diff --git a/.dvc/.gitignore b/.dvc/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..528f30c71c687de473bbb506c071e902beba6cd9 --- /dev/null +++ b/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/.dvc/config b/.dvc/config new file mode 100644 index 0000000000000000000000000000000000000000..a17e958688974c7adb7bec58f64bbe65d2be7550 --- /dev/null +++ b/.dvc/config @@ -0,0 +1,4 @@ +[core] + autostage = true +['remote "origin"'] + url = https://dagshub.com/Aryan-coder-student/Med-VQA.git diff --git a/.dvcignore b/.dvcignore new file mode 100644 index 0000000000000000000000000000000000000000..51973055237895f2d23e65e015793fd302f4b9da --- /dev/null +++ b/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..81370fb356166f28fd3c791ee5c47ed90709401f --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +models/best-saved-model +models/last-saved-model +Deployment/.env diff --git a/Deployment/.gitignore b/Deployment/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5ac11fab32c849281703c72df2bacfa260e8c005 --- /dev/null +++ b/Deployment/.gitignore @@ -0,0 +1,2 @@ +.env +Model/14-last-blip-saved-model/ \ No newline at end of file diff --git a/Deployment/__pycache__/app.cpython-310.pyc b/Deployment/__pycache__/app.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cfe041ef8c484ec3ceac081bda2a9ba92d6e84c9 Binary files /dev/null and b/Deployment/__pycache__/app.cpython-310.pyc differ diff --git a/Deployment/app.py b/Deployment/app.py new file mode 100644 index 0000000000000000000000000000000000000000..aee98cbe1b4b038ae602cb715251d524b65b19ef --- /dev/null +++ b/Deployment/app.py @@ -0,0 +1,98 @@ +import torch +from flask import Flask, request, jsonify +from transformers import BlipProcessor, BlipForQuestionAnswering +from langchain.agents import initialize_agent, AgentType +from langchain.tools import Tool +from langchain_community.utilities import SerpAPIWrapper, PubMedAPIWrapper +from langchain_groq import ChatGroq +from langchain.memory import ConversationBufferMemory +from dotenv import load_dotenv +import os +from PIL import Image +from flask_cors import CORS + + +load_dotenv() +os.getenv("SERPAPI_API_KEY") +os.getenv("GROQ_API_KEY") + + +app = Flask(__name__) +CORS(app) + + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +fine_tuned_model = BlipForQuestionAnswering.from_pretrained(os.path.join("Deployment/Model/14-last-blip-saved-model")).to(device) +processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") + +memory = ConversationBufferMemory() +def chat_bot_query(query: str): + search_tool = Tool( + name="Medical_Web_Search", + func=SerpAPIWrapper().run, + description="Searches the web for medical information related to brain, CT, and MRI scans." + ) + + pubmed_tool = Tool( + name="PubMed_Search", + func=PubMedAPIWrapper().run, + description="Searches PubMed for research papers related to brain, CT, and MRI scans." + ) + + agent = initialize_agent( + agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, + tools=[search_tool, pubmed_tool], + llm=ChatGroq(model="gemma2-9b-it"), + memory=memory, + verbose=False, + max_iterations=10, + handle_parsing_errors=True + ) + + response = agent.run(query) + return response + + + +def predict_answer(image, question): + try: + image = image.convert('RGB') + inputs = processor(image, question, return_tensors="pt").to(device) + fine_tuned_output = fine_tuned_model.generate(**inputs) + fine_tuned_answer = processor.tokenizer.decode(fine_tuned_output[0], skip_special_tokens=True) + return fine_tuned_answer + except Exception as e: + return f"Error in prediction: {e}" + + +@app.route('/predict/', methods=['GET', 'POST']) +def predict(): + try: + file = request.files['file'] + question = request.form['question'] + if not file or not question: + return jsonify({'error': 'No file or question provided'}), 400 + image = Image.open(file) + answer = predict_answer(image, question) + return jsonify({'answer': answer}) + except Exception as e: + return jsonify({'error': str(e)}), 500 + +@app.route('/chat/', methods=['POST']) +def chat(): + try: + data = request.get_json() + query = data.get('query') + if not query: + return jsonify({'error': 'No query provided'}), 400 + + response = chat_bot_query(query) + return jsonify({'response': response}) + except Exception as e: + print(e) + return jsonify({'error': str(e)}), 500 + + +if __name__ == '__main__': + app.run(debug=True, host='0.0.0.0', port='5000') \ No newline at end of file diff --git a/Deployment/streamlit/.gitignore b/Deployment/streamlit/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2eea525d885d5148108f6f3a9a8613863f783d36 --- /dev/null +++ b/Deployment/streamlit/.gitignore @@ -0,0 +1 @@ +.env \ No newline at end of file diff --git a/Deployment/streamlit/main.py b/Deployment/streamlit/main.py new file mode 100644 index 0000000000000000000000000000000000000000..e12ead3d301969c566a1ff044acca59dcc86c17d --- /dev/null +++ b/Deployment/streamlit/main.py @@ -0,0 +1,27 @@ +import streamlit as st +import requests +from PIL import Image +import io + +st.set_page_config(page_title="VQA with BLIP", layout="centered", page_icon="🤖") +st.title("Visual Question Answering (VQA) with BLIP") + +uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"]) +question = st.text_input("Ask a question about the image:") + +if uploaded_file and question: + image = Image.open(uploaded_file) + st.image(image, caption="Uploaded Image", use_column_width=True) + if st.button("Get Answer"): + img_bytes = io.BytesIO() + image.save(img_bytes, format="JPEG") + img_bytes = img_bytes.getvalue() + files = {"file": ("image.jpg", img_bytes, "image/jpeg")} + data = {"question": question} + response = requests.post("http://127.0.0.1:5000//predict/", files=files, data=data) + + if response.status_code == 200: + answer = response.json().get("answer", "No answer received") + st.success(f"Answer: {answer}") + else: + st.error("Error in fetching the answer. Please try again.") diff --git a/Deployment/test_api.py b/Deployment/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..bee91173d9785b58220003bf5e6103111aa64253 --- /dev/null +++ b/Deployment/test_api.py @@ -0,0 +1,31 @@ +import requests + +# Define the URL of the Flask server +url = "http://127.0.0.1:5000/chat/" + +# Define the query you want to send +query = "Provide recent papers links or researches on brain tumors." + +# Create the JSON payload +payload = { + "query": query +} + +# Set the headers to specify JSON content +headers = { + "Content-Type": "application/json" +} + +# Send the POST request +try: + response = requests.post(url, json=payload, headers=headers) + + # Check if the request was successful + if response.status_code == 200: + print("Response from server:") + print(response.json()) + else: + print(f"Error: {response.status_code}") + print(response.json()) +except Exception as e: + print(f"An error occurred: {e}") \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7c69166d2fb49437da6ddd0cfb4bf83e0a5ccfcf --- /dev/null +++ b/Dockerfile @@ -0,0 +1,14 @@ +# FROM python:3.10 +# WORKDIR /app +# COPY requirements.txt . +# RUN pip install -r requirements.txt +# COPY . . +# EXPOSE 5000 8501 +# CMD ["sh", "-c", "python Deployment/app.py & while ! nc -z localhost 5000; do sleep 1; done; streamlit run Deployment/streamlit/main.py --server.port=8501 --server.address=0.0.0.0"] +FROM python:3.10 +WORKDIR /app +COPY requirements.txt . +RUN pip install -r requirements.txt +COPY . . +EXPOSE 5000 +CMD ["python", "Deployment/app.py"] diff --git a/README.md b/README.md index 68768306d59c85fe78f32e16954cd332a4b6ccfc..fb4d0935b01d0bf0f83dac02b6b44b5a0027857c 100644 --- a/README.md +++ b/README.md @@ -1 +1 @@ -# NeuroVision-BHPC-VQA \ No newline at end of file +# NeuroVision-VQA \ No newline at end of file diff --git a/config.yaml b/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae9fa4e2483be22aaa010462da7f3e5daf720bb9 --- /dev/null +++ b/config.yaml @@ -0,0 +1,10 @@ +finetune_model: + best: models/best-saved-model + last: models/last-saved-model + orignal_model_id: Salesforce/blip-vqa-base +data_location: + data: data/bronze/flaviagiammarino___vqa-rad + train_processed_data: data/silver/train_dataset.pkl + test_processed_data: data/silver/test_dataset.pkl + +result: results \ No newline at end of file diff --git a/data/.gitignore b/data/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..34c937ffd2d73b87ff0b63cab285c701f9c8527f --- /dev/null +++ b/data/.gitignore @@ -0,0 +1,2 @@ +/bronze +/silver diff --git a/data/bronze.dvc b/data/bronze.dvc new file mode 100644 index 0000000000000000000000000000000000000000..85705e8c4cb20ac0eb6a065398f383dcbdb6c71e --- /dev/null +++ b/data/bronze.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 32db974481a7ea051c9c852355c7b221.dir + size: 153579281 + nfiles: 7 + hash: md5 + path: bronze diff --git a/dvc.lock b/dvc.lock new file mode 100644 index 0000000000000000000000000000000000000000..ba8739b6207564108f341602ffd3ab9c2125e11a --- /dev/null +++ b/dvc.lock @@ -0,0 +1,20 @@ +schema: '2.0' +stages: + preprocess: + cmd: python src/preprocess_data.py + deps: + - path: data/bronze/downloads + hash: md5 + md5: 1454b6162c5ef2c24c037ba883797c3b.dir + size: 34497027 + nfiles: 4 + - path: src/preprocess_data.py + hash: md5 + md5: b1141ff5738a52bee364a048006112a2 + size: 2333 + outs: + - path: data/silver + hash: md5 + md5: 1b44b8c6bbc048557b5285a27b9cc1f5.dir + size: 941687 + nfiles: 2 diff --git a/dvc.yaml b/dvc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3458ecad1a5d2dc5dd496670bad8684b73a313eb --- /dev/null +++ b/dvc.yaml @@ -0,0 +1,27 @@ +stages: + preprocess: + cmd: python src/preprocess_data.py + deps: + - src/preprocess_data.py + - data/bronze/downloads + outs: + - data/silver + + train: + cmd: python src/train.py + deps: + - src/train.py + - data/silver + - param.yaml + outs: + - models + + + evaluate: + cmd: python src/evaluate.py + deps: + - src/evaluate.py + - models + - data/silver + outs: + - results diff --git a/mlruns/0/meta.yaml b/mlruns/0/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9e2491fe8c3d94298fa338b347a3bd6c91e826ef --- /dev/null +++ b/mlruns/0/meta.yaml @@ -0,0 +1,6 @@ +artifact_location: file:///D:/My%20File/Project/MedHelp-Brain-VQA/End-To-End-Pipline/VQA/mlruns/0 +creation_time: 1738400123102 +experiment_id: '0' +last_update_time: 1738400123102 +lifecycle_stage: active +name: Default diff --git a/mlruns/271729933174040292/0ddd5ed748f54c8883bf1830e5bf029c/meta.yaml b/mlruns/271729933174040292/0ddd5ed748f54c8883bf1830e5bf029c/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..877114bf05f093b132a030ab9cdb7093c6b61cda --- /dev/null +++ b/mlruns/271729933174040292/0ddd5ed748f54c8883bf1830e5bf029c/meta.yaml 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"execution": { + "iopub.execute_input": "2025-01-23T06:20:19.450237Z", + "iopub.status.busy": "2025-01-23T06:20:19.449896Z", + "iopub.status.idle": "2025-01-23T06:20:38.322999Z", + "shell.execute_reply": "2025-01-23T06:20:38.321967Z", + "shell.execute_reply.started": "2025-01-23T06:20:19.450198Z" + }, + "id": "O7EO2jzPqawC", + "outputId": "34cf8a1b-60b4-418a-996f-5b7645ae90ff", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: datasets in f:\\python_file_install\\lib\\site-packages (3.3.2)\n", + "Requirement already satisfied: aiohttp in f:\\python_file_install\\lib\\site-packages (from datasets) (3.11.13)\n", + "Requirement already satisfied: dill<0.3.9,>=0.3.0 in f:\\python_file_install\\lib\\site-packages (from datasets) (0.3.8)\n", + "Requirement already satisfied: pandas in f:\\python_file_install\\lib\\site-packages (from datasets) (2.2.3)\n", + "Requirement already satisfied: xxhash in f:\\python_file_install\\lib\\site-packages (from datasets) (3.5.0)\n", + "Requirement already satisfied: requests>=2.32.2 in f:\\python_file_install\\lib\\site-packages (from datasets) (2.32.3)\n", + "Requirement already satisfied: pyarrow>=15.0.0 in f:\\python_file_install\\lib\\site-packages (from datasets) (19.0.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in f:\\python_file_install\\lib\\site-packages (from datasets) (6.0.2)\n", + "Requirement already satisfied: huggingface-hub>=0.24.0 in f:\\python_file_install\\lib\\site-packages (from datasets) (0.29.2)\n", + "Requirement already satisfied: multiprocess<0.70.17 in f:\\python_file_install\\lib\\site-packages (from datasets) (0.70.16)\n", + "Requirement already satisfied: numpy>=1.17 in f:\\python_file_install\\lib\\site-packages (from datasets) (1.26.4)\n", + "Requirement already satisfied: filelock in f:\\python_file_install\\lib\\site-packages (from datasets) (3.13.1)\n", + "Requirement already satisfied: packaging in f:\\python_file_install\\lib\\site-packages (from datasets) (24.2)\n", + "Requirement already satisfied: fsspec[http]<=2024.12.0,>=2023.1.0 in f:\\python_file_install\\lib\\site-packages (from datasets) (2024.6.1)\n", + "Requirement already satisfied: tqdm>=4.66.3 in f:\\python_file_install\\lib\\site-packages (from datasets) (4.67.