Skip to main content Link Search Menu Expand Document (external link)
MesoNet: Docs
Package cli
Source https://bit.ly/3nydEe5
Description Command-line interface for MesoNet variants.
Depends on mesonet.train, mesonet.model

Table of Contents

Introduction

The project comes equipped with a command line interface (CLI) to train and obtain predictions from MesoNet variants, built using argparse.

The entrypoint for the CLI is the mesonet.py file. You can use the CLI with the following command:

python mesonet.py [sub-command]

There are two sub-commands available:

  • train - Sub-command responsible for training a MesoNet variant. Currently, it only supports training the architecture detailed in the original paper.
  • predict - Sub-command responsible for obtaining predictions on data from a saved MesoNet variant. You can use both an HDF5 file and a directory containing a .pb file.

Training Sub-Command

[source]

The train sub-command is responsible for training a MesoNet variant. Currently, it only supports training the architecture detailed in the original paper.

Usage 

python mesonet.py train
    [-h] [-nda] [-s SPLIT] [-e EPOCHS]
    [-bs BS] [-nc] [-lo LOSS] [-lr LR]
    [-nld] [-dr RATE] [-dl LIMIT] [-nse]
    [-m MONITOR] [--mode {max,min,auto}] [-pa PATIENCE] [-ncp]
    [-ntb] [-np] path

Arguments and Options

Run the following command to get the help text:

python mesonet.py train --help

Data Directory, Augmentations and Validation Split

  • path: Path to the directory containing the training data. See Note on Directory Structure for the expected directory structure.
  • -nda, --no-def-aug: Disable the default augmentations used in the paper. See Note on Augmentations for a list of the augmentations applied.
  • -s SPLIT, --validation-split SPLIT: Size of the validation split as a proportion (between 0 and 1). Defaults to None.

Epochs and Batch Size

  • -e EPOCHS, --epochs EPOCHS: Number of epochs the model should be trained for. Defaults to 30.
  • -bs BS, --batch-size BS: Batch size for the dataset. Defaults to 32.

Compilation, Learning Rate and Learning Rate Decay

  • -nc, --no-compile: Disable model compilation with an optimizer (useful for resuming training). For details on which optimizer is used, see the compile argument in Training.
  • -lo LOSS, --loss LOSS: Loss function. Defaults to binary_crossentropy.
  • -lr LR, --learning-rate LR: Learning rate (initial) of the model. Defaults to 0.001.
  • -nld, --no-decay: Disable learning rate decay.
  • -dr RATE, --decay-rate RATE: Rate at which the learning rate should decay. Defaults to 0.1.
  • -dl LIMIT, --decay-limit LIMIT: Learning rate value after which decay should stop. Defaults to 1e-06. See Note on Learning Rate Schedule to learn more about how this is used.

Early Stopping

  • -nse, --no-stop-early: Disable EarlyStopping callback.
  • -m MONITOR, --monitor MONITOR: Metric to monitor for the EarlyStopping callback. Defaults to val_accuracy.
  • --mode {max,min,auto}: See https://keras.io/api/callbacks/early_stopping/. Defaults to max.
  • -pa PATIENCE, --patience PATIENCE: Number of epochs with no improvement after which training will be stopped. Defaults to 20.

Model Checkpoint, TensorBoard and Loss Curve

  • -ncp, --no-checkpoint: Disable ModelCheckpoint callback.
  • -ntb, --no-tensorboard: Disable TensorBoard callback.
  • -np, --no-plot: Disable plotting of loss curve.

Examples

In all the examples below, data is assumed to be at data/train/.

  • Training with all default options:

    python mesonet.py train data/train/

  • Training for 50 epochs with a batch size of 64:

    python mesonet.py train -e 50 -bs 64 data/train/

  • Turning off TensorBoard:

    python mesonet.py train -ntb data/train/

  • Training for 50 epochs with a batch size of 64, initial learning rate of 0.0001 and decay of 0.25:

    python mesonet.py train -e 50 -bs 64 -lr 1e-4 -dr 0.25 data/train/

Prediction Sub-Command

[source]

The predict sub-command is responsible for obtaining predictions on data from a saved MesoNet variant. You can use both an HDF5 file and a directory containing a .pb file. Note that this only works when the output layer has a sigmoid activation.

Usage 

python mesonet.py predict [-h] [-t THRESHOLD] [-p] [-cl] model data_dir

Arguments and Options

Run the following command to get the help text:

python mesonet.py predict --help

Positional Arguments

  • model: Path to the file/directory containing the trained MesoNet variant.
  • data_dir: Path to the directory containing the dataset. Contrary to train sub-command, it isn’t necessary for your directory to have any specific structure.

