Commit
·
96b7b35
1
Parent(s):
8256d51
remove colab badges
Browse files- ColPali_+_Qwen2_VL.ipynb +1 -4
- Faster_Zero_shot_Object_Detection_with_Optimum.ipynb +0 -0
- Faster_foundation_models_with_torch_compile.ipynb +141 -141
- Fine_tune_Florence_2.ipynb +0 -0
- Fine_tune_PaliGemma.ipynb +423 -433
- Fine_tune_SmolVLM2_on_Video.ipynb +722 -732
- Finetune_ColPali.ipynb +0 -10
- Fit_in_vision_models_using_quanto.ipynb +0 -0
- Gemma_3_for_Video_Understanding.ipynb +0 -0
- Gemma_3n_Video_Vibe_Tests.ipynb +1119 -1129
- PaliGemma_DPO.ipynb +0 -0
- Reduce_any_model_to_fp16_using_🤗_Optimum_DETR.ipynb +0 -0
- ShieldGemma_2_for_Vision_LM_Safety.ipynb +0 -0
ColPali_+_Qwen2_VL.ipynb
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"source": [
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"# Multimodal RAG using ColPali (with Byaldi) and Qwen2-VL\n",
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"\n",
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"[](https://colab.research.google.com/github/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb)\n",
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"\n",
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"\n",
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"[ColPali](https://huggingface.co/blog/manu/colpali) is a multimodal retriever that removes the need for hefty and brittle document processors. It natively handles images and processes and encodes image patches to be compatible with text, thus removing need to do OCR, or image captioning.\n",
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"\n",
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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"source": [
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"# Multimodal RAG using ColPali (with Byaldi) and Qwen2-VL\n",
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"\n",
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"[ColPali](https://huggingface.co/blog/manu/colpali) is a multimodal retriever that removes the need for hefty and brittle document processors. It natively handles images and processes and encodes image patches to be compatible with text, thus removing need to do OCR, or image captioning.\n",
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"\n",
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"\n",
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"nbformat": 4,
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"nbformat_minor": 0
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Faster_Zero_shot_Object_Detection_with_Optimum.ipynb
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Faster_foundation_models_with_torch_compile.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"machine_shape": "hm",
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"gpuType": "L4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Faster Foundation Models with `torch.compile`"
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],
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"metadata": {
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"id": "axYlcDTznci4"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Introduction to `torch.compile()`"
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],
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"metadata": {
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"id": "B-yw8KMWsjfY"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"This guide aims to provide a benchmark on the inference speed-ups introduced with `torch.compile()` with no reduction in model performance for foundation models in 🤗 Transformers.\n",
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"\n",
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"- \"reduce-overhead\" reduces the overhead of python with CUDA graphs, useful for small batches, consumes a lot of memory. As of now only works for CUDA only graphs which do not mutate inputs.\n",
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"\n",
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"If you have a lot of memory to use, the best speed-up is through `reduce-overhead`. How much speed-up one can get depends on the model, so in this tutorial we will check the most used foundation models."
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]
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"metadata": {
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"id": "AmmT4aDnqgOB"
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{
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"cell_type": "markdown",
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"source": [
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"## OWLv2\n",
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"\n",
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"OWLv2 is a zero-shot object detection model released by Google Brain. We will load base version."
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]
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"metadata": {
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"id": "5sCfbPTn7wBE"
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"cell_type": "markdown",
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"source": [
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"Let's load the model and processor for OWLv2."
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],
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"metadata": {
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"id": "joeX3J315K0G"
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}
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{
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"cell_type": "code",
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"source": [
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"from PIL import Image\n",
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"import requests\n",
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"\n",
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"url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg'\n",
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"image = Image.open(requests.get(url, stream=True).raw)"
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]
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"metadata": {
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"id": "Ztfcdqkul62z"
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},
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"execution_count": 1,
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"outputs": []
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{
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"cell_type": "code",
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"
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"from transformers import AutoProcessor, Owlv2ForObjectDetection\n",
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"import torch\n",
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"import numpy as np\n",
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"\n",
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"processor = AutoProcessor.from_pretrained(\"google/owlv2-base-patch16-ensemble\")\n",
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"model = Owlv2ForObjectDetection.from_pretrained(\"google/owlv2-base-patch16-ensemble\").to(\"cuda\")\n",
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"\n",
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"texts = [[\"a photo of a bee\", \"a photo of a bird\"]]\n",
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"inputs = processor(text=texts, images=image, return_tensors=\"pt\").to(\"cuda\")"
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],
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"metadata": {
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"id": "84npPHCQpHZ6",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "f30c41c7-b897-460d-d2a4-a1276bf2263e"
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
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"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
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" warnings.warn(\n"
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}
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"cell_type": "markdown",
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"source": [
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"We can now get to benchmarking. We will benchmark the model itself and the compiled model."
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],
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"metadata": {
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"id": "3AedkjLu5PRo"
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}
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{
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"cell_type": "code",
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"
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"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
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"repetitions = 30\n",
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"timings=np.zeros((repetitions,1))\n",
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"\n",
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"for _ in range(10):\n",
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" _ = model(**inputs)\n",
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"\n",
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"with torch.no_grad():\n",
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" for rep in range(repetitions):\n",
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" torch.cuda.synchronize()\n",
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" starter.record()\n",
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" output = model(**inputs)\n",
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" ender.record()\n",
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" torch.cuda.synchronize()\n",
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" curr_time = starter.elapsed_time(ender)\n",
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" timings[rep] = curr_time\n",
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"\n",
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"mean_syn = np.sum(timings) / repetitions\n",
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"print(mean_syn)\n"
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],
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"metadata": {
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"id": "RQQSEgkQtXEV",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "8003590b-c4bc-4b3d-9b1b-dade853b8dd8"
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},
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"255.7331792195638\n"
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
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"timings=np.zeros((repetitions,1))\n",
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"\n",
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"for _ in range(30):\n",
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" with torch.no_grad():\n",
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" _ = compiled_model(**inputs)\n",
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"\n",
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"\n",
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"with torch.no_grad():\n",
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" for rep in range(repetitions):\n",
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" torch.cuda.synchronize()\n",
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" starter.record()\n",
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" output =
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" ender.record()\n",
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" torch.cuda.synchronize()\n",
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" curr_time = starter.elapsed_time(ender)\n",
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" timings[rep] = curr_time\n",
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"\n",
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"mean_syn = np.sum(timings) / repetitions\n",
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"metadata": {
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"id": "bEZiNgaupOx6",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "e5d47875-1e40-4997-e533-94bf0ff34d14"
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"text": [
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"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
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" self.pid = os.fork()\n",
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"text": [
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"154.6884775797526\n"
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"We got nearly 40 percent speed-up! You can also increase the batch size and see how much further speed-up you can get."
