1.numpy()
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x2_out = x2.numpy()
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ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1)
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self.assertTrue(np.allclose(ex_x0, x0_out))
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self.assertTrue(np.allclose(ex_x1, x1_out))
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self.assertTrue(np.allclose(ex_x2, x2_out))
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if __name__ == '__main__':
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unittest.main()
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