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calincru/prepare.ipynb

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Buildings Classification
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prepare.ipynb
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train.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building Classification - Training & Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imports."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"\n",
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(torch.cuda.device_count())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The directory where the data was saved to as well as the save directory for the model snapshots."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data_save_dir = 'output/run+02-36-14/'\n",
"\n",
"dir_name = 'models+{}'.format(datetime.now().strftime('%H-%M-%S'))\n",
"save_dir = os.path.join(os.getcwd(), 'output', dir_name)\n",
"os.makedirs(save_dir)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Working directory: models+09-36-55\n"
]
}
],
"source": [
"print('Working directory: ', dir_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"Xs = np.load(os.path.join(data_save_dir, 'Xs.npy'))\n",
"ys = np.load(os.path.join(data_save_dir, 'ys.npy'))\n",
"indices = np.load(os.path.join(data_save_dir, 'indices.npy'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"n_classes = np.unique(ys).shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total data points: 19856\n",
"Number of classes: 25\n"
]
}
],
"source": [
"print('Total data points: ', len(Xs))\n",
"print('Number of classes: ', n_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 123 128 129 136 138 185 207 237 237 288 350 383 427 486\n",
" 541 756 946 978 1108 1268 1493 1610 1624 1733 4345]\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Number of total class instances')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
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"source": [
"labels, counts = np.unique(ys, return_counts=True)\n",
"print(np.sort(counts))\n",
"\n",
"plt.bar(labels, counts)\n",
"plt.title('Number of total class instances')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the classes are imbalanced."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test, idx_train, idx_test = \\\n",
" train_test_split(Xs, ys, indices, test_size=0.25, stratify=ys)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
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"text/plain": [
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(1, 2, figsize=(8,4))\n",
"axes[0].bar(np.arange(n_classes), np.bincount(y_train))\n",
"axes[0].set_title('Class counts (train)')\n",
"axes[1].bar(np.arange(n_classes), np.bincount(y_test))\n",
"axes[1].set_title('Class counts (test)');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resample training set"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from imblearn.over_sampling import RandomOverSampler"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"orig_shape = X_train.shape\n",
"X_train = X_train.reshape(orig_shape[0], -1)\n",
"\n",
"X_train_resampled, y_train_resampled = \\\n",
" RandomOverSampler(sampling_strategy='all').fit_resample(X_train, y_train)\n",
"\n",
"X_train = X_train.reshape(orig_shape)\n",
"X_train_resampled = X_train_resampled.reshape(-1, *orig_shape[1:])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3259 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259\n",
" 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259 3259]\n"
]
}
],
"source": [
"print(np.sort(np.bincount(y_train_resampled)))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New training set size: 81475\n"
]
}
],
"source": [
"print('New training set size: ', len(X_train_resampled))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data for training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The PyTorch `Dataset` abstraction."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from torch import Tensor\n",
"from torch.utils.data import TensorDataset\n",
"\n",
"class BuildingsDataset(TensorDataset):\n",
" \n",
" mean = torch.tensor([0.485, 0.456, 0.406]).reshape(3, 1, 1)\n",
" std = torch.tensor([0.229, 0.224, 0.225]).reshape(3, 1, 1)\n",
" \n",
" def __init__(self, Xs: Tensor, ys: Tensor):\n",
" # Transform the datasets as expected by the ImageNet pre-trained model.\n",
" Xs = (Xs / 256.0 - self.mean) / self.std\n",
" super().__init__(Xs, ys)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the train and test datasets."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"X_train_resampled = torch.as_tensor(np.transpose(X_train_resampled, [0, 3, 1, 2]))\n",
"y_train_resampled = torch.as_tensor(y_train_resampled)\n",
"train_ds = BuildingsDataset(X_train_resampled, y_train_resampled)\n",
"\n",
"X_test = torch.as_tensor(np.transpose(X_test, [0, 3, 1, 2]))\n",
"y_test = torch.as_tensor(y_test)\n",
"test_ds = BuildingsDataset(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train ds size: 81475\n",
"Test ds size: 4964\n"
]
}
],
"source": [
"print('Train ds size: {}\\nTest ds size: {}'.format(len(train_ds), len(test_ds)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the feature layers of the VGG11-BN (with BatchNormalization) architecture, except for the layers before the last pooling, since our image sizes is half of ImageNet images.\n",
"\n",
"We add a fully connected layer between the convolutions and the last softmax layer."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import torchvision\n",
"\n",
"class BuildingClassifier(torch.nn.Module):\n",
" \n",
" def __init__(self, pretrained=True):\n",