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"metadata": {
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"id": "d_0d7DwN6gBt"
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"source": [
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"texts = [[\"a photo of a bee\", \"a photo of a bird\"] for _ in range(8)]\n",
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"images = [image for _ in range(8)]\n",
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"inputs = processor(text=texts, images=image, return_tensors=\"pt\").to(\"cuda\")"
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]
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"metadata": {
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"id": "exKoOptB61UL"
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"execution_count": 11,
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"source": [
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"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
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"repetitions = 30\n",
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"mean_syn = np.sum(timings) / repetitions\n",
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"print(mean_syn)"
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "
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"execution_count": 12,
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"source": [
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"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
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"mean_syn = np.sum(timings) / repetitions\n",
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"print(mean_syn)"
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],
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"metadata": {
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"colab": {
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"id": "axYlcDTznci4"
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},
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"source": [
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"# Faster Foundation Models with `torch.compile`"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "B-yw8KMWsjfY"
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},
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"source": [
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"## Introduction to `torch.compile()`"
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+
]
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},
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{
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"cell_type": "markdown",
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+
"metadata": {
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+
"id": "AmmT4aDnqgOB"
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+
},
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"source": [
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"This guide aims to provide a benchmark on the inference speed-ups introduced with `torch.compile()` with no reduction in model performance for foundation models in 🤗 Transformers.\n",
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"\n",
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"- \"reduce-overhead\" reduces the overhead of python with CUDA graphs, useful for small batches, consumes a lot of memory. As of now only works for CUDA only graphs which do not mutate inputs.\n",
|
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"\n",
|
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"If you have a lot of memory to use, the best speed-up is through `reduce-overhead`. How much speed-up one can get depends on the model, so in this tutorial we will check the most used foundation models."
|
36 |
+
]
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},
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{
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"cell_type": "markdown",
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+
"metadata": {
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+
"id": "5sCfbPTn7wBE"
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+
},
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"source": [
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"## OWLv2\n",
|
45 |
"\n",
|
46 |
"OWLv2 is a zero-shot object detection model released by Google Brain. We will load base version."
|
47 |
+
]
|
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|
48 |
},
|
49 |
{
|
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"cell_type": "markdown",
|
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"metadata": {
|
52 |
"id": "joeX3J315K0G"
|
53 |
+
},
|
54 |
+
"source": [
|
55 |
+
"Let's load the model and processor for OWLv2."
|
56 |
+
]
|
57 |
},
|
58 |
{
|
59 |
"cell_type": "code",
|
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+
"execution_count": 1,
|
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+
"metadata": {
|
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+
"id": "Ztfcdqkul62z"
|
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+
},
|
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+
"outputs": [],
|
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"source": [
|
66 |
"from PIL import Image\n",
|
67 |
"import requests\n",
|
68 |
"\n",
|
69 |
"url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg'\n",
|
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"image = Image.open(requests.get(url, stream=True).raw)"
|
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+
]
|
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},
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"execution_count": 2,
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"metadata": {
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
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},
|
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+
"id": "84npPHCQpHZ6",
|
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"outputId": "f30c41c7-b897-460d-d2a4-a1276bf2263e"
|
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},
|
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"outputs": [
|
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{
|
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"name": "stderr",
|
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+
"output_type": "stream",
|
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"text": [
|
88 |
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
|
89 |
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
|
|
93 |
" warnings.warn(\n"
|
94 |
]
|
95 |
}
|
96 |
+
],
|
97 |
+
"source": [
|
98 |
+
"from transformers import AutoProcessor, Owlv2ForObjectDetection\n",
|
99 |
+
"import torch\n",
|
100 |
+
"import numpy as np\n",
|
101 |
+
"\n",
|
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+
"processor = AutoProcessor.from_pretrained(\"google/owlv2-base-patch16-ensemble\")\n",
|
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+
"model = Owlv2ForObjectDetection.from_pretrained(\"google/owlv2-base-patch16-ensemble\").to(\"cuda\")\n",
|
104 |
+
"\n",
|
105 |
+
"texts = [[\"a photo of a bee\", \"a photo of a bird\"]]\n",
|
106 |
+
"inputs = processor(text=texts, images=image, return_tensors=\"pt\").to(\"cuda\")"
|
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]
|
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},
|
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{
|
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"cell_type": "markdown",
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"metadata": {
|
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"id": "3AedkjLu5PRo"
|
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+
},
|
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+
"source": [
|
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+
"We can now get to benchmarking. We will benchmark the model itself and the compiled model."
|
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+
]
|
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},
|
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{
|
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
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},
|
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+
"id": "RQQSEgkQtXEV",
|
126 |
"outputId": "8003590b-c4bc-4b3d-9b1b-dade853b8dd8"
|
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},
|
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"outputs": [
|
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{
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"name": "stdout",
|
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+
"output_type": "stream",
|
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"text": [
|
133 |
"255.7331792195638\n"
|
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]
|
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}
|
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+
],
|
|
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|
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"source": [
|
138 |
"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
|
139 |
+
"repetitions = 30\n",
|
140 |
"timings=np.zeros((repetitions,1))\n",
|
141 |
"\n",
|
142 |
+
"for _ in range(10):\n",
|
143 |
+
" _ = model(**inputs)\n",
|
|
|
|
|
|
|
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|
144 |
"\n",
|
145 |
"with torch.no_grad():\n",
|
146 |
" for rep in range(repetitions):\n",
|
147 |
" torch.cuda.synchronize()\n",
|
148 |
" starter.record()\n",
|
149 |
+
" output = model(**inputs)\n",
|
150 |
" ender.record()\n",
|
151 |
" torch.cuda.synchronize()\n",
|
152 |
" curr_time = starter.elapsed_time(ender)\n",
|
153 |
" timings[rep] = curr_time\n",
|
154 |
"\n",
|
155 |
"mean_syn = np.sum(timings) / repetitions\n",
|
156 |
+
"print(mean_syn)\n"
|
157 |
+
]
|
158 |
+
},
|
159 |
+
{
|
160 |
+
"cell_type": "code",
|
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+
"execution_count": 4,
|
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"metadata": {
|
|
|
163 |
"colab": {
|
164 |
"base_uri": "https://localhost:8080/"
|
165 |
},
|
166 |
+
"id": "bEZiNgaupOx6",
|
167 |
"outputId": "e5d47875-1e40-4997-e533-94bf0ff34d14"
|
168 |
},
|
|
|
169 |
"outputs": [
|
170 |
{
|
|
|
171 |
"name": "stderr",
|
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+
"output_type": "stream",
|
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"text": [
|
174 |
"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
|
175 |
" self.pid = os.fork()\n",
|
|
|
184 |
]
|
185 |
},
|
186 |
{
|
|
|
187 |
"name": "stdout",
|
188 |
+
"output_type": "stream",
|
189 |
"text": [
|
190 |
"154.6884775797526\n"
|
191 |
]
|
192 |
}
|
193 |
+
],
|
194 |
+
"source": [
|
195 |
+
"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
|
196 |
+
"timings=np.zeros((repetitions,1))\n",
|
197 |
+
"\n",
|
198 |
+
"compiled_model = torch.compile(model, mode=\"reduce-overhead\").to(\"cuda\")\n",
|
199 |
+
"\n",
|
200 |
+
"for _ in range(30):\n",
|
201 |
+
" with torch.no_grad():\n",
|
202 |
+
" _ = compiled_model(**inputs)\n",
|
203 |
+
"\n",
|
204 |
+
"\n",
|
205 |
+
"with torch.no_grad():\n",
|
206 |
+
" for rep in range(repetitions):\n",
|
207 |
+
" torch.cuda.synchronize()\n",
|
208 |
+
" starter.record()\n",
|
209 |
+
" output = compiled_model(**inputs)\n",
|
210 |
+
" ender.record()\n",
|
211 |
+
" torch.cuda.synchronize()\n",
|
212 |
+
" curr_time = starter.elapsed_time(ender)\n",
|
213 |
+
" timings[rep] = curr_time\n",
|
214 |
+
"\n",
|
215 |
+
"mean_syn = np.sum(timings) / repetitions\n",
|
216 |
+
"print(mean_syn)"
|
217 |
]
|
218 |
},
|
219 |
{
|
220 |
"cell_type": "markdown",
|
|
|
|
|
|
|
221 |
"metadata": {
|
222 |
"id": "d_0d7DwN6gBt"
|
223 |
+
},
|
224 |
+
"source": [
|
225 |
+
"We got nearly 40 percent speed-up! You can also increase the batch size and see how much further speed-up you can get."
