Questions about Text-to-Image training

[xiaoxiaodadada]

I now have my own image text pair dataset, how can I modify this code to train it? If you can help me, I would be very grateful.

[Siddharth-Latthe-07]

To train a model using your own image-text pair dataset with the given setup, you'll need to modify the code to load your dataset, preprocess the data, and then feed it into the model for training. Here's a steps on how to do this:

1. Prepare Your Dataset: Ensure your dataset is in a structured format, such as a CSV file, where each row contains an image file path and its corresponding text description.
2. Modify the Dataset Loader: Update the data loading part of your code to read your dataset and preprocess the images and text.
3. Update the Training Loop: Adjust the training loop to use your dataset and fine-tune the model accordingly.

sample code snippets:- Modify the Dataset Loader

Copy code
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from transformers import CLIPProcessor

class CustomDataset(Dataset):
    def __init__(self, csv_file, processor, max_length=77):
        self.data = pd.read_csv(csv_file)
        self.processor = processor
        self.max_length = max_length

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        img_path = self.data.iloc[idx, 0]
        text = self.data.iloc[idx, 1]

        image = Image.open(img_path).convert("RGB")
        inputs = self.processor(text=text, images=image, return_tensors="pt", max_length=self.max_length, padding="max_length", truncation=True)

        return inputs

# Initialize processor (modify as per your model)
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
dataset = CustomDataset(csv_file="dataset.csv", processor=processor)

# Create DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

Update the Training Loop

import torch
from transformers import CLIPModel, CLIPProcessor

# Initialize the model (modify as per your model)
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")

# Move the model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Define optimizer and loss function
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
loss_fn = torch.nn.CrossEntropyLoss()

# Training loop
for epoch in range(num_epochs):
    model.train()
    total_loss = 0

    for batch in dataloader:
        optimizer.zero_grad()

        input_ids = batch['input_ids'].squeeze(1).to(device)
        pixel_values = batch['pixel_values'].squeeze(1).to(device)
        attention_mask = batch['attention_mask'].squeeze(1).to(device)

        outputs = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
        logits_per_image = outputs.logits_per_image
        logits_per_text = outputs.logits_per_text

        labels = torch.arange(logits_per_image.size(0), device=device)
        loss = (loss_fn(logits_per_image, labels) + loss_fn(logits_per_text, labels)) / 2
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

    avg_loss = total_loss / len(dataloader)
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")

# Save the model
model.save_pretrained("path_to_save_your_model")
processor.save_pretrained("path_to_save_your_processor")

Hope this helps Thanks

[xiaoxiaodadada]

Thank you very much, this helped me a lot