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import clip
import clip.model
from datasets import Dataset
import json
import numpy as np
import pandas as pd
from PIL import Image
from sklearn.model_selection import train_test_split
import streamlit as st
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import tqdm
import os
def model_training():
dataset_path = st.session_state.get("selected_dataset", None)
if not dataset_path or dataset_path == "":
st.error("Please select a dataset to proceed.")
return
if not os.path.exists(f"annotations/{dataset_path}/annotations.json"):
st.error("No annotations found for the selected dataset.")
return
with open(f"annotations/{dataset_path}/annotations.json", "r") as f:
annotations_dict = json.load(f)
annotations_df = pd.DataFrame(annotations_dict.items(), columns=['image_path', 'annotation'])
annotations_df.columns = ['file_name', 'text']
st.subheader("Data Preview")
st.dataframe(annotations_df.head(), use_container_width=True)
if len(annotations_df) < 2:
st.error("Not enough data to train the model.")
return
test_size = st.selectbox("Select Test Size", options=[0.1, 0.2, 0.3, 0.4, 0.5], index=1)
train_df, val_df = train_test_split(annotations_df, test_size=test_size, random_state=42)
if len(train_df) < 2:
st.error("Not enough data to train the model.")
st.write(f"Train Size: {len(train_df)} | Validation Size: {len(val_df)}")
col1, col2 = st.columns(2)
with col1:
optimizer = st.selectbox("Select Optimizer", options=optim.__all__, index=3)
optimizer = getattr(optim, optimizer)
with col2:
batch_size_options = [2, 4, 8, 16, 32, 64, 128]
ideal_batch_size = int(np.sqrt(len(train_df)))
if ideal_batch_size in batch_size_options:
ideal_batch_size_index = batch_size_options.index(ideal_batch_size)
else:
for batch_size in batch_size_options:
if batch_size > ideal_batch_size:
ideal_batch_size_index = batch_size_options.index(batch_size) - 1
if ideal_batch_size_index < 0:
ideal_batch_size_index = 0
break
batch_size = st.selectbox("Select Batch Size", options=[2, 4, 8, 16, 32, 64, 128], index=ideal_batch_size_index)
col1, col2 = st.columns(2)
with col1:
weight_decay = st.number_input("Weight Decay", value=0.3, format="%.5f")
with col2:
learning_rate = st.number_input("Learning Rate", value=1e-3, format="%.5f")
device = "cuda" if torch.cuda.is_available() else "cpu"
if st.button("Train", key="train_button", use_container_width=True, type="primary"):
def convert_models_to_fp32(model):
for p in model.parameters():
p.data = p.data.float()
p.grad.data = p.grad.data.float()
device = "cuda:0" if torch.cuda.is_available() else "cpu"
with st.spinner("Loading Model..."):
model, preprocess = clip.load("ViT-B/32", device=device, jit=False)
clip.model.convert_weights(model)
loss_img = nn.CrossEntropyLoss()
loss_txt = nn.CrossEntropyLoss()
optimizer = optimizer(model.parameters(), lr=learning_rate, betas=(0.9, 0.98), eps=1e-6, weight_decay=weight_decay)
def collate_fn(batch):
images = []
texts = []
for entry in batch:
img = entry['file_name']
text = entry['text']
images.append(img)
texts.append(text)
images = [preprocess(Image.open(img_path)) for img_path in images]
images = torch.stack(images)
return images, list(texts)
train_df['file_name'] = train_df['file_name'].str.strip()
val_df['file_name'] = val_df['file_name'].str.strip()
dataset = Dataset.from_pandas(train_df)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn
)
val_dataset = Dataset.from_pandas(val_df)
val_dataloader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=collate_fn
)
def calculate_val_loss(model):
model.eval()
total_loss = 0
with torch.no_grad():
for batch_idx, (images, texts) in enumerate(val_dataloader):
texts = clip.tokenize(texts).to(device)
images = images.to(device)
texts = texts.to(device)
logits_per_image, logits_per_text = model(images, texts)
ground_truth = torch.arange(len(images)).to(device)
image_loss = loss_img(logits_per_image, ground_truth)
text_loss = loss_txt(logits_per_text, ground_truth)
total_loss += (image_loss + text_loss) / 2
model.train()
return total_loss / len(val_dataloader)
step = 0
progress_bar = st.progress(0, text=f"Model Training in progress... \nStep: {step}/{len(dataloader)} | {0 / len(dataloader)}% Completed | Loss: 0.0")
for batch_idx, (images, texts) in enumerate(dataloader):
optimizer.zero_grad()
texts = clip.tokenize(texts).to(device)
images = images.to(device)
texts = texts.to(device)
logits_per_image, logits_per_text = model(images, texts)
ground_truth = torch.arange(len(images)).to(device)
image_loss = loss_img(logits_per_image, ground_truth)
text_loss = loss_txt(logits_per_text, ground_truth)
total_loss = (image_loss + text_loss) / 2
total_loss.backward()
if step % 20 == 0:
print("\nStep : ", step)
print("Total Loss : ", total_loss.item())
val_loss = calculate_val_loss(model)
print("\nValidation Loss : ", val_loss.item())
print("\n")
convert_models_to_fp32(model)
optimizer.step()
clip.model.convert_weights(model)
step += 1
progress_bar.progress((batch_idx + 1) / len(dataloader), f"Model Training in progress... \nStep: {step}/{len(dataloader)} | {round((batch_idx + 1) / len(dataloader) * 100)}% Completed | Loss: {val_loss.item():.4f}")
st.toast("Training Completed!", icon="🎉")
with st.spinner("Saving Model..."):
finetuned_model = model.eval()
torch.save(finetuned_model.state_dict(), f"annotations/{dataset_path}/finetuned_model.pt")
st.success("Model Saved Successfully!")
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