Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.

In [3]:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch,numpy
The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
Author: Sebastian Raschka

Python implementation: CPython
Python version       : 3.8.8
IPython version      : 7.30.1

torch: 1.10.1
numpy: 1.22.2

Model Zoo -- Using PyTorch Dataset Loading Utilities for Custom Datasets (Images from Quickdraw)

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

In this example, we are using the Quickdraw dataset consisting of handdrawn objects, which is available at

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

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 ( tool. If you want to install that, you can then use

mkdir quickdraw-npy
gsutil -m cp gs://quickdraw_dataset/full/numpy_bitmap/*.npy quickdraw-npy

Note that if you download the whole dataset, this will take up 37 Gb of storage space.


In [4]:
import pandas as pd
import numpy as np
import os

import torch
from import Dataset
from import DataLoader
from torchvision import transforms

%matplotlib inline
import matplotlib.pyplot as plt
from PIL import Image


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:

In [5]:
label_dict = {
         "lollipop": 0,
         "binoculars": 1,
         "mouse": 2,
         "basket": 3,
         "penguin": 4,
         "washing machine": 5,
         "canoe": 6,
         "eyeglasses": 7,
         "beach": 8,
         "screwdriver": 9,

The dictionary values shall represent class labels that we could use for a classification task, for example.

Conversion to PNG files

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):

In [6]:
# load utilities from ../
import sys
sys.path.insert(0, '..') 
from helper import quickdraw_npy_to_imagefile


Preprocessing into train/valid/test subsets and creating a label files

For convenience, let's create a CSV file mapping file names to class labels. First, let's collect the files and labels.

In [7]:
paths, labels = [], []

main_dir = 'quickdraw-png_set1/'

for d in os.listdir(main_dir):
    subdir = os.path.join(main_dir, d)
    if not os.path.isdir(subdir):
    for f in os.listdir(subdir):
        path = os.path.join(d, f)
print('Num paths:', len(paths))
print('Num labels:', len(labels))
Num paths: 1515745
Num labels: 1515745

Next, we shuffle the dataset and assign 70% of the dataset for training, 10% for validation, and 20% for testing.

In [10]:
from mlxtend.preprocessing import shuffle_arrays_unison

paths2, labels2 = shuffle_arrays_unison(arrays=[np.array(paths), np.array(labels)], random_seed=3)

cut1 = int(len(paths)*0.7)
cut2 = int(len(paths)*0.8)

paths_train, labels_train = paths2[:cut1], labels2[:cut1]
paths_valid, labels_valid = paths2[cut1:cut2], labels2[cut1:cut2]
paths_test, labels_test = paths2[cut2:], labels2[cut2:]

Finally, let us create a CSV file that maps the file paths to the class labels:

In [11]:
df = pd.DataFrame(
    {'Path': paths_train,
     'Label': labels_train,

df = df.set_index('Path')

penguin/penguin_112865.png 4
binoculars/binoculars_040058.png 1
eyeglasses/eyeglasses_208525.png 7
basket/basket_050889.png 3
lollipop/lollipop_037650.png 0
In [20]:
df = pd.DataFrame(
    {'Path': paths_valid,
     'Label': labels_valid,

df = df.set_index('Path')

basket/basket_046788.png 3
basket/basket_074810.png 3
binoculars/binoculars_077567.png 1
screwdriver/screwdriver_007042.png 9
binoculars/binoculars_037327.png 1
In [21]:
df = pd.DataFrame(
    {'Path': paths_test,
     'Label': labels_test,

df = df.set_index('Path')

eyeglasses/eyeglasses_051135.png 7
mouse/mouse_059179.png 2
basket/basket_046866.png 3
penguin/penguin_043578.png 4
screwdriver/screwdriver_081750.png 9

Now, let's open one of the images to make sure they look ok:

In [23]:
main_dir = 'quickdraw-png_set1/'

img =, df.index[1]))
img = np.asarray(img, dtype=np.uint8)
plt.imshow(np.array(img), cmap='binary');
(28, 28)

Implementing a Custom Dataset Class

Now, we implement a custom Dataset for reading the images. The __getitem__ method will

  1. read a single image from disk based on an index (more on batching later)
  2. perform a custom image transformation (if a transform argument is provided in the __init__ construtor)
  3. return a single image and it's corresponding label
In [24]:
class QuickdrawDataset(Dataset):
    """Custom Dataset for loading Quickdraw images"""

    def __init__(self, txt_path, img_dir, transform=None):
        df = pd.read_csv(txt_path, sep=",", index_col=0)
        self.img_dir = img_dir
        self.txt_path = txt_path
        self.img_names = df.index.values
        self.y = df['Label'].values
        self.transform = transform

    def __getitem__(self, index):
        img =,
        if self.transform is not None:
            img = self.transform(img)
        label = self.y[index]
        return img, label

    def __len__(self):
        return self.y.shape[0]

Now that we have created our custom Dataset class, let us add some custom transformations via the transforms utilities from torchvision, we

  1. normalize the images (here: dividing by 255)
  2. converting the image arrays into PyTorch tensors

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).

Finally, we initialize a DataLoader that allows us to read from the dataset.

In [25]:
# Note that transforms.ToTensor()
# already divides pixels by 255. internally

custom_transform = transforms.Compose([#transforms.Lambda(lambda x: x/255.),

train_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv',

train_loader = DataLoader(dataset=train_dataset,

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:

Iterating Through the Custom Dataset

In [26]:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

num_epochs = 2
for epoch in range(num_epochs):

    for batch_idx, (x, y) in enumerate(train_loader):
        print('Epoch:', epoch+1, end='')
        print(' | Batch index:', batch_idx, end='')
        print(' | Batch size:', y.size()[0])
        x =
        y =
Epoch: 1 | Batch index: 0 | Batch size: 128
Epoch: 2 | Batch index: 0 | Batch size: 128

Just to make sure that the batches are being loaded correctly, let's print out the dimensions of the last batch:

In [27]:
torch.Size([128, 1, 28, 28])

As we can see, each batch consists of 128 images, just as specified. However, one thing to keep in mind though is that 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):

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:

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one_image = x[0].permute(1, 2, 0)
torch.Size([28, 28, 1])
In [29]:
# note that imshow also works fine with scaled
# images in [0, 1] range.
plt.imshow('cpu')).squeeze(), cmap='binary');
In [30]:
%watermark -iv
torch      : 1.10.1
PIL        : 9.0.1
sys        : 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) 
[GCC 9.3.0]
torchvision: 0.11.2
pandas     : 1.2.5
numpy      : 1.22.2
matplotlib : 3.3.4