Copy_for_local_computer_vision_pipeline (5).ipynb

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     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "%pip install kaggle transformers[torch] datasets[vision]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d8a7507",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import cv2\n",
    "import torch\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import tqdm, trange\n",
    "from datasets import load_dataset, load_metric, DatasetDict\n",
    "from PIL import ImageDraw, ImageFont, Image\n",
    "from transformers import (\n",
    "    AutoImageProcessor,\n",
    "    AutoFeatureExtractor,\n",
    "    ViTImageProcessor,\n",
    "    ViTForImageClassification,\n",
    "    ResNetForImageClassification,\n",
    "    TrainingArguments,\n",
    "    AutoConfig,\n",
    "    ViTConfig,\n",
    "    TrainerCallback\n",
    ")\n",
    "\n",
    "print(torch.cuda.is_available())"
   ]
  },
  {
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    },
    "tags": []
   },
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1b197cf",
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   "source": [
    "def show_examples(ds, seed: int = 1234, examples_per_class: int = 3, size=(350, 350)):\n",
    "  w, h = size\n",
    "  labels = ds['train'].features['labels'].names\n",
    "  grid = Image.new('RGB', size=(examples_per_class * w, len(labels) * h))\n",
    "  draw = ImageDraw.Draw(grid)\n",
    "  font = ImageFont.truetype(\"/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf\", 24)\n",
    "\n",
    "  for label_id, label in enumerate(labels):\n",
    "    ds_slice = ds['train'].filter(lambda ex: ex['labels'] == label_id).shuffle(seed).select(range(examples_per_class))\n",
    "\n",
    "    for i, example in enumerate(ds_slice):\n",
    "      image = example['image']\n",
    "      idx = examples_per_class * label_id + i\n",
    "      box = (idx % examples_per_class * w, idx // examples_per_class * h)\n",
    "      grid.paste(image.resize(size), box=box)\n",
    "      draw.text(box, label, (255, 255, 255), font=font)\n",
    "\n",
    "  return grid"
   ]
  },
  {
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   "id": "42552a25",
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    "id": "42552a25",
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     "start_time": "2024-06-03T11:47:47.186246",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Tomato leaf dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "399d9cd0",
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   "outputs": [],
   "source": [
    "tomato_dataset = load_dataset(\"wellCh4n/tomato-leaf-disease-image\")\n",
    "\n",
    "tomato_dataset = tomato_dataset.rename_column(\"text\", \"labels\")"
   ]
  },
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   "outputs": [
    {
     "data": {
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       "model_id": "958286e295984813a24078f6c9796c08",
       "version_major": 2,
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       "  0%|          | 0/10 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Spider Mites', 'Healthy', 'Septoria Leaf Spot', 'Mosiac Virus', 'Leaf Mold', 'Yellow Leaf Curl Virus', 'Target Spot', 'Bacterial Spot', 'Early Blight', 'Late Blight']\n"
     ]
    },
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      "text/plain": [
       "Map:   0%|          | 0/18160 [00:00<?, ? examples/s]"
      ]
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     "output_type": "display_data"
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       "model_id": "7564e34896ae445998be950f058bcaaf",
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      },
      "text/plain": [
       "Casting the dataset:   0%|          | 0/18160 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "from datasets import ClassLabel\n",
    "\n",
    "texts = tomato_dataset['train']['labels']\n",
    "\n",
    "name_map = {'A tomato leaf with Septoria Leaf Spot': \"Septoria Leaf Spot\",\n",
    "            'A tomato leaf with Target Spot': \"Target Spot\",\n",
    "            'A tomato leaf with Bacterial Spot': \"Bacterial Spot\",\n",
    "            'A tomato leaf with Tomato Yellow Leaf Curl Virus': \"Yellow Leaf Curl Virus\",\n",
    "            'A healthy tomato leaf': \"Healthy\",\n",
    "            'A tomato leaf with Tomato Mosaic Virus': \"Mosiac Virus\",\n",
    "            'A tomato leaf with Leaf Mold': \"Leaf Mold\",\n",
    "            'A tomato leaf with Late Blight': \"Late Blight\",\n",
    "            'A tomato leaf with Early Blight': \"Early Blight\",\n",
    "            'A tomato leaf with Spider Mites Two-spotted Spider Mite': \"Spider Mites\"}\n",
    "\n",
    "labels = list(set(texts))\n",
    "\n",
    "for idx in trange(len(labels)):\n",
    "    labels[idx] = name_map[labels[idx]]\n",
    "\n",
    "print(labels)\n",
    "class_labels = ClassLabel(names=labels)\n",
    "\n",
    "id2label = {idx: label for idx, label in enumerate(labels)}\n",
    "label2id = {label: idx for idx, label in enumerate(labels)}\n",
    "\n",
    "def encode_labels(example):\n",
    "    simplified_name = name_map[example['labels']]\n",
    "    example['labels'] = label2id[simplified_name]\n",
    "\n",
    "    return example\n",
    "\n",
    "dataset = tomato_dataset.map(encode_labels)\n",
    "dataset = dataset.cast_column(\"labels\", class_labels)"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'labels'],\n",
       "        num_rows: 14528\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'labels'],\n",
       "        num_rows: 1816\n",
       "    })\n",
       "    valid: Dataset({\n",
       "        features: ['image', 'labels'],\n",
       "        num_rows: 1816\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test_sets = dataset['train'].train_test_split(test_size=0.2)\n",
    "test_valid_sets = train_test_sets['test'].train_test_split(test_size=0.5)\n",
    "\n",
    "dataset = DatasetDict({\n",
    "    'train': train_test_sets['train'],\n",
