mmsegmentation/tests/test_utils/test_util_distribution.py

69 lines
2.1 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, patch
import mmcv
import torch
import torch.nn as nn
from mmcv.parallel import (MMDataParallel, MMDistributedDataParallel,
is_module_wrapper)
from mmseg import digit_version
from mmseg.utils import build_ddp, build_dp
def mock(*args, **kwargs):
pass
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(2, 2, 1)
def forward(self, x):
return self.conv(x)
@patch('torch.distributed._broadcast_coalesced', mock)
@patch('torch.distributed.broadcast', mock)
@patch('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
def test_build_dp():
model = Model()
assert not is_module_wrapper(model)
mmdp = build_dp(model, 'cpu')
assert isinstance(mmdp, MMDataParallel)
if torch.cuda.is_available():
mmdp = build_dp(model, 'cuda')
assert isinstance(mmdp, MMDataParallel)
if digit_version(mmcv.__version__) >= digit_version('1.5.0'):
from mmcv.device.mlu import MLUDataParallel
from mmcv.utils import IS_MLU_AVAILABLE
if IS_MLU_AVAILABLE:
mludp = build_dp(model, 'mlu')
assert isinstance(mludp, MLUDataParallel)
@patch('torch.distributed._broadcast_coalesced', mock)
@patch('torch.distributed.broadcast', mock)
@patch('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
def test_build_ddp():
model = Model()
assert not is_module_wrapper(model)
if torch.cuda.is_available():
mmddp = build_ddp(
model, 'cuda', device_ids=[0], process_group=MagicMock())
assert isinstance(mmddp, MMDistributedDataParallel)
if digit_version(mmcv.__version__) >= digit_version('1.5.0'):
from mmcv.device.mlu import MLUDistributedDataParallel
from mmcv.utils import IS_MLU_AVAILABLE
if IS_MLU_AVAILABLE:
mluddp = build_ddp(
model, 'mlu', device_ids=[0], process_group=MagicMock())
assert isinstance(mluddp, MLUDistributedDataParallel)