Smashing Computer Vision Models

This tutorial demonstrates how to use the pruna package to optimize any custom computer vision model. We will use the vit_b_16 model as an example.

Loading the CV Model

First, load your stable diffusion model.

from torchvision.models import resnet50, ResNet50_Weights
import torchvision

model = torchvision.models.vit_b_16(weights="ViT_B_16_Weights.DEFAULT").cuda()

Initializing the Smash Config

Next, initialize the smash_config.

from pruna_engine.SmashConfig import SmashConfig

# Initialize the SmashConfig
smash_config = SmashConfig()
smasher_config['task'] = 'image_classification'
smash_config["compilers"] = "cv-fast"
smash_config['n_quantization_bits'] = 16

Smashing the Model

Now, smash the model.

from pruna.smash import smash

# Smash the model
smashed_model = smash(
    api_key='<your-api-key>',  # replace <your-api-key> with your actual API key

Don’t forget to replace the api_key by the one provided by PrunaAI.

Preparing the Input

import numpy as np
from torchvision import transforms

# Generating a random image
image = np.random.randint(0, 256, size=(224, 224, 3), dtype=np.uint8)
input_tensor = transforms.ToTensor()(image).unsqueeze(0).to(device)

Running the Model

Finally, run the model to transcribe the audio file.

# Display the result

Wrap Up

Congratulations! You have successfully smashed a CV model. You can now use the pruna package to optimize any custom CV model. The only parts that you should modify are step 1 and step 5 to fit your use case. Additionally you can use the compiler ‘all’ which explores many compression methods as well as supporting cpu optimization, albeit it supports less CV models.