    "    'test': test_valid_sets['test'],\n",
    "    'valid': test_valid_sets['train'],\n",
    "})\n",
    "\n",
    "dataset"
   ]
  },
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   "execution_count": 6,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "cannot open resource",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m show_examples(dataset, seed\u001b[38;5;241m=\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1337\u001b[39m), examples_per_class\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m224\u001b[39m, \u001b[38;5;241m224\u001b[39m))\n",
      "Cell \u001b[1;32mIn[2], line 6\u001b[0m, in \u001b[0;36mshow_examples\u001b[1;34m(ds, seed, examples_per_class, size)\u001b[0m\n\u001b[0;32m      4\u001b[0m grid \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mnew(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRGB\u001b[39m\u001b[38;5;124m'\u001b[39m, size\u001b[38;5;241m=\u001b[39m(examples_per_class \u001b[38;5;241m*\u001b[39m w, \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;241m*\u001b[39m h))\n\u001b[0;32m      5\u001b[0m draw \u001b[38;5;241m=\u001b[39m ImageDraw\u001b[38;5;241m.\u001b[39mDraw(grid)\n\u001b[1;32m----> 6\u001b[0m font \u001b[38;5;241m=\u001b[39m ImageFont\u001b[38;5;241m.\u001b[39mtruetype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m24\u001b[39m)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label_id, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels):\n\u001b[0;32m      9\u001b[0m   ds_slice \u001b[38;5;241m=\u001b[39m ds[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mfilter(\u001b[38;5;28;01mlambda\u001b[39;00m ex: ex[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m label_id)\u001b[38;5;241m.\u001b[39mshuffle(seed)\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;28mrange\u001b[39m(examples_per_class))\n",
      "File \u001b[1;32m~\\.conda\\envs\\pytorch_gpu\\Lib\\site-packages\\PIL\\ImageFont.py:807\u001b[0m, in \u001b[0;36mtruetype\u001b[1;34m(font, size, index, encoding, layout_engine)\u001b[0m\n\u001b[0;32m    804\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m FreeTypeFont(font, size, index, encoding, layout_engine)\n\u001b[0;32m    806\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 807\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m freetype(font)\n\u001b[0;32m    808\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    809\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_path(font):\n",
      "File \u001b[1;32m~\\.conda\\envs\\pytorch_gpu\\Lib\\site-packages\\PIL\\ImageFont.py:804\u001b[0m, in \u001b[0;36mtruetype.<locals>.freetype\u001b[1;34m(font)\u001b[0m\n\u001b[0;32m    803\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfreetype\u001b[39m(font):\n\u001b[1;32m--> 804\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m FreeTypeFont(font, size, index, encoding, layout_engine)\n",
      "File \u001b[1;32m~\\.conda\\envs\\pytorch_gpu\\Lib\\site-packages\\PIL\\ImageFont.py:244\u001b[0m, in \u001b[0;36mFreeTypeFont.__init__\u001b[1;34m(self, font, size, index, encoding, layout_engine)\u001b[0m\n\u001b[0;32m    242\u001b[0m                 load_from_bytes(f)\n\u001b[0;32m    243\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 244\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfont \u001b[38;5;241m=\u001b[39m core\u001b[38;5;241m.\u001b[39mgetfont(\n\u001b[0;32m    245\u001b[0m         font, size, index, encoding, layout_engine\u001b[38;5;241m=\u001b[39mlayout_engine\n\u001b[0;32m    246\u001b[0m     )\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    248\u001b[0m     load_from_bytes(font)\n",
      "\u001b[1;31mOSError\u001b[0m: cannot open resource"
     ]
    }
   ],
   "source": [
    "show_examples(dataset, seed=random.randint(0, 1337), examples_per_class=1, size=(224, 224))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c119a894",
   "metadata": {
    "id": "c119a894",
    "papermill": {
     "duration": 0.064877,
     "end_time": "2024-06-03T11:49:37.159430",
     "exception": false,
     "start_time": "2024-06-03T11:49:37.094553",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8409296e",
   "metadata": {
    "id": "8409296e",
    "papermill": {
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     "end_time": "2024-06-03T11:49:37.287418",
     "exception": false,
     "start_time": "2024-06-03T11:49:37.223901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ResNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48ba0f73",
   "metadata": {
    "id": "48ba0f73",
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     "exception": false,
     "start_time": "2024-06-03T11:49:37.399757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "resnet_path = \"microsoft/resnet-50\"\n",
    "resnet_processor = AutoImageProcessor.from_pretrained(resnet_path) # On HG ResNet has a feature extractor, not a preprocessor but they function in the same way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfdf1b2e",
   "metadata": {
    "id": "cfdf1b2e",
    "papermill": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_resnet(example_batch):\n",
    "    inputs = resnet_processor([x for x in example_batch['image']], return_tensors='pt')\n",
    "\n",
    "    inputs['labels'] = example_batch['labels']\n",
    "    return inputs"
   ]
  },
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   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6113db43fd894cf5bd205873d8cadd02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/14528 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "462a8e23e8434311901cec6a73c0ff64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1816 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54722edd6b78493893a9f598cb41db49",
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      "text/plain": [
       "Map:   0%|          | 0/1816 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepared_dataset_resnet = dataset.map(transform_resnet, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc502c2",
   "metadata": {
    "id": "5fc502c2",
