inference.py

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session


get_ipython().getoutput("pip install nltk")


import torch
from transformers import BlipProcessor, BlipForConditionalGeneration
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from tqdm import tqdm


device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "utkarshpise/blip-rsicd-captioning"

processor = BlipProcessor.from_pretrained(model_name)
model = BlipForConditionalGeneration.from_pretrained(model_name)

model.to(device)
model.eval()

print(" Model loaded")


smooth = SmoothingFunction().method1

def evaluate_model(model, loader, processor, device):
    model.eval()

    total_loss = 0
    preds = []
    refs = []

    with torch.no_grad():
        for batch in tqdm(loader, desc="Evaluating"):
            batch = {k: v.to(device) for k, v in batch.items()}

            # 🔥 LM LOSS
            outputs = model(**batch)
            loss = outputs.loss
            total_loss += loss.item()

            # 🔥 Generate captions
            generated_ids = model.generate(
                pixel_values=batch["pixel_values"],
                max_length=50,
                num_beams=5
            )

            pred = processor.batch_decode(generated_ids, skip_special_tokens=True)
            ref = processor.batch_decode(batch["labels"], skip_special_tokens=True)

            preds.extend(pred)
            refs.extend(refs if False else ref)

    avg_loss = total_loss / len(loader)

    bleu_scores = []
    for p, r in zip(preds, refs):
        score = sentence_bleu([r.split()], p.split(), smoothing_function=smooth)
        bleu_scores.append(score)

    bleu = sum(bleu_scores) / len(bleu_scores)

    return avg_loss, bleu



import kagglehub
import os
import pandas as pd

path = kagglehub.dataset_download("thedevastator/rsicd-image-caption-dataset")

print("Dataset path:", path)
print("Files:", os.listdir(path))


TEST_CSV = os.path.join(path, "test.csv")

df = pd.read_csv(TEST_CSV)

print("Total samples:", len(df))


import random



class RSICDDataset(Dataset):
    def __init__(self, df, processor):
        self.df = df
        self.processor = processor

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        row = self.df.iloc[idx]

        # 🔥 IMAGE
        import ast
        from io import BytesIO
        from PIL import Image

        img_data = row["image"]

        if isinstance(img_data, str):
            img_dict = ast.literal_eval(img_data)
            image_bytes = img_dict["bytes"]
        else:
            image_bytes = img_data["bytes"]

        image = Image.open(BytesIO(image_bytes)).convert("RGB")

        # CORRECT CAPTION FIELD
        captions = row["captions"]

        if isinstance(captions, str):
            captions = ast.literal_eval(captions)

        caption = random.choice(captions)

        encoding = self.processor(
            images=image,
            text=caption,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        )

        encoding = {k: v.squeeze(0) for k, v in encoding.items()}
        encoding["labels"] = encoding["input_ids"]

        return encoding


from torch.utils.data import DataLoader

dataset = RSICDDataset(df, processor)

test_loader = DataLoader(dataset, batch_size=8, num_workers=2)


loss, bleu = evaluate_model(model, test_loader, processor, device)

print(f"\nLM Loss: {loss}")
print(f" BLEU Score: {bleu}")


from huggingface_hub import login

login()



from huggingface_hub import upload_file

repo_id = "utkarshpise/blip-rsicd-captioning"

upload_file(
    path_or_fileobj="/kaggle/working/.virtual_documents/__notebook_source__.ipynb", 
    path_in_repo="inference.py",        
    repo_id=repo_id,
    repo_type="model"
)

print(" Code uploaded!")


Evaluating: 100%|██████████| 137/137 [05:44<00:00,  2.51s/it]

LM Loss: 16.873739333048356
 BLEU Score: 0.10002967072689554