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Python/d2l/d2l-zh/pytorch/chapter_computer-vision/image-augmentation.ipynb
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{
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"# 图像增广\n",
":label:`sec_image_augmentation`\n",
"\n",
" :numref:`sec_alexnet`提到过大型数据集是成功应用深度神经网络的先决条件。\n",
"图像增广在对训练图像进行一系列的随机变化之后,生成相似但不同的训练样本,从而扩大了训练集的规模。\n",
"此外,应用图像增广的原因是,随机改变训练样本可以减少模型对某些属性的依赖,从而提高模型的泛化能力。\n",
"例如,我们可以以不同的方式裁剪图像,使感兴趣的对象出现在不同的位置,减少模型对于对象出现位置的依赖。\n",
"我们还可以调整亮度、颜色等因素来降低模型对颜色的敏感度。\n",
"可以说,图像增广技术对于AlexNet的成功是必不可少的。本节将讨论这项广泛应用于计算机视觉的技术。\n"
]
},
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"source": [
"%matplotlib inline\n",
"import torch\n",
"import torchvision\n",
"from torch import nn\n",
"from d2l import torch as d2l"
]
},
{
"cell_type": "markdown",
"id": "25366201",
"metadata": {
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"source": [
"## 常用的图像增广方法\n",
"\n",
"在对常用图像增广方法的探索时,我们将使用下面这个尺寸为$400\\times 500$的图像作为示例。\n"
]
},
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"pytorch"
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"source": [
"d2l.set_figsize()\n",
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"大多数图像增广方法都具有一定的随机性。为了便于观察图像增广的效果,我们下面定义辅助函数`apply`。\n",
"此函数在输入图像`img`上多次运行图像增广方法`aug`并显示所有结果。\n"
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"def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):\n",
" Y = [aug(img) for _ in range(num_rows * num_cols)]\n",
" d2l.show_images(Y, num_rows, num_cols, scale=scale)"
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"### 翻转和裁剪\n",
"\n",
"[**左右翻转图像**]通常不会改变对象的类别。这是最早且最广泛使用的图像增广方法之一。\n",
"接下来,我们使用`transforms`模块来创建`RandomFlipLeftRight`实例,这样就各有50%的几率使图像向左或向右翻转。\n"
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"text/plain": [
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},
"metadata": {
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}
],
"source": [
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]
},
{
"cell_type": "markdown",
"id": "0ba3f1b5",
"metadata": {
"origin_pos": 13
},
"source": [
"[**上下翻转图像**]不如左右图像翻转那样常用。但是,至少对于这个示例图像,上下翻转不会妨碍识别。接下来,我们创建一个`RandomFlipTopBottom`实例,使图像各有50%的几率向上或向下翻转。\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7899eb1",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:04.005369Z",
"iopub.status.busy": "2023-08-18T07:09:04.004737Z",
"iopub.status.idle": "2023-08-18T07:09:04.594173Z",
"shell.execute_reply": "2023-08-18T07:09:04.593347Z"
},
"origin_pos": 15,
"tab": [
"pytorch"
]
},
"outputs": [
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"text/plain": [
"<Figure size 432x216 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"apply(img, torchvision.transforms.RandomVerticalFlip())"
]
},
{
"cell_type": "markdown",
"id": "00ccc140",
"metadata": {
"origin_pos": 17
},
"source": [
"在我们使用的示例图像中,猫位于图像的中间,但并非所有图像都是这样。\n",
"在 :numref:`sec_pooling`中,我们解释了汇聚层可以降低卷积层对目标位置的敏感性。\n",
"另外,我们可以通过对图像进行随机裁剪,使物体以不同的比例出现在图像的不同位置。\n",
"这也可以降低模型对目标位置的敏感性。\n",
"\n",
"下面的代码将[**随机裁剪**]一个面积为原始面积10%到100%的区域,该区域的宽高比从0.5~2之间随机取值。\n",
"然后,区域的宽度和高度都被缩放到200像素。\n",
"在本节中(除非另有说明),$a$和$b$之间的随机数指的是在区间$[a, b]$中通过均匀采样获得的连续值。\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7c50f6a4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:04.598134Z",
"iopub.status.busy": "2023-08-18T07:09:04.597541Z",
"iopub.status.idle": "2023-08-18T07:09:04.957843Z",
"shell.execute_reply": "2023-08-18T07:09:04.956957Z"
},
"origin_pos": 19,
"tab": [
"pytorch"
]
},
"outputs": [
{
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" <image xlink:href=\"data:image/png;base64,\n",
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"text/plain": [