Optional Arguments

  • -t THRESHOLD, --threshold THRESHOLD: Probability threshold for classification. Images having sigmoid probabilities equal to or above this value will be classified into the positive class. Defaults to 0.5.
  • -p, --include-probabilites: Show predicted probabilities along with labels. Note that the probability is always for the image belonging to the predicted class and not for the image belonging to the positive class.
  • -cl, --classification-report: Generate the classification report for the dataset. Note that to be able to use this option, the data directory does need to have the same structure as that mentioned in Note on Directory Structure.

Example

Assuming data is in data/predict/ and using one of the trained models provided in trained_models, below we obtain predictions, enabling the probabilities and classification report options:

$ python mesonet.py predict -p -cl trained_models/model1_18epochs_valacc0.9252.hdf5 data/predict
Found 55 images belonging to 2 classes.
1/1 [==============================] - 2s 2s/step
Classification report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        43
           1       1.00      1.00      1.00        12

    accuracy                           1.00        55
   macro avg       1.00      1.00      1.00        55
weighted avg       1.00      1.00      1.00        55

###############################################
| Filenames            | Predictions   |   Probability |
|----------------------|---------------|---------------|
| deepfake/114_132.jpg | Deepfake      |       99.5502 |
| deepfake/114_144.jpg | Deepfake      |       99.9737 |
| deepfake/114_150.jpg | Deepfake      |       99.6843 |
| deepfake/114_186.jpg | Deepfake      |       99.9748 |
| deepfake/114_204.jpg | Deepfake      |       99.9995 |
| deepfake/114_228.jpg | Deepfake      |       99.9984 |
| deepfake/114_240.jpg | Deepfake      |       99.9991 |
| deepfake/114_246.jpg | Deepfake      |       99.9001 |
| deepfake/114_264.jpg | Deepfake      |       99.9998 |
| deepfake/114_276.jpg | Deepfake      |       99.9985 |
| deepfake/114_288.jpg | Deepfake      |       77.8174 |
| deepfake/114_300.jpg | Deepfake      |       99.9996 |
| deepfake/114_306.jpg | Deepfake      |       99.9999 |
| deepfake/114_324.jpg | Deepfake      |       99.9998 |
| deepfake/114_348.jpg | Deepfake      |      100.0000 |
| deepfake/114_354.jpg | Deepfake      |      100.0000 |
| deepfake/114_390.jpg | Deepfake      |       99.9987 |
| deepfake/114_396.jpg | Deepfake      |      100.0000 |
| deepfake/114_402.jpg | Deepfake      |      100.0000 |
| deepfake/114_408.jpg | Deepfake      |      100.0000 |
| deepfake/114_414.jpg | Deepfake      |       99.9996 |
| deepfake/114_426.jpg | Deepfake      |       99.9993 |
| deepfake/114_438.jpg | Deepfake      |       99.9993 |
| deepfake/114_450.jpg | Deepfake      |       99.9990 |
| deepfake/114_510.jpg | Deepfake      |       99.9999 |
| deepfake/114_516.jpg | Deepfake      |       99.9999 |
| deepfake/114_534.jpg | Deepfake      |       99.9999 |
| deepfake/114_540.jpg | Deepfake      |       99.9997 |
| deepfake/114_564.jpg | Deepfake      |       99.9965 |
| deepfake/114_570.jpg | Deepfake      |       99.9991 |
| deepfake/114_576.jpg | Deepfake      |       99.9998 |
| deepfake/114_588.jpg | Deepfake      |       99.9982 |
| deepfake/114_618.jpg | Deepfake      |       99.9999 |
| deepfake/114_642.jpg | Deepfake      |       99.9999 |
| deepfake/114_648.jpg | Deepfake      |      100.0000 |
| deepfake/114_666.jpg | Deepfake      |       99.9807 |
| deepfake/114_672.jpg | Deepfake      |       99.9989 |
| deepfake/114_702.jpg | Deepfake      |       99.9999 |
| deepfake/114_708.jpg | Deepfake      |      100.0000 |
| deepfake/114_714.jpg | Deepfake      |       99.9998 |
| deepfake/114_720.jpg | Deepfake      |       99.9995 |
| deepfake/114_726.jpg | Deepfake      |       99.9999 |
| deepfake/114_738.jpg | Deepfake      |      100.0000 |
| real/34_102.jpg      | Real          |       75.8813 |
| real/34_108.jpg      | Real          |       85.5676 |
| real/34_114.jpg      | Real          |       95.6834 |
| real/34_120.jpg      | Real          |       71.2622 |
| real/34_54.jpg       | Real          |       97.7189 |
| real/34_60.jpg       | Real          |       98.9342 |
| real/34_66.jpg       | Real          |       99.0665 |
| real/34_72.jpg       | Real          |       99.5065 |
| real/34_78.jpg       | Real          |       99.8474 |
| real/34_84.jpg       | Real          |       95.4600 |
| real/34_90.jpg       | Real          |       92.4506 |
| real/34_96.jpg       | Real          |       97.2753 |