" super().__init__()\n",
" vgg11 = torchvision.models.vgg11_bn(pretrained=pretrained)\n",
" self.features = vgg11.features[:-7] # The feature map has shape [?, 512, 7, 7]\n",
" self.classifier = torch.nn.Sequential(\n",
" torch.nn.Flatten(),\n",
" \n",
" torch.nn.Linear(in_features=25088, out_features=2048, bias=True),\n",
" torch.nn.ReLU(inplace=True),\n",
" torch.nn.Dropout(p=0.5, inplace=False),\n",
" \n",
" torch.nn.Linear(in_features=2048, out_features=n_classes, bias=True),\n",
" )\n",
" \n",
" def forward(self, x):\n",
" x = self.features(x)\n",
" return self.classifier(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train It"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from torch.utils.data import DataLoader\n",
"\n",
"class TrainingEngine:\n",
" \n",
" def __init__(self, model: torch.nn.Module):\n",
" self.model = model\n",
" self.optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
" self.criterion = torch.nn.CrossEntropyLoss()\n",
" self.epochs = 30\n",
" self.batch_size = 256\n",
" self.save_every_epoch = 5\n",
" self.num_workers = 15\n",
" \n",
" def __call__(self, train_ds: BuildingsDataset):\n",
" # Make use of several GPUs if possible.\n",
" self.model = torch.nn.DataParallel(self.model)\n",
" if torch.cuda.is_available():\n",
" self.model = self.model.cuda()\n",
" \n",
" data_loader = DataLoader(train_ds, \n",
" batch_size=self.batch_size, \n",
" shuffle=True,\n",
" num_workers=self.num_workers,\n",
" pin_memory=True)\n",
" for epoch in range(self.epochs):\n",
" if epoch > 0 and epoch % self.save_every_epoch == 0:\n",
" save_path = os.path.join(save_dir, 'model-{}.pth'.format(epoch))\n",
" torch.save(self.model.state_dict(), save_path)\n",
" \n",
" self._train(data_loader, epoch)\n",
" \n",
" def _train(self, data_loader: DataLoader, epoch: int):\n",
" self.model.train()\n",
" \n",
" losses = []\n",
" for X, y in data_loader:\n",
" if torch.cuda.is_available():\n",
" X = X.cuda(non_blocking=True)\n",
" y = y.cuda(non_blocking=True)\n",
" \n",
" output = self.model(X)\n",
" loss = self.criterion(output, y)\n",
" \n",
" self.optimizer.zero_grad()\n",
" loss.backward()\n",
" self.optimizer.step()\n",
" \n",
" losses.append(loss.item())\n",
" \n",
" now = datetime.now().strftime('%H:%M:%S')\n",
" print('[{}] epoch {}: {:.6f}'.format(now, epoch, np.mean(losses)))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"training_engine = TrainingEngine(BuildingClassifier())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[09:39:48] epoch 0: 1.0699\n",
"[09:41:22] epoch 1: 0.2014\n",
"[09:42:57] epoch 2: 0.0481\n",
"[09:44:31] epoch 3: 0.0111\n",
"[09:46:05] epoch 4: 0.0044\n",
"[09:47:40] epoch 5: 0.0023\n",
"[09:49:14] epoch 6: 0.0014\n",
"[09:50:47] epoch 7: 0.0010\n",
"[09:52:21] epoch 8: 0.0007\n",
"[09:53:55] epoch 9: 0.0005\n",
"[09:55:30] epoch 10: 0.0004\n",
"[09:57:04] epoch 11: 0.0003\n",
"[09:58:38] epoch 12: 0.0002\n",
"[10:00:12] epoch 13: 0.0002\n",
"[10:01:45] epoch 14: 0.0001\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"training_engine(train_ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"class InferenceEngine:\n",
" \n",
" def __init__(self, model: torch.nn.Module):\n",
" self.model = model\n",
" self.batch_size = 256\n",
" self.num_workers = 15\n",
" \n",
" @torch.no_grad()\n",
" def __call__(self, test_ds: BuildingsDataset):\n",
" data_loader = DataLoader(test_ds, \n",
" batch_size=self.batch_size, \n",
" shuffle=False, \n",
" num_workers=self.num_workers,\n",
" pin_memory=True)\n",
" \n",
" \n",
" cm = torch.zeros(n_classes, n_classes)\n",
" a1s = 0; a5s = 0\n",
" for X, y in data_loader:\n",
" if torch.cuda.is_available():\n",
" X = X.cuda(non_blocking=True)\n",
" y = y.cuda(non_blocking=True)\n",
"\n",
" # The predictions.\n",
" output = self.model(X)\n",
" y_pred = output.argmax(dim=1)\n",
" \n",
" # Increment the corresponding entries in the confusion matrix.\n",
" for i, j in zip(y, y_pred):\n",
" cm[i, j] += 1\n",
"\n",
" a1, a5 = self._accuracy_at_k(output.data, y, topk=(1,5))\n",
" a1s += a1.item()\n",
" a5s += a5.item()\n",
" \n",
" return cm, a1s / len(data_loader), a5s / len(data_loader)\n",
" \n",
" def _accuracy_at_k(self, output, target, topk=(1,)):\n",
" \"\"\"Computes the precision@k for the specified values of k\"\"\"\n",
" _, pred = output.topk(max(topk), dim=1, largest=True, sorted=True)\n",
" pred = pred.t()\n",
" correct = pred.eq(target.view(1, -1).expand_as(pred))\n",
"\n",
" res = []\n",
" for k in topk:\n",
" correct_k = correct[:k].view(-1).float().sum(0)\n",
" res.append(correct_k.mul_(100.0 / self.batch_size))\n",
" \n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"inference_engine = InferenceEngine(training_engine.model)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"cm, mean_a1, mean_a5 = inference_engine(test_ds)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prec@1: 38.242, Prec@5: 80.215\n"
]
}
],
"source": [
"print('Prec@1: {:.3f}, Prec@5: {:.3f}'.format(mean_a1, mean_a5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The confusion matrix."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<Figure size 432x432 with 2 Axes>"
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},
"metadata": {
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],
"source": [
"import itertools\n",
"\n",
"# Normalize it\n",
"cm = cm / cm.sum(axis=0, keepdims=True)\n",
"\n",
"plt.figure(figsize=(6, 6))\n",
"plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
"plt.colorbar()\n",
"plt.ylabel('True label')\n",
"plt.xlabel('Predicted label')\n",
"plt.title('Normalized Confusion Matrix')\n",
"plt.tight_layout()"
]
}
],
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