Machine-Learning
/
aiuNN
1
0
Fork 0
Code Issues 2 Pull Requests Actions Packages Projects Releases Wiki Activity

develop #4

Merged
Fabel merged 103 commits from develop into main 2025-03-01 21:47:17 +00:00
Conversation 0 Commits 103 Files Changed 13 +3 -3
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files
Showing only changes of commit 443b9f5589 - Show all commits

6
src/aiunn/finetune.py
View File

@ -40,7 +40,7 @@ class UpscaleDataset(Dataset):
def __init__(self, parquet_files: list, transform=None): def __init__(self, parquet_files: list, transform=None):
combined_df = pd.DataFrame() combined_df = pd.DataFrame()
for parquet_file in parquet_files: for parquet_file in parquet_files:
df = pd.read_parquet(parquet_file, columns=['image_512', 'image_1024']).head(500) df = pd.read_parquet(parquet_file, columns=['image_512', 'image_1024']).head(2500)
combined_df = pd.concat([combined_df, df], ignore_index=True) combined_df = pd.concat([combined_df, df], ignore_index=True)
self.df = combined_df.apply(self._validate_row, axis=1) self.df = combined_df.apply(self._validate_row, axis=1)
@ -97,7 +97,7 @@ pretrained_model_path = "/root/vision/AIIA/AIIA-base-512"
base_model = AIIABase.load(pretrained_model_path, precision="bf16") base_model = AIIABase.load(pretrained_model_path, precision="bf16")
model = Upsampler(base_model) model = Upsampler(base_model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cpu")
# Move model to device using channels_last memory format. # Move model to device using channels_last memory format.
model = model.to(device, memory_format=torch.channels_last) model = model.to(device, memory_format=torch.channels_last)
@ -109,7 +109,7 @@ dataset = UpscaleDataset([
"/root/training_data/vision-dataset/image_upscaler.parquet", "/root/training_data/vision-dataset/image_upscaler.parquet",
"/root/training_data/vision-dataset/image_vec_upscaler.parquet" "/root/training_data/vision-dataset/image_vec_upscaler.parquet"
], transform=transform) ], transform=transform)
data_loader = DataLoader(dataset, batch_size=1, shuffle=True) # Consider adjusting num_workers if needed. data_loader = DataLoader(dataset, batch_size=2, shuffle=True) # Consider adjusting num_workers if needed.
# Define loss function and optimizer. # Define loss function and optimizer.
criterion = nn.MSELoss() criterion = nn.MSELoss()