    "papermill": {
     "duration": 0.068377,
     "end_time": "2024-06-03T11:53:24.407957",
     "exception": false,
     "start_time": "2024-06-03T11:53:24.339580",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ViT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "189151d4",
   "metadata": {
    "id": "189151d4",
    "papermill": {
     "duration": 0.186753,
     "end_time": "2024-06-03T11:53:24.663399",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vit_path = 'google/vit-base-patch16-224-in21k'\n",
    "vit_processor = ViTImageProcessor.from_pretrained(vit_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0167746",
   "metadata": {
    "id": "a0167746",
    "papermill": {
     "duration": 0.080436,
     "end_time": "2024-06-03T11:53:24.810913",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_vit(example_batch):\n",
    "  inputs = vit_processor([x for x in example_batch['image']], return_tensors='pt')\n",
    "\n",
    "  inputs['labels'] = example_batch['labels']\n",
    "  return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adf63813",
   "metadata": {
    "id": "adf63813",
    "papermill": {
     "duration": 0.080887,
     "end_time": "2024-06-03T11:53:24.957111",
     "exception": false,
     "start_time": "2024-06-03T11:53:24.876224",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "prepared_dataset_vit = dataset.with_transform(transform_vit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aadf38b",
   "metadata": {
    "id": "6aadf38b",
    "papermill": {
     "duration": 0.066833,
     "end_time": "2024-06-03T11:53:25.105210",
     "exception": false,
     "start_time": "2024-06-03T11:53:25.038377",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training\n",
    "\n",
    "Based on (https://huggingface.co/blog/fine-tune-vit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674568fd",
   "metadata": {
    "id": "674568fd",
    "papermill": {
     "duration": 0.071094,
     "end_time": "2024-06-03T11:53:25.243318",
     "exception": false,
     "start_time": "2024-06-03T11:53:25.172224",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Full training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce578e34",
   "metadata": {
    "id": "ce578e34",
    "papermill": {
     "duration": 0.069167,
     "end_time": "2024-06-03T11:53:25.384589",
     "exception": false,
     "start_time": "2024-06-03T11:53:25.315422",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d799e71",
   "metadata": {
    "id": "8d799e71",
    "papermill": {
     "duration": 0.074398,
     "end_time": "2024-06-03T11:53:25.527034",
     "exception": false,
     "start_time": "2024-06-03T11:53:25.452636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def collate_fn(batch):\n",
    "  # Necessary for ViT\n",
    "  return {\n",
    "      'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
    "      'labels': torch.tensor([x['labels'] for x in batch])\n",
    "  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e358c78",
   "metadata": {
    "id": "7e358c78",
    "papermill": {
     "duration": 1.363334,
     "end_time": "2024-06-03T11:53:26.956707",
     "exception": false,
     "start_time": "2024-06-03T11:53:25.593373",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stanm\\AppData\\Local\\Temp\\ipykernel_6036\\3735508423.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  metric = load_metric(\"accuracy\", trust_remote_code=True)\n"
     ]
    }
   ],
   "source": [
    "metric = load_metric(\"accuracy\", trust_remote_code=True)\n",
    "\n",
    "def compute_metrics(p):\n",
    "  return metric.compute(predictions=np.argmax(p.predictions, axis=1),\n",
    "                        references=p.label_ids,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bffbfee9",
   "metadata": {
    "id": "bffbfee9",
    "papermill": {
     "duration": 0.082013,
     "end_time": "2024-06-03T11:53:27.115275",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TimerTracker(TrainerCallback):\n",
    "    def __init__(self):\n",
    "        self.start_time = None\n",
    "        self.end_time = None\n",
    "\n",
    "    def on_train_begin(self, args, state, control, **kwargs):\n",
    "        self.start_time = time.time()\n",
    "\n",
    "    def on_train_end(self, args, state, control, **kwargs):\n",
    "        self.end_time = time.time()\n",
    "\n",
    "        total_time = self.end_time - self.start_time\n",
    "\n",
    "        if state.is_world_process_zero:\n",
    "            state.log_history.append({'total_time': total_time})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc0b9536",
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    "id": "dc0b9536",
    "outputId": "f7718a09-b96e-436a-8694-c886b2fb7c1a",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Some weights of ResNetForImageClassification were not initialized from the model checkpoint at microsoft/resnet-50 and are newly initialized because the shapes did not match:\n",
      "- classifier.1.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.1.weight: found shape torch.Size([1000, 2048]) in the checkpoint and torch.Size([10, 2048]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import ViTConfig, ResNetConfig\n",
    "\n",
    "##\n",
    "## CREATE VIT MODELS\n",
    "##\n",
    "\n",
    "vit_config = ViTConfig(\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id\n",
    ")\n",
    "\n",
    "vit = ViTForImageClassification(vit_config)\n",
    "\n",
    "vit_pretrained = ViTForImageClassification.from_pretrained(\n",
    "    vit_path,\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id,\n",
    ")\n",
    "\n",
    "\n",
    "##\n",
    "## CREATE RESNET MODELS\n",
    "##\n",
    "\n",
    "resnet_config = ResNetConfig(\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id\n",
    ")\n",
    "\n",
    "resnet = ResNetForImageClassification(resnet_config)\n",
    "\n",
    "resnet_pretrained = ResNetForImageClassification.from_pretrained(\n",
    "    resnet_path,\n",
    "    num_labels=len(labels),\n",
    "    id2label=id2label,\n",