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (6.1.0)\n", + "Requirement already satisfied: attrs>=17.3.0 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (25.1.0)\n", + "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (2.5.0)\n", + "Requirement already satisfied: yarl<2.0,>=1.17.0 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (1.18.3)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (1.5.0)\n", + "Requirement already satisfied: propcache>=0.2.0 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (0.3.0)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (1.3.2)\n", + "Requirement already satisfied: async-timeout<6.0,>=4.0 in f:\\python_file_install\\lib\\site-packages (from aiohttp->datasets) (4.0.3)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in f:\\python_file_install\\lib\\site-packages (from huggingface-hub>=0.24.0->datasets) (4.12.2)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in f:\\python_file_install\\lib\\site-packages (from requests>=2.32.2->datasets) (2.3.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in f:\\python_file_install\\lib\\site-packages (from requests>=2.32.2->datasets) (2025.1.31)\n", + "Requirement already satisfied: idna<4,>=2.5 in f:\\python_file_install\\lib\\site-packages (from requests>=2.32.2->datasets) (3.10)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in f:\\python_file_install\\lib\\site-packages (from requests>=2.32.2->datasets) (3.4.1)\n", + "Requirement already satisfied: colorama in f:\\python_file_install\\lib\\site-packages (from tqdm>=4.66.3->datasets) (0.4.6)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in f:\\python_file_install\\lib\\site-packages (from pandas->datasets) (2.9.0.post0)\n", + "Requirement already satisfied: tzdata>=2022.7 in f:\\python_file_install\\lib\\site-packages (from pandas->datasets) (2025.1)\n", + "Requirement already satisfied: pytz>=2020.1 in f:\\python_file_install\\lib\\site-packages (from pandas->datasets) (2025.1)\n", + "Requirement already satisfied: six>=1.5 in f:\\python_file_install\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.0.1 -> 25.0.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "f:\\python_file_install\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "!pip install datasets\n", + "from datasets import load_dataset\n", + "ds = load_dataset(\"flaviagiammarino/vqa-rad\",cache_dir=\"../data/bronze\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['image', 'question', 'answer'],\n", + " num_rows: 1793\n", + " })\n", + " test: Dataset({\n", + " features: ['image', 'question', 'answer'],\n", + " num_rows: 451\n", + " })\n", + "})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T06:20:38.325207Z", + "iopub.status.busy": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds[\"train\"][0][\"image\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T06:20:38.447713Z", + "iopub.status.busy": "2025-01-23T06:20:38.447469Z", + "iopub.status.idle": "2025-01-23T06:20:38.492220Z", + "shell.execute_reply": "2025-01-23T06:20:38.491431Z", + "shell.execute_reply.started": "2025-01-23T06:20:38.447688Z" + }, + "id": "H_VRvGHoLPq3", + "outputId": "937eac58-ed83-4c2f-8dfa-0da768ad9c23", + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('what is the condition of the patient', 'blind loop syndrome')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds[\"train\"][5][\"question\"] , ds[\"train\"][5][\"answer\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T06:20:38.494490Z", + "iopub.status.busy": "2025-01-23T06:20:38.494233Z", + "iopub.status.idle": "2025-01-23T06:21:49.502273Z", + "shell.execute_reply": "2025-01-23T06:21:49.501413Z", + "shell.execute_reply.started": "2025-01-23T06:20:38.494465Z" + }, + "id": "o2059XmhpQpx", + "trusted": true + }, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from transformers import BlipProcessor, BlipForQuestionAnswering\n", + "from datasets import load_dataset\n", + "import torch\n", + "from PIL import Image\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import tqdm\n", + "import pickle\n", + "\n", + "model = BlipForQuestionAnswering.from_pretrained(\"Salesforce/blip-vqa-base\")\n", + "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)\n", + "\n", + "torch.cuda.empty_cache()\n", + "torch.manual_seed(42)\n", + "\n", + "class VQADataset(torch.utils.data.Dataset):\n", + " \"\"\"VQA (v2) dataset.\"\"\"\n", + " def __init__(self, dataset, processor):\n", + " self.dataset = dataset\n", + " self.processor = processor\n", + "\n", + " def __len__(self):\n", + " return len(self.dataset)\n", + "\n", + " def __getitem__(self, idx):\n", + " # get image + text\n", + " question = self.dataset[idx]['question']\n", + " answer = self.dataset[idx]['answer']\n", + " image = self.dataset[idx]['image']\n", + " image = image.convert(\"RGB\")\n", + " text = question\n", + "\n", + " encoding = self.processor(image, text, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n", + " labels = self.processor.tokenizer.encode(\n", + " answer, max_length=8, padding=\"max_length\", truncation=True,pad_to_max_length=True, return_tensors='pt'\n", + " )\n", + " encoding[\"labels\"] = labels\n", + " for k, v in encoding.items():\n", + " encoding[k] = v.squeeze()\n", + " return encoding\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T06:21:49.504128Z", + "iopub.status.busy": "2025-01-23T06:21:49.503494Z", + "iopub.status.idle": "2025-01-23T06:21:49.510418Z", + "shell.execute_reply": "2025-01-23T06:21:49.509627Z", + "shell.execute_reply.started": "2025-01-23T06:21:49.504095Z" + }, + "id": "UGuPJYmwpe38", + "outputId": "52a75b1f-d386-4370-d2e0-a8236ed8a84c", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training sets: 1793 - Validating set: 451\n" + ] + } + ], + "source": [ + "training_dataset = ds[\"train\"]\n", + "valid_dataset = ds[\"test\"]\n", + "print(\"Training sets: {} - Validating set: {}\".format(len(training_dataset), len(valid_dataset)))\n", + "\n", + "train_dataset = VQADataset(dataset=training_dataset,\n", + " processor=processor)\n", + "valid_dataset = VQADataset(dataset=valid_dataset,\n", + " processor=processor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Saving