Fine_tuning_MedGemma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "445e7bc9-e783-48e6-9f09-3aa295c1d216",
   "metadata": {},
   "source": [
    "## Installing Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1124aadd-600c-4ff3-88b8-db724d3a8071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install --upgrade --quiet transformers bitsandbytes datasets evaluate peft trl scikit-learn kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3b4bee-6824-4c16-ac55-30285152a199",
   "metadata": {},
   "source": [
    "## Loading and Processing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "621190d1-2d43-410d-9bd4-b586918bab81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/orvile/brain-cancer-mri-dataset\n",
      "License(s): CC-BY-SA-4.0\n",
      "Downloading brain-cancer-mri-dataset.zip to /workspace\n",
      " 90%|████████████████████████████████████▉    | 130M/144M [00:00<00:00, 231MB/s]\n",
      "100%|█████████████████████████████████████████| 144M/144M [00:01<00:00, 108MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d orvile/brain-cancer-mri-dataset --unzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66e9b3ee-7220-4a6c-b573-895cdb9438c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc0b52fa43b947aaa860b5819ac93642",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/6056 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0688ef8336b64871b09aea10a9e2c5b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0/6056 [00:00<?, ?files/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f310ad8abe204688b9e1ad7fcaaf326e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['image', 'label'],\n",
      "        num_rows: 4844\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['image', 'label'],\n",
      "        num_rows: 1212\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "data_dir = \"./Brain_Cancer raw MRI data/Brain_Cancer\"\n",
    "\n",
    "# Define proportions for train and validation splits\n",
    "train_size = 0.8  \n",
    "validation_size = 0.2  \n",
    "\n",
    "\n",
    "data = load_dataset(\"imagefolder\", data_dir=data_dir, split=\"train\")\n",
    "\n",
    "# Split the dataset into train and validation sets\n",
    "data = data.train_test_split(\n",
    "    train_size=train_size,\n",
    "    test_size=validation_size,\n",
    "    shuffle=True,\n",
    "    seed=42,\n",
    ")\n",
    "\n",
    "# Rename the 'test' split to 'validation'\n",
    "data[\"validation\"] = data.pop(\"test\")\n",
    "\n",
    "# Display dataset details\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74a77bda-3152-4d97-b66b-fe2cddd85a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first image (as a PIL object)\n",
    "data[\"train\"][0][\"image\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fbd1185-7384-4211-8688-91d51a839f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Display the corresponding label\n",
    "print(data[\"train\"][0][\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85470739-3559-406b-af67-07a6d2ccbc27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected classes: ['brain_glioma', 'brain_menin', 'brain_tumor']\n"
     ]
    }
   ],
   "source": [
    "BRAIN_CANCER_CLASSES = data[\"train\"].features[\"label\"].names\n",
    "print(\"Detected classes:\", BRAIN_CANCER_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8048ede-ccc5-4b88-b8bf-e3e5d4c2ee1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "BRAIN_CANCER_CLASSES = ['A: brain glioma', 'B: brain menin', 'C: brain tumor']\n",
    "\n",
    "options = \"\\n\".join(BRAIN_CANCER_CLASSES)\n",
    "PROMPT = f\"What is the most likely type of brain cancer shown in the MRI image?\\n{options}\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20e7b335-2b7c-40d9-96e8-c7c7d568cd3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb322285f490480ca5a9343c48996eeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/4844 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "665dcf639c8e468499e5954a23c81f27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1212 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'content': [{'text': None, 'type': 'image'},\n",
       "   {'text': 'What is the most likely type of brain cancer shown in the MRI image?\\nA: brain glioma\\nB: brain menin\\nC: brain tumor',\n",
       "    'type': 'text'}],\n",
       "  'role': 'user'},\n",
       " {'content': [{'text': 'B: brain menin', 'type': 'text'}],\n",
       "  'role': 'assistant'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_data(example: dict[str, any]) -> dict[str, any]:\n",
    "    example[\"messages\"] = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"image\",\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": PROMPT,\n",
    "                },\n",
    "            ],\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"assistant\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": BRAIN_CANCER_CLASSES[example[\"label\"]],\n",
    "                },\n",
    "            ],\n",
    "        },\n",
    "    ]\n",
    "    return example\n",