|
226 |
+
]
|
227 |
},
|
228 |
{
|
229 |
"cell_type": "code",
|
230 |
+
"execution_count": 11,
|
231 |
+
"metadata": {
|
232 |
+
"id": "exKoOptB61UL"
|
233 |
+
},
|
234 |
+
"outputs": [],
|
235 |
"source": [
|
236 |
"texts = [[\"a photo of a bee\", \"a photo of a bird\"] for _ in range(8)]\n",
|
237 |
"images = [image for _ in range(8)]\n",
|
238 |
"inputs = processor(text=texts, images=image, return_tensors=\"pt\").to(\"cuda\")"
|
239 |
+
]
|
|
|
|
|
|
|
|
|
|
|
240 |
},
|
241 |
{
|
242 |
"cell_type": "code",
|
243 |
+
"execution_count": 12,
|
244 |
+
"metadata": {
|
245 |
+
"colab": {
|
246 |
+
"base_uri": "https://localhost:8080/"
|
247 |
+
},
|
248 |
+
"id": "EFj9Pgra7Km8",
|
249 |
+
"outputId": "5fefb8c0-9e86-478c-e9e2-0dbc0fa8a37b"
|
250 |
+
},
|
251 |
+
"outputs": [
|
252 |
+
{
|
253 |
+
"name": "stdout",
|
254 |
+
"output_type": "stream",
|
255 |
+
"text": [
|
256 |
+
"269.3023401896159\n"
|
257 |
+
]
|
258 |
+
}
|
259 |
+
],
|
260 |
"source": [
|
261 |
"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
|
262 |
"repetitions = 30\n",
|
|
|
277 |
"\n",
|
278 |
"mean_syn = np.sum(timings) / repetitions\n",
|
279 |
"print(mean_syn)"
|
280 |
+
]
|
281 |
+
},
|
282 |
+
{
|
283 |
+
"cell_type": "code",
|
284 |
+
"execution_count": 13,
|
285 |
"metadata": {
|
286 |
"colab": {
|
287 |
"base_uri": "https://localhost:8080/"
|
288 |
},
|
289 |
+
"id": "OuQZmgTK7UCo",
|
290 |
+
"outputId": "7184eb1d-b545-4bb6-b544-3effd5c2545a"
|
291 |
},
|
|
|
292 |
"outputs": [
|
293 |
{
|
|
|
294 |
"name": "stdout",
|
295 |
+
"output_type": "stream",
|
296 |
"text": [
|
297 |
+
"159.77137603759766\n"
|
298 |
]
|
299 |
}
|
300 |
+
],
|
|
|
|
|
|
|
301 |
"source": [
|
302 |
"starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)\n",
|
303 |
"timings=np.zeros((repetitions,1))\n",
|
|
|
321 |
"\n",
|
322 |
"mean_syn = np.sum(timings) / repetitions\n",
|
323 |
"print(mean_syn)"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
324 |
]
|
325 |
}
|
326 |
+
],
|
327 |
+
"metadata": {
|
328 |
+
"accelerator": "GPU",
|
329 |
+
"colab": {
|
330 |
+
"gpuType": "L4",
|
331 |
+
"machine_shape": "hm",
|
332 |
+
"provenance": []
|
333 |
+
},
|
334 |
+
"kernelspec": {
|
335 |
+
"display_name": "Python 3",
|
336 |
+
"name": "python3"
|
337 |
+
},
|
338 |
+
"language_info": {
|
339 |
+
"name": "python"
|
340 |
+
}
|
341 |
+
},
|
342 |
+
"nbformat": 4,
|
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+
"nbformat_minor": 0
|
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+
}
|
Fine_tune_Florence_2.ipynb
CHANGED
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|
Fine_tune_PaliGemma.ipynb
CHANGED
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|
|
1 |
{
|
2 |
"cells": [
|
3 |
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{
|
4 |
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"cell_type": "markdown",
|
5 |
-
"metadata": {
|
6 |
-
"id": "view-in-github",
|
7 |
-
"colab_type": "text"
|
8 |
-
},
|
9 |
-
"source": [
|
10 |
-
"<a href=\"https://colab.research.google.com/github/merveenoyan/smol-vision/blob/main/Fine_tune_PaliGemma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
11 |
-
]
|
12 |
-
},
|
13 |
{
|
14 |
"cell_type": "markdown",
|
15 |
"metadata": {
|
@@ -23,21 +13,18 @@
|
|
23 |
},
|
24 |
{
|
25 |
"cell_type": "code",
|
26 |
-
"
|
27 |
-
"!pip install -q -U datasets bitsandbytes peft git+https://github.com/huggingface/transformers.git"
|
28 |
-
],
|
29 |
"metadata": {
|
30 |
-
"id": "EB0gv8OzHfLV",
|
31 |
"colab": {
|
32 |
"base_uri": "https://localhost:8080/"
|
33 |
},
|
|
|
34 |
"outputId": "9de07e75-ddf4-4347-fc41-432a23774e2c"
|
35 |
},
|
36 |
-
"execution_count": 1,
|
37 |
"outputs": [
|
38 |
{
|
39 |
-
"output_type": "stream",
|
40 |
"name": "stdout",
|
|
|
41 |
"text": [
|
42 |
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
|
43 |
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
|
@@ -55,6 +42,9 @@
|
|
55 |
"\u001b[0m"
|
56 |
]
|
57 |
}
|
|
|
|
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|
|
58 |
]
|
59 |
},
|
60 |
{
|
@@ -70,7 +60,6 @@
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|
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"cell_type": "code",
|
71 |
"execution_count": 2,
|
72 |
"metadata": {
|
73 |
-
"id": "NzJZSHD8tZZy",
|
74 |
"colab": {
|
75 |
"base_uri": "https://localhost:8080/",
|
76 |
"height": 17,
|
@@ -97,22 +86,23 @@
|
|
97 |
"80df5f3cd6c646808b09d99daed5bfd2"
|
98 |
]
|
99 |
},
|
|
|
100 |
"outputId": "c01b2b6f-3c1e-45da-9fc0-f4f518bcca24"
|
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},
|
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"outputs": [
|
103 |
{
|
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"output_type": "display_data",
|
105 |
"data": {
|
106 |
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"text/plain": [
|
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"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
|
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],
|
109 |
"application/vnd.jupyter.widget-view+json": {
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|
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|
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"metadata": {}
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|
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|
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@@ -133,16 +123,16 @@
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|
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"execution_count": 1,
|
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"metadata": {
|
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"id": "az5kdSbNpjgH",
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
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},
|
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"outputId": "2d9f379c-eb31-45b0-b84c-79c2a2577d01"
|
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},
|
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"outputs": [
|
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{
|
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"output_type": "stream",
|
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"name": "stderr",
|
|
|
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"text": [
|
147 |
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
148 |
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
@@ -174,15 +164,14 @@
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|
174 |
"cell_type": "code",
|
175 |
"execution_count": 3,
|
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"metadata": {
|
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"id": "TNJW2ty4yy4L",
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
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},
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"outputId": "f76414b2-8f37-48ae-d369-b977323fa892"
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},
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"outputs": [
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{
|
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"Dataset({\n",
|
@@ -191,8 +180,9 @@
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"})"
|
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|
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},
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"metadata": {},
|
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-
"
|
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}
|
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],
|
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"source": [
|
@@ -224,7 +214,6 @@
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"cell_type": "code",
|
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"execution_count": 5,
|
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"metadata": {
|
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-
"id": "iZRvrfUquH1y",
|
