EfficientDet_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch>=1.4.0 in /root/anaconda3/envs/rcnn/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (1.7.0)\n",
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      "Requirement already satisfied: six>=1.5 in /root/anaconda3/envs/rcnn/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->pycocotools>=2.0.2->-r requirements.txt (line 8)) (1.16.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "cTHDyODENsgr"
   },
   "outputs": [],
   "source": [
    "# 라이브러리 및 모듈 import\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools.cocoeval import COCOeval\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "from effdet import get_efficientdet_config, EfficientDet, DetBenchTrain\n",
    "from effdet.efficientdet import HeadNet\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: albumentations in /opt/conda/envs/detection/lib/python3.7/site-packages (1.1.0)\n",
      "Requirement already satisfied: PyYAML in /opt/conda/envs/detection/lib/python3.7/site-packages (from albumentations) (6.0)\n",
      "Requirement already satisfied: opencv-python-headless>=4.1.1 in /opt/conda/envs/detection/lib/python3.7/site-packages (from albumentations) (4.5.5.64)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/detection/lib/python3.7/site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/detection/lib/python3.7/site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.1.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "SENfg9eZNsgu"
   },
   "outputs": [],
   "source": [
    "# CustomDataset class 선언\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    '''\n",
    "      data_dir: data가 존재하는 폴더 경로\n",
    "      transforms: data transform (resize, crop, Totensor, etc,,,)\n",
    "    '''\n",
    "\n",
    "    def __init__(self, annotation, data_dir, transforms=None):\n",
    "        super().__init__()\n",
    "        self.data_dir = data_dir\n",
    "        \n",
    "        # coco annotation 불러오기 (by. coco API)\n",
    "        self.coco = COCO(annotation)\n",
    "        self.predictions = {\n",
    "            \"images\": self.coco.dataset[\"images\"].copy(),\n",
    "            \"categories\": self.coco.dataset[\"categories\"].copy(),\n",
    "            \"annotations\": None\n",
    "        }\n",
    "        self.transforms = transforms\n",
    "\n",
    "    def __getitem__(self, index: int):\n",
    "        image_id = self.coco.getImgIds(imgIds=index)\n",
    "\n",
    "        image_info = self.coco.loadImgs(image_id)[0]\n",
    "        \n",
    "        image = cv2.imread(os.path.join(self.data_dir, image_info['file_name']))\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n",
    "        image /= 255.0\n",
    "\n",
    "        ann_ids = self.coco.getAnnIds(imgIds=image_info['id'])\n",
    "        anns = self.coco.loadAnns(ann_ids)\n",
    "\n",
    "        # boxes (x, y, w, h)\n",
    "        boxes = np.array([x['bbox'] for x in anns])\n",
    "\n",
    "        # (x,y,w,h)의 coco format에서 (x_min, y_min, x_max, y_max)로 변경\n",
    "        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n",
    "        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n",
    "        \n",
    "        # box별 label\n",
    "        labels = np.array([x['category_id'] for x in anns])\n",
    "        labels = torch.as_tensor(labels, dtype=torch.int64)\n",
    "        \n",
    "        areas = np.array([x['area'] for x in anns])\n",
    "        areas = torch.as_tensor(areas, dtype=torch.float32)\n",
    "        \n",
    "        is_crowds = np.array([x['iscrowd'] for x in anns])\n",
    "        is_crowds = torch.as_tensor(is_crowds, dtype=torch.int64)\n",
    "\n",
    "        target = {'boxes': boxes, 'labels': labels, 'image_id': torch.tensor([index]), 'area': areas,\n",
    "                  'iscrowd': is_crowds}\n",
    "\n",
    "        # transform\n",
    "        if self.transforms:\n",
    "            while True:\n",
    "                sample = self.transforms(**{\n",
    "                    'image': image,\n",
    "                    'bboxes': target['boxes'],\n",
    "                    'labels': labels\n",
    "                })\n",
    "                if len(sample['bboxes']) > 0:\n",
    "                    image = sample['image']\n",
    "                    target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)\n",
    "                    target['boxes'][:,[0,1,2,3]] = target['boxes'][:,[1,0,3,2]]  #yxyx: be warning\n",
    "                    target['labels'] = torch.tensor(sample['labels'])\n",
    "                    break\n",
    "            \n",
    "        return image, target, image_id\n",
    "    \n",
    "    def __len__(self) -> int:\n",
    "        return len(self.coco.getImgIds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "W8FQbYrjNsgx"
   },
   "outputs": [],
   "source": [
    "# Albumentation을 이용, augmentation 선언\n",
    "def get_train_transform():\n",
    "    return A.Compose([\n",
    "        A.Resize(512, 512),\n",
    "        A.Flip(p=0.5),\n",
    "        A.RandomBrightness(limit=0.24, p=0.5),\n",
    "        ToTensorV2(p=1.0)\n",
    "    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n",
    "\n",
    "\n",
    "def get_valid_transform():\n",
    "    return A.Compose([\n",
    "        ToTensorV2(p=1.0)\n",
    "    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Kc0uStKpNsgy"
   },
   "outputs": [],
   "source": [
    "# loss 추적\n",
    "class Averager:\n",
    "    def __init__(self):\n",
    "        self.current_total = 0.0\n",
    "        self.iterations = 0.0\n",
    "\n",
    "    def send(self, value):\n",
    "        self.current_total += value\n",