    "\n",
    "# Apply the formatting to the dataset\n",
    "formatted_data = data.map(format_data)\n",
    "\n",
    "# Display a sample formatted data point\n",
    "formatted_data[\"train\"][0][\"messages\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892b4731-e9f5-4ba2-bd23-ff6cb3ac7c50",
   "metadata": {},
   "source": [
    "## Loading the Model and Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e814414a-d800-452b-b664-9dcd7182eb21",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "import os\n",
    "\n",
    "hf_token = os.environ.get(\"HF_TOKEN\")\n",
    "login(hf_token)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3b40bcf-6e10-458c-9712-b8425457dcf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e0e87b0141344b099a77009b004f932",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig\n",
    "\n",
    "model_id = \"google/medgemma-4b-it\"\n",
    "\n",
    "# Check if GPU supports bfloat16\n",
    "if torch.cuda.get_device_capability()[0] < 8:\n",
    "    raise ValueError(\"GPU does not support bfloat16, please use a GPU that supports bfloat16.\")\n",
    "\n",
    "model_kwargs = dict(\n",
    "    attn_implementation=\"eager\",\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "\n",
    "model = AutoModelForImageTextToText.from_pretrained(model_id, **model_kwargs)\n",
    "processor = AutoProcessor.from_pretrained(model_id)\n",
    "\n",
    "# Use right padding to avoid issues during training\n",
    "processor.tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39285917-2c44-46bf-a052-145446d0967a",
   "metadata": {},
   "source": [
    "## Setting up the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30020ce0-cde2-4f1f-9a0d-659f6953ce25",
   "metadata": {},
   "outputs": [],
   "source": [
    "from peft import LoraConfig\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.05,\n",
    "    r=16,\n",
    "    bias=\"none\",\n",
    "    target_modules=\"all-linear\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    "    modules_to_save=[\n",
    "        \"lm_head\",\n",
    "        \"embed_tokens\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d03e219b-6228-4516-9e1b-0593a6cfefb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(examples: list[dict[str, any]]):\n",
    "    texts = []\n",
    "    images = []\n",
    "    for example in examples:\n",
    "        images.append([example[\"image\"]])\n",
    "        texts.append(\n",
    "            processor.apply_chat_template(\n",
    "                example[\"messages\"], add_generation_prompt=False, tokenize=False\n",
    "            ).strip()\n",
    "        )\n",
    "\n",
    "    # Tokenize the texts and process the images\n",
    "    batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n",
    "\n",
    "    # The labels are the input_ids, with the padding and image tokens masked in\n",
    "    # the loss computation\n",
    "    labels = batch[\"input_ids\"].clone()\n",
    "\n",
    "    # Mask image tokens\n",
    "    image_token_id = [\n",
    "        processor.tokenizer.convert_tokens_to_ids(\n",
    "            processor.tokenizer.special_tokens_map[\"boi_token\"]\n",
    "        )\n",
    "    ]\n",
    "    # Mask tokens that are not used in the loss computation\n",
    "    labels[labels == processor.tokenizer.pad_token_id] = -100\n",
    "    labels[labels == image_token_id] = -100\n",
    "    labels[labels == 262144] = -100\n",
    "\n",
    "    batch[\"labels\"] = labels\n",
    "    return batch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d42066d-7d0f-45ba-b410-574ca30f3bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trl import SFTConfig\n",
    "\n",
    "args = SFTConfig(\n",
    "    output_dir=\"medgemma-brain-cancer\",            \n",
    "    num_train_epochs=1,                       \n",
    "    per_device_train_batch_size=8,                           \n",
    "    per_device_eval_batch_size=8,                            \n",
    "    gradient_accumulation_steps=8,                           \n",
    "    gradient_checkpointing=True,                             \n",
    "    optim=\"adamw_torch_fused\",                               \n",
    "    logging_steps=0.1,                                        \n",
    "    save_strategy=\"epoch\",                                   \n",
    "    eval_strategy=\"steps\",                                   \n",
    "    eval_steps=0.1,                                           \n",
    "    learning_rate=2e-4,                             \n",
    "    bf16=True,                                               \n",
    "    max_grad_norm=0.3,                                       \n",
    "    warmup_ratio=0.03,                                       \n",
    "    lr_scheduler_type=\"linear\",                              \n",
    "    push_to_hub=True,                                        \n",
    "    report_to=\"none\",\n",
    "    gradient_checkpointing_kwargs={\"use_reentrant\": False},  \n",
    "    dataset_kwargs={\"skip_prepare_dataset\": True},           \n",
