fixed paths
This commit is contained in:
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from .finetune.trainer import aiuNNTrainer
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from .upsampler.aiunn import aiuNN
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from .upsampler.config import aiuNNConfig
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from .finetune import *
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from .inference import UpScaler
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__version__ = "0.1.0"
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__version__ = "0.1.1"
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import pandas as pd
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import io
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import csv
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import base64
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from PIL import Image, ImageFile
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from torch.amp import autocast, GradScaler
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms
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from tqdm import tqdm
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from torch.utils.checkpoint import checkpoint
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import gc
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from aiia import AIIABase
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from upsampler import Upsampler
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# Define a simple EarlyStopping class to monitor the epoch loss.
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class EarlyStopping:
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def __init__(self, patience=3, min_delta=0.001):
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self.patience = patience # Number of epochs with no significant improvement before stopping.
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self.min_delta = min_delta # Minimum change in loss required to count as an improvement.
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self.best_loss = float('inf')
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self.counter = 0
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self.early_stop = False
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def __call__(self, epoch_loss):
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if epoch_loss < self.best_loss - self.min_delta:
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self.best_loss = epoch_loss
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self.counter = 0
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else:
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self.counter += 1
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if self.counter >= self.patience:
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self.early_stop = True
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return self.early_stop
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# UpscaleDataset to load and preprocess your data.
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class UpscaleDataset(Dataset):
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def __init__(self, parquet_files: list, transform=None):
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combined_df = pd.DataFrame()
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for parquet_file in parquet_files:
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# Load a subset (head(2500)) from each parquet file
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df = pd.read_parquet(parquet_file, columns=['image_512', 'image_1024']).head(2500)
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combined_df = pd.concat([combined_df, df], ignore_index=True)
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# Validate rows (ensuring each value is bytes or str)
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self.df = combined_df.apply(self._validate_row, axis=1)
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self.transform = transform
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self.failed_indices = set()
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def _validate_row(self, row):
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for col in ['image_512', 'image_1024']:
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if not isinstance(row[col], (bytes, str)):
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raise ValueError(f"Invalid data type in column {col}: {type(row[col])}")
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return row
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def _decode_image(self, data):
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try:
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if isinstance(data, str):
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return base64.b64decode(data)
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elif isinstance(data, bytes):
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return data
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raise ValueError(f"Unsupported data type: {type(data)}")
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except Exception as e:
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raise RuntimeError(f"Decoding failed: {str(e)}")
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def __len__(self):
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return len(self.df)
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def __getitem__(self, idx):
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# If previous call failed for this index, use a different index.
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if idx in self.failed_indices:
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return self[(idx + 1) % len(self)]
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try:
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row = self.df.iloc[idx]
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low_res_bytes = self._decode_image(row['image_512'])
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high_res_bytes = self._decode_image(row['image_1024'])
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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# Open image bytes with Pillow and convert to RGBA first
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low_res_rgba = Image.open(io.BytesIO(low_res_bytes)).convert('RGBA')
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high_res_rgba = Image.open(io.BytesIO(high_res_bytes)).convert('RGBA')
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# Create a new RGB image with black background
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low_res_rgb = Image.new("RGB", low_res_rgba.size, (0, 0, 0))
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high_res_rgb = Image.new("RGB", high_res_rgba.size, (0, 0, 0))
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# Composite the original image over the black background
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low_res_rgb.paste(low_res_rgba, mask=low_res_rgba.split()[3])
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high_res_rgb.paste(high_res_rgba, mask=high_res_rgba.split()[3])
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# Now we have true 3-channel RGB images with transparent areas converted to black
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low_res = low_res_rgb
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high_res = high_res_rgb
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# Resize the images to reduce VRAM usage.
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low_res = low_res.resize((384, 384), Image.LANCZOS)
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high_res = high_res.resize((768, 768), Image.LANCZOS)
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# If a transform is provided (e.g. conversion to Tensor), apply it.
