TorchFlare
TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework to train your models without much effort. It provides an almost Keras-like experience for training your models with all the callbacks, metrics, etc
Features
- A high-level module for Keras-like training.
- Flexibility to write custom training and validation loops for advanced use cases.
- Off-the-shelf Pytorch style Datasets/Dataloaders for standard tasks such as Image classification, Image segmentation,
Text Classification, etc - Callbacks for model checkpoints, early stopping, and much more!
- Metrics and much more.
- Reduction of the boiler plate code required for training your models.
Currently, TorchFlare supports CPU and GPU training. DDP and TPU support will be coming soon!
Installation
pip install torchflare
Documentation
The Documentation is available here
Getting Started
The core idea around TorchFlare is the Experiment
class. It handles all the internal stuff like boiler plate code for training,
calling callbacks,metrics,etc. The only thing you need to focus on is creating you PyTorch Model.
Also, there are off-the-shelf pytorch style datasets/dataloaders available for standard tasks, so that you don't
have to worry about creating Pytorch Datasets/Dataloaders.
Here is an easy-to-understand example to show how Experiment class works.
import torch
import torch.nn as nn
from torchflare.experiments import Experiment, ModelConfig
import torchflare.callbacks as cbs
import torchflare.metrics as metrics
#Some dummy dataloaders
train_dl = SomeTrainingDataloader()
valid_dl = SomeValidationDataloader()
test_dl = SomeTestingDataloader()
Create a pytorch Model
class Net(nn.Module):
def __init__(self, n_classes, p_dropout):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d(p=p_dropout)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, n_classes)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return x
Define callbacks and metrics
metric_list = [metrics.Accuracy(num_classes=num_classes, multilabel=False),
metrics.F1Score(num_classes=num_classes, multilabel=False)]
callbacks = [cbs.EarlyStopping(monitor="val_accuracy", mode="max"), cbs.ModelCheckpoint(monitor="val_accuracy"),
cbs.ReduceLROnPlateau(mode="max" , patience = 2)]
Define your experiment
# Set some constants for training
exp = Experiment(
num_epochs=5,
fp16=False,
device="cuda",
seed=42,
)
# Compile your experiment with model, optimizer, schedulers, etc
config = ModelConfig(nn_module = Net,
module_params = {"n_classes" : 10 , "p_dropout" : 0.3},
optimizer = "Adam"
optimizer_params = {"lr" : 3e-4},
criterion = "cross_entropy")
exp.compile_experiment(model_config = config,
callbacks = callbacks,
metrics = metric_list,
main_metrics = "accuracy")
# Run your experiment with training dataloader and validation dataloader.
exp.fit_loader(train_dl=train_dl, valid_dl= valid_dl)
For inference, you can use infer method, which yields output per batch. You can use it as follows
outputs = []
for op in exp.predict_on_loader(test_dl=test_dl , path_to_model='./models/model.bin' , device = 'cuda'):
op = some_post_process_function(op)
outputs.extend(op)
If you want to access your experiments history or get as a dataframe. You can do it as follows.
history = exp.history # This will return a dict
exp.get_logs() #This will return a dataframe constructed from model-history.