    "    remove_unused_columns = False,                           \n",
    "    label_names=[\"labels\"],                                  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70e4e881-467e-4804-9ef1-3706ed85b7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trl import SFTTrainer\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    args=args,\n",
    "    train_dataset=formatted_data[\"train\"],\n",
    "    eval_dataset=formatted_data[\"validation\"].shuffle().select(range(50)), \n",
    "    peft_config=peft_config,\n",
    "    processing_class=processor,\n",
    "    data_collator=collate_fn,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec612ba-9006-484c-bfeb-77d2d03eade5",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c6f3de5-df14-432b-890b-a0467087ef5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='76' max='76' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [76/76 1:07:40, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>31.499300</td>\n",
       "      <td>1.393780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.916200</td>\n",
       "      <td>0.373517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.435700</td>\n",
       "      <td>0.088616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.532700</td>\n",
       "      <td>0.060583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.417600</td>\n",
       "      <td>0.046456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.355700</td>\n",
       "      <td>0.039753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.316100</td>\n",
       "      <td>0.043567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.322100</td>\n",
       "      <td>0.037666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.287500</td>\n",
       "      <td>0.035154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=76, training_loss=4.338119332727633, metrics={'train_runtime': 4112.8912, 'train_samples_per_second': 1.178, 'train_steps_per_second': 0.018, 'total_flos': 3.853875156358656e+16, 'train_loss': 4.338119332727633})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80e25489-48cd-4152-b762-2c58572f9489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6237933d3e3449f291a6dfab32e4dc4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading...:   0%|          | 0.00/2.87G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.save_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e4751d-1958-4ae8-948b-c50c429e79ce",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b9c57d34-fdbe-4b3e-90ea-5042bb612d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model\n",
    "del trainer\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f3360e-c844-4624-b985-29191b063082",
   "metadata": {},
   "source": [
    "### Setting up for model testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb1d216b-1dcc-4ab4-9825-18fe5b2ecfc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_test_data(example: dict[str, any]) -> dict[str, any]:\n",
    "    example[\"messages\"] = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\n",
    "                    \"type\": \"image\",\n",
    "                },\n",
    "                {\n",
    "                    \"type\": \"text\",\n",
    "                    \"text\": PROMPT,\n",
    "                },\n",
    "            ],\n",
    "        },\n",
    "    ]\n",
    "    return example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "806d411a-32aa-41c0-bbbd-a25a6a67c844",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = data[\"validation\"]\n",
    "test_data = test_data.map(format_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "badca756-d6f7-4bc0-9009-5fd2bb483666",
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "accuracy_metric = evaluate.load(\"accuracy\")\n",
    "f1_metric = evaluate.load(\"f1\")\n",
    "\n",
    "# Ground-truth labels\n",
    "REFERENCES = test_data[\"label\"]\n",
    "\n",
    "\n",
    "def compute_metrics(predictions: list[int]) -> dict[str, float]:\n",
    "    metrics = {}\n",
    "    metrics.update(\n",
    "        accuracy_metric.compute(\n",
    "            predictions=predictions,\n",
    "            references=REFERENCES,\n",
    "        )\n",
    "    )\n",
    "    metrics.update(\n",
    "        f1_metric.compute(\n",
    "            predictions=predictions,\n",
    "            references=REFERENCES,\n",
    "            average=\"weighted\",\n",
    "        )\n",
    "    )\n",
    "    return metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "70b28c91-c377-4667-b045-27fd2ecbad6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import ClassLabel\n",
    "\n",
    "test_data = test_data.cast_column(\n",
    "    \"label\",\n",
    "    ClassLabel(names=BRAIN_CANCER_CLASSES)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "01ad709b-5b7d-4355-bc2e-c03fa87c76bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "LABEL_FEATURE = test_data.features[\"label\"]\n",
    "\n",
    "ALT_LABELS = dict([\n",
