DL-Art-School/codes/utils/loss_accumulator.py

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import torch
# Utility class that stores detached, named losses in a rotating buffer for smooth metric outputting.
class LossAccumulator:
def __init__(self, buffer_sz=50):
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self.buffer_sz = buffer_sz
self.buffers = {}
def add_loss(self, name, tensor):
if name not in self.buffers.keys():
self.buffers[name] = (0, torch.zeros(self.buffer_sz), False)
i, buf, filled = self.buffers[name]
# Can take tensors or just plain python numbers.
if isinstance(tensor, torch.Tensor):
buf[i] = tensor.detach().cpu()
else:
buf[i] = tensor
filled = i+1 >= self.buffer_sz or filled
self.buffers[name] = ((i+1) % self.buffer_sz, buf, filled)
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def as_dict(self):
result = {}
for k, v in self.buffers.items():
i, buf, filled = v
if filled:
result["loss_" + k] = torch.mean(buf)
else:
result["loss_" + k] = torch.mean(buf[:i])
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return result