{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
"- Author: Sebastian Raschka\n",
"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"\n",
"CPython 3.7.1\n",
"IPython 7.2.0\n",
"\n",
"torch 1.0.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Zoo -- Using PyTorch Dataset Loading Utilities for Custom Datasets (Images from Quickdraw)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook provides an example for how to load an image dataset, stored as individual PNG files, using PyTorch's data loading utilities. For a more in-depth discussion, please see the official\n",
"\n",
"- [Data Loading and Processing Tutorial](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html)\n",
"- [torch.utils.data](http://pytorch.org/docs/master/data.html) API documentation\n",
"\n",
"In this example, we are using the Quickdraw dataset consisting of handdrawn objects, which is available at https://quickdraw.withgoogle.com. \n",
"\n",
"To execute the following examples, you need to download the \".npy\" (bitmap files in NumPy). You don't need to download all of the 345 categories but only a subset you are interested in. The groups/subsets can be individually downloaded from https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap\n",
"\n",
"Unfortunately, the Google cloud storage currently does not support selecting and downloading multiple groups at once. Thus, in order to download all groups most coneniently, we need to use their `gsutil` (https://cloud.google.com/storage/docs/gsutil_install) tool. If you want to install that, you can then use \n",
"\n",
" mkdir quickdraw-npy\n",
" gsutil -m cp gs://quickdraw_dataset/full/numpy_bitmap/*.npy quickdraw-npy\n",
"\n",
"Note that if you download the whole dataset, this will take up 37 Gb of storage space.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"\n",
"import torch\n",
"from torch.utils.data import Dataset\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import transforms\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from PIL import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After downloading the dataset to a local directory, `quickdraw-npy`, the next step is to select certain groups we are interested in analyzing. Let's say we are interested in the following groups defined in the `label_dict` in the next code cell:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"label_dict = {\n",
" \"lollipop\": 0,\n",
" \"binoculars\": 1,\n",
" \"mouse\": 2,\n",
" \"basket\": 3,\n",
" \"penguin\": 4,\n",
" \"washing machine\": 5,\n",
" \"canoe\": 6,\n",
" \"eyeglasses\": 7,\n",
" \"beach\": 8,\n",
" \"screwdriver\": 9,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dictionary values shall represent class labels that we could use for a classification task, for example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conversion to PNG files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we are going to convert the groups we are interested in (specified in the dictionary above) to individual PNG files using a helper function (note that this might take a while):"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# load utilities from ../helper.py\n",
"import sys\n",
"sys.path.insert(0, '..') \n",
"from helper import quickdraw_npy_to_imagefile\n",
"\n",
" \n",
"quickdraw_npy_to_imagefile(inpath='quickdraw-npy',\n",
" outpath='quickdraw-png_set1',\n",
" subset=label_dict.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocessing into train/valid/test subsets and creating a label files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, let's create a CSV file mapping file names to class labels. First, let's collect the files and labels."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num paths: 1515745\n",
"Num labels: 1515745\n"
]
}
],
"source": [
"paths, labels = [], []\n",
"\n",
"main_dir = 'quickdraw-png_set1/'\n",
"\n",
"for d in os.listdir(main_dir):\n",
" subdir = os.path.join(main_dir, d)\n",
" if not os.path.isdir(subdir):\n",
" continue\n",
" for f in os.listdir(subdir):\n",
" path = os.path.join(d, f)\n",
" paths.append(path)\n",
" labels.append(label_dict[d])\n",
" \n",
"print('Num paths:', len(paths))\n",
"print('Num labels:', len(labels))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we shuffle the dataset and assign 70% of the dataset for training, 10% for validation, and 20% for testing."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from mlxtend.preprocessing import shuffle_arrays_unison\n",
"\n",
"\n",
"paths2, labels2 = shuffle_arrays_unison(arrays=[np.array(paths), np.array(labels)], random_seed=3)\n",
"\n",
"\n",
"cut1 = int(len(paths)*0.7)\n",
"cut2 = int(len(paths)*0.8)\n",
"\n",
"paths_train, labels_train = paths2[:cut1], labels2[:cut1]\n",
"paths_valid, labels_valid = paths2[cut1:cut2], labels2[cut1:cut2]\n",
"paths_test, labels_test = paths2[cut2:], labels2[cut2:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let us create a CSV file that maps the file paths to the class labels (here only shown for the training set for simplicity):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Path</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>penguin/penguin_182463.png</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mouse/mouse_139942.png</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>screwdriver/screwdriver_066105.png</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beach/beach_026711.png</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eyeglasses/eyeglasses_035833.png</th>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Label\n",
"Path \n",
"penguin/penguin_182463.png 4\n",
"mouse/mouse_139942.png 2\n",
"screwdriver/screwdriver_066105.png 9\n",
