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from transformers import AutoModelForCausalLM
from datasets import load_dataset
from transformers import AutoProcessor
import torch
import json
import time
from tqdm import tqdm
val_dataset = load_dataset("SimulaMet-HOST/Kvasir-VQA")['raw'].select(range(5))
predictions = [] # List to store predictions
gpu_name = torch.cuda.get_device_name(
0) if torch.cuda.is_available() else "cpu"
device = "CUDA" if torch.cuda.is_available() else "cpu"
def get_mem(): return torch.cuda.memory_allocated(device) / \
(1024 ** 2) if torch.cuda.is_available() else 0
initial_mem = get_mem()
# ✏️✏️--------EDIT SECTION 1: SUBMISISON DETAILS and MODEL LOADING --------✏️✏️#
SUBMISSION_INFO = {
# 🔹 TODO: PARTICIPANTS MUST ADD PROPER SUBMISSION INFO FOR THE SUBMISSION 🔹
# This will be visible to the organizers
# DONT change the keys, only add your info
"Participant_Names": "Sushant Gautam, Steven Hicks and Vajita Thambawita",
"Affiliations": "SimulaMet",
"Contact_emails": ["[email protected]", "[email protected]"],
# But, the first email only will be used for correspondance
"Team_Name": "SimulaMetmedVQA Rangers",
"Country": "Norway",
"Notes_to_organizers": '''
eg, We have finetund XXX model
This is optional . .
Used data augmentations . .
Custom info about the model . .
Any insights. .
+ Any informal things you like to share about this submission.
'''
}
# 🔹 TODO: PARTICIPANTS MUST LOAD THEIR MODEL HERE, EDIT AS NECESSARY FOR YOUR MODEL 🔹
# can add necessary library imports here
model_hf = AutoModelForCausalLM.from_pretrained(
"SushantGautam/Florence-2-vqa-demo", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained(
"microsoft/Florence-2-base-ft", trust_remote_code=True)
model_hf.eval() # Ensure model is in evaluation mode
# 🏁----------------END SUBMISISON DETAILS and MODEL LOADING -----------------🏁#
start_time, post_model_mem = time.time(), get_mem()
total_time, final_mem = round(
time.time() - start_time, 4), round(get_mem() - post_model_mem, 2)
model_mem_used = round(post_model_mem - initial_mem, 2)
for idx, ex in enumerate(tqdm(val_dataset, desc="Validating")):
question = ex["question"]
image = ex["image"].convert(
"RGB") if ex["image"].mode != "RGB" else ex["image"]
# you have access to 'question' and 'image' variables for each example
# ✏️✏️___________EDIT SECTION 2: ANSWER GENERATION___________✏️✏️#
# 🔹 TODO: PARTICIPANTS CAN MODIFY THIS TOKENIZATION STEP IF NEEDED 🔹
inputs = processor(text=[question], images=[image],
return_tensors="pt", padding=True)
inputs = {k: v.to(device) for k, v in inputs.items()
if k not in ['labels', 'attention_mask']}
# 🔹 TODO: PARTICIPANTS CAN MODIFY THE GENERATION AND DECODING METHOD HERE 🔹
with torch.no_grad():
output = model_hf.generate(**inputs)
answer = processor.tokenizer.decode(output[0], skip_special_tokens=True)
# make sure 'answer' variable will hold answer (sentence/word) as str
# 🏁________________ END ANSWER GENERATION ________________🏁#
# ⛔ DO NOT EDIT any lines below from here, can edit only upto decoding step above as required. ⛔
# Ensures answer is a string
assert isinstance(
answer, str), f"Generated answer at index {idx} is not a string"
# Appends prediction
predictions.append(
{"index": idx, "img_id": ex["img_id"], "question": ex["question"], "answer": answer})
# Ensure all predictions match dataset length
assert len(predictions) == len(
val_dataset), "Mismatch between predictions and dataset length"
# Saves predictions to a JSON file
total_time, final_mem = round(
time.time() - start_time, 4), round(get_mem() - post_model_mem, 2)
model_mem_used = round(post_model_mem - initial_mem, 2)
output_data = {"submission_info": SUBMISSION_INFO,
"predictions": predictions, "total_time": total_time, "time_per_item": total_time / len(val_dataset),
"memory_used_mb": final_mem, "model_memory_mb": model_mem_used, "gpu_name": gpu_name, }
with open("predictions_1.json", "w") as f:
json.dump(output_data, f, indent=4)
print(f"Time: {total_time}s | Mem: {final_mem}MB | Model Load Mem: {model_mem_used}MB | GPU: {gpu_name}")
print("✅ Scripts Looks Good! Generation process completed successfully. Results saved to 'predictions_1.json'.")
print("Next Step:\n 1) Upload this submission_task1.py script file to HuggingFace model repository.")
print('''\n 2) Make a submission to the competition:\n Run:: medvqa validate_and_submit --competition=gi-2025 --task=1 --repo_id=...''')
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