wehub-resource-sync
144 lines
4.8 KiB
Python
144 lines
4.8 KiB
Python
# 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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"""This is unit test of Test shuffle_batch Op."""
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import os
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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place, is_custom_device
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import paddle
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from paddle import base
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class TestShuffleBatchOpBase(OpTest):
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def gen_random_array(self, shape, low=0, high=1):
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rnd = (high - low) * np.random.random(shape) + low
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return rnd.astype(self.dtype)
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def get_shape(self):
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return (10, 10, 5)
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def _get_places(self):
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# NOTE: shuffle_batch is not supported on Windows
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if os.name == 'nt':
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return [base.CPUPlace()]
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return super()._get_places()
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def setUp(self):
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self.op_type = 'shuffle_batch'
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self.python_api = paddle.incubate.layers.shuffle_batch
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self.python_out_sig = ["Out"]
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self.dtype = np.float64
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self.shape = self.get_shape()
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x = self.gen_random_array(self.shape)
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seed = np.random.random_integers(low=10, high=100, size=(1,)).astype(
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'int64'
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)
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self.inputs = {'X': x, 'Seed': seed}
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self.outputs = {
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'Out': np.array([]).astype(x.dtype),
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'ShuffleIdx': np.array([]).astype('int64'),
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'SeedOut': np.array([]).astype(seed.dtype),
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}
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self.attrs = {'startup_seed': 1}
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def test_check_output(self):
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self.check_output_customized(self.verify_output, check_pir=True)
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def verify_output(self, outs):
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x = np.copy(self.inputs['X'])
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y = None
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for out in outs:
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if out.shape == x.shape:
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y = np.copy(out)
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break
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assert y is not None
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sort_x = self.sort_array(x)
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sort_y = self.sort_array(y)
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np.testing.assert_array_equal(sort_x, sort_y)
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def sort_array(self, array):
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shape = array.shape
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new_shape = [-1, shape[-1]]
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arr_list = np.reshape(array, new_shape).tolist()
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arr_list.sort(key=lambda x: x[0])
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return np.reshape(np.array(arr_list), shape)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_dygraph=False, check_pir=True)
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class TestShuffleBatchOp2(TestShuffleBatchOpBase):
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def get_shape(self):
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return (4, 30)
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class TestShuffleBatchAPI(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.CPUPlace()]
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if not os.name == 'nt' and (
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paddle.is_compiled_with_cuda() or is_custom_device()
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):
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self.places.append(get_device_place())
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paddle.enable_static()
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def tearDown(self):
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paddle.disable_static()
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def test_seed_without_tensor(self):
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def api_run(seed, place=paddle.CPUPlace()):
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main_prog, startup_prog = (
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paddle.static.Program(),
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paddle.static.Program(),
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)
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
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out = paddle.incubate.layers.shuffle_batch(x, seed=seed)
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exe = paddle.static.Executor(place=place)
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feed = {'x': np.random.random((10, 4)).astype('float32')}
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exe.run(startup_prog)
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_ = exe.run(main_prog, feed=feed, fetch_list=[out])
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for place in self.places:
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api_run(None, place=place)
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api_run(1, place=place)
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def test_seed_with_tensor(self):
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def api_run(place=paddle.CPUPlace()):
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main_prog, startup_prog = (
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paddle.static.Program(),
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paddle.static.Program(),
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)
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(name='x', shape=[-1, 4], dtype='float32')
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seed = paddle.static.data(name='seed', shape=[1], dtype='int64')
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out = paddle.incubate.layers.shuffle_batch(x, seed=seed)
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exe = paddle.static.Executor(place=place)
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feed = {
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'x': np.random.random((10, 4)).astype('float32'),
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'seed': np.array([1]).astype('int64'),
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}
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exe.run(startup_prog)
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_ = exe.run(main_prog, feed=feed, fetch_list=[out])
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for place in self.places:
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api_run(place=place)
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if __name__ == '__main__':
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unittest.main()
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