"beach/beach_026711.png 8\n",
"eyeglasses/eyeglasses_035833.png 7"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(\n",
" {'Path': paths_train,\n",
" 'Label': labels_train,\n",
" })\n",
"\n",
"df = df.set_index('Path')\n",
"df.to_csv('quickdraw_png_set1_train.csv')\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's open one of the images to make sure they look ok:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(28, 28)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"main_dir = 'quickdraw-png_set1/'\n",
"\n",
"img = Image.open(os.path.join(main_dir, df.index[99]))\n",
"img = np.asarray(img, dtype=np.uint8)\n",
"print(img.shape)\n",
"plt.imshow(np.array(img), cmap='binary');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementing a Custom Dataset Class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we implement a custom `Dataset` for reading the images. The `__getitem__` method will\n",
"\n",
"1. read a single image from disk based on an `index` (more on batching later)\n",
"2. perform a custom image transformation (if a `transform` argument is provided in the `__init__` construtor)\n",
"3. return a single image and it's corresponding label"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class QuickdrawDataset(Dataset):\n",
" \"\"\"Custom Dataset for loading Quickdraw images\"\"\"\n",
"\n",
" def __init__(self, txt_path, img_dir, transform=None):\n",
" \n",
" df = pd.read_csv(txt_path, sep=\",\", index_col=0)\n",
" self.img_dir = img_dir\n",
" self.txt_path = txt_path\n",
" self.img_names = df.index.values\n",
" self.y = df['Label'].values\n",
" self.transform = transform\n",
"\n",
" def __getitem__(self, index):\n",
" img = Image.open(os.path.join(self.img_dir,\n",
" self.img_names[index]))\n",
" \n",
" if self.transform is not None:\n",
" img = self.transform(img)\n",
" \n",
" label = self.y[index]\n",
" return img, label\n",
"\n",
" def __len__(self):\n",
" return self.y.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have created our custom Dataset class, let us add some custom transformations via the `transforms` utilities from `torchvision`, we\n",
"\n",
"1. normalize the images (here: dividing by 255)\n",
"2. converting the image arrays into PyTorch tensors\n",
"\n",
"Then, we initialize a Dataset instance for the training images using the 'quickdraw_png_set1_train.csv' label file (we omit the test set, but the same concepts apply).\n",
"\n",
"Finally, we initialize a `DataLoader` that allows us to read from the dataset."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Note that transforms.ToTensor()\n",
"# already divides pixels by 255. internally\n",
"\n",
"custom_transform = transforms.Compose([#transforms.Lambda(lambda x: x/255.),\n",
" transforms.ToTensor()])\n",
"\n",
"train_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv',\n",
" img_dir='quickdraw-png_set1/',\n",
" transform=custom_transform)\n",
"\n",
"train_loader = DataLoader(dataset=train_dataset,\n",
" batch_size=128,\n",
" shuffle=True,\n",
" num_workers=4) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it, now we can iterate over an epoch using the train_loader as an iterator and use the features and labels from the training dataset for model training:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Iterating Through the Custom Dataset"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 | Batch index: 0 | Batch size: 128\n",
"Epoch: 2 | Batch index: 0 | Batch size: 128\n"
]
}
],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"torch.manual_seed(0)\n",
"\n",
"num_epochs = 2\n",
"for epoch in range(num_epochs):\n",
"\n",
" for batch_idx, (x, y) in enumerate(train_loader):\n",
" \n",
" print('Epoch:', epoch+1, end='')\n",
" print(' | Batch index:', batch_idx, end='')\n",
" print(' | Batch size:', y.size()[0])\n",
" \n",
" x = x.to(device)\n",
" y = y.to(device)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to make sure that the batches are being loaded correctly, let's print out the dimensions of the last batch:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([128, 1, 28, 28])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, each batch consists of 128 images, just as specified. However, one thing to keep in mind though is that\n",
"PyTorch uses a different image layout (which is more efficient when working with CUDA); here, the image axes are \"num_images x channels x height x width\" (NCHW) instead of \"num_images height x width x channels\" (NHWC):"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To visually check that the images that coming of the data loader are intact, let's swap the axes to NHWC and convert an image from a Torch Tensor to a NumPy array so that we can visualize the image via `imshow`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([28, 28, 1])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_image = x[0].permute(1, 2, 0)\n",
"one_image.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Cz10rrXhdRPjfknSamZ1qZt+TdL2krgL6+BYzG599ECMzGy/pR2q91Ye7JC3Kbi+S9FKBvfyNVlm5OW9laRX83LXaiteFfMknm8r4L0nHSFrt7g83vYkhmNnfa+BoLw1c2fhXRfZmZs9LmquBs772SFoqab2kdZJOlvSRpGvdvekfvOX0NlcDL13/unLz0ffYTe7tQkkbJb0n6Ui2uVMD768Le+4SfS1QAc8b3/ADguIbfkBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgvp/hck5eusCwwAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# note that imshow also works fine with scaled\n",
"# images in [0, 1] range.\n",
"plt.imshow(one_image.to(torch.device('cpu')).squeeze(), cmap='binary');"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy 1.15.4\n",
"pandas 0.23.4\n",
"torchvision 0.2.1\n",
"torch 1.0.0\n",
"PIL.Image 5.3.0\n",
"matplotlib 3.0.2\n",
"\n"
]
}
],
"source": [
"%watermark -iv"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}