    "    (label, f\"({label.replace(': ', ') ')}\") for label in BRAIN_CANCER_CLASSES\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "849c8293-14b2-4b26-a3ea-5749a3d844e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def postprocess(prediction, do_full_match: bool = False) -> int:\n",
    "    if isinstance(prediction, str):\n",
    "        response_text = prediction\n",
    "    else:\n",
    "        response_text = prediction[0][\"generated_text\"]\n",
    "\n",
    "    if do_full_match:\n",
    "        return LABEL_FEATURE.str2int(response_text)\n",
    "\n",
    "    for label in BRAIN_CANCER_CLASSES:\n",
    "        # accept canonical or alternative wording\n",
    "        if label in response_text or ALT_LABELS[label] in response_text:\n",
    "            return LABEL_FEATURE.str2int(label)\n",
    "\n",
    "    return -1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1a23f4-beff-4303-8630-6320512c46c8",
   "metadata": {},
   "source": [
    "### Model performance on the base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ff8164fc-a5d8-4a78-9d68-93542a4c3f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "372a690e2ad24ed2926befbcb7c28573",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForImageTextToText, AutoProcessor\n",
    "\n",
    "model_kwargs = dict(\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "\n",
    "model = AutoModelForImageTextToText.from_pretrained(\n",
    "    model_id, **model_kwargs\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0c5a762a-1726-40ed-bed6-4dfc0d88a785",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import GenerationConfig\n",
    "gen_cfg = GenerationConfig.from_pretrained(model_id)\n",
    "gen_cfg.update(\n",
    "    do_sample          = False, \n",
    "    top_k              = None, \n",
    "    top_p              = None,\n",
    "    cache_implementation = \"dynamic\" \n",
    ")\n",
    "model.generation_config = gen_cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "136391e5-20e1-4f6e-84de-58621dfb6b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "processor  = AutoProcessor.from_pretrained(args.output_dir)\n",
    "tok = processor.tokenizer\n",
    "\n",
    "model.config.pad_token_id            = tok.pad_token_id\n",
    "model.generation_config.pad_token_id = tok.pad_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d5fad38-4d36-4d88-9182-904d6ff2d6f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat_to_prompt(chat_turns):\n",
    "    return processor.apply_chat_template(\n",
    "        chat_turns,\n",
    "        add_generation_prompt=True,   # tells the model \"your turn\"\n",
    "        tokenize=False                # we want raw text, not ids\n",
    "    )\n",
    "\n",
    "prompts = [chat_to_prompt(c) for c in test_data[\"messages\"]]\n",
    "images  = test_data[\"image\"]                         # already a list of PIL images\n",
    "assert len(prompts) == len(images), \"1 prompt must match 1 image!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "94d5cc7a-7ba9-418f-ac4b-8e4c1c4c11cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from typing import List, Any, Callable\n",
    "\n",
    "\n",
    "def batch_predict(\n",
    "    prompts,\n",
    "    images,\n",
    "    model,\n",
    "    processor,\n",
    "    postprocess,\n",
    "    *,\n",
    "    batch_size=64,\n",
    "    device=\"cuda\",\n",
    "    dtype=torch.bfloat16,\n",
    "    **gen_kwargs\n",
    "):\n",
    "    preds = []\n",
    "    for i in range(0, len(prompts), batch_size):\n",
    "        texts = prompts[i : i + batch_size]\n",
    "        imgs = [[img] for img in images[i : i + batch_size]]\n",
    "        enc = processor(text=texts, images=imgs, padding=True, return_tensors=\"pt\").to(\n",
    "            device, dtype=dtype\n",
    "        )\n",
    "        lens = enc[\"attention_mask\"].sum(dim=1)\n",
    "        with torch.inference_mode():\n",
    "            out = model.generate(\n",
    "                **enc,\n",
    "                disable_compile=True,\n",
    "                **gen_kwargs\n",
    "            )\n",
    "        for seq, ln in zip(out, lens):\n",
    "            ans = processor.decode(seq[ln:], skip_special_tokens=True)\n",
    "            preds.append(postprocess(ans))\n",
    "    return preds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5c4f07cf-ba46-473b-afc1-80e61de55bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 1212 samples\n"
     ]
    }
   ],
   "source": [
    "bf_preds = batch_predict(\n",
    "    model         = model,\n",
    "    processor     = processor,\n",
    "    prompts       = prompts,\n",
    "    images        = images,\n",
    "    batch_size    = 64,\n",
    "    max_new_tokens= 40,          # forwarded to generate\n",
    "    postprocess= postprocess, # your label-mapping function\n",
    ")\n",
    "\n",
    "print(\"Completed\", len(bf_preds), \"samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7b67d17e-b38f-4315-9070-2efd6dbcbf49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline metrics: {'accuracy': 0.33745874587458746, 'f1': 0.1737287617650654}\n"
     ]
    }
   ],
