Summary

This simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset with several common and useful features:

Choose between two different neural network architectures
Make architectures parametrizable
Read input arguments from config file or command line
- (command line arguments override config file ones)
Download FashionMNIST dataset if not already downloaded
Monitor training progress on the terminal and/or with TensorBoard logs
- Accuracy, loss, confusion matrix

More details about FashionMNIST can be found here.

It may be useful as a starting point for people who are starting to learn about PyTorch and neural networks.

Prerequisites

We assume that most users will have a GPU driver correctly configured, although the script can also be run on the CPU.

The project should work with your preferred python environment, but I have only tested it with conda (MiniConda 3) local environments. To create a local environment for this project,

conda create --name simple_pytorch_example python=3.9

and then activate it with

conda activate simple_pytorch_example

Installation on Ubuntu Linux

(Tested on Ubuntu Linux Focal 20.04.3 LTS)

Go to the directory where you want to have the project, e.g.

cd Software

Clone the simple_pytorch_example github repository

git clone https://github.com/rcasero/simple_pytorch_example.git

Install the python dependencies

cd simple_pytorch_example
python setup.py install

`train_simple_pytorch_example.py`: Main script to train the neural network

You can run the script train_simple_pytorch_example.py as

./train_simple_pytorch_example.py [options]

python train_simple_pytorch_example.py [options]

Usage summary

usage: train_simple_pytorch_example.py [-h] [-c CONFIG_FILE] [-v] [--workdir DIR] [-d STR] [-e N] [-b N] [-l F] [--validation_ratio F] [-n STR] [--conv_out_features N [N ...]]
                                       [--conv_kernel_size N] [--maxpool_kernel_size N]

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config CONFIG_FILE
                        config file path
  -v, --verbose         verbose output for debugging
  --workdir DIR         working directory to place data, logs, weights, etc subdirectories (def .)
  -d STR, --device STR  device to train on (def 'cuda', 'cpu')
  -e N, --epochs N      number of epochs for training (def 10)
  -b N, --batch_size N  batch size for training (def 64)
  -l F, --learning_rate F
                        learning rate for training (def 1e-3)
  --validation_ratio F  ratio of training dataset reserved for validation (def 0.0)
  -n STR, --nn STR      neural network architecture (def 'SimpleCNN', 'SimpleLinearNN')
  --conv_out_features N [N ...]
                        (SimpleCNN only) number of output features for each convolutional block (def 8 16)
  --conv_kernel_size N  (SimpleCNN only) kernel size of convolutional layers (def 3)
  --maxpool_kernel_size N
                        (SimpleCNN only) kernel size of max pool layers (def 2)

Args that start with '--' (eg. -v) can also be set in a config file (specified via -c). Config file syntax allows: key=value, flag=true, stuff=[a,b,c]
(for details, see syntax at https://goo.gl/R74nmi). If an arg is specified in more than one place, then commandline values override config file values
which override defaults.

Options not provided to the script take default values, e.g. running ./train_simple_pytorch_example.py -v produces the output

** Arg breakdown (defaults / config file / command line):
Command Line Args:   -v
Defaults:
  --workdir:         .
  --device:          cuda
  --epochs:          10
  --batch_size:      64
  --learning_rate:   0.001
  --validation_ratio:0.0
  --nn:              SimpleCNN
  --conv_out_features:[8, 16]
  --conv_kernel_size:3
  --maxpool_kernel_size:2

Arguments that start with -- can have their default values overridden using a configuration file (-c CONFIG_FILE). A configuration file is just a text file (e.g. config.txt) that looks like this:

device = cuda
epochs = 20
batch_size = 64
learning_rate = 1e-3
validation_ratio = 0.2
nn = SimpleCNN
conv_out_features = [8, 16]
conv_kernel_size = 3
maxpool_kernel_size = 2

Note that when running ./train_simple_pytorch_example.py -v -c config.txt the defaults have been replaced by the arguments provided in the config file:

** Arg breakdown (defaults / config file / command line):
Command Line Args:   -v -c config.txt
Config File (config.txt):
  device:            cuda
  epochs:            20
  batch_size:        64
  learning_rate:     1e-3
  validation_ratio:  0.2
  nn:                SimpleCNN
  conv_out_features: [8, 16]
  conv_kernel_size:  3
  maxpool_kernel_size:2
Defaults:
  --workdir:         .

