# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import numpy as np from paddle.fluid import Program, program_guard from paddle import fluid import paddle class TestChunkOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): # The type of axis in chunk_op should be int or Variable. def test_axis_type(): x1 = paddle.data(shape=[4], dtype='float16', name='x3') paddle.chunk(x=x1, chunks=2, axis=3.2) self.assertRaises(TypeError, test_axis_type) # The type of axis in chunk op should be int or Variable. def test_axis_variable_type(): x2 = paddle.data(shape=[4], dtype='float16', name='x9') x3 = paddle.data(shape=[1], dtype='float16', name='x10') paddle.chunk(input=x2, chunks=2, axis=x3) self.assertRaises(TypeError, test_axis_variable_type) # The type of num_or_sections in chunk_op should be int, tuple or list. def test_chunks_type(): x4 = paddle.data(shape=[4], dtype='float16', name='x4') paddle.chunk(input=x4, chunks=2.1, axis=3) self.assertRaises(TypeError, test_chunks_type) def test_axis_type_tensor(): x5 = paddle.data(shape=[4], dtype='float16', name='x6') paddle.chunk(input=x5, chunks=2, axis=3.2) self.assertRaises(TypeError, test_axis_type_tensor) class API_TestChunk(unittest.TestCase): def test_out(self): with fluid.program_guard(fluid.Program(), fluid.Program()): data1 = paddle.data('data1', shape=[4, 6, 6], dtype='float64') data2 = paddle.data('data2', shape=[1], dtype='int32') x0, x1, x2 = paddle.chunk(data1, chunks=3, axis=data2) place = paddle.CPUPlace() exe = paddle.static.Executor(place) input1 = np.random.random([4, 6, 6]).astype('float64') input2 = np.array([2]).astype('int32') r0, r1, r2, = exe.run(feed={"data1": input1, "data2": input2}, fetch_list=[x0, x1, x2]) ex_x0, ex_x1, ex_x2 = np.array_split(input1, 3, axis=2) self.assertTrue(np.allclose(ex_x0, r0)) self.assertTrue(np.allclose(ex_x1, r1)) self.assertTrue(np.allclose(ex_x2, r2)) class API_TestChunk1(unittest.TestCase): def test_out(self): with fluid.program_guard(fluid.Program(), fluid.Program()): data1 = paddle.data('data1', shape=[4, 6, 6], dtype='float64') x0, x1, x2 = paddle.chunk(data1, chunks=3, axis=2) place = paddle.CPUPlace() exe = paddle.static.Executor(place) input1 = np.random.random([4, 6, 6]).astype('float64') r0, r1, r2, = exe.run(feed={"data1": input1}, fetch_list=[x0, x1, x2]) ex_x0, ex_x1, ex_x2 = np.array_split(input1, 3, axis=2) self.assertTrue(np.allclose(ex_x0, r0)) self.assertTrue(np.allclose(ex_x1, r1)) self.assertTrue(np.allclose(ex_x2, r2)) class API_TestDygraphChunk(unittest.TestCase): def test_out1(self): with fluid.dygraph.guard(): input_1 = np.random.random([4, 6, 6]).astype("int32") # input is a variable which shape is [4, 6, 6] input = fluid.dygraph.to_variable(input_1) x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1) x0_out = x0.numpy() x1_out = x1.numpy() x2_out = x2.numpy() ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1) self.assertTrue(np.allclose(ex_x0, x0_out)) self.assertTrue(np.allclose(ex_x1, x1_out)) self.assertTrue(np.allclose(ex_x2, x2_out)) def test_out2(self): with fluid.dygraph.guard(): input_1 = np.random.random([4, 6, 6]).astype("bool") # input is a variable which shape is [4, 6, 6] input = fluid.dygraph.to_variable(input_1) x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1) x0_out = x0.numpy() x1_out = x1.numpy() x2_out = x2.numpy() ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1) self.assertTrue(np.allclose(ex_x0, x0_out)) self.assertTrue(np.allclose(ex_x1, x1_out)) self.assertTrue(np.allclose(ex_x2, x2_out)) def test_axis_tensor_input(self): with fluid.dygraph.guard(): input_1 = np.random.random([4, 6, 6]).astype("int32") # input is a variable which shape is [4, 6, 6] input = fluid.dygraph.to_variable(input_1) num1 = paddle.full(shape=[1], fill_value=1, dtype='int32') x0, x1, x2 = paddle.chunk(input, chunks=3, axis=num1) x0_out = x0.numpy() x1_out = x1.numpy() x2_out = x2.numpy() ex_x0, ex_x1, ex_x2 = np.array_split(input_1, 3, axis=1) self.assertTrue(np.allclose(ex_x0, x0_out)) self.assertTrue(np.allclose(ex_x1, x1_out)) self.assertTrue(np.allclose(ex_x2, x2_out)) if __name__ == '__main__': unittest.main()