   "source": [
    "bf_metrics = compute_metrics(bf_preds)\n",
    "print(f\"Baseline metrics: {bf_metrics}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b762150-0b52-494f-b741-4bfc09bc4eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from typing import Union, Dict, Any, List\n",
    "from transformers import AutoModelForImageTextToText, AutoProcessor\n",
    "\n",
    "\n",
    "def predict_one(\n",
    "    prompt,\n",
    "    image,\n",
    "    model,\n",
    "    processor,\n",
    "    *,\n",
    "    device=\"cuda\",\n",
    "    dtype=torch.bfloat16,\n",
    "    disable_compile=True,\n",
    "    **gen_kwargs\n",
    ") -> str:\n",
    "    inputs = processor(text=prompt, images=image, return_tensors=\"pt\").to(\n",
    "        device, dtype=dtype\n",
    "    )\n",
    "    plen = inputs[\"input_ids\"].shape[-1]\n",
    "    with torch.inference_mode():\n",
    "        ids = model.generate(\n",
    "            **inputs,\n",
    "            disable_compile=disable_compile,\n",
    "            **gen_kwargs\n",
    "        )\n",
    "    return processor.decode(ids[0, plen:], skip_special_tokens=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "50064a57-57b4-493e-9c33-6fb1a0bb99d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model answer: Based on the MRI image, the most likely type of brain cancer is **A: brain glioma**.\n",
      "\n",
      "Here's why:\n",
      "\n",
      "*   **Gliomas** are a common type of brain tumor\n"
     ]
    }
   ],
   "source": [
    "idx     = 10\n",
    "chat    = test_data[\"messages\"][idx]\n",
    "prompt  = processor.apply_chat_template(\n",
    "            chat,\n",
    "            add_generation_prompt=True,\n",
    "            tokenize=False\n",
    "          )\n",
    "\n",
    "# run the one-sample helper\n",
    "answer = predict_one(\n",
    "    prompt   = prompt,\n",
    "    image    = test_data[\"image\"][idx],\n",
    "    model    = model,\n",
    "    processor= processor,\n",
    "    max_new_tokens = 40 \n",
    ")\n",
    "\n",
    "print(\"Model answer:\", answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802654b8-00e6-450d-a030-4fc5eca0fab2",
   "metadata": {},
   "source": [
    "### Model performance on the fine-tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8f8961c1-8db0-4e28-a7ce-92c2ca524141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "328c05c9ec8746759dc8ac0c98901d66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = AutoModelForImageTextToText.from_pretrained(\n",
    "    args.output_dir, **model_kwargs\n",
    ")\n",
    "model.generation_config = gen_cfg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8cb2ee3b-34e8-4c06-9bf2-bad6aca9a78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "processor  = AutoProcessor.from_pretrained(args.output_dir)\n",
    "tok = processor.tokenizer\n",
    "\n",
    "model.config.pad_token_id            = tok.pad_token_id\n",
    "model.generation_config.pad_token_id = tok.pad_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ae9c0a3e-4e23-4f8a-98f4-6494e7259436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed 1212 samples\n"
     ]
    }
   ],
   "source": [
    "af_preds = batch_predict(\n",
    "    model         = model,\n",
    "    processor     = processor,\n",
    "    prompts       = prompts,\n",
    "    images        = images,\n",
    "    batch_size    = 64,\n",
    "    max_new_tokens= 40,          # forwarded to generate\n",
    "    postprocess= postprocess, # your label-mapping function\n",
    ")\n",
    "\n",
    "print(\"Completed\", len(af_preds), \"samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0c0ae2b6-feb4-4f26-b45c-38f908bec232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fine-tuned metrics: {'accuracy': 0.8927392739273927, 'f1': 0.892641793935792}\n"
     ]
    }
   ],
   "source": [
    "af_metrics = compute_metrics(af_preds)\n",
    "print(f\"Fine-tuned metrics: {af_metrics}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70bc842e-41f7-49ee-84e4-c54417eb76aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model answer: C: brain tumor\n"
     ]
    }
   ],
   "source": [
    "idx     = 10\n",
    "chat    = test_data[\"messages\"][idx]\n",
    "prompt  = processor.apply_chat_template(\n",
    "            chat,\n",
    "            add_generation_prompt=True,\n",
    "            tokenize=False\n",
    "          )\n",
    "\n",
    "# run the one-sample helper\n",
    "answer = predict_one(\n",
    "    prompt   = prompt,\n",
    "    image    = test_data[\"image\"][idx],\n",
    "    model    = model,\n",
    "    processor= processor,\n",
    "    max_new_tokens = 40              # any generate-kwargs you need\n",
    ")\n",
    "\n",
    "print(\"Model answer:\", answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c1d4b328-c1bb-415a-b879-d95dfb062940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512>,\n",
       " 'label': 2}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"validation\"][10]"
   ]
  }
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