Command line arguments override both defaults and configuration file arguments, e.g.

./train_simple_pytorch_example.py --nn SimpleCNN -v --conv_out_features 8 16 32 -e 5

FashionMNIST data download

When train_simple_pytorch_example.py runs, it checks whether the FashionMNIST data has already been downloaded to WORKDIR/data, and if not, it downloads it automatically.

Network architectures

We provide two neural network architectures that can be selected with option --nn SimpleLinearNN or --nn SimpleCNN.

SimpleLinearNN is a network with fully connected layers

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
SimpleLinearNN                           --                        --
├─Flatten: 1-1                           [1, 784]                  --
├─Sequential: 1-2                        [1, 10]                   --
│    └─Linear: 2-1                       [1, 512]                  401,920
│    └─ReLU: 2-2                         [1, 512]                  --
│    └─Linear: 2-3                       [1, 512]                  262,656
│    └─ReLU: 2-4                         [1, 512]                  --
│    └─Linear: 2-5                       [1, 10]                   5,130
==========================================================================================

SimpleCNN is a traditional convolutional neural network (CNN) formed by concatenation of convolutional blocks (Conv2d + ReLU + MaxPool2d + BatchNorm2d). Those blocks are followed by a 1x1 convolution and a fully connected layer with 10 outputs. The hyperparameters that the user can configure are (they are ignored for the other network):

--conv_kernel_size N: Size of the convolutional kernels (NxN, dafault 3x3).
--maxpool_kernel_size N: Size of the maxpool kernels (NxN, dafault 2x2).
--conv_out_features N1 [N2 ...]: Each number adds a convolutional block with the corresponding number of output features. E.g. --conv_out_features 8 16 32 creates a network with 3 blocks

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
SimpleCNN                                --                        --
├─ModuleList: 1-1                        --                        --
│    └─Conv2d: 2-1                       [1, 8, 28, 28]            80
│    └─ReLU: 2-2                         [1, 8, 28, 28]            --
│    └─MaxPool2d: 2-3                    [1, 8, 14, 14]            --
│    └─BatchNorm2d: 2-4                  [1, 8, 14, 14]            16
│    └─Conv2d: 2-5                       [1, 16, 14, 14]           1,168
│    └─ReLU: 2-6                         [1, 16, 14, 14]           --
│    └─MaxPool2d: 2-7                    [1, 16, 7, 7]             --
│    └─BatchNorm2d: 2-8                  [1, 16, 7, 7]             32
│    └─Conv2d: 2-9                       [1, 32, 7, 7]             4,640
│    └─ReLU: 2-10                        [1, 32, 7, 7]             --
│    └─MaxPool2d: 2-11                   [1, 32, 3, 3]             --
│    └─BatchNorm2d: 2-12                 [1, 32, 3, 3]             64
│    └─Conv2d: 2-13                      [1, 1, 3, 3]              289
│    └─Flatten: 2-14                     [1, 9]                    --
│    └─Linear: 2-15                      [1, 10]                   100
==========================================================================================

General training options

Currently, the loss (torch.nn.CrossEntropyLoss) and optimizer (torch.optim.SGD) are fixed.

Parameters common to both architectures are

--epochs N: number of training epochs.
--batch_size N: size of the training batch (if the dataset size is not a multiple of the batch size, the last batch will be smaller).
--learning_rate F: learning rate.
--validation_ratio F: by default, the script uses all the training data in FashionMNIST for training. But the user can choose to split the training data between training and validation. (The test data is a separate dataset in FashionMNIST).

