Spaces:
Sleeping
Sleeping
File size: 2,058 Bytes
d7e58f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from torch.utils.data import Dataset
from mmcv.parallel.data_container import DataContainer
from mmcv.utils import IS_IPU_AVAILABLE
if IS_IPU_AVAILABLE:
from mmcv.device.ipu import IPUDataLoader, cfg2options
from mmcv.device.ipu.dataloader import collate
skip_no_ipu = pytest.mark.skipif(
not IS_IPU_AVAILABLE, reason='test case under ipu environment')
class ToyDataset(Dataset):
def __getitem__(self, index):
return 111
def __len__(self, ):
return 3
@skip_no_ipu
def test_ipu_dataloader():
# test lazy initialization
dataloader = IPUDataLoader(
ToyDataset(), None, batch_size=256, num_workers=1, mode='async')
options_cfg = {'train_cfg': {}, 'eval_cfg': {}}
ipu_options = cfg2options(options_cfg)
dataloader.init(ipu_options['training'])
# test normal initialization
options_cfg = {'train_cfg': {}, 'eval_cfg': {}}
ipu_options = cfg2options(options_cfg)['training']
dataloader = IPUDataLoader(
ToyDataset(), ipu_options, batch_size=256, num_workers=1, mode='async')
@skip_no_ipu
def test_ipu_collate():
with pytest.raises(TypeError, match='`batch` should be a sequence'):
collate(123)
with pytest.raises(TypeError, match='DataContainer is not supported'):
collate([DataContainer(666)])
data_list = [[1, 2, 3], [2, 3, 4], DataContainer(666)]
batch0 = {
'tensor': torch.rand(3, 4, 5),
'arr': np.random.rand(3, 4, 5, 6),
'data_list': data_list
}
batch1 = {
'tensor': torch.rand(3, 4, 5),
'arr': np.random.rand(3, 4, 5, 6),
'data_list': data_list
}
batch = [batch1, batch0]
results = collate(batch)
assert results['tensor'].shape == (2, 3, 4, 5)
assert results['arr'].shape == (2, 3, 4, 5, 6)
for data in results['data_list']:
for tensor in data:
assert not isinstance(tensor, DataContainer)
assert tensor.shape == (2, )
|