Machine-Learning
/
aiuNN
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request 'corrected MOTrainer' (#27) from feat/change_mef into main
Run VectorLoader Script / Explore-Gitea-Actions (push) Successful in 20s Details
Gitea Actions For AIIA / Explore-Gitea-Actions (push) Successful in 9m42s Details 

Reviewed-on: #27
This commit is contained in:
Falko Victor Habel 2025-07-03 12:22:39 +00:00
parent e7b9da37d6 505b383605
commit dc12ec50ff
4 changed files with 6 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
pyproject.toml
View File

@ -3,7 +3,7 @@ requires = ["setuptools>=45", "wheel"]
build-backend = "setuptools.build_meta" build-backend = "setuptools.build_meta"
[project] [project]
name = "aiunn" name = "aiunn"
version = "0.4.0" version = "0.4.1"
description = "Finetuner for image upscaling using AIIA" description = "Finetuner for image upscaling using AIIA"
readme = "README.md" readme = "README.md"
requires-python = ">=3.10" requires-python = ">=3.10"

2
setup.py
View File

@ -2,7 +2,7 @@ from setuptools import setup, find_packages
setup( setup(
name="aiunn", name="aiunn",
version="0.4.0", version="0.4.1",
packages=find_packages(where="src"), packages=find_packages(where="src"),
package_dir={"": "src"}, package_dir={"": "src"},
install_requires=[ install_requires=[

2
src/aiunn/__init__.py
View File

@ -4,4 +4,4 @@ from .upsampler.aiunn import aiuNN
from .upsampler.config import aiuNNConfig from .upsampler.config import aiuNNConfig
from .inference.inference import aiuNNInference from .inference.inference import aiuNNInference
__version__ = "0.4.0" __version__ = "0.4.1"

5
src/aiunn/finetune/memory_trainer.py
View File

@ -177,6 +177,7 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
with autocast(device_type=self.device.type): with autocast(device_type=self.device.type):
outputs = self.model(low_res) outputs = self.model(low_res)
outputs = outputs.clone()
loss = self.criterion(outputs, high_res) loss = self.criterion(outputs, high_res)
val_loss += loss.item() val_loss += loss.item()
@ -252,10 +253,10 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
if hasattr(self, 'use_checkpointing') and self.use_checkpointing: if hasattr(self, 'use_checkpointing') and self.use_checkpointing:
low_res.requires_grad_() low_res.requires_grad_()
outputs = checkpoint(self.model, low_res) outputs = checkpoint(self.model, low_res)
outputs = outputs.clone() # <-- Clone added here outputs = outputs.clone()
else: else:
outputs = self.model(low_res) outputs = self.model(low_res)
outputs = outputs.clone() # <-- Clone added here outputs = outputs.clone()
loss = self.criterion(outputs, high_res) loss = self.criterion(outputs, high_res)
# Scale loss for gradient accumulation # Scale loss for gradient accumulation