crosstyan
00fcda4fe3
Move demo implementation into opengait_studio, retire Sports2D runtime integration, and align packaging with root-level monorepo dependency management.
60 lines
1.4 KiB
Python
60 lines
1.4 KiB
Python
from ctypes import ArgumentError
|
|
import torch.nn as nn
|
|
import torch
|
|
from opengait.utils import Odict
|
|
import functools
|
|
from opengait.utils import ddp_all_gather
|
|
|
|
|
|
def gather_and_scale_wrapper(func):
|
|
"""Internal wrapper: gather the input from multple cards to one card, and scale the loss by the number of cards.
|
|
"""
|
|
|
|
@functools.wraps(func)
|
|
def inner(*args, **kwds):
|
|
try:
|
|
|
|
for k, v in kwds.items():
|
|
kwds[k] = ddp_all_gather(v)
|
|
|
|
loss, loss_info = func(*args, **kwds)
|
|
loss *= torch.distributed.get_world_size()
|
|
return loss, loss_info
|
|
except:
|
|
raise ArgumentError
|
|
return inner
|
|
|
|
|
|
class BaseLoss(nn.Module):
|
|
"""
|
|
Base class for all losses.
|
|
|
|
Your loss should also subclass this class.
|
|
"""
|
|
|
|
def __init__(self, loss_term_weight=1.0):
|
|
"""
|
|
Initialize the base class.
|
|
|
|
Args:
|
|
loss_term_weight: the weight of the loss term.
|
|
"""
|
|
super(BaseLoss, self).__init__()
|
|
self.loss_term_weight = loss_term_weight
|
|
self.info = Odict()
|
|
|
|
def forward(self, logits, labels):
|
|
"""
|
|
The default forward function.
|
|
|
|
This function should be overridden by the subclass.
|
|
|
|
Args:
|
|
logits: the logits of the model.
|
|
labels: the labels of the data.
|
|
|
|
Returns:
|
|
tuple of loss and info.
|
|
"""
|
|
return .0, self.info
|