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if self.transform:
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low_res = self.transform(low_res)
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high_res = self.transform(high_res)
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return low_res, high_res
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except Exception as e:
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print(f"\nError at index {idx}: {str(e)}")
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self.failed_indices.add(idx)
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return self[(idx + 1) % len(self)]
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# Define any transformations you require.
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transform = transforms.Compose([
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transforms.ToTensor(),
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])
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# Load the base AIIABase model and wrap it with the Upsampler.
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pretrained_model_path = "/root/vision/AIIA/AIIA-base-512"
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base_model = AIIABase.load(pretrained_model_path, precision="bf16")
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model = Upsampler(base_model)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Move model to device using channels_last memory format.
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model = model.to(device, memory_format=torch.channels_last)
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# Optional: flag to enable gradient checkpointing.
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use_checkpointing = True
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# Create the dataset and dataloader.
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dataset = UpscaleDataset([
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"/root/training_data/vision-dataset/image_upscaler.parquet",
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"/root/training_data/vision-dataset/image_vec_upscaler.parquet"
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], transform=transform)
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data_loader = DataLoader(dataset, batch_size=1, shuffle=True) # Consider adjusting num_workers if needed.
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# Define loss function and optimizer.
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=1e-4)
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num_epochs = 10
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model.train()
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# Prepare a CSV file for logging training loss.
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csv_file = 'losses.csv'
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with open(csv_file, mode='a', newline='') as file:
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writer = csv.writer(file)
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if file.tell() == 0:
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writer.writerow(['Epoch', 'Train Loss'])
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scaler = GradScaler()
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early_stopping = EarlyStopping(patience=3, min_delta=0.001)
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# Training loop with early stopping.
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for epoch in range(num_epochs):
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epoch_loss = 0.0
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progress_bar = tqdm(data_loader, desc=f"Epoch {epoch + 1}")
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print(f"Epoch: {epoch + 1}")
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for low_res, high_res in progress_bar:
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# Move data to GPU with channels_last format where possible.
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low_res = low_res.to(device, non_blocking=True).to(memory_format=torch.channels_last)
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high_res = high_res.to(device, non_blocking=True)
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optimizer.zero_grad()
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with autocast(device_type=device.type):
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if use_checkpointing:
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# Ensure the input tensor requires gradient so that checkpointing records the computation graph.
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low_res.requires_grad_()
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outputs = checkpoint(model, low_res)
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else:
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outputs = model(low_res)
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loss = criterion(outputs, high_res)
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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epoch_loss += loss.item()
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progress_bar.set_postfix({'loss': loss.item()})
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# Optionally delete variables to free memory.
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del low_res, high_res, outputs, loss
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# Perform garbage collection and clear GPU cache after each epoch.
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gc.collect()
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torch.cuda.empty_cache()
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print(f"Epoch {epoch + 1}, Loss: {epoch_loss}")
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# Record the loss in the CSV log.
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with open(csv_file, mode='a', newline='') as file:
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writer = csv.writer(file)
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writer.writerow([epoch + 1, epoch_loss])
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if early_stopping(epoch_loss):
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print(f"Early stopping triggered at epoch {epoch + 1} with loss {epoch_loss}")
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break
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# Optionally save the fine-tuned model.