    "    label2id=label2id,\n",
    "    ignore_mismatched_sizes=True,\n",
    ")\n",
    "\n",
    "# The way HuggingFace initializes the weights makes it more likely we experience\n",
    "# gradient vanishing (for some reason)\n",
    "in_features = resnet_pretrained.classifier[-1].in_features\n",
    "resnet_pretrained.classifier[-1] = torch.nn.Linear(in_features, len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed749fca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ed749fca",
    "outputId": "0e1cf0d1-dc0c-4e7c-8b29-489b22bd346a",
    "papermill": {
     "duration": 0.220951,
     "end_time": "2024-06-03T11:53:33.229229",
     "exception": false,
     "start_time": "2024-06-03T11:53:33.008278",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stanm\\.conda\\envs\\pytorch_gpu\\Lib\\site-packages\\transformers\\training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 3\n",
    "BATCH_SIZE = 32\n",
    "SEED = 42\n",
    "EVAL_STEPS = 100\n",
    "LOGGING_STEPS = 100\n",
    "\n",
    "training_args_vit = TrainingArguments(\n",
    "  output_dir=\"./vit-tomato-leaf\",\n",
    "  per_device_train_batch_size=BATCH_SIZE,\n",
    "  per_device_eval_batch_size=BATCH_SIZE,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  seed = SEED,\n",
    "  num_train_epochs=EPOCHS,\n",
    "  eval_steps=EVAL_STEPS,\n",
    "  logging_steps=LOGGING_STEPS,\n",
    "  learning_rate=2e-4,\n",
    "  remove_unused_columns=False,\n",
    "  push_to_hub=False,\n",
    "  report_to='tensorboard',\n",
    "  load_best_model_at_end=True,\n",
    ")\n",
    "\n",
    "training_args_vit_pretrained = TrainingArguments(\n",
    "  output_dir=\"./vit-pretrained-tomato-leaf\",\n",
    "  per_device_train_batch_size=BATCH_SIZE,\n",
    "  per_device_eval_batch_size=BATCH_SIZE,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  seed = SEED,\n",
    "  num_train_epochs=EPOCHS,\n",
    "  eval_steps=EVAL_STEPS,\n",
    "  logging_steps=LOGGING_STEPS,\n",
    "  learning_rate=2e-4,\n",
    "  remove_unused_columns=False,\n",
    "  push_to_hub=False,\n",
    "  report_to='tensorboard',\n",
    "  load_best_model_at_end=True,\n",
    ")\n",
    "\n",
    "training_args_resnet = TrainingArguments(\n",
    "  output_dir=\"./resnet-tomato-leaf\",\n",
    "  per_device_train_batch_size=BATCH_SIZE,\n",
    "  per_device_eval_batch_size=BATCH_SIZE,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  seed = SEED,\n",
    "  num_train_epochs=EPOCHS,\n",
    "  logging_dir='./logs',\n",
    "  eval_steps=EVAL_STEPS,\n",
    "  logging_steps=LOGGING_STEPS,\n",
    "  learning_rate=5e-5,\n",
    "  push_to_hub=False,\n",
    "  report_to='tensorboard',\n",
    "  load_best_model_at_end=True,\n",
    ")\n",
    "\n",
    "training_args_resnet_pretrained = TrainingArguments(\n",
    "  output_dir=\"./resnet-pretrained-tomato-leaf\",\n",
    "  per_device_train_batch_size=BATCH_SIZE,\n",
    "  per_device_eval_batch_size=BATCH_SIZE,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  seed = SEED,\n",
    "  num_train_epochs=EPOCHS,\n",
    "  logging_dir='./logs',\n",
    "  eval_steps=EVAL_STEPS,\n",
    "  logging_steps=LOGGING_STEPS,\n",
    "  learning_rate=5e-5,\n",
    "  push_to_hub=False,\n",
    "  report_to='tensorboard',\n",
    "  load_best_model_at_end=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2bb2bedd",
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     "end_time": "2024-06-03T11:53:33.418075",
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     "start_time": "2024-06-03T11:53:33.310632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import DefaultDataCollator\n",
    "\n",
    "model_stack  = [\n",
    "    (\"resnet\", resnet, training_args_resnet, prepared_dataset_resnet, resnet_processor, DefaultDataCollator()),\n",
    "    (\"vit\", vit, training_args_vit, prepared_dataset_vit, vit_processor, collate_fn),\n",
    "    (\"resnet_pretrained\", resnet_pretrained, training_args_resnet_pretrained, prepared_dataset_resnet, resnet_processor, DefaultDataCollator()),\n",
    "    (\"vit_pretrained\", vit_pretrained, training_args_vit_pretrained, prepared_dataset_vit, vit_processor, collate_fn),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e445d3d7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "ee3ef67d7fc44401b86ae70763676e38",
      "a89901357eab4f359980e36041231dbe",
      "3f3db8f176684ac8949e472b9e864af6",
      "d054624c2d0140eeb15a11781b521fc2",
      "b7eef11f9183415b905047a9e025bd6d",
      "10876af80028444f85f3b2ebdd9ee93d",
      "5a097b0ca1714533afe840c1dbf717b7",
      "7dd1a12d59e3471d89bb3f0ac22a79fe",
      "7c4a75f953ae4164afb34b56cdee3585",
      "f70ef00d817244d6beba30c38fad7b49",
      "3166b1f4fd8d4072bee9e88d9d6da98b"
     ]
    },
    "id": "e445d3d7",
    "outputId": "463a2313-409f-4c10-fb5a-fc37989d2800",
    "papermill": {
     "duration": 25513.769345,
     "end_time": "2024-06-03T18:58:47.263942",
     "exception": false,
     "start_time": "2024-06-03T11:53:33.494597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  0%|          | 0/4 [00:00<?, ?it/s]"
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "Training: resnet\n"
     ]
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1362' max='1362' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1362/1362 2:21:03, Epoch 3/3]\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",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.747400</td>\n",
       "      <td>1.384149</td>\n",
       "      <td>0.519824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.222400</td>\n",
       "      <td>1.008597</td>\n",
       "      <td>0.642070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.947900</td>\n",
       "      <td>0.898375</td>\n",
       "      <td>0.689427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.824700</td>\n",
       "      <td>0.779903</td>\n",
       "      <td>0.713656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.747800</td>\n",
       "      <td>0.740646</td>\n",
       "      <td>0.722467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.635800</td>\n",
