train.py

"""
train.py — Finetune a Hugging Face vision model (e.g., ViT) on breast ultrasound images
"""

from datasets import load_dataset
from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
    TrainingArguments,
    Trainer,
)
import evaluate
import numpy as np
import torch

# ---- 1. Load dataset ----
dataset = load_dataset("gymprathap/Breast-Cancer-Ultrasound-Images-Dataset")

# Dataset info
labels = dataset["train"].features["label"].names
num_labels = len(labels)
print(f"Classes: {labels}")

# ---- 2. Preprocessing ----
checkpoint = "google/vit-base-patch16-224-in21k"  # choose your model
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

def transform_examples(examples):
    images = [img.convert("RGB") for img in examples["image"]]  # ensure 3-channel
    inputs = image_processor(images, return_tensors="pt")
    inputs["labels"] = examples["label"]
    return inputs

prepared_ds = dataset.with_transform(transform_examples)

# Split dataset
splits = prepared_ds["train"].train_test_split(test_size=0.2, seed=42)
train_ds, val_ds = splits["train"], splits["test"]

# ---- 3. Load model ----
model = AutoModelForImageClassification.from_pretrained(
    checkpoint,
    num_labels=num_labels,
    ignore_mismatched_sizes=True,  # handles final layer shape mismatch
)

# ---- 4. Metrics ----
accuracy = evaluate.load("accuracy")
f1 = evaluate.load("f1")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=-1)
    acc = accuracy.compute(predictions=preds, references=labels)["accuracy"]
    f1_score = f1.compute(predictions=preds, references=labels, average="macro")["f1"]
    return {"accuracy": acc, "f1": f1_score}

# ---- 5. Training setup ----
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    eval_strategy="epoch", 
    save_strategy="epoch",
    num_train_epochs=3,
    learning_rate=5e-5,
    logging_dir="./logs",
    load_best_model_at_end=True,
    remove_unused_columns=False,
    push_to_hub=True, 
    hub_model_id="hugging-science/sample-breast-cancer-classification",
    report_to="none",
)

# ---- 6. Trainer ----
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    tokenizer=image_processor,
    compute_metrics=compute_metrics,
)

# ---- 7. Train ----
trainer.train()

# ---- 8. Save locally ----
model.save_pretrained("./finetuned-ultrasound-model")
image_processor.save_pretrained("./finetuned-ultrasound-model")

print("✅ Training complete. Model saved to ./finetuned-ultrasound-model")