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Fix loss_dict problem

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aria1th 2023-01-16 02:46:21 +09:00
parent 205991df78
commit 598f7fcd84
1 changed files with 3 additions and 1 deletions
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4
modules/hypernetworks/hypernetwork.py
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@ -561,6 +561,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
_loss_step = 0 #internal _loss_step = 0 #internal
# size = len(ds.indexes) # size = len(ds.indexes)
# loss_dict = defaultdict(lambda : deque(maxlen = 1024)) # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
loss_logging = []
# losses = torch.zeros((size,)) # losses = torch.zeros((size,))
# previous_mean_losses = [0] # previous_mean_losses = [0]
# previous_mean_loss = 0 # previous_mean_loss = 0
@ -601,6 +602,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
else: else:
c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
loss = shared.sd_model(x, c)[0] / gradient_step loss = shared.sd_model(x, c)[0] / gradient_step
loss_logging.append(loss.item())
del x del x
del c del c
@ -644,7 +646,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
if shared.opts.training_enable_tensorboard: if shared.opts.training_enable_tensorboard:
epoch_num = hypernetwork.step // len(ds) epoch_num = hypernetwork.step // len(ds)
epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1 epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
mean_loss = sum(sum(x) for x in loss_dict.values()) / sum(len(x) for x in loss_dict.values()) mean_loss = sum(loss_logging) / len(loss_logging)
textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num) textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, { textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {