A set of scripts to make the Neural Network training with pytorch faster
Scorch: utilities for network training with PyTorch
This package is a set of codes that may be reused quite often for different networks trainings.
You will need Python 3 to use this package.
You will need the following packages installed:
To use the notebooks for testing the model and the dataset you will need Jupyter Notebook or JupyterLab installed.
Here is the minimal command to run to train the model specified in MODEL_FILE with a dataset specified in DATASET_FILE, the data is located in the DATASET_PATH.
python train.py --model MODEL_FILE --dataset DATASET_FILE --dataset-path DATASET_PATH
Here is a list of parameters of the script (it will be soon updated):
-b BATCH_SIZE, --batch-size BATCH_SIZE Batch size to train or validate your model -w WORKERS, --workers WORKERS Number of workers in a dataloader --pretraining Pretraining mode -lr LEARNING_RATE, --learning-rate LEARNING_RATE Learning rate -d DUMP_PERIOD, --dump-period DUMP_PERIOD Dump period -e EPOCHS, --epochs EPOCHS Number of epochs to perform -c CHECKPOINT, --checkpoint CHECKPOINT Checkpoint to load from --use-cuda Use cuda for training --validate-on-train Flag showing that you want to perform validation on training dataset along with the validation on the validation set --model MODEL File with a model specification --dataset DATASET File with a dataset sepcification --max-train-iterations MAX_TRAIN_ITERATIONS Maximum training iterations --max-valid-iterations MAX_VALID_ITERATIONS Maximum validation iterations -dp DATASET_PATH, --dataset-path DATASET_PATH Path to the dataset -v VERBOSITY, --verbosity VERBOSITY -1 for no output, 0 for epoch output, positive number is printout frequency during the training -cp CHECKPOINT_PREFIX, --checkpoint-prefix CHECKPOINT_PREFIX Prefix to the checkpoint name
Model module syntax
The syntax for the model file is the following:
class Network(torch.nn.Module): def __init__(self): super(Network, self).__init__() pass def forward(self, input): return [output1, output2] def __call__(self, input): return self.forward(input) class Socket: def __init__(self, model): self.model = model def criterion(self, pred, target): pass def metrics(self, pred, target): pass
The requirements are as follows:
- Network should have (obviously) the constructor
- Network should have forward function which:
- Takes as input a list of inputs.
- Outputs a list of outputs.
- There should be also the
__call__function specified that is a proxy for the forward function.
- There should be a
Socketclass defined in order to specify how to handle the model, it should contain:
criterionmethod that takes as inputs a list of tensors with predictions and a list of tensors with targets. The output should be a number.
metricsmethod that specifies the metrics which are of the interest for your experiment. It should take as inputs a list of tensors with predictions and a list of tensors with targets and return a list of metrics which.
The reason there are lists everywhere is the following: the network may have more than one input and more than one output. We have to deal with this fact smart enough to reuse the code. Thus, the best way to do things is to pass the values of interests in lists.
Dataset module syntax
Here is a syntax for the Dataset module:
class DataSetIndex(): def __init__(self, path): pass class DataSet(): def __init__(self, ds_index, mode='train'): self.ds_index = ds_index def __len__(self): if self.mode == 'test': pass elif self.mode == 'valid': pass else: pass def __getitem__(self, index): img = None target = None if self.mode == 'test': pass elif self.mode == 'valid': pass else: pass return [img1, img2], [target1, target2]
The dataset script should have at least the class DataSet which should have the following specified:
__init__, the constructor that defines all three parts of the dataset. The mode of the dataset should be defined here.
__len__function that returns the length of the dataset
__getitem__function that returns a list of input tensors and a list of target tensors
Although it is enough to have only the DataSet specified, it is recommended to specify also the DataSetIndex class that contains the information about the dataset's data. It is recommended to share one instance of the DataSetIndex between all the instances of the DataSet with different modes to avoid doubling or tripling the memory used to store this index and also to avoid collecting the dataset index several times.
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