       "      <td>0.528925</td>\n",
       "      <td>0.805066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.577400</td>\n",
       "      <td>0.493329</td>\n",
       "      <td>0.821035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.532600</td>\n",
       "      <td>0.468809</td>\n",
       "      <td>0.835903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.551300</td>\n",
       "      <td>0.402855</td>\n",
       "      <td>0.851872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.459200</td>\n",
       "      <td>0.383450</td>\n",
       "      <td>0.863987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.440800</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.870595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.460600</td>\n",
       "      <td>0.349669</td>\n",
       "      <td>0.878855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.424100</td>\n",
       "      <td>0.336852</td>\n",
       "      <td>0.878304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =         3.0\n",
      "  total_flos               = 862562164GF\n",
      "  train_loss               =      0.7218\n",
      "  train_runtime            =  2:21:16.35\n",
      "  train_samples_per_second =       5.142\n",
      "  train_steps_per_second   =       0.161\n",
      "***** train metrics *****\n",
      "  epoch                    =         3.0\n",
      "  total_flos               = 862562164GF\n",
      "  train_loss               =      0.7218\n",
      "  train_runtime            =  2:21:16.35\n",
      "  train_samples_per_second =       5.142\n",
      "  train_steps_per_second   =       0.161\n",
      "Metrics for: resnet after training for 8476.359677553177\n"
     ]
    },
    {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [57/57 03:17]\n",
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       "    "
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "***** eval metrics *****\n",
      "  epoch                   =        3.0\n",
      "  eval_accuracy           =     0.8651\n",
      "  eval_loss               =      0.377\n",
      "  eval_runtime            = 0:03:24.67\n",
      "  eval_samples_per_second =      8.872\n",
      "  eval_steps_per_second   =      0.278\n",
      "Training: vit\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stanm\\.conda\\envs\\pytorch_gpu\\Lib\\site-packages\\transformers\\models\\vit\\modeling_vit.py:253: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:455.)\n",
      "  context_layer = torch.nn.functional.scaled_dot_product_attention(\n"
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    },
    {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1362' max='1362' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1362/1362 4:34:16, Epoch 3/3]\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",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.720900</td>\n",
       "      <td>1.327215</td>\n",
       "      <td>0.529736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.186300</td>\n",
       "      <td>1.143225</td>\n",
       "      <td>0.620595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.989100</td>\n",
       "      <td>1.162088</td>\n",
       "      <td>0.551211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.812900</td>\n",
       "      <td>0.759277</td>\n",
       "      <td>0.731828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.668900</td>\n",
       "      <td>0.510452</td>\n",
       "      <td>0.838106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.494700</td>\n",
       "      <td>0.520120</td>\n",
       "      <td>0.820485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.454800</td>\n",
       "      <td>0.516084</td>\n",
       "      <td>0.829846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.421800</td>\n",
       "      <td>0.351454</td>\n",
       "      <td>0.877753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.362400</td>\n",
       "      <td>0.340381</td>\n",
       "      <td>0.876652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.271400</td>\n",
       "      <td>0.277942</td>\n",
       "      <td>0.906388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>0.261844</td>\n",
       "      <td>0.908590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.219500</td>\n",
       "      <td>0.207315</td>\n",
       "      <td>0.932269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.178200</td>\n",
       "      <td>0.178874</td>\n",
       "      <td>0.941630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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    {
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     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =          3.0\n",
      "  total_flos               = 3145684526GF\n",
      "  train_loss               =       0.5952\n",
      "  train_runtime            =   4:34:25.95\n",
      "  train_samples_per_second =        2.647\n",
      "  train_steps_per_second   =        0.083\n",
      "***** train metrics *****\n",
      "  epoch                    =          3.0\n",
      "  total_flos               = 3145684526GF\n",
      "  train_loss               =       0.5952\n",
      "  train_runtime            =   4:34:25.95\n",
      "  train_samples_per_second =        2.647\n",
      "  train_steps_per_second   =        0.083\n",
      "Metrics for: vit after training for 16465.955320119858\n"
     ]
    },
    {
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       "\n",
       "    <div>\n",
       "      \n",
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       "      [57/57 01:09]\n",
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       "    "
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "***** eval metrics *****\n",
      "  epoch                   =        3.0\n",
      "  eval_accuracy           =     0.9058\n",
      "  eval_loss               =     0.2668\n",
      "  eval_runtime            = 0:01:10.55\n",
      "  eval_samples_per_second =     25.739\n",
