pipeline_tag: zero-shot-classification
base_model: laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K
inference: false
tags:
- deepsparse
This is a SparseML quantized version of https://huggingface.co/laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K that is ready to use with DeepSparse. It achieves 71.1% one-shot accuracy on ImageNet and 95.6% one-shot accuracy on Imagenette.
Notebook for basic usage: 
Notebook for Imagenette evaluation:
Setup for usage
First, install DeepSparse with extensions for CLIP:
pip install deepsparse-nightly[clip]>=1.7.0.20231210
Download some test images of a church, a dog, and elephants:
wget -O basilica.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolo/sample_images/basilica.jpg
wget -O buddy.jpeg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/tests/deepsparse/pipelines/sample_images/buddy.jpeg
wget -O thailand.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolact/sample_images/thailand.jpg
For this model there is a second input that is the length of tokens, so run this input override code before making a text pipeline:
import numpy as np
from deepsparse.clip import CLIPTextPipeline
def custom_process_inputs(self, inputs):
if not isinstance(inputs.text, list):
inputs.text = [inputs.text]
if not isinstance(inputs.text[0], str):
return inputs.text
tokens = [np.array(t).astype(np.int32) for t in self.tokenizer(inputs.text)]
tokens = np.stack(tokens, axis=0)
tokens_lengths = np.array(tokens.shape[0] * [tokens.shape[1] - 1])
return [tokens, tokens_lengths]
# This overrides the process_inputs function globally for all CLIPTextPipeline classes
CLIPTextPipeline.process_inputs = custom_process_inputs
Text embedding pipeline
Here is an example of how to create and use a DeepSparse pipeline for text embeddings.
from deepsparse import Pipeline
from huggingface_hub import snapshot_download
# Download the model from HF
model_folder = snapshot_download(repo_id="neuralmagic/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K-quant-ds")
text_embed_pipeline = Pipeline.create(task="clip_text", model_path=model_folder + "/textual.onnx")
text = ["ice cream", "an elephant", "a dog", "a building", "a church"]
embeddings = text_embed_pipeline(text=text).text_embeddings
for i in range(len(embeddings)):
print(embeddings[i].shape)
print(embeddings[i])
Image embedding pipeline
Here is an example of how to create and use a DeepSparse pipeline for image embeddings.
from deepsparse import Pipeline
from huggingface_hub import snapshot_download
# Download the model from HF
model_folder = snapshot_download(repo_id="neuralmagic/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K-quant-ds")
image_embed_pipeline = Pipeline.create(task="clip_visual", model_path=model_folder + "/visual.onnx")
images = ["basilica.jpg", "buddy.jpeg", "thailand.jpg"]
embeddings = image_embed_pipeline(images=images).image_embeddings
for i in range(len(embeddings)):
print(embeddings[i].shape)
print(embeddings[i])
Zero-shot image classification pipeline
Since CLIP trained both the text and image embedding models in tandem, we can generate embeddings for both and relate them together without retraining. Here is an example of how to create and use a DeepSparse pipeline for zero-shot image classification.
from deepsparse import Pipeline
from deepsparse.clip import (
CLIPTextInput,
CLIPVisualInput,
CLIPZeroShotInput
)
from huggingface_hub import snapshot_download
# Download the model from HF
model_folder = snapshot_download(repo_id="neuralmagic/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K-quant-ds")
possible_classes = ["ice cream", "an elephant", "a dog", "a building", "a church"]
images = ["basilica.jpg", "buddy.jpeg", "thailand.jpg"]
# Load the model into DeepSparse
pipeline = Pipeline.create(
task="clip_zeroshot",
visual_model_path=model_folder + "/visual.onnx",
text_model_path=model_folder + "/textual.onnx"
)
# Infer
output = pipeline(
image=CLIPVisualInput(images=images),
text=CLIPTextInput(text=possible_classes),
).text_scores
for i in range(len(output)):
prediction = possible_classes[np.argmax(output[i])]
print(f"Image {images[i]} is a picture of {prediction}")
"""
Image basilica.jpg is a picture of a church
Image buddy.jpeg is a picture of a dog
Image thailand.jpg is a picture of an elephant
"""