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File size: 33,264 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sYaX1Rf8pCWN",
"outputId": "f52aaf57-323d-46ff-908f-f188525b830a",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ftfy\n",
" Downloading ftfy-6.2.0-py3-none-any.whl (54 kB)\n",
"\u001b[K |████████████████████████████████| 54 kB 3.5 MB/s eta 0:00:011\n",
"\u001b[?25hCollecting regex\n",
" Downloading regex-2024.5.15-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (774 kB)\n",
"\u001b[K |████████████████████████████████| 774 kB 4.9 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: tqdm in /home/user/miniconda/lib/python3.9/site-packages (4.61.2)\n",
"Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /home/user/miniconda/lib/python3.9/site-packages (from ftfy) (0.2.13)\n",
"Installing collected packages: regex, ftfy\n",
"Successfully installed ftfy-6.2.0 regex-2024.5.15\n",
"Collecting git+https://github.com/openai/CLIP.git\n",
" Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-7h9f8ksf\n",
" Running command git clone -q https://github.com/openai/CLIP.git /tmp/pip-req-build-7h9f8ksf\n",
"Requirement already satisfied: ftfy in /home/user/miniconda/lib/python3.9/site-packages (from clip==1.0) (6.2.0)\n",
"Requirement already satisfied: regex in /home/user/miniconda/lib/python3.9/site-packages (from clip==1.0) (2024.5.15)\n",
"Requirement already satisfied: tqdm in /home/user/miniconda/lib/python3.9/site-packages (from clip==1.0) (4.61.2)\n",
"Collecting torch\n",
" Downloading torch-2.3.0-cp39-cp39-manylinux1_x86_64.whl (779.1 MB)\n",
"\u001b[K |█████████████▎ | 322.4 MB 155.1 MB/s eta 0:00:03"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |█████████████████████████████▉ | 726.2 MB 140.6 MB/s eta 0:00:01"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 779.1 MB 39 kB/s \n",
"\u001b[?25hCollecting torchvision\n",
" Downloading torchvision-0.18.0-cp39-cp39-manylinux1_x86_64.whl (7.0 MB)\n",
"\u001b[K |████████████████████████████████| 7.0 MB 117.1 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /home/user/miniconda/lib/python3.9/site-packages (from ftfy->clip==1.0) (0.2.13)\n",
"Collecting filelock\n",
" Downloading filelock-3.14.0-py3-none-any.whl (12 kB)\n",
"Requirement already satisfied: jinja2 in /home/user/miniconda/lib/python3.9/site-packages (from torch->clip==1.0) (3.1.4)\n",
"Collecting nvidia-cuda-nvrtc-cu12==12.1.105\n",
" Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
"\u001b[K |████████████████████████████████| 23.7 MB 111.3 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26\n",
" Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
"\u001b[K |████████████▎ | 281.1 MB 157.5 MB/s eta 0:00:03"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |██████████████████████████████ | 687.7 MB 121.2 MB/s eta 0:00:01"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 731.7 MB 27 kB/s \n",
"\u001b[?25hCollecting triton==2.3.0\n",
" Downloading triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (168.1 MB)\n",
"\u001b[K |████████████████████████████████| 168.1 MB 163.1 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-nccl-cu12==2.20.5\n",
" Downloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
"\u001b[K |████████████████████████████████| 176.2 MB 157 kB/s s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1\n",
" Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
"\u001b[K |██████████████████████▊ | 291.1 MB 155.6 MB/s eta 0:00:01"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 410.6 MB 11 kB/s /s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106\n",
" Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
"\u001b[K |████████████████████████████████| 56.5 MB 125.6 MB/s eta 0:00:01███████████████████▉ | 35.0 MB 125.6 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: typing-extensions>=4.8.0 in /home/user/miniconda/lib/python3.9/site-packages (from torch->clip==1.0) (4.11.0)\n",
"Collecting nvidia-cusolver-cu12==11.4.5.107\n",
" Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
"\u001b[K |████████████████████████████████| 124.2 MB 144.5 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting sympy\n",
" Downloading sympy-1.12-py3-none-any.whl (5.7 MB)\n",
"\u001b[K |████████████████████████████████| 5.7 MB 109.2 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting fsspec\n",
" Downloading fsspec-2024.5.0-py3-none-any.whl (316 kB)\n",
"\u001b[K |████████████████████████████████| 316 kB 119.1 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105\n",
" Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
"\u001b[K |████████████████████████████████| 823 kB 119.5 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105\n",
" Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
"\u001b[K |████████████████████████████████| 14.1 MB 126.1 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54\n",
" Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
"\u001b[K |████████████████████████████████| 121.6 MB 4.8 MB/s eta 0:00:011\n",
"\u001b[?25hCollecting networkx\n",
" Downloading networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