      "  eval_steps_per_second   =      0.808\n",
      "Training: resnet_pretrained\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1362' max='1362' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1362/1362 3:15:44, Epoch 3/3]\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",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>2.167500</td>\n",
       "      <td>1.972697</td>\n",
       "      <td>0.309471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.817400</td>\n",
       "      <td>1.612785</td>\n",
       "      <td>0.351322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1.609000</td>\n",
       "      <td>1.464275</td>\n",
       "      <td>0.537445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>1.446900</td>\n",
       "      <td>1.278250</td>\n",
       "      <td>0.627753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.261400</td>\n",
       "      <td>1.102868</td>\n",
       "      <td>0.701542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>1.073900</td>\n",
       "      <td>0.925075</td>\n",
       "      <td>0.744493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.932000</td>\n",
       "      <td>0.789578</td>\n",
       "      <td>0.779185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.779800</td>\n",
       "      <td>0.685544</td>\n",
       "      <td>0.800110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.728100</td>\n",
       "      <td>0.604512</td>\n",
       "      <td>0.819934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.643600</td>\n",
       "      <td>0.550776</td>\n",
       "      <td>0.832048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.588400</td>\n",
       "      <td>0.514590</td>\n",
       "      <td>0.852423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.573000</td>\n",
       "      <td>0.488952</td>\n",
       "      <td>0.863436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.571200</td>\n",
       "      <td>0.475524</td>\n",
       "      <td>0.865088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =         3.0\n",
      "  total_flos               = 862562164GF\n",
      "  train_loss               =      1.0671\n",
      "  train_runtime            =  3:15:58.64\n",
      "  train_samples_per_second =       3.707\n",
      "  train_steps_per_second   =       0.116\n",
      "***** train metrics *****\n",
      "  epoch                    =         3.0\n",
      "  total_flos               = 862562164GF\n",
      "  train_loss               =      1.0671\n",
      "  train_runtime            =  3:15:58.64\n",
      "  train_samples_per_second =       3.707\n",
      "  train_steps_per_second   =       0.116\n",
      "Metrics for: resnet_pretrained after training for 11758.625509738922\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "      [57/57 03:22]\n",
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       "    "
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     "output_type": "stream",
     "text": [
      "***** eval metrics *****\n",
      "  epoch                   =        3.0\n",
      "  eval_accuracy           =     0.8216\n",
      "  eval_loss               =     0.5517\n",
      "  eval_runtime            = 0:03:30.02\n",
      "  eval_samples_per_second =      8.647\n",
      "  eval_steps_per_second   =      0.271\n",
      "Training: vit_pretrained\n"
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    },
    {
     "data": {
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       "    <div>\n",
       "      \n",
       "      <progress value='1362' max='1362' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1362/1362 5:12:59, Epoch 3/3]\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",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.658400</td>\n",
       "      <td>0.220406</td>\n",
       "      <td>0.959251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>0.206465</td>\n",
       "      <td>0.941630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.106470</td>\n",
       "      <td>0.973568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.084500</td>\n",
       "      <td>0.065736</td>\n",
       "      <td>0.983480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.071800</td>\n",
       "      <td>0.151506</td>\n",
       "      <td>0.954846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.058900</td>\n",
       "      <td>0.067276</td>\n",
       "      <td>0.984581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.027700</td>\n",
       "      <td>0.036966</td>\n",
       "      <td>0.991189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.019500</td>\n",
       "      <td>0.027644</td>\n",
       "      <td>0.995044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.030450</td>\n",
       "      <td>0.993943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.029469</td>\n",
       "      <td>0.994493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.006300</td>\n",
       "      <td>0.020232</td>\n",
       "      <td>0.996696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>0.997797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.997797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** train metrics *****\n",
      "  epoch                    =          3.0\n",
      "  total_flos               = 3145684526GF\n",
      "  train_loss               =        0.095\n",
      "  train_runtime            =   5:13:12.04\n",
      "  train_samples_per_second =        2.319\n",
      "  train_steps_per_second   =        0.072\n",
      "***** train metrics *****\n",
      "  epoch                    =          3.0\n",
      "  total_flos               = 3145684526GF\n",
      "  train_loss               =        0.095\n",
      "  train_runtime            =   5:13:12.04\n",
      "  train_samples_per_second =        2.319\n",
      "  train_steps_per_second   =        0.072\n",
      "Metrics for: vit_pretrained after training for 18792.063464403152\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='57' max='57' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [57/57 01:08]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** eval metrics *****\n",