Output network parameters

Once the network is trained, the model.state_dict() is saved to WORKDIR/models/LOGFILENAME.state_dict.

Monitoring

Option --verbose outputs detailed information about the script arguments, datasets, network architecture and training progress.

** Training:
Epoch 1/10
-------------------------------
train mean loss: 2.3913  [     0/ 60000]
train mean loss: 2.1813  [  6400/ 60000]
train mean loss: 2.1227  [ 12800/ 60000]
train mean loss: 2.0780  [ 19200/ 60000]
train mean loss: 1.9196  [ 25600/ 60000]
train mean loss: 1.6919  [ 32000/ 60000]
train mean loss: 1.4112  [ 38400/ 60000]
train mean loss: 1.2632  [ 44800/ 60000]
train mean loss: 1.0215  [ 51200/ 60000]
train mean loss: 0.8559  [ 57600/ 60000]
Training: Mean loss: 1.6672
Test: Accuracy: 63.8%, Mean loss: 0.9794
Validation: Accuracy: nan%, Mean loss:    nan
Epoch 2/10
-------------------------------
train mean loss: 1.0026  [     0/ 60000]
train mean loss: 0.8822  [  6400/ 60000]
...

Training progress can also be monitored with TensorBoard. The script saves TensorBoard logs to WORKDIR/runs, with a filename formed by the date (YYYY-MM-DD), time (HH-MM-SS), hostname and network architecture (e.g. 2021-11-25_01-15-49_marcel_SimpleCNN). To monitor the logs either during training or afterwards, run

tensorboard --logdir=runs &

and browse the URL displayed on the terminal, e.g. http://localhost:6006/.

If you are working remotely on the GPU server, you need to forward the remote server's port to your local machine

ssh -L 6006:localhost:6006 [email protected]_IP

We provide plots for Accuracy (%), Mean loss and the Confusion Matrix

Results

SimpleLinearNN

Experiment 2021-11-26_01-33-52_marcel_SimpleLinearNN run with parameters:

./train_simple_pytorch_example.py -v --nn SimpleLinearNN --validation_ratio 0.2 -e 100

** All args:
Namespace(config_file=None, verbose=True, workdir='.', device='cuda', epochs=100, batch_size=64, learning_rate=0.001, validation_ratio=0.2, nn='SimpleLinearNN', conv_out_features=[8, 16], conv_kernel_size=3, maxpool_kernel_size=2)
** Arg breakdown (defaults / config file / command line):
Command Line Args:   -v --nn SimpleLinearNN --validation_ratio 0.2 -e 100
Defaults:
  --workdir:         .
  --device:          cuda
  --batch_size:      64
  --learning_rate:   0.001
  --conv_out_features:[8, 16]
  --conv_kernel_size:3
  --maxpool_kernel_size:2

** GPU found:
NVIDIA GeForce GTX 1050
** Datasets:
Image size (H, W): (28, 28)
Training samples: 48000
Validation samples: 12000
Testing samples: 10000
Classes: {'T-shirt/top': 0, 'Trouser': 1, 'Pullover': 2, 'Dress': 3, 'Coat': 4, 'Sandal': 5, 'Shirt': 6, 'Sneaker': 7, 'Bag': 8, 'Ankle boot': 9}
** Neural network architecture:
==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
SimpleLinearNN                           --                        --
├─Flatten: 1-1                           [1, 784]                  --
├─Sequential: 1-2                        [1, 10]                   --
│    └─Linear: 2-1                       [1, 512]                  401,920
│    └─ReLU: 2-2                         [1, 512]                  --
│    └─Linear: 2-3                       [1, 512]                  262,656
│    └─ReLU: 2-4                         [1, 512]                  --
│    └─Linear: 2-5                       [1, 10]                   5,130
==========================================================================================
Total params: 669,706
Trainable params: 669,706
Non-trainable params: 0
Total mult-adds (M): 0.67
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.01
Params size (MB): 2.68
Estimated Total Size (MB): 2.69
==========================================================================================