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"colab": {
|
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"base_uri": "https://localhost:8080/",
|
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"height": 49,
|
@@ -242,22 +231,23 @@
|
|
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"a9a5503caf384b93bf987e5271a577d2"
|
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]
|
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},
|
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|
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"outputId": "34f12289-6ef4-49d9-9257-ad0328961190"
|
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},
|
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"outputs": [
|
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{
|
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-
"output_type": "display_data",
|
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"data": {
|
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"text/plain": [
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|
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|
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"metadata": {}
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Fine_tune_SmolVLM2_on_Video.ipynb
CHANGED
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"cell_type": "markdown",
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"source": [
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"## Gemma 3n Video with Audio Inference"
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],
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"metadata": {
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"id": "onFz3_7AqnaB"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"In this notebook we'll infer Gemma-3n videos with audios inside."
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],
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"metadata": {
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"id": "KKUnhy4JqqAg"
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"source": [
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"!pip install -U -q transformers timm datasets"
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],
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"metadata": {
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"id": "Vf-VvnrNjuxF"
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"execution_count": null,
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"outputs": []
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"source": [
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"We will load three examples from FineVideo dataset and Gemma-3n model so make sure you have access to both and provide access token."
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"metadata": {
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"id": "gcJbxIPLqvjH"
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{
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"source": [
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"from huggingface_hub import login\n",
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"login()"
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{
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"66f82e7ef3694c699e3d4a2bd826392b",
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"outputId": "7351e21a-3c82-4d0c-c827-24b66812f181"
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"outputs": [
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
1102 |
-
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
1103 |
-
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
1104 |
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"You will be able to reuse this secret in all of your notebooks.\n",
|
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"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
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" warnings.warn(\n"
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"metadata": {}
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"source": [
|
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"from transformers import AutoProcessor, Gemma3nForConditionalGeneration\n",
|
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"import torch\n",
|
1127 |
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"model = Gemma3nForConditionalGeneration.from_pretrained(\n",
|
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" \"google/gemma-3n-E4B-it\", torch_dtype=torch.bfloat16,\n",
|
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").to(\"cuda\")\n",
|
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"processor = AutoProcessor.from_pretrained(\n",
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" \"google/gemma-3n-E4B-it\",\n",
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{
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"cell_type": "markdown",
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"source": [
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"Download video for inference."
|
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],
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"metadata": {
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"id": "mQzrURJlNRwW"
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{
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"source": [
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"!wget https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/IMG_8137.mp4"
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|
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|
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" generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)"
|
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{
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2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "markdown",
|
5 |
+
"metadata": {
|
6 |
+
"id": "onFz3_7AqnaB"
|
7 |
+
},
|
8 |
+
"source": [
|
9 |
+
"## Gemma 3n Video with Audio Inference"
|
10 |
+
]
|
11 |
},
|
12 |
+
{
|
13 |
+
"cell_type": "markdown",
|
14 |
+
"metadata": {
|
15 |
+
"id": "KKUnhy4JqqAg"
|
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+
},
|
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"source": [
|
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+
"In this notebook we'll infer Gemma-3n videos with audios inside."
|
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+
]
|
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},
|
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+
{
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+
"cell_type": "code",
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+
"execution_count": null,
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+
"metadata": {
|
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+
"id": "Vf-VvnrNjuxF"
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+
},
|
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+
"outputs": [],
|
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+
"source": [
|
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+
"!pip install -U -q transformers timm datasets"
|
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+
]
|
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},
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+
{
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+
"cell_type": "markdown",
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"metadata": {
|
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+
"id": "gcJbxIPLqvjH"
|
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+
},
|
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+
"source": [
|
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+
"We will load three examples from FineVideo dataset and Gemma-3n model so make sure you have access to both and provide access token."