The Process dataset to reduce trainiq time " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed data saved to ../data/silver\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os\n", + "\n", + "# Define the path where you want to save the processed data\n", + "save_dir = \"../data/silver\"\n", + "os.makedirs(save_dir, exist_ok=True)\n", + "\n", + "# Save the training and validation datasets\n", + "with open(os.path.join(save_dir, \"train_dataset.pkl\"), \"wb\") as f:\n", + " pickle.dump(train_dataset, f)\n", + "\n", + "with open(os.path.join(save_dir, \"valid_dataset.pkl\"), \"wb\") as f:\n", + " pickle.dump(valid_dataset, f)\n", + "\n", + "print(\"Processed data saved to ../data/silver\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded processed data from ../data/silver\n" + ] + } + ], + "source": [ + "if os.path.exists(os.path.join(save_dir, \"train_dataset.pkl\")) and os.path.exists(os.path.join(save_dir, \"valid_dataset.pkl\")):\n", + " with open(os.path.join(save_dir, \"train_dataset.pkl\"), \"rb\") as f:\n", + " train_dataset = pickle.load(f)\n", + "\n", + " with open(os.path.join(save_dir, \"valid_dataset.pkl\"), \"rb\") as f:\n", + " valid_dataset = pickle.load(f)\n", + " print(\"Loaded processed data from ../data/silver\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 4\n", + "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True)\n", + "valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, pin_memory=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1793" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T07:04:34.452286Z", + "iopub.status.busy": "2025-01-23T07:04:34.451915Z", + "iopub.status.idle": "2025-01-23T07:05:09.178326Z", + "shell.execute_reply": "2025-01-23T07:05:09.177423Z", + "shell.execute_reply.started": "2025-01-23T07:04:34.452255Z" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "# !pip install nltk\n", + "# import nltk\n", + "# nltk.download('all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use the train_dataset.pkl and validation_dataset.pkl from the data directory to train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T07:08:25.868499Z", + "iopub.status.busy": "2025-01-23T07:08:25.868101Z", + "iopub.status.idle": "2025-01-23T07:08:25.947129Z", + "shell.execute_reply": "2025-01-23T07:08:25.946299Z", + "shell.execute_reply.started": "2025-01-23T07:08:25.868464Z" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "from transformers import get_linear_schedule_with_warmup\n", + "from nltk.translate.bleu_score import sentence_bleu\n", + "from nltk.tokenize import word_tokenize\n", + " \n", + "num_epochs = 100\n", + "patience = 10\n", + "min_bleu_score = 0\n", + "early_stopping_hook = 0\n", + "tracking_information = []\n", + "gradient_accumulation_steps = 4 \n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-5, weight_decay=1e-4)\n", + "total_steps = len(train_dataloader) * num_epochs\n", + "warmup_steps = total_steps // 10\n", + "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)\n", + "scaler = torch.amp.GradScaler('cuda')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "editable": false, + "execution": { + "iopub.execute_input": "2025-01-23T07:08:26.892831Z", + "iopub.status.busy": "2025-01-23T07:08:26.892502Z" + }, + "id": "EN5f7fZRpunj", + "outputId": "1a64cb53-6211-4f7f-b68b-965f4a2c3657", + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1 Training: 0%| | 0/449 [00:00 12\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 13\u001b[0m loss \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;241m/\u001b[39m gradient_accumulation_steps \u001b[38;5;66;03m# Normalize loss\u001b[39;00m\n\u001b[0;32m 15\u001b[0m scaler\u001b[38;5;241m.\u001b[39mscale(loss)\u001b[38;5;241m.\u001b[39mbackward()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip.py:1311\u001b[0m, in \u001b[0;36mBlipForQuestionAnswering.forward\u001b[1;34m(self, input_ids, pixel_values, decoder_input_ids, decoder_attention_mask, attention_mask, output_attentions, output_hidden_states, labels, return_dict, interpolate_pos_encoding)\u001b[0m\n\u001b[0;32m 1308\u001b[0m image_embeds \u001b[38;5;241m=\u001b[39m vision_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 1309\u001b[0m image_attention_mask \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mones(image_embeds\u001b[38;5;241m.\u001b[39msize()[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n\u001b[1;32m-> 1311\u001b[0m question_embeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1316\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1317\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m decoder_input_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1320\u001b[0m \u001b[38;5;66;03m# labels are already shifted right, see: https://github.com/huggingface/transformers/pull/23153\u001b[39;00m\n\u001b[0;32m 1321\u001b[0m decoder_input_ids \u001b[38;5;241m=\u001b[39m labels\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip_text.py:783\u001b[0m, in \u001b[0;36mBlipTextModel.forward\u001b[1;34m(self, input_ids, attention_mask, position_ids, head_mask, inputs_embeds, encoder_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, is_decoder)\u001b[0m\n\u001b[0;32m 780\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 781\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m encoder_embeds\n\u001b[1;32m--> 783\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 784\u001b[0m \u001b[43m \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 785\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 786\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 787\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 788\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 789\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 794\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 795\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 796\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip_text.py:437\u001b[0m, in \u001b[0;36mBlipTextEncoder.