wehub-resource-sync
308 lines
12 KiB
Python
308 lines
12 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from utils import dygraph_guard, static_guard
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import paddle
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from paddle import base
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from paddle.base import Program, program_guard
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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.static.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 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.static.data(shape=[4], dtype='float16', name='x4')
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paddle.chunk(x=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.static.data(shape=[4], dtype='float16', name='x6')
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paddle.chunk(x=x5, chunks=2, axis=3.2)
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self.assertRaises(TypeError, test_axis_type_tensor)
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with paddle.base.dygraph.guard():
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def test_0_chunks_tensor():
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x = paddle.uniform([1, 1, 1], dtype='float32')
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paddle.chunk(x, chunks=0)
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self.assertRaises(ValueError, test_0_chunks_tensor)
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def test_negative_chunks_tensor():
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x = paddle.uniform([2, 3, 4], dtype='float32')
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paddle.chunk(x, chunks=-1)
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self.assertRaises(ValueError, test_negative_chunks_tensor)
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def test_chunks_greater_than_dim():
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x = paddle.uniform([2, 3, 4], dtype='float32')
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# axis=1, shape=3, chunks=5 > 3
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paddle.chunk(x, chunks=5, axis=1)
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self.assertRaises(ValueError, test_chunks_greater_than_dim)
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class API_TestChunk(unittest.TestCase):
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def test_out(self):
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with base.program_guard(base.Program(), base.Program()):
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data1 = paddle.static.data(
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'data1', shape=[4, 6, 6], dtype='float64'
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)
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data2 = paddle.static.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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(
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r0,
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r1,
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r2,
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) = exe.run(
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feed={"data1": input1, "data2": input2}, fetch_list=[x0, x1, x2]
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)
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ex_x0, ex_x1, ex_x2 = np.array_split(input1, 3, axis=2)
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np.testing.assert_allclose(ex_x0, r0, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, r1, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, r2, rtol=1e-05)
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class API_TestChunk1(unittest.TestCase):
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def test_out(self):
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with base.program_guard(base.Program(), base.Program()):
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data1 = paddle.static.data(
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'data1', shape=[4, 6, 6], dtype='float64'
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)
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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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(
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r0,
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r1,
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r2,
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) = exe.run(feed={"data1": input1}, 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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np.testing.assert_allclose(ex_x0, r0, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, r1, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, r2, rtol=1e-05)
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class API_TestChunkZeroSize1(unittest.TestCase):
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def test_out(self):
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with base.program_guard(base.Program(), base.Program()):
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data1 = paddle.static.data(
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'data1', shape=[0, 1, 1, 4], dtype='float32'
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)
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x0, x1, x2, x3 = paddle.chunk(data1, chunks=4, axis=-1)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([0, 1, 1, 4]).astype('float32')
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(
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r0,
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r1,
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r2,
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r3,
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) = exe.run(feed={"data1": input1}, fetch_list=[x0, x1, x2, x3])
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ex_x0, ex_x1, ex_x2, ex_x3 = np.array_split(input1, 4, axis=-1)
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np.testing.assert_allclose(ex_x0, r0, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, r1, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, r2, rtol=1e-05)
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np.testing.assert_allclose(ex_x3, r3, rtol=1e-05)
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class API_TestDygraphChunk(unittest.TestCase):
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def test_out1(self):
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with base.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 = paddle.to_tensor(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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np.testing.assert_allclose(ex_x0, x0_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, x1_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, x2_out, rtol=1e-05)
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def test_out2(self):
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with base.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 = paddle.to_tensor(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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np.testing.assert_allclose(ex_x0, x0_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, x1_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, x2_out, rtol=1e-05)
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def test_axis_tensor_input(self):
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with base.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 = paddle.to_tensor(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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np.testing.assert_allclose(ex_x0, x0_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x1, x1_out, rtol=1e-05)
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np.testing.assert_allclose(ex_x2, x2_out, rtol=1e-05)
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class TestChunkCompatibility(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.CPUPlace()]
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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self.places.append(get_device_place())
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self.func = paddle.chunk
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self.init_data()
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self.init_case()
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def init_data(self):
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self.shape = [6, 4]
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self.dtype = 'float32'
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self.np_input = np.random.random(self.shape).astype(self.dtype)
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self.chunks = 2
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self.axis = 0
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self.np_out = np.array_split(self.np_input, self.chunks, axis=self.axis)
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def init_case(self):
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params = [
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['x', 'input'], # param1
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['chunks'], # param2
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['axis', 'dim'], # param3
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]
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# Generate all valid combinations
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def generate_cases(param_groups, case_list):
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from itertools import product
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for combo in product(*[[None, *names] for names in param_groups]):
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args = ['pos' if p is None else 'kw' for p in combo]
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if args == sorted(args, key=lambda x: x != 'pos'):
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case_list.append(combo)
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# paddle.chunk()
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self.test_cases = []
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generate_cases(params, self.test_cases)
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# x.chunk()
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self.tensor_test_cases = []
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generate_cases(params[1:], self.tensor_test_cases)
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def _build_args_kwargs(self, param_names, params):
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args = []
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kwargs = {}
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for name, param in zip(param_names, params):
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if name is None:
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args.append(param)
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else:
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kwargs[name] = param
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return args, kwargs
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def test_dygraph_compatibility(self):
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with dygraph_guard():
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for place in self.places:
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paddle.device.set_device(place)
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x = paddle.to_tensor(self.np_input)
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# paddle.
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for param_names in self.test_cases:
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args, kwargs = self._build_args_kwargs(
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param_names, (x, self.chunks, self.axis)
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)
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outs = self.func(*args, **kwargs)
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for out, np_out in zip(outs, self.np_out):
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np.testing.assert_allclose(
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np_out, out.numpy(), rtol=1e-10
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)
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# paddle.Tensor.
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for param_names in self.tensor_test_cases:
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args, kwargs = self._build_args_kwargs(
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param_names, (self.chunks, self.axis)
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)
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outs = x.chunk(*args, **kwargs)
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for out, np_out in zip(outs, self.np_out):
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np.testing.assert_allclose(
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np_out, out.numpy(), rtol=1e-10
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)
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def test_static_compatibility(self):
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with static_guard():
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for place in self.places:
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype=self.dtype
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)
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# paddle.
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for param_names in self.test_cases:
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args, kwargs = self._build_args_kwargs(
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param_names, (x, self.chunks, self.axis)
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)
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outs = self.func(*args, **kwargs)
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exe = base.Executor(place)
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fetches = exe.run(
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main,
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feed={"x": self.np_input},
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fetch_list=outs,
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)
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for fetch, np_out in zip(fetches, self.np_out):
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np.testing.assert_allclose(
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np_out, fetch, rtol=1e-10
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)
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# paddle.Tensor.
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for param_names in self.tensor_test_cases:
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args, kwargs = self._build_args_kwargs(
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param_names, (self.chunks, self.axis)
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)
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outs = x.chunk(*args, **kwargs)
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exe = base.Executor(place)
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fetches = exe.run(
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main,
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feed={"x": self.np_input},
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fetch_list=outs,
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)
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for fetch, np_out in zip(fetches, self.np_out):
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np.testing.assert_allclose(
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np_out, fetch, rtol=1e-10
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)
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if __name__ == '__main__':
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unittest.main()
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