"<Figure size 432x216 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shape_aug = torchvision.transforms.RandomResizedCrop(\n",
" (200, 200), scale=(0.1, 1), ratio=(0.5, 2))\n",
"apply(img, shape_aug)"
]
},
{
"cell_type": "markdown",
"id": "ae160672",
"metadata": {
"origin_pos": 21
},
"source": [
"### 改变颜色\n",
"\n",
"另一种增广方法是改变颜色。\n",
"我们可以改变图像颜色的四个方面:亮度、对比度、饱和度和色调。\n",
"在下面的示例中,我们[**随机更改图像的亮度**],随机值为原始图像的50%$1-0.5$)到150%$1+0.5$)之间。\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f38c67b5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:04.961969Z",
"iopub.status.busy": "2023-08-18T07:09:04.961381Z",
"iopub.status.idle": "2023-08-18T07:09:05.497682Z",
"shell.execute_reply": "2023-08-18T07:09:05.496805Z"
},
"origin_pos": 23,
"tab": [
"pytorch"
]
},
"outputs": [
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"text/plain": [
"<Figure size 432x216 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"apply(img, torchvision.transforms.ColorJitter(\n",
" brightness=0.5, contrast=0, saturation=0, hue=0))"
]
},
{
"cell_type": "markdown",
"id": "bead8ed4",
"metadata": {
"origin_pos": 25
},
"source": [
"同样,我们可以[**随机更改图像的色调**]。\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "72d28919",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:05.501452Z",
"iopub.status.busy": "2023-08-18T07:09:05.500816Z",
"iopub.status.idle": "2023-08-18T07:09:06.125837Z",
"shell.execute_reply": "2023-08-18T07:09:06.124959Z"
},
"origin_pos": 27,
"tab": [
"pytorch"
]
},
"outputs": [
{
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"text/plain": [
"<Figure size 432x216 with 8 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"apply(img, torchvision.transforms.ColorJitter(\n",
" brightness=0, contrast=0, saturation=0, hue=0.5))"
]
},
{
"cell_type": "markdown",
"id": "de49c0fc",
"metadata": {
"origin_pos": 29
},
"source": [
"我们还可以创建一个`RandomColorJitter`实例,并设置如何同时[**随机更改图像的亮度(`brightness`)、对比度(`contrast`)、饱和度(`saturation`)和色调(`hue`**]。\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8f62c430",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:06.129806Z",
"iopub.status.busy": "2023-08-18T07:09:06.129220Z",
"iopub.status.idle": "2023-08-18T07:09:06.890747Z",
"shell.execute_reply": "2023-08-18T07:09:06.889906Z"
},
"origin_pos": 31,
"tab": [
"pytorch"
]
},
"outputs": [
{
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"text/plain": [
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]
},
"metadata": {
"needs_background": "light"
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"output_type": "display_data"
}
],
"source": [
"color_aug = torchvision.transforms.ColorJitter(\n",
" brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n",
"apply(img, color_aug)"
]
},
{
"cell_type": "markdown",
"id": "da1a57e0",
"metadata": {
"origin_pos": 33
},
"source": [
"### [**结合多种图像增广方法**]\n",
"\n",
"在实践中,我们将结合多种图像增广方法。比如,我们可以通过使用一个`Compose`实例来综合上面定义的不同的图像增广方法,并将它们应用到每个图像。\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2b060294",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:06.894513Z",
"iopub.status.busy": "2023-08-18T07:09:06.893920Z",
"iopub.status.idle": "2023-08-18T07:09:07.407841Z",
"shell.execute_reply": "2023-08-18T07:09:07.406996Z"
},
"origin_pos": 35,
"tab": [
"pytorch"
]
},
"outputs": [
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],
"source": [
"augs = torchvision.transforms.Compose([\n",
" torchvision.transforms.RandomHorizontalFlip(), color_aug, shape_aug])\n",
"apply(img, augs)"
]
},
{
"cell_type": "markdown",
"id": "7ad3846c",
"metadata": {
"origin_pos": 37
},
"source": [
"## [**使用图像增广进行训练**]\n",
"\n",
"让我们使用图像增广来训练模型。\n",
"这里,我们使用CIFAR-10数据集,而不是我们之前使用的Fashion-MNIST数据集。\n",
"这是因为Fashion-MNIST数据集中对象的位置和大小已被规范化,而CIFAR-10数据集中对象的颜色和大小差异更明显。\n",
"CIFAR-10数据集中的前32个训练图像如下所示。\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8a539625",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:09:07.412048Z",
"iopub.status.busy": "2023-08-18T07:09:07.411327Z",
"iopub.status.idle": "2023-08-18T07:12:53.687290Z",
"shell.execute_reply": "2023-08-18T07:12:53.686473Z"
},
"origin_pos": 39,
"tab": [
"pytorch"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ../data/cifar-10-python.tar.gz\n"
]
},
{
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},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"all_images = torchvision.datasets.CIFAR10(train=True, root=\"../data\",\n",
" download=True)\n",
"d2l.show_images([all_images[i][0] for i in range(32)], 4, 8, scale=0.8);"
]
},
{
"cell_type": "markdown",
"id": "efa3598c",
"metadata": {
"origin_pos": 41
},
"source": [
"为了在预测过程中得到确切的结果,我们通常对训练样本只进行图像增广,且在预测过程中不使用随机操作的图像增广。\n",
"在这里,我们[**只使用最简单的随机左右翻转**]。\n",
"此外,我们使用`ToTensor`实例将一批图像转换为深度学习框架所要求的格式,即形状为(批量大小,通道数,高度,宽度)的32位浮点数,取值范围为0~1。\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "afeeb232",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:12:53.692393Z",
"iopub.status.busy": "2023-08-18T07:12:53.691826Z",
"iopub.status.idle": "2023-08-18T07:12:53.696255Z",
"shell.execute_reply": "2023-08-18T07:12:53.695504Z"
},
"origin_pos": 43,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"train_augs = torchvision.transforms.Compose([\n",
" torchvision.transforms.RandomHorizontalFlip(),\n",
" torchvision.transforms.ToTensor()])\n",
"\n",
"test_augs = torchvision.transforms.Compose([\n",
" torchvision.transforms.ToTensor()])"
]
},
{
"cell_type": "markdown",
"id": "272f6e78",
"metadata": {
"origin_pos": 46,
"tab": [
"pytorch"
]
},
"source": [
"接下来,我们[**定义一个辅助函数,以便于读取图像和应用图像增广**]。PyTorch数据集提供的`transform`参数应用图像增广来转化图像。有关`DataLoader`的详细介绍,请参阅 :numref:`sec_fashion_mnist`。\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e78239ec",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:12:53.699803Z",
"iopub.status.busy": "2023-08-18T07:12:53.699195Z",
"iopub.status.idle": "2023-08-18T07:12:53.703723Z",
"shell.execute_reply": "2023-08-18T07:12:53.702970Z"
},
"origin_pos": 48,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"def load_cifar10(is_train, augs, batch_size):\n",
" dataset = torchvision.datasets.CIFAR10(root=\"../data\", train=is_train,\n",
" transform=augs, download=True)\n",
" dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
" shuffle=is_train, num_workers=d2l.get_dataloader_workers())\n",
" return dataloader"
]
},
{
"cell_type": "markdown",
"id": "4ddf567c",
"metadata": {
"origin_pos": 50
},
"source": [
"### 多GPU训练\n",
"\n",
"我们在CIFAR-10数据集上训练 :numref:`sec_resnet`中的ResNet-18模型。\n",
"回想一下 :numref:`sec_multi_gpu_concise`中对多GPU训练的介绍。\n",
"接下来,我们[**定义一个函数,使用多GPU对模型进行训练和评估**]。\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "bcf2640f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:12:53.707111Z",
"iopub.status.busy": "2023-08-18T07:12:53.706612Z",
"iopub.status.idle": "2023-08-18T07:12:53.712395Z",
"shell.execute_reply": "2023-08-18T07:12:53.711671Z"
},
"origin_pos": 52,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"#@save\n",
"def train_batch_ch13(net, X, y, loss, trainer, devices):\n",
" \"\"\"用多GPU进行小批量训练\"\"\"\n",
" if isinstance(X, list):\n",
" # 微调BERT中所需\n",
" X = [x.to(devices[0]) for x in X]\n",
" else:\n",
" X = X.to(devices[0])\n",
" y = y.to(devices[0])\n",
" net.train()\n",
" trainer.zero_grad()\n",
" pred = net(X)\n",
" l = loss(pred, y)\n",
" l.sum().backward()\n",
" trainer.step()\n",
" train_loss_sum = l.sum()\n",
" train_acc_sum = d2l.accuracy(pred, y)\n",
