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cleanup some code

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AngelBottomless 2022-10-23 21:07:07 +09:00 committed by AUTOMATIC1111
parent b297cc3324
commit 40b56c9289
1 changed files with 3 additions and 11 deletions
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14
modules/hypernetworks/hypernetwork.py
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@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum from torch import einsum
from collections import defaultdict, deque
from statistics import stdev, mean from statistics import stdev, mean
class HypernetworkModule(torch.nn.Module): class HypernetworkModule(torch.nn.Module):
@ -269,15 +270,6 @@ def stack_conds(conds):
return torch.stack(conds) return torch.stack(conds)
def log_statistics(loss_info:dict, key, value):
if key not in loss_info:
loss_info[key] = [value]
else:
loss_info[key].append(value)
if len(loss_info[key]) > 1024:
loss_info[key].pop(0)
def statistics(data): def statistics(data):
total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})" total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
recent_data = data[-32:] recent_data = data[-32:]
@ -341,7 +333,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
weight.requires_grad = True weight.requires_grad = True
size = len(ds.indexes) size = len(ds.indexes)
loss_dict = {} loss_dict = defaultdict(lambda : deque(maxlen = 1024))
losses = torch.zeros((size,)) losses = torch.zeros((size,))
previous_mean_loss = 0 previous_mean_loss = 0
print("Mean loss of {} elements".format(size)) print("Mean loss of {} elements".format(size))
@ -383,7 +375,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
losses[hypernetwork.step % losses.shape[0]] = loss.item() losses[hypernetwork.step % losses.shape[0]] = loss.item()
for entry in entries: for entry in entries:
log_statistics(loss_dict, entry.filename, loss.item()) loss_dict[entry.filename].append(loss.item())
optimizer.zero_grad() optimizer.zero_grad()
weights[0].grad = None weights[0].grad = None