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{
"cells": [
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"import requests\n",
"from PIL import Image\n",
"from transformers import CLIPModel, CLIPProcessor, CLIPFeatureExtractor\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
"image = Image.open(requests.get(url, stream=True).raw)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class ClipWrapper:\n",
" def __init__(self):\n",
" self.model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
" self.processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"\n",
" def images2vec(self, images: List[Image.Image]) -> torch.Tensor:\n",
" inputs = self.processor(images=images, return_tensors=\"pt\")\n",
" with torch.no_grad():\n",
" model_inputs = {k: v.to(self.model.device) for k, v in inputs.items()}\n",
" image_embeds = self.model.vision_model(**model_inputs)\n",
" clip_vectors = self.model.visual_projection(image_embeds[1])\n",
" return clip_vectors / clip_vectors.norm(dim=-1, keepdim=True)\n",
"\n",
" def texts2vec(self, texts: List[str]) -> torch.Tensor:\n",
" inputs = self.processor(text=texts, return_tensors=\"pt\", padding=True)\n",
" with torch.no_grad():\n",
" model_inputs = {k: v.to(self.model.device) for k, v in inputs.items()}\n",
" text_embeds = self.model.text_model(**model_inputs)\n",
" text_vectors = self.model.text_projection(text_embeds[1])\n",
" return text_vectors / text_vectors.norm(dim=-1, keepdim=True)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([2, 512])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def images2vec(images: List[Image.Image]) -> torch.Tensor:\n",
" inputs = processor(images=images, return_tensors=\"pt\")\n",
" with torch.no_grad():\n",
" model_inputs = {k: v.to(model.device) for k, v in inputs.items()}\n",
" image_embeds = model.vision_model(**model_inputs)\n",
" clip_vectors = model.visual_projection(image_embeds[1])\n",
" return clip_vectors / clip_vectors.norm(dim=-1, keepdim=True)\n",
"\n",
"\n",
"result = images2vec([image, image])\n",
"result.shape"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([2, 512])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def texts2vec(texts: List[str]) -> torch.Tensor:\n",
" inputs = processor(text=texts, return_tensors=\"pt\", padding=True)\n",
" with torch.no_grad():\n",
" model_inputs = {k: v.to(model.device) for k, v in inputs.items()}\n",
" text_embeds = model.text_model(**model_inputs)\n",
" text_vectors = model.text_projection(text_embeds[1])\n",
" return text_vectors / text_vectors.norm(dim=-1, keepdim=True)\n",
"\n",
"\n",
"texts2vec([\"a photo of a cat\", \"a photo of a dog\"]).shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "semvideo-hackathon-230523",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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