|
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+
]
|
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+
},
|
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+
{
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+
"cell_type": "code",
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+
"execution_count": null,
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+
"metadata": {
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+
"colab": {
|
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+
"base_uri": "https://localhost:8080/",
|
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+
"height": 17,
|
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+
"referenced_widgets": [
|
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+
"542490f74e974451bc44009a6fa174bd",
|
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+
"409f985be1134b468b81136fbdb54408",
|
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+
"57cb1e931c614980a4147cb125524d7d",
|
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+
"87dc7aaf52e349a7bb43bb1b8bc137ee",
|
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+
"983ed4cb4eea42daa9ae8c0417021a21",
|
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+
"40c381fd7bb04b43a879044a4e988cc6",
|
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+
"8d0e5abdd7c549f1a66ee198c9fa1430",
|
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+
"c72dd3d6a4c246cfa6590c314783c8f0",
|
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+
"c0e471e664dd41eab98efe08301ef5e1",
|
58 |
+
"868f63ea9455442d837dc2c422918800",
|
59 |
+
"5b7b4707b1bf4159a10bf7e289bde435",
|
60 |
+
"889d0d1ed24e4de2b89896511d008e60",
|
61 |
+
"68fc757825dd44a48ab2383db20958db",
|
62 |
+
"cb76f933e6e640d9a688f7838e5fb0b3",
|
63 |
+
"8704264bff4d46c9813ac9acf92da962",
|
64 |
+
"9b5d87960dde401baeaf8b6144fb8bad",
|
65 |
+
"76e06881e5e94197a24944e07fdf3189",
|
66 |
+
"f40dd696acc64c6284c6f8f485f3ce9d",
|
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+
"4488de26dce74cbbb39d99ae09bd21fa",
|
68 |
+
"ded62e6c032745ec88ca0ab694b0d397"
|
69 |
+
]
|
70 |
},
|
71 |
+
"id": "bROdG2-Jj9lT",
|
72 |
+
"outputId": "1978e9bd-3b52-40b8-e643-418f9872476d"
|
73 |
+
},
|
74 |
+
"outputs": [
|
75 |
+
{
|
76 |
+
"data": {
|
77 |
+
"application/vnd.jupyter.widget-view+json": {
|
78 |
+
"model_id": "542490f74e974451bc44009a6fa174bd",
|
79 |
+
"version_major": 2,
|
80 |
+
"version_minor": 0
|
81 |
+
},
|
82 |
+
"text/plain": [
|
83 |
+
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
|
84 |
+
]
|
85 |
+
},
|
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+
"metadata": {},
|
87 |
+
"output_type": "display_data"
|
88 |
+
}
|
89 |
+
],
|
90 |
+
"source": [
|
91 |
+
"from huggingface_hub import login\n",
|
92 |
+
"login()"
|
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+
]
|
94 |
+
},
|
95 |
+
{
|
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+
"cell_type": "code",
|
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+
"execution_count": null,
|
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"metadata": {
|
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+
"colab": {
|
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+
"base_uri": "https://localhost:8080/",
|
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"height": 173,
|
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"referenced_widgets": [
|
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"be523e956910487ca263d943a7a58395",
|
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"01dc23faab3d42cda41fdfdd2a7dfed5",
|
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"777d7addfb144fd8896b77a1e0d54f25",
|
106 |
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"c518268069244b21810e84380502c190",
|
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+
"fee72c1c455549b59092028b855a082a",
|
108 |
+
"ed0fa93199b94fb486c125d4f322d59f",
|
109 |
+
"66f82e7ef3694c699e3d4a2bd826392b",
|
110 |
+
"2bfd51e3ae954008ae83704c24dbd6cb",
|
111 |
+
"f8b84d8c06384680973ef6fe787b5a5d",
|
112 |
+
"770341dc116148a8b7571cce3a2f2baf",
|
113 |
+
"29416122cc0b4a5592668ddced7686ba"
|
114 |
+
]
|
115 |
},
|
116 |
+
"id": "TMiKyRtAjjAc",
|
117 |
+
"outputId": "7351e21a-3c82-4d0c-c827-24b66812f181"
|
118 |
+
},
|
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+
"outputs": [
|
120 |
+
{
|
121 |
+
"name": "stderr",
|
122 |
+
"output_type": "stream",
|
123 |
+
"text": [
|
124 |
+
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
125 |
+
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
126 |
+
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
127 |
+
"You will be able to reuse this secret in all of your notebooks.\n",
|
128 |
+
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
129 |
+
" warnings.warn(\n"
|
130 |
+
]
|
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|
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},
|
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+
{
|
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+
"data": {
|
134 |
+
"application/vnd.jupyter.widget-view+json": {
|
135 |
+
"model_id": "be523e956910487ca263d943a7a58395",
|
136 |
+
"version_major": 2,
|
137 |
+
"version_minor": 0
|
138 |
+
},
|
139 |
+
"text/plain": [
|
140 |
+
"Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]"
|
141 |
+
]
|
142 |
+
},
|
143 |
+
"metadata": {},
|
144 |
+
"output_type": "display_data"
|
145 |
+
}
|
146 |
+
],
|
147 |
+
"source": [
|
148 |
+
"from transformers import AutoProcessor, Gemma3nForConditionalGeneration\n",
|
149 |
+
"import torch\n",
|
150 |
+
"model = Gemma3nForConditionalGeneration.from_pretrained(\n",
|
151 |
+
" \"google/gemma-3n-E4B-it\", torch_dtype=torch.bfloat16,\n",
|
152 |
+
").to(\"cuda\")\n",
|
153 |
+
"processor = AutoProcessor.from_pretrained(\n",
|
154 |
+
" \"google/gemma-3n-E4B-it\",\n",
|
155 |
+
")\n",
|
156 |
+
"processor.tokenizer.padding_side = \"right\""
|
157 |
+
]
|
158 |
+
},
|
159 |
+
{
|
160 |
+
"cell_type": "markdown",
|
161 |
+
"metadata": {
|
162 |
+
"id": "mQzrURJlNRwW"
|
163 |
+
},
|
164 |
+
"source": [
|
165 |
+
"Download video for inference."