    "        self.iterations += 1\n",
    "\n",
    "    @property\n",
    "    def value(self):\n",
    "        if self.iterations == 0:\n",
    "            return 0\n",
    "        else:\n",
    "            return 1.0 * self.current_total / self.iterations\n",
    "\n",
    "    def reset(self):\n",
    "        self.current_total = 0.0\n",
    "        self.iterations = 0.0\n",
    "\n",
    "def collate_fn(batch):\n",
    "    return tuple(zip(*batch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kGjDutKiNsgy"
   },
   "outputs": [],
   "source": [
    "# Effdet config\n",
    "# https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/config/model_config.py\n",
    "\n",
    "# Effdet config를 통해 모델 불러오기\n",
    "def get_net(checkpoint_path=None):\n",
    "    \n",
    "    config = get_efficientdet_config('tf_efficientdet_d3')\n",
    "    config.num_classes = 10\n",
    "    config.image_size = (512,512)\n",
    "    \n",
    "    config.soft_nms = False\n",
    "    config.max_det_per_image = 200\n",
    "    \n",
    "    net = EfficientDet(config, pretrained_backbone=True)\n",
    "    net.class_net = HeadNet(config, num_outputs=config.num_classes)\n",
    "    \n",
    "    if checkpoint_path:\n",
    "        checkpoint = torch.load(checkpoint_path)\n",
    "        net.load_state_dict(checkpoint['model_state_dict'])\n",
    "        \n",
    "    return DetBenchTrain(net)\n",
    "    \n",
    "# train function\n",
    "def train_fn(num_epochs, train_data_loader, optimizer, model, device, clip=35):\n",
    "    loss_hist = Averager()\n",
    "    model.train()\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        loss_hist.reset()\n",
    "        \n",
    "        for images, targets, image_ids in tqdm(train_data_loader):\n",
    "            \n",
    "                images = torch.stack(images) # bs, ch, w, h - 16, 3, 512, 512\n",
    "                images = images.to(device).float()\n",
    "                boxes = [target['boxes'].to(device).float() for target in targets]\n",
    "                labels = [target['labels'].to(device).float() + 1.0 for target in targets]\n",
    "                target = {\"bbox\": boxes, \"cls\": labels}\n",
    "\n",
    "                # calculate loss\n",
    "                loss, cls_loss, box_loss = model(images, target).values()\n",
    "                loss_value = loss.detach().item()\n",
    "                \n",
    "                loss_hist.send(loss_value)\n",
    "                \n",
    "                # backward\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                # grad clip\n",
    "                torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "                \n",
    "                optimizer.step()\n",
    "\n",
    "        print(f\"Epoch #{epoch+1} loss: {loss_hist.value}\")\n",
    "        torch.save(model.state_dict(), f'epoch_{epoch+1}.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "OS1UR7dqNsgz"
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    annotation = './datasets/train.json'\n",
    "    data_dir = './datasets'\n",
    "    train_dataset = CustomDataset(annotation, data_dir, get_train_transform())\n",
    "\n",
    "    train_data_loader = DataLoader(\n",
    "        train_dataset,\n",
    "        batch_size=8,\n",
    "        shuffle=False,\n",
    "        num_workers=4,\n",
    "        collate_fn=collate_fn\n",
    "    )\n",
    "    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "    print(device)\n",
    "\n",
    "    model = get_net()\n",
    "    model.to(device)\n",
    "    \n",
    "    params = [p for p in model.parameters() if p.requires_grad]\n",
    "    optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)\n",
    "\n",
    "    num_epochs = 50\n",
    "\n",
    "    loss = train_fn(num_epochs, train_data_loader, optimizer, model, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "h5kBcuWbNsg0",
    "outputId": "63068363-da84-466a-96d3-5a7cf28a7aad",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/detection/lib/python3.7/site-packages/albumentations/augmentations/transforms.py:1802: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrast\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.21s)\n",
      "creating index...\n",
      "index created!\n",
      "cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:27<00:00,  2.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 loss: 12.628878612955345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:26<00:00,  2.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 loss: 1.2212420425906705\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:25<00:00,  2.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 loss: 1.099484641013481\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:27<00:00,  2.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 loss: 1.0233381330088975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:25<00:00,  2.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 loss: 0.9709092502508148\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:27<00:00,  2.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 loss: 0.9248376504294026\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:27<00:00,  2.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 loss: 0.8884299958396075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:26<00:00,  2.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 loss: 0.8580591691301224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 611/611 [04:24<00:00,  2.31it/s]\n"
     ]
    },
    {
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   "metadata": {},
   "outputs": [],
   "source": []
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