mirror of https://github.com/hpcaitech/ColossalAI
[unittest] refactored unit tests for change in dependency (#838)
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f271f34716
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@ -5,34 +5,21 @@ import os
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from pathlib import Path
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from pathlib import Path
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import pytest
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import pytest
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from torchvision import transforms
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from torchvision import transforms, datasets
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from colossalai.builder import build_dataset, build_transform
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from colossalai.context import Config
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from torchvision.transforms import ToTensor
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TRAIN_DATA = dict(dataset=dict(type='CIFAR10', root=Path(os.environ['DATA']), train=True, download=True),
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dataloader=dict(batch_size=4, shuffle=True, num_workers=2))
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@pytest.mark.cpu
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@pytest.mark.cpu
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def test_cifar10_dataset():
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def test_cifar10_dataset():
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config = Config(TRAIN_DATA)
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dataset_cfg = config.dataset
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dataloader_cfg = config.dataloader
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transform_cfg = config.transform_pipeline
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# build transform
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# build transform
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transform_pipeline = [ToTensor()]
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transform_pipeline = [transforms.ToTensor()]
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transform_pipeline = transforms.Compose(transform_pipeline)
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transform_pipeline = transforms.Compose(transform_pipeline)
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dataset_cfg['transform'] = transform_pipeline
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# build dataset
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# build dataset
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dataset = build_dataset(dataset_cfg)
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dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
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# build dataloader
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# build dataloader
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dataloader = DataLoader(dataset=dataset, **dataloader_cfg)
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dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=2)
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data_iter = iter(dataloader)
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data_iter = iter(dataloader)
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img, label = data_iter.next()
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img, label = data_iter.next()
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@ -9,34 +9,21 @@ import pytest
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import torch
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import torch
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import torch.distributed as dist
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.multiprocessing as mp
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from torch.utils.data import DataLoader
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import colossalai
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import colossalai
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from colossalai.builder import build_dataset
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from torchvision import transforms, datasets
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from torchvision import transforms
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from colossalai.context import ParallelMode, Config
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from colossalai.context import ParallelMode, Config
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from colossalai.core import global_context as gpc
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from colossalai.core import global_context as gpc
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from colossalai.utils import get_dataloader, free_port
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from colossalai.utils import get_dataloader, free_port
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.testing import rerun_if_address_is_in_use
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from torchvision.transforms import ToTensor
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CONFIG = Config(
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CONFIG = Config(dict(
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dict(
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parallel=dict(
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train_data=dict(
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pipeline=dict(size=1),
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dataset=dict(
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tensor=dict(size=1, mode=None),
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type='CIFAR10',
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),
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root=Path(os.environ['DATA']),
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seed=1024,
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train=True,
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))
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download=True,
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),
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dataloader=dict(batch_size=8,),
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),
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parallel=dict(
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pipeline=dict(size=1),
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tensor=dict(size=1, mode=None),
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),
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seed=1024,
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))
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def run_data_sampler(rank, world_size, port):
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def run_data_sampler(rank, world_size, port):
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@ -44,11 +31,14 @@ def run_data_sampler(rank, world_size, port):
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colossalai.launch(**dist_args)
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colossalai.launch(**dist_args)
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print('finished initialization')
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print('finished initialization')
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transform_pipeline = [ToTensor()]
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# build dataset
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transform_pipeline = [transforms.ToTensor()]
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transform_pipeline = transforms.Compose(transform_pipeline)
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transform_pipeline = transforms.Compose(transform_pipeline)
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gpc.config.train_data.dataset['transform'] = transform_pipeline
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dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
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dataset = build_dataset(gpc.config.train_data.dataset)
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dataloader = get_dataloader(dataset, **gpc.config.train_data.dataloader)
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# build dataloader
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dataloader = get_dataloader(dataset, batch_size=8, add_sampler=True)
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data_iter = iter(dataloader)
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data_iter = iter(dataloader)
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img, label = data_iter.next()
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img, label = data_iter.next()
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img = img[0]
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img = img[0]
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@ -9,14 +9,12 @@ import pytest
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import torch
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import torch
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import torch.distributed as dist
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.multiprocessing as mp
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from torchvision import transforms
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from torchvision import transforms, datasets
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from torch.utils.data import DataLoader
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import colossalai
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import colossalai
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from colossalai.builder import build_dataset
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from colossalai.context import ParallelMode, Config
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from colossalai.context import ParallelMode, Config
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from colossalai.core import global_context as gpc
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from colossalai.core import global_context as gpc
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from colossalai.utils import free_port
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from colossalai.utils import get_dataloader, free_port
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.testing import rerun_if_address_is_in_use
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from torchvision import transforms
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from torchvision import transforms
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@ -43,20 +41,13 @@ def run_data_sampler(rank, world_size, port):
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dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
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dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
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colossalai.launch(**dist_args)
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colossalai.launch(**dist_args)
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dataset_cfg = gpc.config.train_data.dataset
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dataloader_cfg = gpc.config.train_data.dataloader
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transform_cfg = gpc.config.train_data.transform_pipeline
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# build transform
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transform_pipeline = [transforms.ToTensor(), transforms.RandomCrop(size=32)]
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transform_pipeline = transforms.Compose(transform_pipeline)
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dataset_cfg['transform'] = transform_pipeline
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# build dataset
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# build dataset
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dataset = build_dataset(dataset_cfg)
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transform_pipeline = [transforms.ToTensor(), transforms.RandomCrop(size=32, padding=4)]
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transform_pipeline = transforms.Compose(transform_pipeline)
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dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
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# build dataloader
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# build dataloader
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dataloader = DataLoader(dataset=dataset, **dataloader_cfg)
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dataloader = get_dataloader(dataset, batch_size=8, add_sampler=False)
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data_iter = iter(dataloader)
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data_iter = iter(dataloader)
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img, label = data_iter.next()
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img, label = data_iter.next()
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@ -76,7 +67,6 @@ def run_data_sampler(rank, world_size, port):
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torch.cuda.empty_cache()
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torch.cuda.empty_cache()
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@pytest.mark.skip
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@pytest.mark.cpu
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@pytest.mark.cpu
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@rerun_if_address_is_in_use()
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@rerun_if_address_is_in_use()
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def test_data_sampler():
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def test_data_sampler():
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