      "  epoch                   =        3.0\n",
      "  eval_accuracy           =     0.9983\n",
      "  eval_loss               =     0.0122\n",
      "  eval_runtime            = 0:01:10.33\n",
      "  eval_samples_per_second =     25.818\n",
      "  eval_steps_per_second   =       0.81\n"
     ]
    }
   ],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "for (name, model, training_args, dataset, processor, collate) in tqdm(model_stack):\n",
    "  print(f\"Training: {name}\")\n",
    "\n",
    "  trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=collate,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=dataset[\"train\"],\n",
    "    eval_dataset=dataset[\"valid\"],\n",
    "    tokenizer=processor,\n",
    "    callbacks=[TimerTracker()]\n",
    "  )\n",
    "\n",
    "  train_results = trainer.train()\n",
    "\n",
    "  trainer.save_model()\n",
    "  trainer.log_metrics(\"train\", train_results.metrics)\n",
    "  trainer.save_metrics(\"train\", train_results.metrics)\n",
    "\n",
    "  trainer.log_metrics(\"train\", train_results.metrics)\n",
    "  trainer.save_metrics(\"train\", train_results.metrics)\n",
    "  trainer.save_state()\n",
    "\n",
    "  print(f\"Metrics for: {name} after training for {trainer.state.log_history[-1]['total_time']}\")\n",
    "  metrics = trainer.evaluate(dataset['test'])\n",
    "  trainer.log_metrics(\"eval\", metrics)\n",
    "  trainer.save_metrics(\"eval\", metrics)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "RPzn5pXfUMWu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "b63a497428054b8c9e7bac221b7adfc4",
      "78104c54173f43b6a9fcb46dc04fd57c",
      "a43ba925d09b4a24bbf8436807844e08",
      "9c93456b09b1415abc75df63002e09df",
      "8a1fcbda18d2447f85605b6e3cb833ef",
      "2070ab6315ab458bb94eed849573adde",
      "5dfdc8bb37da43fdb38655c34ba8876e",
      "ceda16c6a259493492c645deb03eebbe",
      "aa390f6d93084ff48aa6a86859ab4f53",
      "20f293ed7cfd4123987ce601212e4381",
      "7720c57ac69d442e9342a41e9a0d7bfb",
      "54458f02e5284903be9c0ac0473af215",
      "7e818b1a427942c5a40f7dd925cb4de4",
      "73911cddd4ec4d24a7799111b4448f4e",
      "8ab49a6683094dac9d43c6e02da9bb71",
      "79c2a48d2dce469abe4e58231dd9068d",
      "7991856621a7465183f5a2b93fefc665",
      "5b316525c5df4f8382450ada634b86f4",
      "50e4730b7bb84683a924c6a469e0a149",
      "3890966a4d7c41e19d3b4069df49c2d2",
      "1ba04e94c8f34b7381a17a75e1847e04",
      "fc08c2d6644a4bc7b5f48e830343a65a"
     ]
    },
    "id": "RPzn5pXfUMWu",
    "outputId": "34f09b37-5f6a-4618-b400-620e5cc32737"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      " 89%|██████████████████████████████████████████████████████████████████████████████▎         | 89/100 [00:20<00:02,  4.40it/s]\u001b[A\n",
      " 90%|███████████████████████████████████████████████████████████████████████████████▏        | 90/100 [00:21<00:02,  4.40it/s]\u001b[A\n",
      " 91%|████████████████████████████████████████████████████████████████████████████████        | 91/100 [00:21<00:02,  4.40it/s]\u001b[A\n",
      " 92%|████████████████████████████████████████████████████████████████████████████████▉       | 92/100 [00:21<00:01,  4.41it/s]\u001b[A\n",
      " 93%|█████████████████████████████████████████████████████████████████████████████████▊      | 93/100 [00:21<00:01,  4.40it/s]\u001b[A\n",
      " 94%|██████████████████████████████████████████████████████████████████████████████████▋     | 94/100 [00:21<00:01,  4.39it/s]\u001b[A\n",
      " 95%|███████████████████████████████████████████████████████████████████████████████████▌    | 95/100 [00:22<00:01,  4.41it/s]\u001b[A\n",
      " 96%|████████████████████████████████████████████████████████████████████████████████████▍   | 96/100 [00:22<00:00,  4.39it/s]\u001b[A\n",
      " 97%|█████████████████████████████████████████████████████████████████████████████████████▎  | 97/100 [00:22<00:00,  4.41it/s]\u001b[A\n",
      " 98%|██████████████████████████████████████████████████████████████████████████████████████▏ | 98/100 [00:22<00:00,  4.43it/s]\u001b[A\n",
      " 99%|███████████████████████████████████████████████████████████████████████████████████████ | 99/100 [00:23<00:00,  4.41it/s]\u001b[A\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:23<00:00,  4.29it/s]\u001b[A\n",
      " 25%|██████████████████████▊                                                                    | 1/4 [00:23<01:10, 23.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total inference time for resnet: 22.281595468521118s (0.22281595468521118s per sample)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 60%|████████████████████████████████████████████████████▊                                   | 60/100 [00:05<00:03, 11.82it/s]\u001b[A\n",
      " 62%|██████████████████████████████████████████████████████▌                                 | 62/100 [00:05<00:03, 11.80it/s]\u001b[A\n",
      " 64%|████████████████████████████████████████████████████████▎                               | 64/100 [00:05<00:03, 11.84it/s]\u001b[A\n",
      " 66%|██████████████████████████████████████████████████████████                              | 66/100 [00:05<00:02, 11.87it/s]\u001b[A\n",
      " 68%|███████████████████████████████████████████████████████████▊                            | 68/100 [00:05<00:02, 11.82it/s]\u001b[A\n",
      " 70%|█████████████████████████████████████████████████████████████▌                          | 70/100 [00:05<00:02, 12.02it/s]\u001b[A\n",
      " 72%|███████████████████████████████████████████████████████████████▎                        | 72/100 [00:06<00:02, 12.12it/s]\u001b[A\n",
      " 74%|█████████████████████████████████████████████████████████████████                       | 74/100 [00:06<00:02, 12.09it/s]\u001b[A\n",
      " 76%|██████████████████████████████████████████████████████████████████▉                     | 76/100 [00:06<00:02, 12.00it/s]\u001b[A\n",
