[unittest] refactored unit tests for change in dependency (#838)

tests/test_data/test_cifar10_dataset.py

@@ -5,34 +5,21 @@ import os
 from pathlib import Path
 
 import pytest
-from torchvision import transforms
+from torchvision import transforms, datasets
 from torch.utils.data import DataLoader
 
-from colossalai.builder import build_dataset, build_transform
-from colossalai.context import Config
-from torchvision.transforms import ToTensor
-
-TRAIN_DATA = dict(dataset=dict(type='CIFAR10', root=Path(os.environ['DATA']), train=True, download=True),
-                  dataloader=dict(batch_size=4, shuffle=True, num_workers=2))
-
 
 @pytest.mark.cpu
 def test_cifar10_dataset():
-    config = Config(TRAIN_DATA)
-    dataset_cfg = config.dataset
-    dataloader_cfg = config.dataloader
-    transform_cfg = config.transform_pipeline
-
     # build transform
-    transform_pipeline = [ToTensor()]
+    transform_pipeline = [transforms.ToTensor()]
     transform_pipeline = transforms.Compose(transform_pipeline)
-    dataset_cfg['transform'] = transform_pipeline
 
     # build dataset
-    dataset = build_dataset(dataset_cfg)
+    dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
 
     # build dataloader
-    dataloader = DataLoader(dataset=dataset, **dataloader_cfg)
+    dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=2)
     data_iter = iter(dataloader)
     img, label = data_iter.next()
 

tests/test_data/test_data_parallel_sampler.py

@@ -9,28 +9,15 @@ import pytest
 import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
-from torch.utils.data import DataLoader
 
 import colossalai
-from colossalai.builder import build_dataset
+from torchvision import transforms, datasets
-from torchvision import transforms
 from colossalai.context import ParallelMode, Config
 from colossalai.core import global_context as gpc
 from colossalai.utils import get_dataloader, free_port
 from colossalai.testing import rerun_if_address_is_in_use
-from torchvision.transforms import ToTensor
 
-CONFIG = Config(
+CONFIG = Config(dict(
-    dict(
-        train_data=dict(
-            dataset=dict(
-                type='CIFAR10',
-                root=Path(os.environ['DATA']),
-                train=True,
-                download=True,
-            ),
-            dataloader=dict(batch_size=8,),
-        ),
     parallel=dict(
         pipeline=dict(size=1),
         tensor=dict(size=1, mode=None),
@@ -44,11 +31,14 @@ def run_data_sampler(rank, world_size, port):
     colossalai.launch(**dist_args)
     print('finished initialization')
 
-    transform_pipeline = [ToTensor()]
+    # build dataset
+    transform_pipeline = [transforms.ToTensor()]
     transform_pipeline = transforms.Compose(transform_pipeline)
-    gpc.config.train_data.dataset['transform'] = transform_pipeline
+    dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
-    dataset = build_dataset(gpc.config.train_data.dataset)
+
-    dataloader = get_dataloader(dataset, **gpc.config.train_data.dataloader)
+    # build dataloader
+    dataloader = get_dataloader(dataset, batch_size=8, add_sampler=True)
+
     data_iter = iter(dataloader)
     img, label = data_iter.next()
     img = img[0]

tests/test_data/test_deterministic_dataloader.py

@@ -9,14 +9,12 @@ import pytest
 import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
-from torchvision import transforms
+from torchvision import transforms, datasets
-from torch.utils.data import DataLoader
 
 import colossalai
-from colossalai.builder import build_dataset
 from colossalai.context import ParallelMode, Config
 from colossalai.core import global_context as gpc
-from colossalai.utils import free_port
+from colossalai.utils import get_dataloader, free_port
 from colossalai.testing import rerun_if_address_is_in_use
 from torchvision import transforms
 
@@ -43,20 +41,13 @@ def run_data_sampler(rank, world_size, port):
     dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
     colossalai.launch(**dist_args)
 
-    dataset_cfg = gpc.config.train_data.dataset
-    dataloader_cfg = gpc.config.train_data.dataloader
-    transform_cfg = gpc.config.train_data.transform_pipeline
-
-    # build transform
-    transform_pipeline = [transforms.ToTensor(), transforms.RandomCrop(size=32)]
-    transform_pipeline = transforms.Compose(transform_pipeline)
-    dataset_cfg['transform'] = transform_pipeline
-
     # build dataset
-    dataset = build_dataset(dataset_cfg)
+    transform_pipeline = [transforms.ToTensor(), transforms.RandomCrop(size=32, padding=4)]
+    transform_pipeline = transforms.Compose(transform_pipeline)
+    dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
 
     # build dataloader
-    dataloader = DataLoader(dataset=dataset, **dataloader_cfg)
+    dataloader = get_dataloader(dataset, batch_size=8, add_sampler=False)
 
     data_iter = iter(dataloader)
     img, label = data_iter.next()
@@ -76,7 +67,6 @@ def run_data_sampler(rank, world_size, port):
     torch.cuda.empty_cache()
 
 
-@pytest.mark.skip
 @pytest.mark.cpu
 @rerun_if_address_is_in_use()
 def test_data_sampler():