add submission sample

medvqa/submission_samples/gi-2025/submission_task1.py

@@ -0,0 +1,106 @@
+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=...''')