"\u001b[K |████████████████████████████████| 1.6 MB 112.8 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106\n",
" Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
"\u001b[K |████████████████████████████████| 196.0 MB 154.4 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105\n",
" Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
"\u001b[K |████████████████████████████████| 99 kB 39.0 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting nvidia-nvjitlink-cu12\n",
" Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
"\u001b[K |████████████████████████████████| 21.1 MB 123.7 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /home/user/miniconda/lib/python3.9/site-packages (from jinja2->torch->clip==1.0) (2.1.5)\n",
"Collecting mpmath>=0.19\n",
" Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
"\u001b[K |████████████████████████████████| 536 kB 125.5 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting pillow!=8.3.*,>=5.3.0\n",
" Downloading pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (4.5 MB)\n",
"\u001b[K |████████████████████████████████| 4.5 MB 123.5 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting numpy\n",
" Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
"\u001b[K |████████████████████████████████| 18.2 MB 113.2 MB/s eta 0:00:01 | 1.1 MB 113.2 MB/s eta 0:00:01\n",
"\u001b[?25hBuilding wheels for collected packages: clip\n",
" Building wheel for clip (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369525 sha256=2d16eeced15e3729c52334f9be57fd2ddca900110e745c1af86ab5aade88cd62\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-8vr04co8/wheels/c8/e4/e1/11374c111387672fc2068dfbe0d4b424cb9cdd1b2e184a71b5\n",
"Successfully built clip\n",
"Installing collected packages: nvidia-nvjitlink-cu12, nvidia-cusparse-cu12, nvidia-cublas-cu12, mpmath, filelock, triton, sympy, nvidia-nvtx-cu12, nvidia-nccl-cu12, nvidia-cusolver-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, networkx, fsspec, torch, pillow, numpy, torchvision, clip\n",
"Successfully installed clip-1.0 filelock-3.14.0 fsspec-2024.5.0 mpmath-1.3.0 networkx-3.2.1 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 pillow-10.3.0 sympy-1.12 torch-2.3.0 torchvision-0.18.0 triton-2.3.0\n",
"\u001b[33mWARNING: Requirement 'sentencepiece-0.1.98-cp311-cp311-win_amd64.whl' looks like a filename, but the file does not exist\u001b[0m\n",
"\u001b[31mERROR: sentencepiece-0.1.98-cp311-cp311-win_amd64.whl is not a supported wheel on this platform.\u001b[0m\n"
]
}
],
"source": [
"!pip install ftfy regex tqdm\n",
"!pip install git+https://github.com/openai/CLIP.git\n",
"!pip install sentencepiece-0.1.98-cp311-cp311-win_amd64.whl\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zuat0Supqs7r",
"outputId": "f3ec0a32-0d58-4241-d3f2-621828297c43",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting transformers\n",
" Downloading transformers-4.41.0-py3-none-any.whl (9.1 MB)\n",
"\u001b[K |████████████████████████████████| 9.1 MB 4.3 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (4.61.2)\n",
"Collecting tokenizers<0.20,>=0.19\n",
" Downloading tokenizers-0.19.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
"\u001b[K |████████████████████████████████| 3.6 MB 104.9 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (6.0.1)\n",
"Requirement already satisfied: filelock in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (3.14.0)\n",
"Requirement already satisfied: numpy>=1.17 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (1.26.4)\n",
"Collecting huggingface-hub<1.0,>=0.23.0\n",
" Downloading huggingface_hub-0.23.0-py3-none-any.whl (401 kB)\n",
"\u001b[K |████████████████████████████████| 401 kB 120.0 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: packaging>=20.0 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (24.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (2024.5.15)\n",
"Requirement already satisfied: requests in /home/user/miniconda/lib/python3.9/site-packages (from transformers) (2.31.0)\n",
"Collecting safetensors>=0.4.1\n",
" Downloading safetensors-0.4.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
"\u001b[K |████████████████████████████████| 1.2 MB 95.0 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /home/user/miniconda/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.11.0)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /home/user/miniconda/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2024.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (2021.5.30)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/user/miniconda/lib/python3.9/site-packages (from requests->transformers) (1.26.6)\n",
"Installing collected packages: huggingface-hub, tokenizers, safetensors, transformers\n",
"Successfully installed huggingface-hub-0.23.0 safetensors-0.4.3 tokenizers-0.19.1 transformers-4.41.0\n"
]
}
],
"source": [
"# prompt: install transformers\n",
"\n",
"!pip install transformers\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "8xOP6veIq5LM",
"tags": []
},
"outputs": [
{
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"source": [
"from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer\n",
"\n",
"\n",