" return train_loss_sum, train_acc_sum"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "94b378e3",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:12:53.715853Z",
"iopub.status.busy": "2023-08-18T07:12:53.715212Z",
"iopub.status.idle": "2023-08-18T07:12:53.723420Z",
"shell.execute_reply": "2023-08-18T07:12:53.722651Z"
},
"origin_pos": 55,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"#@save\n",
"def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,\n",
" devices=d2l.try_all_gpus()):\n",
" \"\"\"用多GPU进行模型训练\"\"\"\n",
" timer, num_batches = d2l.Timer(), len(train_iter)\n",
" animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],\n",
" legend=['train loss', 'train acc', 'test acc'])\n",
" net = nn.DataParallel(net, device_ids=devices).to(devices[0])\n",
" for epoch in range(num_epochs):\n",
" # 4个维度:储存训练损失,训练准确度,实例数,特点数\n",
" metric = d2l.Accumulator(4)\n",
" for i, (features, labels) in enumerate(train_iter):\n",
" timer.start()\n",
" l, acc = train_batch_ch13(\n",
" net, features, labels, loss, trainer, devices)\n",
" metric.add(l, acc, labels.shape[0], labels.numel())\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[2], metric[1] / metric[3],\n",
" None))\n",
" test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)\n",
" animator.add(epoch + 1, (None, None, test_acc))\n",
" print(f'loss {metric[0] / metric[2]:.3f}, train acc '\n",
" f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')\n",
" print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '\n",
" f'{str(devices)}')"
]
},
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"source": [
"现在,我们可以[**定义`train_with_data_aug`函数,使用图像增广来训练模型**]。该函数获取所有的GPU,并使用Adam作为训练的优化算法,将图像增广应用于训练集,最后调用刚刚定义的用于训练和评估模型的`train_ch13`函数。\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3b314880",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T07:12:53.726893Z",
"iopub.status.busy": "2023-08-18T07:12:53.726383Z",
"iopub.status.idle": "2023-08-18T07:12:53.922963Z",
"shell.execute_reply": "2023-08-18T07:12:53.922077Z"
},
"origin_pos": 59,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)\n",
"\n",
"def init_weights(m):\n",
" if type(m) in [nn.Linear, nn.Conv2d]:\n",
" nn.init.xavier_uniform_(m.weight)\n",
"\n",
"net.apply(init_weights)\n",
"\n",
"def train_with_data_aug(train_augs, test_augs, net, lr=0.001):\n",
" train_iter = load_cifar10(True, train_augs, batch_size)\n",
" test_iter = load_cifar10(False, test_augs, batch_size)\n",
" loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
" trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
" train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)"
]
},
{
"cell_type": "markdown",
"id": "83461623",
"metadata": {
"origin_pos": 61
},
"source": [
"让我们使用基于随机左右翻转的图像增广来[**训练模型**]。\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "00b33b59",
"metadata": {
"execution": {
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"iopub.status.busy": "2023-08-18T07:12:53.926516Z",
"iopub.status.idle": "2023-08-18T07:15:26.031335Z",
"shell.execute_reply": "2023-08-18T07:15:26.030442Z"
},
"origin_pos": 62,
"tab": [
"pytorch"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"loss 0.173, train acc 0.941, test acc 0.854\n",
"4183.9 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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"## 小结\n",
"\n",
"* 图像增广基于现有的训练数据生成随机图像,来提高模型的泛化能力。\n",
"* 为了在预测过程中得到确切的结果,我们通常对训练样本只进行图像增广,而在预测过程中不使用带随机操作的图像增广。\n",
"* 深度学习框架提供了许多不同的图像增广方法,这些方法可以被同时应用。\n",
"\n",
"## 练习\n",
"\n",
"1. 在不使用图像增广的情况下训练模型:`train_with_data_aug(no_aug, no_aug)`。比较使用和不使用图像增广的训练结果和测试精度。这个对比实验能支持图像增广可以减轻过拟合的论点吗?为什么?\n",
"2. 在基于CIFAR-10数据集的模型训练中结合多种不同的图像增广方法。它能提高测试准确性吗?\n",
"3. 参阅深度学习框架的在线文档。它还提供了哪些其他的图像增广方法?\n"
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