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finetuned_model_path = "aiuNN"
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model.save(finetuned_model_path)
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from .trainer import aiuNNTrainer
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__all__ = ["aiuNNTrainer" ]
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import os
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import csv
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from torch.amp import autocast, GradScaler
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from torch.utils.checkpoint import checkpoint
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import gc
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import time
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import shutil
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class EarlyStopping:
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def __init__(self, patience=3, min_delta=0.001):
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# Number of epochs with no significant improvement before stopping
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# Minimum change in loss required to count as an improvement
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self.patience = patience
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self.min_delta = min_delta
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self.best_loss = float('inf')
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self.counter = 0
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self.early_stop = False
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def __call__(self, epoch_loss):
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if epoch_loss < self.best_loss - self.min_delta:
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self.best_loss = epoch_loss
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self.counter = 0
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return True # Improved
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else:
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self.counter += 1
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if self.counter >= self.patience:
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self.early_stop = True
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return False # Not improved
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class aiuNNTrainer:
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def __init__(self, upscaler_model, dataset_class=None):
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"""
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Initialize the upscaler trainer
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Args:
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upscaler_model: The model to fine-tune
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dataset_class: The dataset class to use for loading data (optional)
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"""
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self.model = upscaler_model
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self.dataset_class = dataset_class
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.model = self.model.to(self.device, memory_format=torch.channels_last)
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self.criterion = nn.MSELoss()
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self.optimizer = None
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self.scaler = GradScaler()
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self.best_loss = float('inf')
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self.use_checkpointing = True
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self.data_loader = None
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self.validation_loader = None
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self.log_dir = None
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def load_data(self, dataset_params=None, batch_size=1, validation_split=0.2, custom_train_dataset=None, custom_val_dataset=None):
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"""
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Load data using either a custom dataset instance or the dataset class provided at initialization
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Args:
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dataset_params (dict/list): Parameters to pass to the dataset class constructor
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batch_size (int): Batch size for training
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validation_split (float): Proportion of data to use for validation
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custom_train_dataset: A pre-instantiated dataset to use for training (optional)
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custom_val_dataset: A pre-instantiated dataset to use for validation (optional)
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"""
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# If custom datasets are provided directly, use them
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if custom_train_dataset is not None:
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train_dataset = custom_train_dataset
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val_dataset = custom_val_dataset if custom_val_dataset is not None else None
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else:
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# Otherwise instantiate dataset using the class and parameters
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if self.dataset_class is None:
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raise ValueError("No dataset class provided. Either provide a dataset class at initialization or custom datasets.")
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# Create dataset instance
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dataset = self.dataset_class(**dataset_params if isinstance(dataset_params, dict) else {'parquet_files': dataset_params})
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# Split into train and validation sets
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dataset_size = len(dataset)
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val_size = int(validation_split * dataset_size)
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train_size = dataset_size - val_size
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train_dataset, val_dataset = torch.utils.data.random_split(
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dataset, [train_size, val_size]
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)
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# Create data loaders
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self.data_loader = DataLoader(
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train_dataset,
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batch_size=batch_size,
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shuffle=True,
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pin_memory=True
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)
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if val_dataset is not None:
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self.validation_loader = DataLoader(
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val_dataset,
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batch_size=batch_size,
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shuffle=False,
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pin_memory=True
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)
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print(f"Loaded {len(train_dataset)} training samples and {len(val_dataset)} validation samples")
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else:
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self.validation_loader = None
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print(f"Loaded {len(train_dataset)} training samples (no validation set)")
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return self.data_loader, self.validation_loader
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def _setup_logging(self, output_path):
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"""Set up directory structure for logging and model checkpoints"""
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timestamp = time.strftime("%Y%m%d-%H%M%S")
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self.log_dir = os.path.join(output_path, f"training_run_{timestamp}")
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os.makedirs(self.log_dir, exist_ok=True)
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# Create checkpoint directory
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self.checkpoint_dir = os.path.join(self.log_dir, "checkpoints")
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os.makedirs(self.checkpoint_dir, exist_ok=True)
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# Set up CSV logging
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self.csv_path = os.path.join(self.log_dir, 'training_log.csv')
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with open(self.csv_path, mode='w', newline='') as file:
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writer = csv.writer(file)
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if self.validation_loader:
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writer.writerow(['Epoch', 'Train Loss', 'Validation Loss', 'Improved'])
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else:
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writer.writerow(['Epoch', 'Train Loss', 'Improved'])
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def _evaluate(self):
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"""Evaluate the model on validation data"""
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if self.validation_loader is None:
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return 0.0
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self.model.eval()
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val_loss = 0.0
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with torch.no_grad():
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for low_res, high_res in tqdm(self.validation_loader, desc="Validating"):
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low_res = low_res.to(self.device, non_blocking=True).to(memory_format=torch.channels_last)
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high_res = high_res.to(self.device, non_blocking=True)
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with autocast(device_type=self.device.type):
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outputs = self.model(low_res)
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loss = self.criterion(outputs, high_res)
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val_loss += loss.item()
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del low_res, high_res, outputs, loss
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self.model.train()
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return val_loss
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def _save_checkpoint(self, epoch, is_best=False):
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"""Save model checkpoint"""
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checkpoint_path = os.path.join(self.checkpoint_dir, f"epoch_{epoch}.pt")
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best_model_path = os.path.join(self.log_dir, "best_model")
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# Save the model checkpoint
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self.model.save(checkpoint_path)
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# If this is the best model so far, copy it to best_model
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if is_best:
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if os.path.exists(best_model_path):
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shutil.rmtree(best_model_path)
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self.model.save(best_model_path)
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print(f"Saved new best model with loss: {self.best_loss:.6f}")
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def finetune(self, output_path, epochs=10, lr=1e-4, patience=3, min_delta=0.001):
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"""
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Finetune the upscaler model
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Args:
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output_path (str): Directory to save models and logs
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epochs (int): Maximum number of training epochs
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lr (float): Learning rate
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patience (int): Early stopping patience
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min_delta (float): Minimum improvement for early stopping
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"""
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# Check if data is loaded
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if self.data_loader is None:
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raise ValueError("Data not loaded. Call load_data first.")
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# Setup optimizer
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self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
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# Set up logging
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self._setup_logging(output_path)
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# Setup early stopping
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early_stopping = EarlyStopping(patience=patience, min_delta=min_delta)
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# Training loop
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self.model.train()
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for epoch in range(epochs):
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# Training phase
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epoch_loss = 0.0
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progress_bar = tqdm(self.data_loader, desc=f"Epoch {epoch + 1}/{epochs}")
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for low_res, high_res in progress_bar:
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# Move data to GPU with channels_last format where possible
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low_res = low_res.to(self.device, non_blocking=True).to(memory_format=torch.channels_last)
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high_res = high_res.to(self.device, non_blocking=True)
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self.optimizer.zero_grad()
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with autocast(device_type=self.device.type):
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if self.use_checkpointing:
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# Ensure the input tensor requires gradient so that checkpointing records the computation graph
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low_res.requires_grad_()
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outputs = checkpoint(self.model, low_res)
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else:
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outputs = self.model(low_res)
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loss = self.criterion(outputs, high_res)
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self.scaler.scale(loss).backward()
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self.scaler.step(self.optimizer)
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self.scaler.update()
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epoch_loss += loss.item()
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progress_bar.set_postfix({'loss': loss.item()})
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# Optionally delete variables to free memory
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del low_res, high_res, outputs, loss
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# Calculate average epoch loss
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avg_train_loss = epoch_loss / len(self.data_loader)
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# Validation phase (if validation loader exists)
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if self.validation_loader:
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val_loss = self._evaluate() / len(self.validation_loader)
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is_improved = val_loss < self.best_loss
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if is_improved:
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self.best_loss = val_loss
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# Log results
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print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}, Val Loss: {val_loss:.6f}")
|
||||
with open(self.csv_path, mode='a', newline='') as file:
|
||||
writer = csv.writer(file)
|
||||
writer.writerow([epoch + 1, avg_train_loss, val_loss, "Yes" if is_improved else "No"])
|
||||
else:
|
||||
# If no validation, use training loss for improvement tracking
|
||||
is_improved = avg_train_loss < self.best_loss
|
||||
if is_improved:
|
||||
self.best_loss = avg_train_loss
|
||||
|
||||
# Log results
|
||||
print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}")
|
||||
with open(self.csv_path, mode='a', newline='') as file:
|
||||
writer = csv.writer(file)
|
||||
writer.writerow([epoch + 1, avg_train_loss, "Yes" if is_improved else "No"])
|
||||
|
||||
# Save checkpoint
|
||||
self._save_checkpoint(epoch + 1, is_best=is_improved)
|
||||
|
||||
# Perform garbage collection and clear GPU cache after each epoch
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# Check early stopping
|
||||
if early_stopping(val_loss if self.validation_loader else avg_train_loss):
|
||||
print(f"Early stopping triggered at epoch {epoch + 1}")
|
||||
break
|
||||
|
||||
return self.best_loss
|
||||
|
||||
def save(self, output_path=None):
|
||||
"""
|
||||
Save the best model to the specified path
|
||||
|
||||
Args:
|
||||
output_path (str, optional): Path to save the model. If None, uses the best model from training.