"feature_extractor = ViTFeatureExtractor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uYLlkIWgqGwX"
},
"source": [
"## Import the necessary libraries and load the CLIP model:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "dLxPnrUQqDZU",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████| 338M/338M [00:12<00:00, 28.0MiB/s]\n"
]
}
],
"source": [
"from PIL import Image\n",
"import clip\n",
"import torch\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"clip_model, preprocess = clip.load(\"ViT-B/32\", device=device)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gt1Q-d1iqM9F"
},
"source": [
"## Define a function to generate product descriptions:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "u2XdvaffqGMr",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n",
"You may ignore this warning if your `pad_token_id` (50256) is identical to the `bos_token_id` (50256), `eos_token_id` (50256), or the `sep_token_id` (None), and your input is not padded.\n"
]
}
],
"source": [
"image = Image.open(\"data/download.jpeg\")\n",
"pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n",
"output_ids = model.generate(pixel_values, max_length=50, num_beams=4, early_stopping=True)\n",
"captions = tokenizer.batch_decode(output_ids, skip_special_tokens=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lOf9lcUAqVlm",
"outputId": "d00cdc05-6652-4fba-b40c-03ad803d54e3",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a vase sitting on top of a table \n"
]
}
],
"source": [
"image = preprocess(image).unsqueeze(0).to(device)\n",
"with torch.no_grad():\n",
" image_features = clip_model.encode_image(image)\n",
"\n",
"text_inputs = torch.cat([clip.tokenize(caption).to(device) for caption in captions]).to(device)\n",
"with torch.no_grad():\n",
" text_features = clip_model.encode_text(text_inputs)\n",
"\n",
"similarity_scores = image_features @ text_features.T\n",
"best_caption_idx = similarity_scores.argmax().item()\n",
"product_description = captions[best_caption_idx]\n",
"print(product_description)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RM6RXXvT4xSN"
},
"source": [
"# Using SigLip"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting protobuf\n",
" Downloading protobuf-5.26.1-cp37-abi3-manylinux2014_x86_64.whl (302 kB)\n",
"\u001b[K |████████████████████████████████| 302 kB 4.3 MB/s eta 0:00:01\n",
"\u001b[?25hInstalling collected packages: protobuf\n",
"Successfully installed protobuf-5.26.1\n"
]
}
],
"source": [
"!pip install sentencepiece\n",
"!pip install protobuf"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fR9c1mv3qXGz",
"outputId": "5b222c53-e0f8-4545-f191-ad6a90ab1373",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/user/miniconda/lib/python3.9/site-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"an old fashioned clock sitting on top of a table \n"
]
}
],
"source": [
"from transformers import AutoProcessor, AutoModel, VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer\n",
"import torch\n",
"from PIL import Image\n",
"\n",
"\n",
"model = AutoModel.from_pretrained(\"google/siglip-base-patch16-224\")\n",
"processor = AutoProcessor.from_pretrained(\"google/siglip-base-patch16-224\")\n",
"\n",
"\n",
"image = Image.open(\"data/avito4.jpeg\")\n",
"inputs = processor(images=image, return_tensors=\"pt\")\n",
"\n",
"\n",
"feature_extractor = ViTFeatureExtractor.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"model = VisionEncoderDecoderModel.from_pretrained(\"nlpconnect/vit-gpt2-image-captioning\")\n",
"\n",
"pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n",
"output_ids = model.generate(pixel_values, max_length=100, num_beams=5, early_stopping=True)\n",
"captions = tokenizer.batch_decode(output_ids, skip_special_tokens=True)\n",
"\n",
"image = preprocess(image).unsqueeze(0).to(device)\n",
"with torch.no_grad():\n",
" image_features = clip_model.encode_image(image)\n",
"\n",
"text_inputs = torch.cat([clip.tokenize(caption).to(device) for caption in captions]).to(device)\n",
"with torch.no_grad():\n",
" text_features = clip_model.encode_text(text_inputs)\n",
"\n",
"similarity_scores = image_features @ text_features.T\n",
"best_caption_idx = similarity_scores.argmax().item()\n",
"product_description = captions[best_caption_idx]\n",
"print(product_description)\n",
"\n",
"# a vase sitting on a shelf in a store => thuya\n",
"# a wooden bench sitting on top of a wooden floor => avito\n",
"## two old fashioned vases sitting next to each other => avito2\n",
"## three wooden vases sitting on top of a wooden floor => avito3\n",
"# an old fashioned clock sitting on top of a table => avito4\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fR9c1mv3qXGz",
"outputId": "5b222c53-e0f8-4545-f191-ad6a90ab1373",
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qRkGmKyYB7DM"
},
"source": [
"# Implemeting LLaVa"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u6jq8q__zoOt"
},
"source": [
"https://colab.research.google.com/drive/1veefV17NcD1S4ou4nF8ABkfm8-TgU0Dr#scrollTo=XN2vJCPZk1UY"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QyO2UcBjzl71"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
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