235_实战Kaggle比赛狗的品种识别ImageNetDogs.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 实战Kaggle比赛狗的品种识别ImageNetDogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 比赛网址是 https://www.kaggle.com/c/dog-breed-identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ..\\data\\kaggle_dog_tiny.zip from http://d2l-data.s3-accelerate.amazonaws.com/kaggle_dog_tiny.zip...\n"
     ]
    }
   ],
   "source": [
    "d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',\n",
    "                           '0cb91d09b814ecdc07b50f31f8dcad3e816a86d')\n",
    "\n",
    "demo = True\n",
    "if demo:\n",
    "    data_dir = d2l.download_extract('dog_tiny')\n",
    "else:\n",
    "    data_dir = os.path_join('..','data','dog_breed-identification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 整理数据集\n",
    "def reorg_dog_data(data_dir, valid_ratio):\n",
    "    labels = d2l.read_csv_labels(os.path.join(data_dir,'labels.csv'))\n",
    "    d2l.reorg_train_valid(data_dir,labels,valid_ratio)\n",
    "    d2l.reorg_test(data_dir)\n",
    "    \n",
    "batch_size = 32 if demo else 128\n",
    "valid_ratio = 0.1\n",
    "reorg_dog_data(data_dir, valid_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图像增广\n",
    "transform_train = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.RandomResizedCrop(224,scale=(0.08,1.0),ratio=(3.0/4.0, 4.0/3.0)),\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.ColorJitter(brightness=0.4,contrast=0.4,saturation=0.4),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.485,0.456,0.406],\n",
    "                                    [0.229,0.224,0.225])])\n",
    "\n",
    "transform_test = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize(256),\n",
    "    torchvision.transforms.CenterCrop(224),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize([0.485,0.456,0.406],\n",
    "                                     [0.229,0.224,0.225])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds, train_valid_ds = [\n",
    "    torchvision.datasets.ImageFolder(\n",
    "        os.path.join(data_dir,'train_valid_test',folder),\n",
    "        transform=transform_train) for folder in ['train','train_valid']]       \n",
    "\n",
    "valid_ds, test_ds = [\n",
    "    torchvision.datasets.ImageFolder(\n",
    "    os.path.join(data_dir,'train_valid_test',folder),\n",
    "    transform=transform_test) for folder in ['valid','test']]\n",
    "\n",
    "train_iter, train_valid_iter = [\n",
    "    torch.utils.data.DataLoader(dataset,batch_size,shuffle=True,drop_last=True) for dataset in (train_ds, train_valid_ds)]\n",
    "\n",
    "valid_iter = torch.utils.data.DataLoader(valid_ds,batch_size,shuffle=False,drop_last=True)\n",
    "test_iter = torch.utils.data.DataLoader(test_ds,batch_size,shuffle=False,drop_last=False)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 微调预训练模型\n",
    "# 除了最后一层外，前面的层固定住参数不变\n",
    "def get_net(device):\n",
    "    finetune_net = nn.Sequential()\n",
    "    finetune_net.features = torchvision.models.resnet34(pretrained=True)\n",
    "    print(\"finetune_net：\", finetune_net)\n",
    "    finetune_net.output_new = nn.Sequential(nn.Linear(1000,256),nn.ReLU(),nn.Linear(256,120)) #  在原始网络后又加了一层 \n",
    "    print(\"finetune_net：\", finetune_net)\n",
    "    finetune_net = finetune_net.to(devices[0])\n",
    "    for param in finetune_net.features.parameters(): # 遍历features的所有参数\n",
    "        param.requires_grad = False  \n",
    "    return finetune_net # 返回整个网络，这个网络中原始层的参数固定住了，保持不变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算损失\n",
    "loss = nn.CrossEntropyLoss(reduction='none')\n",
    "\n",
    "def evaluate_loss(data_iter, net, devices):\n",
    "    l_sum, n = 0.0, 0\n",
    "    for features, labels in data_iter:\n",
    "        features, labels = features.to(devices[0]), labels.to(devices[0])\n",
    "        outputs = net(features)\n",
    "        l = loss(outputs, labels)\n",
    "        l_sum += l.sum()\n",
    "        n += labels.numel()\n",
    "    return l_sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数\n",
    "def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,lr_decay):\n",
    "    net = nn.DataParallel(net,device_ids=devices).to(devices[0])\n",
    "    trainer = torch.optim.SGD(\n",
    "        (param for param in net.parameters() if param.requires_grad), \n",
    "        lr = lr, momentum = 0.9, weight_decay=wd)\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)\n",
    "    num_batches, timer = len(train_iter), d2l.Timer()\n",
    "    legend = ['train loss']\n",
    "    if valid_iter is not None:\n",
    "        legend.append('valid loss')\n",
    "    animator = d2l.Animator(xlabel='epoch',xlim=[1,num_epochs],legend=legend)\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        metric = d2l.Accumulator(2)\n",
    "        for i, (features, labels) in enumerate(train_iter):\n",
    "            timer.start()\n",
    "            features, labels = features.to(devices[0]), labels.to(devices[0])\n",
    "            trainer.zero_grad()\n",
    "            output = net(features)\n",
    "            l = loss(output, labels).sum()\n",
    "            l.backward()\n",
    "            trainer.step()\n",
    "            metric.add(l,labels.shape[0])\n",
    "            timer.stop()\n",
    "            if (i+1) % (num_batches // 5) == 0 or i == num_batches -1:\n",
    "                animator.add(epoch + (i+1) / num_batches,\n",
    "                             (metric[0] / metric[1], None))\n",
    "        measures = f'train loss {metric[0] / metric[1]:.3f}'\n",
    "        if valid_iter is not None:\n",
    "            valid_loss = evaluate_loss(valid_iter, net, devices)\n",
    "            animator.add(epoch + 1, (None, valid_loss.detach()))\n",
    "        scheduler.step()\n",
    "    if valid_iter is not None:\n",
    "        measures += f', valid loss {valid_loss:.3f}'\n",
    "    print(measures + f'\\n{metric[1] * num_epochs / timer.sum():.1f}'\n",
    "         f' examples/sec on {str(devices)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss 1.119, valid loss 1.561\n",
      "569.0 examples/sec on [device(type='cuda', index=0)]\n"
     ]
    },
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   "source": [
    "devices, num_epochs, lr, wd = d2l.try_all_gpus(), 10, 1e-4, 1e-4\n",
    "lr_period, lr_decay, net = 2, 0.9, get_net(devices)\n",
    "train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period, lr_decay)   "
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss 1.068\n",
      "761.3 examples/sec on [device(type='cuda', index=0)]\n"
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     "metadata": {
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   ],
   "source": [
    "net = get_net(devices)\n",
    "train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period, lr_decay)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "preds = []\n",
    "for data, label in test_iter:\n",
    "    # 计算每一个样本对每一类的概率是多少\n",
    "    output = torch.nn.functional.softmax(net(data.to(devices[0])), dim=0)\n",
    "    preds.extend(output.cpu().detach().numpy())\n",
    "    print(len(preds))\n",
    "ids = sorted(os.listdir(os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')))    \n",
    "with open('submission.csv','w') as f:\n",
    "    f.write('id,' + ','.join(train_valid_ds.classes)+'\\n')\n",
    "    for i, output in zip(ids, preds):\n",
    "        f.write(i.split('.')[0] + ',' + ','.join([str(num) for num in output]) + '\\n')   "
   ]
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