wehub-resource-sync
269 lines
8.2 KiB
Python
269 lines
8.2 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 multiprocessing
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import unittest
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import numpy as np
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from op_test import is_custom_device
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from paddle import base
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from paddle.base import core
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from paddle.io import BatchSampler, DataLoader, Dataset, IterableDataset
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from paddle.io.dataloader.worker import _worker_loop
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class RandomDataset(Dataset):
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def __init__(self, sample_num):
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self.sample_num = sample_num
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def __getitem__(self, idx):
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np.random.seed(idx)
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image = np.random.random([784]).astype('float32')
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label = np.random.randint(0, 9, (1,)).astype('int64')
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return image, label
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def __len__(self):
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return self.sample_num
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class TestDataLoaderAssert(unittest.TestCase):
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def test_main(self):
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place = base.cpu_places()[0]
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with base.dygraph.guard(place):
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dataset = RandomDataset(100)
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batch_sampler = BatchSampler(dataset=dataset, batch_size=4)
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# dataset is not instance of Dataset
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try:
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loader = DataLoader(dataset=batch_sampler, places=place)
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self.assertTrue(False)
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except AssertionError:
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pass
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# places is None
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try:
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loader = DataLoader(dataset=dataset, places=None)
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self.assertTrue(False)
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except AssertionError:
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pass
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# num_workers < 0
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try:
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loader = DataLoader(
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dataset=dataset, places=place, num_workers=-1
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)
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self.assertTrue(False)
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except AssertionError:
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pass
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# timeout < 0
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try:
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loader = DataLoader(dataset=dataset, places=place, timeout=-1)
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self.assertTrue(False)
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except AssertionError:
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pass
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# set batch_sampler and shuffle/batch_size/drop_last
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try:
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loader = DataLoader(
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dataset=dataset,
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places=place,
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batch_sampler=batch_sampler,
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shuffle=True,
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drop_last=True,
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)
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self.assertTrue(False)
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except AssertionError:
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pass
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# set batch_sampler correctly
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try:
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loader = DataLoader(
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dataset=dataset, places=place, batch_sampler=batch_sampler
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)
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self.assertTrue(True)
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except AssertionError:
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self.assertTrue(False)
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class TestDatasetRuntimeError(unittest.TestCase):
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def test_main(self):
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dataset = Dataset()
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# __getitem__ not implement
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try:
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d = dataset[0]
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self.assertTrue(False)
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except NotImplementedError:
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pass
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# __len__ not implement
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try:
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l = len(dataset)
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self.assertTrue(False)
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except NotImplementedError:
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pass
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dataset = IterableDataset()
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# __iter__ not implement
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try:
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d = iter(dataset)
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self.assertTrue(False)
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except NotImplementedError:
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pass
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# __getitem__ runtime error
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try:
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d = dataset[0]
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self.assertTrue(False)
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except RuntimeError:
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pass
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# __len__ runtime error
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try:
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l = len(dataset)
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self.assertTrue(False)
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except RuntimeError:
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pass
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# CI Coverage cannot record stub in subprocess,
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# HACK a _worker_loop in main process call here
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestDataLoaderWorkerLoop(unittest.TestCase):
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def run_without_worker_done(self, use_shared_memory=True):
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try:
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place = base.cpu_places()[0]
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with base.dygraph.guard(place):
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dataset = RandomDataset(800)
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# test init_fn
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def _init_fn(worker_id):
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pass
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# test collate_fn
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def _collate_fn(sample_list):
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return [
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np.stack(s, axis=0) for s in list(zip(*sample_list))
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]
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loader = DataLoader(
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dataset,
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num_workers=1,
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places=place,
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use_shared_memory=use_shared_memory,
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)
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assert loader.num_workers > 0, (
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"go to AssertionError and pass in Mac and Windows"
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)
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loader = iter(loader)
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print("loader length", len(loader))
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indices_queue = multiprocessing.Queue()
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for i in range(10):
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indices_queue.put([i, i + 10])
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indices_queue.put(None)
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base_seed = 1234
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_worker_loop(
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loader._dataset,
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0,
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indices_queue,
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loader._data_queue,
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loader._workers_done_event,
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True,
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_collate_fn,
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True,
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_init_fn,
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0,
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1,
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loader._use_shared_memory,
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base_seed,
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)
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self.assertTrue(False)
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except AssertionError:
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pass
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except Exception as e:
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print("Exception", e)
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import sys
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sys.stdout.flush()
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self.assertTrue(False)
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def run_with_worker_done(self, use_shared_memory=True):
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try:
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place = base.CPUPlace()
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with base.dygraph.guard(place):
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dataset = RandomDataset(800)
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# test init_fn
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def _init_fn(worker_id):
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pass
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# test collate_fn
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def _collate_fn(sample_list):
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return [
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np.stack(s, axis=0) for s in list(zip(*sample_list))
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]
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loader = DataLoader(
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dataset,
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num_workers=1,
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places=place,
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use_shared_memory=use_shared_memory,
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)
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assert loader.num_workers > 0, (
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"go to AssertionError and pass in Mac and Windows"
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)
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loader = iter(loader)
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print("loader length", len(loader))
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indices_queue = multiprocessing.Queue()
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for i in range(10):
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indices_queue.put([i, i + 10])
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indices_queue.put(None)
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loader._workers_done_event.set()
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base_seed = 1234
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_worker_loop(
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loader._dataset,
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0,
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indices_queue,
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loader._data_queue,
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loader._workers_done_event,
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True,
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_collate_fn,
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True,
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_init_fn,
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0,
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1,
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loader._use_shared_memory,
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base_seed,
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)
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self.assertTrue(True)
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except AssertionError:
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pass
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except Exception:
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self.assertTrue(False)
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def test_main(self):
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# only HACK a subprocess call here, do not need to use_shared_memory
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for use_shared_memory in [False]:
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self.run_without_worker_done(use_shared_memory)
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self.run_with_worker_done(use_shared_memory)
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if __name__ == '__main__':
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unittest.main()
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