      " 78%|████████████████████████████████████████████████████████████████████▋                   | 78/100 [00:06<00:01, 11.96it/s]\u001b[A\n",
      " 80%|██████████████████████████████████████████████████████████████████████▍                 | 80/100 [00:06<00:01, 12.07it/s]\u001b[A\n",
      " 82%|████████████████████████████████████████████████████████████████████████▏               | 82/100 [00:06<00:01, 12.07it/s]\u001b[A\n",
      " 84%|█████████████████████████████████████████████████████████████████████████▉              | 84/100 [00:07<00:01, 12.13it/s]\u001b[A\n",
      " 86%|███████████████████████████████████████████████████████████████████████████▋            | 86/100 [00:07<00:01, 12.07it/s]\u001b[A\n",
      " 88%|█████████████████████████████████████████████████████████████████████████████▍          | 88/100 [00:07<00:00, 12.21it/s]\u001b[A\n",
      " 90%|███████████████████████████████████████████████████████████████████████████████▏        | 90/100 [00:07<00:00, 11.78it/s]\u001b[A\n",
      " 92%|████████████████████████████████████████████████████████████████████████████████▉       | 92/100 [00:07<00:00, 11.73it/s]\u001b[A\n",
      " 94%|██████████████████████████████████████████████████████████████████████████████████▋     | 94/100 [00:07<00:00, 11.94it/s]\u001b[A\n",
      " 96%|████████████████████████████████████████████████████████████████████████████████████▍   | 96/100 [00:08<00:00, 11.71it/s]\u001b[A\n",
      " 98%|██████████████████████████████████████████████████████████████████████████████████████▏ | 98/100 [00:08<00:00, 11.80it/s]\u001b[A\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:08<00:00, 11.98it/s]\u001b[A\n",
      " 50%|█████████████████████████████████████████████▌                                             | 2/4 [00:31<00:29, 14.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total inference time for vit: 7.144994020462036s (0.07144994020462037s per sample)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 64%|████████████████████████████████████████████████████████▎                               | 64/100 [00:03<00:01, 18.49it/s]\u001b[A\n",
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      " 68%|███████████████████████████████████████████████████████████▊                            | 68/100 [00:03<00:01, 18.88it/s]\u001b[A\n",
      " 70%|█████████████████████████████████████████████████████████████▌                          | 70/100 [00:03<00:01, 18.63it/s]\u001b[A\n",
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      " 74%|█████████████████████████████████████████████████████████████████                       | 74/100 [00:04<00:01, 18.73it/s]\u001b[A\n",
      " 76%|██████████████████████████████████████████████████████████████████▉                     | 76/100 [00:04<00:01, 18.81it/s]\u001b[A\n",
      " 78%|████████████████████████████████████████████████████████████████████▋                   | 78/100 [00:04<00:01, 18.37it/s]\u001b[A\n",
      " 80%|██████████████████████████████████████████████████████████████████████▍                 | 80/100 [00:04<00:01, 18.51it/s]\u001b[A\n",
      " 82%|████████████████████████████████████████████████████████████████████████▏               | 82/100 [00:04<00:00, 18.16it/s]\u001b[A\n",
      " 84%|█████████████████████████████████████████████████████████████████████████▉              | 84/100 [00:04<00:00, 18.41it/s]\u001b[A\n",
      " 86%|███████████████████████████████████████████████████████████████████████████▋            | 86/100 [00:04<00:00, 18.14it/s]\u001b[A\n",
      " 88%|█████████████████████████████████████████████████████████████████████████████▍          | 88/100 [00:04<00:00, 18.49it/s]\u001b[A\n",
      " 90%|███████████████████████████████████████████████████████████████████████████████▏        | 90/100 [00:04<00:00, 18.66it/s]\u001b[A\n",
      " 92%|████████████████████████████████████████████████████████████████████████████████▉       | 92/100 [00:04<00:00, 18.54it/s]\u001b[A\n",
      " 94%|██████████████████████████████████████████████████████████████████████████████████▋     | 94/100 [00:05<00:00, 18.36it/s]\u001b[A\n",
      " 96%|████████████████████████████████████████████████████████████████████████████████████▍   | 96/100 [00:05<00:00, 18.49it/s]\u001b[A\n",
      " 98%|██████████████████████████████████████████████████████████████████████████████████████▏ | 98/100 [00:05<00:00, 18.82it/s]\u001b[A\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:05<00:00, 18.40it/s]\u001b[A\n",
      " 75%|████████████████████████████████████████████████████████████████████▎                      | 3/4 [00:37<00:10, 10.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total inference time for resnet_pretrained: 4.461665153503418s (0.04461665153503418s per sample)\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total inference time for vit_pretrained: 4.880549192428589s (0.04880549192428589s per sample)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "NUM_ROWS = 100\n",
    "\n",
    "for (name, model, training_args, _, processor, collate) in tqdm(model_stack):\n",
    "    rows = [randint(0, tomato_dataset['train'].num_rows-1) for _ in range(NUM_ROWS)]\n",
    "\n",
    "    samples = tomato_dataset['train'].select(rows)\n",
    "    total_time = 0\n",
    "    \n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    for sample in tqdm(samples):\n",
    "        inputs = processor(sample['image'], return_tensors=\"pt\")\n",
    "        inputs = {k: v.to(device) for k, v in inputs.items()}\n",
    "        \n",
    "        correct_label = name_map[sample['labels']]\n",
    "        with torch.no_grad():\n",
    "            start = time.time()\n",
    "            outputs = model(**inputs)\n",
    "            prediction = outputs.logits.argmax(-1).item()\n",
    "            prediction = id2label[prediction]\n",
    "            end = time.time()\n",
    "\n",
    "            total_time += (end - start)\n",
    "    print(f\"Total inference time for {name}: {total_time}s ({total_time / NUM_ROWS}s per sample)\")"
   ]
  }
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