|
166 |
+
]
|
167 |
+
},
|
168 |
+
{
|
169 |
+
"cell_type": "code",
|
170 |
+
"execution_count": null,
|
171 |
+
"metadata": {
|
172 |
+
"colab": {
|
173 |
+
"base_uri": "https://localhost:8080/"
|
174 |
},
|
175 |
+
"id": "PAQ1S2uDMIzj",
|
176 |
+
"outputId": "c584ee8c-b960-4f82-f2c6-be194709256f"
|
177 |
+
},
|
178 |
+
"outputs": [
|
179 |
+
{
|
180 |
+
"name": "stdout",
|
181 |
+
"output_type": "stream",
|
182 |
+
"text": [
|
183 |
+
"--2025-07-01 13:39:22-- https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/IMG_8137.mp4\n",
|
184 |
+
"Resolving huggingface.co (huggingface.co)... 18.172.134.4, 18.172.134.24, 18.172.134.124, ...\n",
|
185 |
+
"Connecting to huggingface.co (huggingface.co)|18.172.134.4|:443... connected.\n",
|
186 |
+
"HTTP request sent, awaiting response... 302 Found\n",
|
187 |
+
"Location: https://cdn-lfs-us-1.hf.co/repos/7b/14/7b14679bb56cefbf7829be71f3f444110ccc308f431bd8596f534e743367ea5c/6331cbb913feb48349e3b7015a7969e04ce3cd594b1bda7278e4e33fe4a3f5f3?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27IMG_8137.mp4%3B+filename%3D%22IMG_8137.mp4%22%3B&response-content-type=video%2Fmp4&Expires=1751380762&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc1MTM4MDc2Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzdiLzE0LzdiMTQ2NzliYjU2Y2VmYmY3ODI5YmU3MWYzZjQ0NDExMGNjYzMwOGY0MzFiZDg1OTZmNTM0ZTc0MzM2N2VhNWMvNjMzMWNiYjkxM2ZlYjQ4MzQ5ZTNiNzAxNWE3OTY5ZTA0Y2UzY2Q1OTRiMWJkYTcyNzhlNGUzM2ZlNGEzZjVmMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=MsPaMyO17sK%7Eo3U41ncCYEHd2vpjR6Jvv2IiqrhIy45kp-2WPdIGaYg5F7g9ENDJfFqmYavs6VH26AdLbX3HLPBUoR%7EAV8Iew8V1lFK1SpMkyCkh0SMtYNHqSw27jJ1ZSIhMKnHA7hRGi5b8LAhBiGzmlikz4a%7EtZAjjQZ18ZyN8GxCvTironzCp3uKUExWpRQF%7EwEwqurBb%7EKs-uJ6KDLvshYInzF%7Eo1LEoRNlXdxmDk8Q5Q7ZnBFM5m%7EPvBt-OQ4WWDPQZ86qblHwtoAgf483cdviYLPd8PjGzarQxgrjxbqELMvXM-nvUdXcOuAwhbBzpzSwBGQManPZxOFKTFw__&Key-Pair-Id=K24J24Z295AEI9 [following]\n",
|
188 |
+
"--2025-07-01 13:39:22-- https://cdn-lfs-us-1.hf.co/repos/7b/14/7b14679bb56cefbf7829be71f3f444110ccc308f431bd8596f534e743367ea5c/6331cbb913feb48349e3b7015a7969e04ce3cd594b1bda7278e4e33fe4a3f5f3?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27IMG_8137.mp4%3B+filename%3D%22IMG_8137.mp4%22%3B&response-content-type=video%2Fmp4&Expires=1751380762&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc1MTM4MDc2Mn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzdiLzE0LzdiMTQ2NzliYjU2Y2VmYmY3ODI5YmU3MWYzZjQ0NDExMGNjYzMwOGY0MzFiZDg1OTZmNTM0ZTc0MzM2N2VhNWMvNjMzMWNiYjkxM2ZlYjQ4MzQ5ZTNiNzAxNWE3OTY5ZTA0Y2UzY2Q1OTRiMWJkYTcyNzhlNGUzM2ZlNGEzZjVmMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=MsPaMyO17sK%7Eo3U41ncCYEHd2vpjR6Jvv2IiqrhIy45kp-2WPdIGaYg5F7g9ENDJfFqmYavs6VH26AdLbX3HLPBUoR%7EAV8Iew8V1lFK1SpMkyCkh0SMtYNHqSw27jJ1ZSIhMKnHA7hRGi5b8LAhBiGzmlikz4a%7EtZAjjQZ18ZyN8GxCvTironzCp3uKUExWpRQF%7EwEwqurBb%7EKs-uJ6KDLvshYInzF%7Eo1LEoRNlXdxmDk8Q5Q7ZnBFM5m%7EPvBt-OQ4WWDPQZ86qblHwtoAgf483cdviYLPd8PjGzarQxgrjxbqELMvXM-nvUdXcOuAwhbBzpzSwBGQManPZxOFKTFw__&Key-Pair-Id=K24J24Z295AEI9\n",
|
189 |
+
"Resolving cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)... 3.167.138.114, 3.167.138.90, 3.167.138.39, ...\n",
|
190 |
+
"Connecting to cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)|3.167.138.114|:443... connected.\n",
|
191 |
+
"HTTP request sent, awaiting response... 200 OK\n",
|
192 |
+
"Length: 5340706 (5.1M) [video/mp4]\n",
|
193 |
+
"Saving to: ‘IMG_8137.mp4’\n",
|
194 |
+
"\n",
|
195 |
+
"IMG_8137.mp4 100%[===================>] 5.09M 27.1MB/s in 0.2s \n",
|
196 |
+
"\n",
|
197 |
+
"2025-07-01 13:39:22 (27.1 MB/s) - ‘IMG_8137.mp4’ saved [5340706/5340706]\n",
|
198 |
+
"\n"
|
199 |
+
]
|
200 |
+
}
|
201 |
+
],
|
202 |
+
"source": [
|
203 |
+
"!wget https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/IMG_8137.mp4"
|
204 |
+
]
|
205 |
+
},
|
206 |
+
{
|
207 |
+
"cell_type": "markdown",
|
208 |
+
"metadata": {
|
209 |
+
"id": "KXlBj7dVtUFZ"
|
210 |
+
},
|
211 |
+
"source": [
|
212 |
+
"Strip audios from video."
|
213 |
+
]
|
214 |
+
},
|
215 |
+
{
|
216 |
+
"cell_type": "code",
|
217 |
+
"execution_count": null,
|
218 |
+
"metadata": {
|
219 |
+
"colab": {
|
220 |
+
"base_uri": "https://localhost:8080/"
|
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|
221 |
},
|
222 |
+
"id": "FQhKimtlMOHe",
|
223 |
+
"outputId": "ef05231a-ce56-4733-b0be-d6b423a143ae"
|
224 |
+
},
|
225 |
+
"outputs": [
|
226 |
+
{
|
227 |
+
"data": {
|
228 |
+
"text/plain": [
|
229 |
+
"CompletedProcess(args=['ffmpeg', '-i', 'IMG_8137.mp4', '-q:a', '0', '-map', 'a', 'audios/audio.wav', '-y'], returncode=0)"
|
230 |
+
]
|
231 |
+
},
|
232 |
+
"execution_count": 57,
|
233 |
+
"metadata": {},
|
234 |
+
"output_type": "execute_result"
|
235 |
+
}
|
236 |
+
],
|
237 |
+
"source": [
|
238 |
+
"import os\n",
|
239 |
+
"import subprocess\n",
|
240 |
+
"filename = \"IMG_8137.mp4\"\n",
|
241 |
+
"audio_path = os.path.join(\"audios\", f\"audio.wav\")\n",
|
242 |
+
"\n",
|
243 |
+
"subprocess.run([\n",
|
244 |
+
" \"ffmpeg\", \"-i\", filename,\n",
|
245 |
+
" \"-q:a\", \"0\", \"-map\", \"a\",\n",
|
246 |
+
" audio_path,\n",
|
247 |
+
" \"-y\"\n",
|
248 |
+
"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)"
|
249 |
+
]
|
250 |
+
},
|
251 |
+
{
|
252 |
+
"cell_type": "code",
|
253 |
+
"execution_count": null,
|
254 |
+
"metadata": {
|
255 |
+
"id": "6e_cExwMjx7v"
|
256 |
+
},
|
257 |
+
"outputs": [],
|
258 |
+
"source": [
|
259 |
+
"import cv2\n",
|
260 |
+
"from PIL import Image\n",
|
261 |
+
"import numpy as np\n",
|
262 |
+
"\n",
|
263 |
+
"def downsample_video(video_path):\n",
|
264 |
+
" vidcap = cv2.VideoCapture(video_path)\n",
|
265 |
+
" total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
|
266 |
+
" fps = vidcap.get(cv2.CAP_PROP_FPS)\n",
|
267 |
+
"\n",
|
268 |
+
" frames = []\n",
|
269 |
+
" frame_indices = np.linspace(0, total_frames - 1, 7, dtype=int)\n",
|
270 |
+
"\n",
|
271 |
+
" for i in frame_indices:\n",
|
272 |
+
" vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)\n",
|
273 |
+
" success, image = vidcap.read()\n",
|
274 |
+
" if success:\n",
|
275 |
+
" image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert from BGR to RGB\n",
|
276 |
+
" pil_image = Image.fromarray(image)\n",
|
277 |
+
" timestamp = round(i / fps, 2)\n",
|
278 |
+
" frames.append((pil_image, timestamp))\n",
|
279 |
+
"\n",
|
280 |
+
" vidcap.release()\n",
|
281 |
+
" return frames\n"
|
282 |
+
]
|
283 |
+
},
|
284 |
+
{
|
285 |
+
"cell_type": "markdown",
|
286 |
+
"metadata": {
|
287 |
+
"id": "mRKCPRabuMs6"
|
288 |
+
},
|
289 |
+
"source": [
|
290 |
+
"We will generate descriptions to videos and compare them to irl description in the metadata for the vibecheck.\n",
|
291 |
+
"\n",
|
292 |
+
"We need to downsample video to frames."
