Spaces:
Sleeping
Sleeping
File size: 3,912 Bytes
251e479 |
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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
# Copyright (c) OpenMMLab. All rights reserved.
from itertools import chain
from torch.nn.parallel import DataParallel
from .scatter_gather import scatter_kwargs
class MMDataParallel(DataParallel):
"""The DataParallel module that supports DataContainer.
MMDataParallel has two main differences with PyTorch DataParallel:
- It supports a custom type :class:`DataContainer` which allows more
flexible control of input data during both GPU and CPU inference.
- It implement two more APIs ``train_step()`` and ``val_step()``.
Args:
module (:class:`nn.Module`): Module to be encapsulated.
device_ids (list[int]): Device IDS of modules to be scattered to.
Defaults to None when GPU is not available.
output_device (str | int): Device ID for output. Defaults to None.
dim (int): Dimension used to scatter the data. Defaults to 0.
"""
def __init__(self, *args, dim=0, **kwargs):
super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs)
self.dim = dim
def forward(self, *inputs, **kwargs):
"""Override the original forward function.
The main difference lies in the CPU inference where the data in
:class:`DataContainers` will still be gathered.
"""
if not self.device_ids:
# We add the following line thus the module could gather and
# convert data containers as those in GPU inference
inputs, kwargs = self.scatter(inputs, kwargs, [-1])
return self.module(*inputs[0], **kwargs[0])
else:
return super().forward(*inputs, **kwargs)
def scatter(self, inputs, kwargs, device_ids):
return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
def train_step(self, *inputs, **kwargs):
if not self.device_ids:
# We add the following line thus the module could gather and
# convert data containers as those in GPU inference
inputs, kwargs = self.scatter(inputs, kwargs, [-1])
return self.module.train_step(*inputs[0], **kwargs[0])
assert len(self.device_ids) == 1, \
('MMDataParallel only supports single GPU training, if you need to'
' train with multiple GPUs, please use MMDistributedDataParallel'
'instead.')
for t in chain(self.module.parameters(), self.module.buffers()):
if t.device != self.src_device_obj:
raise RuntimeError(
'module must have its parameters and buffers '
f'on device {self.src_device_obj} (device_ids[0]) but '
f'found one of them on device: {t.device}')
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
return self.module.train_step(*inputs[0], **kwargs[0])
def val_step(self, *inputs, **kwargs):
if not self.device_ids:
# We add the following line thus the module could gather and
# convert data containers as those in GPU inference
inputs, kwargs = self.scatter(inputs, kwargs, [-1])
return self.module.val_step(*inputs[0], **kwargs[0])
assert len(self.device_ids) == 1, \
('MMDataParallel only supports single GPU training, if you need to'
' train with multiple GPUs, please use MMDistributedDataParallel'
' instead.')
for t in chain(self.module.parameters(), self.module.buffers()):
if t.device != self.src_device_obj:
raise RuntimeError(
'module must have its parameters and buffers '
f'on device {self.src_device_obj} (device_ids[0]) but '
f'found one of them on device: {t.device}')
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
return self.module.val_step(*inputs[0], **kwargs[0])
|