Google Colab Training Flow and File Structure

1. Project File Structure

Transformer_Project/
├── datasets/
│   └── coco_dataset/               # Data for training and testing
│       ├── train2017/
│       └── annotations/
├── models/
│   ├── text_to_image_transformer_original.pth   # Original trained model
│   └── text_to_image_transformer_optimized.pth  # Optimized model
├── results/
│   ├── generated_images_original/   # Images generated by the original model
│   └── generated_images_optimized/  # Images generated by the optimized model
└── notebooks/
    ├── data_preprocessing.ipynb     # Data loading and preprocessing
    ├── model_training.ipynb         # Model building and training
    ├── optimization_training.ipynb  # Optimized training with techniques like mixed precision
    ├── generate_images.ipynb        # Generating images with trained models
    └── analysis_and_visualization.ipynb  # Analyzing and visualizing results
    

2. Data Preprocessing Notebook (data_preprocessing.ipynb)

This notebook handles data loading and preprocessing using COCO dataset. It includes steps to mount Google Drive, install necessary libraries, and preprocess data.

Code Example:

from google.colab import drive
# Mount Google Drive
drive.mount('/content/drive')

# Install necessary libraries
!pip install transformers
!pip install datasets

# Set dataset path
dataset_path = '/content/drive/My Drive/Transformer_Project/datasets/coco_dataset/'

from datasets import load_dataset
from transformers import BertTokenizer

# Load and preprocess dataset
dataset = load_dataset("coco", split="train")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

def preprocess_data(example):
    tokens = tokenizer(example['text'], padding="max_length", truncation=True, return_tensors="pt")
    return tokens

dataset = dataset.map(preprocess_data)
    

3. Model Training Notebook (model_training.ipynb)

This notebook is used to build the Transformer model and perform initial training.

Code Example:

import torch
import torch.nn as nn
from transformers import BertModel

# Transformer Model Class
class TextToImageTransformer(nn.Module):
    def __init__(self):
        super(TextToImageTransformer, self).__init__()
        self.text_encoder = BertModel.from_pretrained('bert-base-uncased')
        self.gan_decoder = nn.Sequential(
            nn.Linear(768, 1024),
            nn.ReLU(),
            nn.Linear(1024, 256*256*3),
            nn.Tanh()
        )

    def forward(self, text_inputs):
        text_features = self.text_encoder(**text_inputs).last_hidden_state.mean(dim=1)
        generated_image = self.gan_decoder(text_features)
        return generated_image.view(-1, 3, 256, 256)

# Set device and instantiate model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TextToImageTransformer().to(device)
    

4. Optimized Training Notebook (optimization_training.ipynb)

This notebook focuses on optimizing training with techniques like mixed precision.

Code Example:

from torch.cuda.amp import GradScaler, autocast

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = nn.MSELoss()
scaler = GradScaler()

# Assume we have a dataloader
for epoch in range(10):
    for batch in dataloader:
        text_inputs = {k: v.to(device) for k, v in batch['text_inputs'].items()}
        target_images = batch['images'].to(device)

        optimizer.zero_grad()
        with autocast():
            output_images = model(text_inputs)
            loss = loss_fn(output_images, target_images)
        
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
    
    print(f'Epoch {epoch+1}, Loss: {loss.item()}')
    

5. Image Generation Notebook (generate_images.ipynb)

Generate images using the trained models, and save them to Google Drive.

Code Example:

import torch
from PIL import Image
import numpy as np

# Load trained model
model_path = '/content/drive/My Drive/Transformer_Project/models/text_to_image_transformer_original.pth'
model.load_state_dict(torch.load(model_path))
model.eval()

# Input text and generate image
input_text = "A dog playing with a ball in the park."
tokens = tokenizer(input_text, padding="max_length", truncation=True, return_tensors="pt")

with torch.no_grad():
    generated_image = model(tokens)

# Save generated image
image_array = (np.clip(generated_image[0].permute(1, 2, 0).numpy(), 0, 1) * 255).astype(np.uint8)
image = Image.fromarray(image_array)
image.save('/content/drive/My Drive/Transformer_Project/results/generated_images_original/generated_image_1.png')
    

6. Analysis and Visualization Notebook (analysis_and_visualization.ipynb)

Analyze the results by comparing images generated by different versions of the model, and visualize using Matplotlib.

Code Example:

import matplotlib.pyplot as plt
from PIL import Image

# Load images
img_original = Image.open('/content/drive/My Drive/Transformer_Project/results/generated_images_original/generated_image_1.png')
img_optimized = Image.open('/content/drive/My Drive/Transformer_Project/results/generated_images_optimized/generated_image_1.png')

# Visualization
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].imshow(img_original)
ax[0].set_title("Original Model")
ax[0].axis('off')

ax[1].imshow(img_optimized)
ax[1].set_title("Optimized Model")
ax[1].axis('off')

plt.show()