|
293 |
+
]
|
294 |
+
},
|
295 |
+
{
|
296 |
+
"cell_type": "code",
|
297 |
+
"execution_count": null,
|
298 |
+
"metadata": {
|
299 |
+
"id": "UMJESbFulYTi"
|
300 |
+
},
|
301 |
+
"outputs": [],
|
302 |
+
"source": [
|
303 |
+
"frames = downsample_video(filename)"
|
304 |
+
]
|
305 |
+
},
|
306 |
+
{
|
307 |
+
"cell_type": "code",
|
308 |
+
"execution_count": null,
|
309 |
+
"metadata": {
|
310 |
+
"colab": {
|
311 |
+
"base_uri": "https://localhost:8080/"
|
312 |
},
|
313 |
+
"id": "wJKdYXasMfEG",
|
314 |
+
"outputId": "2cff578c-df4d-41ca-8d9e-f85b4fed3456"
|
315 |
+
},
|
316 |
+
"outputs": [
|
317 |
+
{
|
318 |
+
"data": {
|
319 |
+
"text/plain": [
|
320 |
+
"[(<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(0.0)),\n",
|
321 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(1.03)),\n",
|
322 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(2.09)),\n",
|
323 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(3.12)),\n",
|
324 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(4.17)),\n",
|
325 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(5.21)),\n",
|
326 |
+
" (<PIL.Image.Image image mode=RGB size=1080x1920>, np.float64(6.26))]"
|
327 |
+
]
|
328 |
+
},
|
329 |
+
"execution_count": 52,
|
330 |
+
"metadata": {},
|
331 |
+
"output_type": "execute_result"
|
332 |
+
}
|
333 |
+
],
|
334 |
+
"source": [
|
335 |
+
"frames"
|
336 |
+
]
|
337 |
+
},
|
338 |
+
{
|
339 |
+
"cell_type": "code",
|
340 |
+
"execution_count": null,
|
341 |
+
"metadata": {
|
342 |
+
"id": "u8itVHCflZYQ"
|
343 |
+
},
|
344 |
+
"outputs": [],
|
345 |
+
"source": [
|
346 |
+
"messages = [\n",
|
347 |
+
" {\n",
|
348 |
+
" \"role\": \"system\",\n",
|
349 |
+
" \"content\": [{\"type\": \"text\", \"text\": \"You are a helpful assistant.\"}]\n",
|
350 |
+
" },\n",
|
351 |
+
" {\n",
|
352 |
+
" \"role\": \"user\",\n",
|
353 |
+
" \"content\": [\n",
|
354 |
+
" {\"type\": \"text\", \"text\": f\"What is happening in this video? Summarize the events.\"}]\n",
|
355 |
+
" }\n",
|
356 |
+
"]\n",
|
357 |
+
"for frame in frames:\n",
|
358 |
+
" image, timestamp = frame\n",
|
359 |
+
" messages[1][\"content\"].append({\"type\": \"text\", \"text\": f\"Frame {timestamp}: \"})\n",
|
360 |
+
" image.save(f\"image_{timestamp}.png\")\n",
|
361 |
+
" messages[1][\"content\"].append({\"type\": \"image\", \"url\": f\"./image_{timestamp}.png\"})\n",
|
362 |
+
"messages[1][\"content\"].append({\"type\": \"audio\", \"audio\": f\"audios/audio.wav\"})"
|
363 |
+
]
|
364 |
+
},
|
365 |
+
{
|
366 |
+
"cell_type": "code",
|
367 |
+
"execution_count": null,
|
368 |
+
"metadata": {
|
369 |
+
"colab": {
|
370 |
+
"base_uri": "https://localhost:8080/"
|
371 |
},
|
372 |
+
"id": "dBX4mNxXxGoC",
|
373 |
+
"outputId": "b738e828-bf9b-4f13-bbb2-9f38bea50b6a"
|
374 |
+
},
|
375 |
+
"outputs": [
|
376 |
+
{
|
377 |
+
"data": {
|
378 |
+
"text/plain": [
|
379 |
+
"[{'role': 'system',\n",
|
380 |
+
" 'content': [{'type': 'text', 'text': 'You are a helpful assistant.'}]},\n",
|
381 |
+
" {'role': 'user',\n",
|
382 |
+
" 'content': [{'type': 'text',\n",
|
383 |
+
" 'text': 'What is happening in this video? Summarize the events.'},\n",
|
384 |
+
" {'type': 'text', 'text': 'Frame 0.0: '},\n",
|
385 |
+
" {'type': 'image', 'url': './image_0.0.png'},\n",
|
386 |
+
" {'type': 'text', 'text': 'Frame 1.03: '},\n",
|
387 |
+
" {'type': 'image', 'url': './image_1.03.png'},\n",
|
388 |
+
" {'type': 'text', 'text': 'Frame 2.09: '},\n",
|
389 |
+
" {'type': 'image', 'url': './image_2.09.png'},\n",
|
390 |
+
" {'type': 'text', 'text': 'Frame 3.12: '},\n",
|
391 |
+
" {'type': 'image', 'url': './image_3.12.png'},\n",
|
392 |
+
" {'type': 'text', 'text': 'Frame 4.17: '},\n",
|
393 |
+
" {'type': 'image', 'url': './image_4.17.png'},\n",
|
394 |
+
" {'type': 'text', 'text': 'Frame 5.21: '},\n",
|
395 |
+
" {'type': 'image', 'url': './image_5.21.png'},\n",
|
396 |
+
" {'type': 'text', 'text': 'Frame 6.26: '},\n",
|
397 |
+
" {'type': 'image', 'url': './image_6.26.png'},\n",
|
398 |
+
" {'type': 'audio', 'audio': 'audios/audio.wav'}]}]"
|
399 |
+
]
|
400 |
+
},
|
401 |
+
"execution_count": 59,
|
402 |
+
"metadata": {},
|
403 |
+
"output_type": "execute_result"
|
404 |
+
}
|
405 |
+
],
|
406 |
+
"source": [
|
407 |
+
"messages"
|
408 |
+
]
|
409 |
+
},
|
410 |
+
{
|
411 |
+
"cell_type": "code",
|
412 |
+
"execution_count": null,
|
413 |
+
"metadata": {
|
414 |
+
"id": "e4f0qr67lcjo"
|
415 |
+
},
|
416 |
+
"outputs": [],
|
417 |
+
"source": [
|
418 |
+
"#processor.tokenizer.padding_side = \"right\"\n",
|
419 |
+
"inputs = processor.apply_chat_template(\n",
|
420 |
+
" messages, add_generation_prompt=True, tokenize=True,\n",
|
421 |
+
" return_dict=True, return_tensors=\"pt\"\n",
|
422 |
+
").to(model.device).to(model.dtype)"
|
423 |
+
]
|
424 |
+
},
|
425 |
+
{
|
426 |
+
"cell_type": "code",