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[0;32m 426\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[0;32m 427\u001b[0m layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[0;32m 428\u001b[0m hidden_states,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 434\u001b[0m output_attentions,\n\u001b[0;32m 435\u001b[0m )\n\u001b[0;32m 436\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 437\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 438\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 439\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 440\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 441\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 442\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 443\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 444\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 445\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 447\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip_text.py:359\u001b[0m, in \u001b[0;36mBlipTextLayer.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m 356\u001b[0m present_key_value \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m encoder_hidden_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 359\u001b[0m cross_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcrossattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 360\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 361\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 362\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 363\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 364\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 365\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 366\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 367\u001b[0m attention_output \u001b[38;5;241m=\u001b[39m cross_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 368\u001b[0m outputs \u001b[38;5;241m=\u001b[39m outputs \u001b[38;5;241m+\u001b[39m cross_attention_outputs[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;66;03m# add cross attentions if we output attention weights\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip_text.py:276\u001b[0m, in \u001b[0;36mBlipTextAttention.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[0;32m 267\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 268\u001b[0m hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 274\u001b[0m output_attentions: Optional[\u001b[38;5;28mbool\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 275\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m--> 276\u001b[0m self_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 278\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 279\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 280\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 281\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 283\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 284\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 285\u001b[0m attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(self_outputs[\u001b[38;5;241m0\u001b[39m], hidden_states)\n\u001b[0;32m 286\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (attention_output,) \u001b[38;5;241m+\u001b[39m self_outputs[\u001b[38;5;241m1\u001b[39m:] \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[1;32mc:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\models\\blip\\modeling_blip_text.py:213\u001b[0m, in \u001b[0;36mBlipTextSelfAttention.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 211\u001b[0m attention_probs_dropped \u001b[38;5;241m=\u001b[39m attention_probs_dropped \u001b[38;5;241m*\u001b[39m head_mask\n\u001b[1;32m--> 213\u001b[0m context_layer \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_probs_dropped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_layer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 215\u001b[0m context_layer \u001b[38;5;241m=\u001b[39m context_layer\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m)\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[0;32m 216\u001b[0m new_context_layer_shape \u001b[38;5;241m=\u001b[39m context_layer\u001b[38;5;241m.\u001b[39msize()[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mall_head_size,)\n", + "\u001b[1;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 28.00 MiB. GPU 0 has a total capacity of 4.00 GiB of which 0 bytes is free. Of the allocated memory 10.41 GiB is allocated by PyTorch, and 353.48 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" + ] + } + ], + "source": [ + "for epoch in range(num_epochs):\n", + " model.train()\n", + " epoch_loss = 0\n", + "\n", + " for step, batch in enumerate(tqdm(train_dataloader, desc=f\"Epoch {epoch+1} Training\")):\n", + " input_ids = batch.pop('input_ids').to(device)\n", + " pixel_values = batch.pop('pixel_values').to(device)\n", + " attention_mask = batch.pop('attention_mask').to(device)\n", + " labels = batch.pop('labels').to(device)\n", + "\n", + " with torch.amp.autocast('cuda', dtype=torch.float16):\n", + " outputs = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, labels=labels)\n", + " loss = outputs.loss / gradient_accumulation_steps # Normalize loss\n", + "\n", + " scaler.scale(loss).backward()\n", + "\n", + " if (step + 1) % gradient_accumulation_steps == 0 or (step + 1) == len(train_dataloader):\n", + " scaler.unscale_(optimizer) # Unscales gradients before clipping\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) # Gradient clipping\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + " optimizer.zero_grad()\n", + "\n", + " epoch_loss += loss.item() * gradient_accumulation_steps\n", + "\n", + " # Validation Loop\n", + " model.eval()\n", + " eval_loss = 0\n", + " bleu_scores = []\n", + " \n", + " with torch.no_grad():\n", + " for batch in tqdm(valid_dataloader, desc=f\"Epoch {epoch+1} Validating\"):\n", + " input_ids = batch.pop('input_ids').to(device)\n", + " pixel_values = batch.pop('pixel_values').to(device)\n", + " attention_mask = batch.pop('attention_mask').to(device)\n", + " labels = batch.pop('labels').to(device)\n", + " \n", + " # Forward pass\n", + " with torch.amp.autocast('cuda', dtype=torch.float16):\n", + " outputs = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, labels=labels)\n", + " eval_loss += outputs.loss.item()\n", + " \n", + " # Generate predictions using model.generate()\n", + " generated_ids = model.generate(input_ids=input_ids,pixel_values=pixel_values, attention_mask=attention_mask, max_length=8)\n", + " predictions = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\n", + " \n", + " # Decode references (true answers) from labels\n", + " references = processor.tokenizer.batch_decode(labels, skip_special_tokens=True)\n", + " \n", + " # Calculate BLEU score for each prediction-reference pair\n", + " for pred, ref in zip(predictions, references):\n", + " bleu_scores.append(sentence_bleu([word_tokenize(ref)], word_tokenize(pred)))\n", + " \n", + " # Calculate averages\n", + " avg_train_loss = epoch_loss / len(train_dataloader)\n", + " avg_eval_loss = eval_loss / len(valid_dataloader)\n", + " avg_bleu_score = sum(bleu_scores) / len(bleu_scores)\n", + " tracking_information.append((avg_train_loss, avg_eval_loss, avg_bleu_score, optimizer.param_groups[0][\"lr\"]))\n", + " \n", + " print(f\"Epoch {epoch+1} - Train Loss: {avg_train_loss:.4f} - Eval Loss: {avg_eval_loss:.4f} - BLEU Score: {avg_bleu_score:.4f} - LR: {optimizer.param_groups[0]['lr']}\")\n", + " \n", + " # Save model if BLEU improves\n", + " if avg_bleu_score >= min_bleu_score:\n", + " model.save_pretrained(\"Model/blip-saved-model\", from_pt=True)\n", + " print(f\"Model improved (BLEU: {avg_bleu_score:.4f})! Saved to Model/blip-saved-model.\")\n", + " min_bleu_score = avg_bleu_score\n", + " early_stopping_hook = 0\n", + " else:\n", + " early_stopping_hook += 1\n", + " if early_stopping_hook > patience:\n", + " print(\"Early stopping triggered.\")\n", + " break\n", + " \n", + " scheduler.step() # Update learning rate\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model improved (BLEU: 0.0000)! Saved to Model/blip-saved-model.\n" + ] + } + ], + "source": [ + "model.save_pretrained(f\"Model/{epoch}-last-blip-saved-model\", from_pt=True)\n", + "print(f\"Model improved (BLEU: {avg_bleu_score:.4f})! Saved to Model/blip-saved-model.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": false, + "trusted": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": false, + "execution": { + "iopub.status.busy": "2025-01-23T06:21:50.093389Z", + "iopub.status.idle": "2025-01-23T06:21:50.093757Z", + "shell.execute_reply": "2025-01-23T06:21:50.093609Z", + "shell.execute_reply.started": "2025-01-23T06:21:50.093593Z" + }, + "trusted": true + }, + "outputs": [], + "source": [ + "pickle.dump(tracking_information, open(\"tracking_information.pkl\", \"wb\"))\n", + "train_losses, eval_losses, bleu_scores, lrs = zip(*tracking_information)\n", + "epochs = range(1, len(train_losses) + 1)\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs, train_losses, label=\"Train Loss\")\n", + "plt.plot(epochs, eval_losses, label=\"Eval Loss\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.title(\"Training and Evaluation Loss\")\n", + "plt.legend()\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs, bleu_scores, label=\"BLEU Score\", color=\"green\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"BLEU Score\")\n", + "plt.title(\"BLEU Score Over Epochs\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig(\"training_progress.png\")\n", + "plt.show()\n", + "\n", + "print(\"Fine-tuning process completed!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "editable": false, + "execution": { + "iopub.status.busy": "2025-01-23T06:21:50.094669Z", + "iopub.status.idle": "2025-01-23T06:21:50.094979Z", + "shell.execute_reply": "2025-01-23T06:21:50.094848Z", + "shell.execute_reply.started": "2025-01-23T06:21:50.094832Z" + }, + "id": "gT3arNapp1iy", + "trusted": true + }, + "outputs": [], + "source": [ + "def compare_output_model(original_model_path, finetuned_model_path,image,question,device):\n", + " pre_trained_model = BlipForQuestionAnswering.from_pretrained(original_model_path).to(device)\n", + " fine_tuned_model = BlipForQuestionAnswering.from_pretrained(finetuned_model_path).to(device)\n", + " image = image.convert('RGB')\n", + " inputs = processor(image, question, return_tensors=\"pt\").to(device)\n", + " fine_tuned_output = fine_tuned_model.generate(**inputs)\n", + " pre_trained_output = pre_trained_model.generate(**inputs)\n", + " fine_tuned_answer = processor.tokenizer.decode(fine_tuned_output[0], skip_special_tokens=True)\n", + " pre_trained_answer = processor.tokenizer.decode(pre_trained_output[0], skip_special_tokens=True)\n", + " print(\"Question : \",question)\n", + " print('Model Answer : ',pre_trained_answer)\n", + " print('Finetuned Answer : ',fine_tuned_answer)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": false, + "execution": { + "iopub.status.busy": "2025-01-23T06:21:50.096074Z", + "iopub.status.idle": "2025-01-23T06:21:50.096378Z", + "shell.execute_reply": "2025-01-23T06:21:50.096246Z", + "shell.execute_reply.started": "2025-01-23T06:21:50.096230Z" + }, + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\transformers\\generation\\utils.py:1375: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question : what are the characteristics of the mass?\n", + "Model Answer : white\n", + "Finetuned Answer : triceps\n", + "Original Answer: isointense\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_index = 21\n", + "sample_image = ds[\"train\"][sample_index]['image']\n", + "sample_question = ds[\"train\"][sample_index]['question']\n", + "compare_output_model(\n", + " original_model_path=\"Salesforce/blip-vqa-base\",\n", + " finetuned_model_path=\"Model/14-last-blip-saved-model\",\n", + " image=sample_image,\n", + " question=sample_question,\n", + " device=device\n", + ")\n", + "print(\"Original Answer: \" , ds[\"train\"][sample_index]['answer'])\n", + "ds[\"train\"][sample_index]['image']" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kaggle": { + "accelerator": "tpu1vmV38", + "dataSources": [], + "dockerImageVersionId": 30805, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": 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1e-4 + gradient_accumulation_steps: 4 + patience : 10 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..80d041871492c20cf91824bf50321769d32a18c9 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,20 @@ +torch --index-url https://download.pytorch.org/whl/cu118 +torchvision --index-url https://download.pytorch.org/whl/cu118 +transformers +streamlit +pandas +numpy +dvc +nltk[all] +spacy +mlflow +scikit-learn +PyYAML +flask +flask-cors +langchain +langchain-groq +langchain-community +google-search-results +serpapi +xmltodict \ No newline at end of file diff --git a/results/14-last-blip-saved-model_evaluation.json b/results/14-last-blip-saved-model_evaluation.json new file mode 100644 index 