This is an extension of MAML-Pytorch by Liangqu Long.
This version allows easy use of a custom dataset, and offers an option for "further training".
The further training step is typically referred to as fine-tuning in machine-learning.
However, this term is already used in MAML to
describe the step where the query set is evaluated.
Logits and predictions are exported to CSV files (All_logits.csv and test_predictions_and_labels, respectively).
Test accuracy (mean values for analysis of the query set) are output to mean_test_accuracy.txt .
Various metrics are also calculated and output to mean_metrics.csv and metrics_summary.csv .
mean_metrics.csv includes training and validation (i.e., support and query) loss and accuracy values for each epoch.
The summary file includes mean support loss, mean test/query loss, test AUC, and test F1 scores (macro and micro).
The code has been tested using Google Colab.
It requires Python 3.x, PyTorch 0.4+ and CUDA-powered GPUs.
The training, validation and testing, directories are expected to contain all relevant images,
along with a CSV file listing the names of all relevant files. This can be generated using os.listdir() .
For this version, each training directory (i.e, including the further training directory) must contain a file named train.csv.
If further training is being used, each training dataset is expected to be unique.
The validation set (query set/fine-tuning set) and the testing set are generated from the target dataset.
Below is an example of the contents of train.csv:
filename label
0 sample0001.png group1
1 sample0002.png group2
3 sample0003.png group1
...
In a Jupyter/Colab notebook:
Retrieve the project files
!git clone https://github.com/MTynes/MAML-Pytorch.git maml_pytorch
Set the parameters
train_directory ='/content/miniimagenet/images'
validation_directory ='/content/all_validation_images'
test_directory ='/content/all_test_images'
further_training_directory = '/content/all_further_training_images'
n_epochs = 200 * 10000 # As with the original implementation, this value must be a multiple of 10000
ft_n_epochs = 200 * 10000
Run the training file
!python /content/maml_pytorch/train_custom_dataset.py --run_further_training 'true' --epochs {n_epochs} --train_dir {train_directory} --validation_dir {validation_directory} --test_dir {test_directory} --further_training_dir {further_training_directory} --further_training_epochs {ft_n_epochs}
Examples of use with spectrogram images generated from EEG data are available here:
https://github.com/WinAIML/schizophrenia/blob/master/MLModels/Meta%20Learning%20Models/MAML_Pytorch_with_Dataset-1.ipynb
https://github.com/WinAIML/schizophrenia/blob/master/MLModels/Meta%20Learning%20Models/MAML_Pytorch_with_further_training_Test_Dataset-1.ipynb
Read the help summary for further details
!python /content/maml_pytorch/train_custom_dataset.py --help
usage: train_custom_dataset.py [-h] [--train_dir TRAIN_DIR]
[--further_training_dir FURTHER_TRAINING_DIR]
[--validation_dir VALIDATION_DIR]
[--test_dir TEST_DIR]
[--run_further_training RUN_FURTHER_TRAINING]
[--epochs EPOCHS]
[--further_training_epochs FURTHER_TRAINING_EPOCHS]
[--n_way N_WAY] [--k_spt K_SPT] [--k_qry K_QRY]
[--imgsz IMGSZ] [--imgc IMGC]
[--task_num TASK_NUM] [--meta_lr META_LR]
[--update_lr UPDATE_LR]
[--update_step UPDATE_STEP]
[--update_step_test UPDATE_STEP_TEST]
[--accuracy_log_file ACCURACY_LOG_FILE]
optional arguments:
-h, --help show this help message and exit
--train_dir TRAIN_DIR
train data directory
--fine_tune_dir FINE_TUNE_DIR
fine tuning data directory
--validation_dir VALIDATION_DIR
validation data directory
--test_dir TEST_DIR test data directory
--run_fine_tuning RUN_FINE_TUNING
Boolean for adding a second dataset for further
training. Set as string. Case insensitive.
--epochs EPOCHS Number of epochs
--fine_tuning_epochs FINE_TUNING_EPOCHS
Number of epochs for fine tuning cycle
--n_way N_WAY n way
--k_spt K_SPT k shot for support set
--k_qry K_QRY k shot for query set
--imgsz IMGSZ imgsz
--imgc IMGC imgc
--task_num TASK_NUM meta batch size, namely task num
--meta_lr META_LR meta-level outer learning rate
--update_lr UPDATE_LR
task-level inner update learning rate
--update_step UPDATE_STEP
task-level inner update steps
--update_step_test UPDATE_STEP_TEST
update steps for finetunning
--accuracy_log_file ACCURACY_LOG_FILE
Output file for mean test accuracy
Please refer to the original MAML paper here: Model-Agnostic Meta-Learning (MAML).
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