|
427 |
+
"execution_count": null,
|
428 |
+
"metadata": {
|
429 |
+
"colab": {
|
430 |
+
"base_uri": "https://localhost:8080/"
|
431 |
},
|
432 |
+
"id": "EOiBpgkI9kXi",
|
433 |
+
"outputId": "911a6013-f76f-4fed-c402-8039d67b1e05"
|
434 |
+
},
|
435 |
+
"outputs": [
|
436 |
+
{
|
437 |
+
"data": {
|
438 |
+
"text/plain": [
|
439 |
+
"2087"
|
440 |
+
]
|
441 |
+
},
|
442 |
+
"execution_count": 61,
|
443 |
+
"metadata": {},
|
444 |
+
"output_type": "execute_result"
|
445 |
+
}
|
446 |
+
],
|
447 |
+
"source": [
|
448 |
+
"inputs[\"input_ids\"].shape[-1]"
|
449 |
+
]
|
450 |
+
},
|
451 |
+
{
|
452 |
+
"cell_type": "code",
|
453 |
+
"execution_count": null,
|
454 |
+
"metadata": {
|
455 |
+
"colab": {
|
456 |
+
"base_uri": "https://localhost:8080/"
|
457 |
},
|
458 |
+
"id": "yJ95UXBqvXPM",
|
459 |
+
"outputId": "721839dc-aa78-401b-e802-b858690980da"
|
460 |
+
},
|
461 |
+
"outputs": [
|
462 |
+
{
|
463 |
+
"name": "stderr",
|
464 |
+
"output_type": "stream",
|
465 |
+
"text": [
|
466 |
+
"The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
|
467 |
+
]
|
468 |
+
}
|
469 |
+
],
|
470 |
+
"source": [
|
471 |
+
"with torch.inference_mode():\n",
|
472 |
+
" generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)"
|
473 |
+
]
|
474 |
+
},
|
475 |
+
{
|
476 |
+
"cell_type": "code",
|
477 |
+
"execution_count": null,
|
478 |
+
"metadata": {
|
479 |
+
"colab": {
|
480 |
+
"base_uri": "https://localhost:8080/"
|
481 |
+
},
|
482 |
+
"id": "3ifVZy9c74St",
|
483 |
+
"outputId": "f8ab51c6-e5a3-4a16-875b-d07404041396"
|
484 |
+
},
|
485 |
+
"outputs": [
|
486 |
+
{
|
487 |
+
"name": "stdout",
|
488 |
+
"output_type": "stream",
|
489 |
+
"text": [
|
490 |
+
"Here's a summary of what's happening in the video:\n",
|
491 |
+
"\n",
|
492 |
+
"The video appears to be taken at a ski resort. The main subject is a person snowboarding down a snowy slope. \n",
|
493 |
+
"\n",
|
494 |
+
"**Initial Scene (0.0 - 1.03):** The snowboarder is initially positioned on the slope, seemingly having fallen or stopped. Other skiers and snowboarders are visible in the background, waiting at what looks like a lift station.\n",
|
495 |
+
"\n",
|
496 |
+
"**Mid-Video (1.03 - 6.26):** The snowboarder gets back up and continues down the slope. They navigate past other people, including skiers and snowboarders, and eventually reach a lift station. The video shows the snowboarder interacting with others at the lift, possibly waiting for the lift to start or having just gotten off. There are also other skiers and snowboarders around the lift station.\n",
|
497 |
+
"\n",
|
498 |
+
"**End Scene (6.26):** The snowboarder is still at the lift station,\n"
|
499 |
+
]
|
500 |
+
}
|
501 |
+
],
|
502 |
+
"source": [
|
503 |
+
"input_len = inputs[\"input_ids\"].shape[-1]\n",
|
504 |
+
"\n",
|
505 |
+
"generation = generation[0][input_len:]\n",
|
506 |
+
"\n",
|
507 |
+
"decoded = processor.decode(generation, skip_special_tokens=True)\n",
|
508 |
+
"print(decoded)"
|
509 |
+
]
|
510 |
+
}
|
511 |
+
],
|
512 |
+
"metadata": {
|
513 |
+
"accelerator": "GPU",
|
514 |
+
"colab": {
|
515 |
+
"gpuType": "A100",
|
516 |
+
"include_colab_link": true,
|
517 |
+
"machine_shape": "hm",
|
518 |
+
"provenance": []
|
519 |
+
},
|
520 |
+
"kernelspec": {
|
521 |
+
"display_name": "Python 3",
|
522 |
+
"name": "python3"
|
523 |
+
},
|
524 |
+
"language_info": {
|
525 |
+
"name": "python"
|
526 |
+
},
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527 |
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"_model_name": "HTMLModel",
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"_view_module": "@jupyter-widgets/controls",
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540 |
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"_view_module_version": "1.5.0",
|
541 |
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"_view_name": "HTMLView",
|
542 |
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"description": "",
|
543 |
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"description_tooltip": null,
|
544 |
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"layout": "IPY_MODEL_ed0fa93199b94fb486c125d4f322d59f",
|
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|
546 |
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"style": "IPY_MODEL_66f82e7ef3694c699e3d4a2bd826392b",
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547 |
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"value": "Loading checkpoint shards: 100%"
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PaliGemma_DPO.ipynb
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Reduce_any_model_to_fp16_using_🤗_Optimum_DETR.ipynb
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ShieldGemma_2_for_Vision_LM_Safety.ipynb
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|