0000000000000000000000000000000000000000..5b938407efca390bfac0ea09bbe1a32d6cb0a181 --- /dev/null +++ b/results/14-last-blip-saved-model_evaluation.json @@ -0,0 +1,5 @@ +{ + "model_path": "models/14-last-blip-saved-model", + "avg_bleu": 0.05889530477414862, + "corpus_bleu": 0.000788282690784758 +} \ No newline at end of file diff --git a/src/__pycache__/model.cpython-310.pyc b/src/__pycache__/model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cfee5cbe239efda74b7dea43dd00cb23f3c31038 Binary files /dev/null and b/src/__pycache__/model.cpython-310.pyc differ diff --git a/src/__pycache__/preprocess_data.cpython-310.pyc b/src/__pycache__/preprocess_data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..42722a3219a7d6d2e2ca8340013aa3682b8d313b Binary files /dev/null and b/src/__pycache__/preprocess_data.cpython-310.pyc differ diff --git a/src/model.py b/src/model.py new file mode 100644 index 0000000000000000000000000000000000000000..da8086cb1951110b9c04a4c9334d7bf890f16250 --- /dev/null +++ b/src/model.py @@ -0,0 +1,13 @@ +import torch +from transformers import BlipProcessor, BlipForQuestionAnswering +import yaml +# Initialize the BLIP model and processor +config = yaml.safe_load(open("./config.yaml", "r")) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model_id = config["finetune_model"]["orignal_model_id"] +def load_model_processor(model_path = model_id): + print("Loading Model and Processor................") + model = BlipForQuestionAnswering.from_pretrained(model_path).to(device) + processor = BlipProcessor.from_pretrained(model_id) + print(f"Model and Processor loaded successfully {model_path} !!!") + return model, processor diff --git a/src/preprocess_data.py b/src/preprocess_data.py new file mode 100644 index 0000000000000000000000000000000000000000..b5ea4e3817345f9391a9456db394386ea0ca38e5 --- /dev/null +++ b/src/preprocess_data.py @@ -0,0 +1,68 @@ +import os +import torch +import pickle +import yaml +from datasets import load_dataset +from model import load_model_processor + +config = yaml.safe_load(open("./config.yaml", "r"))["data_location"] +class VQADataset(torch.utils.data.Dataset): + def __init__(self, dataset, processor): + self.dataset = dataset + self.processor = processor + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, idx): + question = self.dataset[idx]['question'] + answer = self.dataset[idx]['answer'] + image = self.dataset[idx]['image'] + image = image.convert("RGB") + text = question + + encoding = self.processor(image, text, padding="max_length", truncation=True, return_tensors="pt") + labels = self.processor.tokenizer.encode( + answer, max_length=128, padding="max_length", truncation=True,pad_to_max_length=True, return_tensors='pt' + ) + encoding["labels"] = labels + for k, v in encoding.items(): + encoding[k] = v.squeeze() + return encoding + +if __name__ == "__main__": + _, processor = load_model_processor() + + + print("Loading VQA dataset................") + data = load_dataset(config["data"],cache_dir="../data/bronze") + train_data = data["train"] + test_data = data["test"] + + print("VQA dataset loaded successfully!!! lenght of train data is ", len(train_data), " and test data is ", len(test_data)) + save_dir = "./data/silver" + os.makedirs(save_dir, exist_ok=True) + print("Processesing data to save in ../data/silver") + train_dataset = VQADataset(dataset=train_data, + processor=processor) + test_dataset = VQADataset(dataset=test_data, + processor=processor) + + + print(f"Data processed successfully !!! ") + print(f"Saving to {os.path.join(save_dir,'train_dataset.pkl')} and {os.path.join(save_dir,'test_dataset.pkl')}") + with open(os.path.join(save_dir,"train_dataset.pkl"), "wb") as f: + pickle.dump(train_dataset, f) + with open(os.path.join(save_dir,"test_dataset.pkl"), "wb") as f: + pickle.dump(test_dataset, f) + print(f"Processed data , saved to {config['train_processed_data']} and {config['test_processed_data']}") + + + + + + + + + + \ No newline at end of file diff --git a/src/test/evaluate.py b/src/test/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0654d26744ce0ba92bace13c2f11358dbd8c46 --- /dev/null +++ b/src/test/evaluate.py @@ -0,0 +1,84 @@ +import os +import torch +import pickle +import yaml +import nltk +import json +from tqdm import tqdm +from torch.utils.data import DataLoader +from nltk.tokenize import word_tokenize +from nltk.translate.bleu_score import sentence_bleu, corpus_bleu, SmoothingFunction +from model import load_model_processor +from preprocess_data import VQADataset + +def evaluate_model(): + """Evaluates both the last saved model and the best model based on multiple evaluation metrics.""" + config = yaml.safe_load(open("./config.yaml", "r")) + model_config = config["finetune_model"] + data_config = config["data_location"] + result_dir = config["result"] + params = yaml.safe_load(open("./param.yaml", "r"))["params"] + + os.makedirs(result_dir, exist_ok=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + _, processor = load_model_processor() + + # Load test dataset + with open(data_config["test_processed_data"], "rb") as f: + test_dataset = pickle.load(f) + + test_dataloader = DataLoader(test_dataset, batch_size=params["batch_size"], shuffle=False, pin_memory=True) + + def evaluate(model_path): + model = load_model_processor(model_path=model_path)[0].to(device) + model.eval() + bleu_scores = [] + references_list = [] + predictions_list = [] + smooth_fn = SmoothingFunction().method1 + + with torch.no_grad(): + for batch in tqdm(test_dataloader, desc=f"Evaluating {model_path}"): + input_ids = batch.pop('input_ids').to(device) + pixel_values = batch.pop('pixel_values').to(device) + attention_mask = batch.pop('attention_mask').to(device) + labels = batch.pop('labels').to(device) + + generated_ids = model.generate(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, max_length=8) + predictions = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + references = processor.tokenizer.batch_decode(labels, skip_special_tokens=True) + + for pred, ref in zip(predictions, references): + bleu_scores.append(sentence_bleu([word_tokenize(ref)], word_tokenize(pred), smoothing_function=smooth_fn)) + references_list.append([word_tokenize(ref)]) + predictions_list.append(word_tokenize(pred)) + + avg_bleu = sum(bleu_scores) / len(bleu_scores) + corpus_bleu_score = corpus_bleu(references_list, predictions_list, smoothing_function=smooth_fn) + results = { + "model_path": model_path, + "avg_bleu": avg_bleu, + "corpus_bleu": corpus_bleu_score + } + + result_file = os.path.join(result_dir, f"{os.path.basename(model_path)}_evaluation.json") + with open(result_file, "w") as f: + json.dump(results, f, indent=4) + + print(f"Results saved to {result_file}") + return avg_bleu, corpus_bleu_score + + last_model_bleu, last_model_corpus_bleu = evaluate(model_config["last"]) + best_model_bleu, best_model_corpus_bleu = evaluate(model_config["best"]) + + print("Comparison:") + print(f"Last Model BLEU Score: {last_model_bleu:.4f}, Corpus BLEU Score: {last_model_corpus_bleu:.4f}") + print(f"Best Model BLEU Score: {best_model_bleu:.4f}, Corpus BLEU Score: {best_model_corpus_bleu:.4f}") + + if best_model_bleu >= last_model_bleu: + print("Best model performs better or equal.") + else: + print("Warning: The last model outperforms the best model!") + +if __name__ == "__main__": + evaluate_model() \ No newline at end of file diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000000000000000000000000000000000000..f68f8923e86253197bcdf018d93de96d266c6a37 --- /dev/null +++ b/src/train.py @@ -0,0 +1,128 @@ +import os +import yaml +import nltk +import pickle +import torch +import mlflow +import mlflow.pytorch +from tqdm import tqdm +from preprocess_data import VQADataset +from nltk.tokenize import word_tokenize +from torch.utils.data import DataLoader +from model import load_model_processor +from transformers import get_linear_schedule_with_warmup +from nltk.translate.bleu_score import sentence_bleu , SmoothingFunction +from mlflow.models import infer_signature + +nltk.download('all') + +config = yaml.safe_load(open("./config.yaml", "r"))["data_location"] +model_config = yaml.safe_load(open("./config.yaml", "r"))["finetune_model"] +params = yaml.safe_load(open("./param.yaml", "r"))["params"] +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model, processor = load_model_processor() + +print(f"Loading processed data from {config['train_processed_data']} and {config['test_processed_data']}.") +with open(f'./{config["train_processed_data"]}', "rb") as f: + train_dataset = pickle.load(f) +with open(f'./{config["test_processed_data"]}', "rb") as f: + test_dataset = pickle.load(f) +print(f"Loaded processed data successfully!!!") +print(f"Length of train dataset is {len(train_dataset)} and test dataset is {len(test_dataset)}") + +batch_size = params["batch_size"] +num_epochs = params["num_epochs"] +patience = params["patience"] +gradient_accumulation_steps = params["gradient_accumulation_steps"] +print("Preparing dataloaders................") +train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True) +valid_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, pin_memory=True) +print("Dataloaders prepared successfully!!!") + + +mlflow.set_experiment("VQA_Model_Training") + + +min_bleu_score = 0 +early_stopping_hook = 0 +tracking_information = [] +optimizer = torch.optim.AdamW(model.parameters(), lr=float(params["learning_rate"]), weight_decay=float(params["weight_decay"])) +total_steps = len(train_dataloader) * num_epochs +warmup_steps = total_steps // 10 +scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps) +scaler = torch.amp.GradScaler('cuda') + +with mlflow.start_run(): + for epoch in range(num_epochs): + model.train() + epoch_loss = 0 + for step, batch in enumerate(tqdm(train_dataloader, desc=f"Epoch {epoch+1} Training")): + input_ids = batch.pop('input_ids').to(device) + pixel_values = batch.pop('pixel_values').to(device) + attention_mask = batch.pop('attention_mask').to(device) + labels = batch.pop('labels').to(device) + + with torch.amp.autocast('cuda', dtype=torch.float16): + outputs = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, labels=labels) + loss = outputs.loss / gradient_accumulation_steps + + scaler.scale(loss).backward() + if (step + 1) % gradient_accumulation_steps == 0 or (step + 1) == len(train_dataloader): + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + epoch_loss += loss.item() * gradient_accumulation_steps + + # Validation Loop + model.eval() + eval_loss = 0 + bleu_scores = [] + smooth_fn = SmoothingFunction().method1 + with torch.no_grad(): + for batch in tqdm(valid_dataloader, desc=f"Epoch {epoch+1} Validating"): + input_ids = batch.pop('input_ids').to(device) + pixel_values = batch.pop('pixel_values').to(device) + attention_mask = batch.pop('attention_mask').to(device) + labels = batch.pop('labels').to(device) + + with torch.amp.autocast('cuda', dtype=torch.float16): + outputs = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, labels=labels) + eval_loss += outputs.loss.item() + + generated_ids = model.generate(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, max_length=8) + predictions = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + references = processor.tokenizer.batch_decode(labels, skip_special_tokens=True) + + for pred, ref in zip(predictions, references): + bleu_scores.append(sentence_bleu([word_tokenize(ref)], word_tokenize(pred), smoothing_function=smooth_fn)) + + avg_train_loss = epoch_loss / len(train_dataloader) + avg_eval_loss = eval_loss / len(valid_dataloader) + avg_bleu_score = sum(bleu_scores) / len(bleu_scores) + tracking_information.append((avg_train_loss, avg_eval_loss, avg_bleu_score, optimizer.param_groups[0]["lr"])) + + mlflow.log_metrics({"train_loss": avg_train_loss, "eval_loss": avg_eval_loss, "bleu_score": avg_bleu_score}, step=epoch) + mlflow.log_param(f"learning_rate_{epoch}", optimizer.param_groups[0]['lr']) + + print(f"Epoch {epoch+1} - Train Loss: {avg_train_loss:.4f} - Eval Loss: {avg_eval_loss:.4f} - BLEU Score: {avg_bleu_score:.4f} - LR: {optimizer.param_groups[0]['lr']}") + + if avg_bleu_score >= min_bleu_score: + model.save_pretrained(model_config["best"], from_pt=True) + print(f"Model improved (BLEU: {avg_bleu_score:.4f})! Saved to {model_config['best']}") + min_bleu_score = avg_bleu_score + early_stopping_hook = 0 + else: + early_stopping_hook += 1 + if early_stopping_hook > patience: + print("Early stopping triggered.") + break + model.save_pretrained(model_config["last"], from_pt=True) + scheduler.step() + + + signature = infer_signature(input_ids.cpu().numpy(), model(input_ids).logits.cpu().detach().numpy()) + mlflow.pytorch.log_model(model, "VQA_model", signature=signature) + print("Model logged with MLflow.")