The final metrics (after 100 epochs) are shown under each corresponding figure:

Mean loss:
- Training (brown): 0.4125
- Test (dark blue): 0.4571
- Validation (cyan): 0.4478

Accuracy:
- Test (pink): 83.8%
- Validation (green): 84.3%

SimpleCNN

Experiment 2021-11-26_02-17-18_marcel_SimpleCNN run with parameters:

./train_simple_pytorch_example.py -v --nn SimpleCNN --validation_ratio 0.2 -e 100 --conv_out_features 8 16 --conv_kernel_size 3 --maxpool_kernel_size 2

** All args:
Namespace(config_file=None, verbose=True, workdir='.', device='cuda', epochs=100, batch_size=64, learning_rate=0.001, validation_ratio=0.2, nn='SimpleCNN', conv_out_features=[8, 16], conv_kernel_size=3, maxpool_kernel_size=2)
** Arg breakdown (defaults / config file / command line):
Command Line Args:   -v --nn SimpleCNN --validation_ratio 0.2 -e 100 --conv_out_features 8 16 --conv_kernel_size 3 --maxpool_kernel_size 2
Defaults:
  --workdir:         .
  --device:          cuda
  --batch_size:      64
  --learning_rate:   0.001

** GPU found:
NVIDIA GeForce GTX 1050
** Datasets:
Image size (H, W): (28, 28)
Training samples: 48000
Validation samples: 12000
Testing samples: 10000
Classes: {'T-shirt/top': 0, 'Trouser': 1, 'Pullover': 2, 'Dress': 3, 'Coat': 4, 'Sandal': 5, 'Shirt': 6, 'Sneaker': 7, 'Bag': 8, 'Ankle boot': 9}
** Neural network architecture:
==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
SimpleCNN                                --                        --
├─ModuleList: 1-1                        --                        --
│    └─Conv2d: 2-1                       [1, 8, 28, 28]            80
│    └─ReLU: 2-2                         [1, 8, 28, 28]            --
│    └─MaxPool2d: 2-3                    [1, 8, 14, 14]            --
│    └─BatchNorm2d: 2-4                  [1, 8, 14, 14]            16
│    └─Conv2d: 2-5                       [1, 16, 14, 14]           1,168
│    └─ReLU: 2-6                         [1, 16, 14, 14]           --
│    └─MaxPool2d: 2-7                    [1, 16, 7, 7]             --
│    └─BatchNorm2d: 2-8                  [1, 16, 7, 7]             32
│    └─Conv2d: 2-9                       [1, 1, 7, 7]              145
│    └─Flatten: 2-10                     [1, 49]                   --
│    └─Linear: 2-11                      [1, 10]                   500
==========================================================================================
Total params: 1,941
Trainable params: 1,941
Non-trainable params: 0
Total mult-adds (M): 0.30
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.09
Params size (MB): 0.01
Estimated Total Size (MB): 0.11
==========================================================================================

Mean loss:
- Training (dark blue): 0.3186
- Test (orange): 0.3686
- Validation (brown): 0.3372

Accuracy:
- Test (cyan): 87.2%
- Validation (pink): 88.1%

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v1.0.0(Jan 7, 2022)
Toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset with several common and useful features:

Choose between two different neural network architectures

Make architectures parametrizable

Read input arguments from config file or command line

(command line arguments override config file ones)

Download FashionMNIST dataset if not already downloaded

Monitor training progress on the terminal and/or with TensorBoard logs

Accuracy, loss, confusion matrix

Source code(tar.gz)
Source code(zip)

simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

Related tags

Overview

Summary

Prerequisites

Installation on Ubuntu Linux

train_simple_pytorch_example.py: Main script to train the neural network

Usage summary

FashionMNIST data download

Network architectures

General training options

Output network parameters

Monitoring

Results

SimpleLinearNN

SimpleCNN

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`train_simple_pytorch_example.py`: Main script to train the neural network