sconce 0.58

sconce: torch pipeliner

==================================== sconce (Model Compression Made Easy)

.. image:: https://readthedocs.org/projects/sconce/badge/?version=latest :target: https://sconce.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

.. image:: https://github.com/satabios/sconce/blob/master/docs/source/images/sconce-punch-bk_removed.png?raw=true :align: center :width: 400

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This is a Pytorch Helper package aimed to aid the workflow of deep learning model development, compression and deployment.

• This packages has boiler plate defintions that can ease the development of torch model development
• This package has a set of compression techniques that can be used to compress the model

NOTE: * Pruning Techniques are imported from Tomoco Package(deperecated) * Model Quantization and Deployment features are in the development pipeline which will be available for use soon.

Package install:

.. code:: python

pip install sconce

Example:

Define Network and Config’s:

.. code:: python

Define your network

class Net(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(3, 8, 3) self.bn1 = nn.BatchNorm2d(8) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(8, 16, 3) self.bn2 = nn.BatchNorm2d(16) self.fc1 = nn.Linear(1666, 32) self.fc2 = nn.Linear(32, 10)

   def forward(self, x):
       x = self.pool(self.bn1(F.relu(self.conv1(x))))
       x = self.pool(self.bn2(F.relu(self.conv2(x))))
       x = torch.flatten(x, 1)
       x = F.relu(self.fc1(x))
       x = self.fc2(x)
       return x

Make a Dict for Dataloader

.. code:: python

image_size = 32 transforms = { "train": Compose([ RandomCrop(image_size, padding=4), RandomHorizontalFlip(), ToTensor(), ]), "test": ToTensor(), } dataset = {} for split in ["train", "test"]: dataset[split] = CIFAR10( root="data/cifar10", train=(split == "train"), download=True, transform=transforms[split], ) dataloader = {} for split in ['train', 'test']: dataloader[split] = DataLoader( dataset[split], batch_size=512, shuffle=(split == 'train'), num_workers=0, pin_memory=True, )

Define your Configurations:

.. code:: python

Define all parameters

from sconce import sconce

sconces = sconce() sconces.model= Net() # Model Definition sconces.criterion = nn.CrossEntropyLoss() # Loss sconces.optimizer= optim.Adam(sconces.model.parameters(), lr=1e-4) sconces.scheduler = optim.lr_scheduler.CosineAnnealingLR(sconces.optimizer, T_max=200) sconces.dataloader = dataloader sconces.epochs = 5 #Number of time we iterate over the data sconces.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") sconces.experiment_name = "vgg-gmp" # Define your experiment name here sconces.prune_mode = "GMP" # Prune Mode: Currently supporting "GMP"(Supports Automated Pruning Ratio Detection), "CWP". Future supports for "OBC" and "sparseGPT"

One Roof Solution [Train -> Compress -> Deploy]:

.. code:: python

sconces.compress()

To-Do

-  ☒ Universal Channel-Wise Pruning

-  ☒ Update Tutorials

-  ☒ Fine Grained Purning

-  ☒ Channel Wise Purning

-  [+] OBC Compression (In-Progress)

-  [+] Spasegpt like Pruning (In-Progress)

-  [+] Quantisation (In-Progress)

-  ☐ Universal AutoML package

-  ☐ Introduction of Sparsification in Pipeline