|
||||
"""
|
||||
if output_path is None and self.log_dir is not None:
|
||||
best_model_path = os.path.join(self.log_dir, "best_model")
|
||||
if os.path.exists(best_model_path):
|
||||
print(f"Best model already saved at {best_model_path}")
|
||||
return best_model_path
|
||||
else:
|
||||
output_path = os.path.join(self.log_dir, "final_model")
|
||||
|
||||
if output_path is None:
|
||||
raise ValueError("No output path specified and no training has been done yet.")
|
||||
|
||||
self.model.save(output_path)
|
||||
print(f"Model saved to {output_path}")
|
||||
return output_path
|
||||
|
|
@ -31,6 +31,8 @@ class Upscaler(nn.Module):
|
|||
def forward(self, x):
|
||||
features = self.base_model(x)
|
||||
return self.last_transform(features)
|
||||
|
||||
|
||||
class ImageUpscaler:
|
||||
def __init__(self, model_path: str, device: str = 'cuda' if torch.cuda.is_available() else 'cpu'):
|
||||
self.device = torch.device(device)
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
from .aiunn import aiuNN
|
||||
from .config import aiuNNConfig
|
||||
|
||||
|
||||
__all__ = ["aiuNN", "aiuNNConfig"]
|
||||
|
|
@ -3,17 +3,17 @@ import torch
|
|||
import torch.nn as nn
|
||||
import warnings
|
||||
from aiia import AIIA, AIIAConfig, AIIABase
|
||||
from config import UpsamplerConfig
|
||||
from .config import aiuNNConfig
|
||||
import warnings
|
||||
|
||||
|
||||
class Upsampler(AIIA):
|
||||
class aiuNN(AIIA):
|
||||
def __init__(self, base_model: AIIABase):
|
||||
super().__init__(base_model.config)
|
||||
self.base_model = base_model
|
||||
|
||||
# Pass the unified base configuration using the new parameter.
|
||||
self.config = UpsamplerConfig(base_config=base_model.config)
|
||||
self.config = aiuNNConfig(base_config=base_model.config)
|
||||
|
||||
self.upsample = nn.Upsample(
|
||||
scale_factor=self.config.upsample_scale,
|
||||
|
|
@ -72,11 +72,11 @@ if __name__ == "__main__":
|
|||
config = AIIAConfig()
|
||||
base_model = AIIABase(config)
|
||||
# Instantiate Upsampler from the base model (works correctly).
|
||||
upsampler = Upsampler(base_model)
|
||||
upsampler = aiuNN(base_model)
|
||||
|
||||
# Save the model (both configuration and weights).
|
||||
upsampler.save("hehe")
|
||||
|
||||
# Now load using the overridden load method; this will load the complete model.
|
||||
upsampler_loaded = Upsampler.load("hehe", precision="bf16")
|
||||
upsampler_loaded = aiuNN.load("hehe", precision="bf16")
|
||||
print("Updated configuration:", upsampler_loaded.config.__dict__)
|
||||
Loading…
Reference in New Issue