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EveryDream2trainer
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add pbar back to preloading, remove cruft from testing loss stuff

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Victor Hall 2023-03-15 12:06:29 -04:00
parent d1bc94fe3e
commit ba687de8b4
2 changed files with 3 additions and 7 deletions
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4
data/dataset.py
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@ -9,6 +9,8 @@ from data.image_train_item import ImageCaption, ImageTrainItem
from utils.fs_helpers import * from utils.fs_helpers import *
from typing import TypeVar, Iterable from typing import TypeVar, Iterable
from tqdm import tqdm
def overlay(overlay, base): def overlay(overlay, base):
return overlay if overlay is not None else base return overlay if overlay is not None else base
@ -216,7 +218,7 @@ class Dataset:
def image_train_items(self, aspects): def image_train_items(self, aspects):
items = [] items = []
for image in self.image_configs: for image in tqdm(self.image_configs, desc="preloading", dynamic_ncols=True):
config = self.image_configs[image] config = self.image_configs[image]
if len(config.main_prompts) > 1: if len(config.main_prompts) > 1:
logging.warning(f" *** Found multiple multiple main_prompts for image {image}, but only one will be applied: {config.main_prompts}") logging.warning(f" *** Found multiple multiple main_prompts for image {image}, but only one will be applied: {config.main_prompts}")

6
train.py
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@ -811,12 +811,6 @@ def main(args):
model_pred, target = get_model_prediction_and_target(batch["image"], batch["tokens"], args.zero_frequency_noise_ratio) model_pred, target = get_model_prediction_and_target(batch["image"], batch["tokens"], args.zero_frequency_noise_ratio)
# with torch.no_grad():
# loss_l1 = F.l1_loss(model_pred.float(), target.float(), reduction="mean")
# log_writer.add_scalar(tag="loss/l1", scalar_value=loss_l1, global_step=global_step)
# loss_hinge = F.hinge_embedding_loss(model_pred.float(), target.float(), reduction="mean")
# log_writer.add_scalar(tag="loss/hinge", scalar_value=loss_hinge, global_step=global_step)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
del target, model_pred del target, model_pred