{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "71GTxOD71mEn" }, "source": [ "## Introduction\n", "\n", "In this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.77%** of the original trainable parameters of the model. \n", "\n", "LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685). \n", "\n", "Let's get started by installing the dependencies. \n", "\n", "__*Note that this notebook builds on top the [official image classification example notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb).*__" ] }, { "cell_type": "markdown", "metadata": { "id": "0a_bETbqv4P7" }, "source": [ "## Install dependencies\n", "\n", "Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z0_5BYt8hobv", "outputId": "aafcbc39-b972-493a-8922-2141b1621926" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m46.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m102.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install transformers accelerate evaluate datasets git+https://github.com/huggingface/peft -q" ] }, { "cell_type": "markdown", "metadata": { "id": "Y8dSVHoIv7HC" }, "source": [ "## Authentication\n", "\n", "We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "5d2f5fb454bc4c16b520e4e96381758f", "dfd2baceac524fe29c0f4a8443b60a71", "90d8e83a6af54184a82e0b81ae7054b9", "1f96ca356b6f41b59275abe93df33f43", "eef81e9bea0c4f5d85e7efa8ebe0463a", "cab6d36980c0423fb75299c09c33facc", "dd38a658218d42d7b051c66de4d4180a", "f34be236ef9c42448ecf2957160990f7", "38deee504dab482983a8b8f340472282", "b2688e34899a449e8d1f6ddb5a66bb85", "dd4edb4de5e14dfbbee418dba0bb3573", "516c6d75bc654d62b95ac235ce84c59c", "14c23f636609458ca4493854826c1a8e", "c778798c234d45b5a4ae2f250e3706f9", "d5c5396ea2f54ff0aeb9be58b59c253b", "15bd2dcdbf4b4e74b9db09bdb8822e61", "ecf73dd75420460399bfd04d8cd81f90" ] }, "id": "31Zv6rFYr37d", "outputId": "6476ebcf-6d71-4b7d-ee38-dc4f8e8d024e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token is valid.\n", "Your token has been saved in your configured git credential helpers (store).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "markdown", "metadata": { "id": "AX7aJaIKjbCF" }, "source": [ "## Check the library versions" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ejkn8GBzh_DB", "outputId": "777afbdf-e026-43d8-8efa-80bb958d0ca3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "===================================BUG REPORT===================================\n", "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", "================================================================================\n" ] } ], "source": [ "import transformers\n", "import accelerate\n", "import peft" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A833xxo3ir28", "outputId": "da71ef1c-b6d7-43e2-a78b-23556785ef02" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transformers version: 4.26.0\n", "Accelerate version: 0.16.0\n", "PEFT version: 0.1.0.dev0\n" ] } ], "source": [ "print(f\"Transformers version: {transformers.__version__}\")\n", "print(f\"Accelerate version: {accelerate.__version__}\")\n", "print(f\"PEFT version: {peft.__version__}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Po1Ve9u5v_Ul" }, "source": [ "## Select a model checkpoint to fine-tune" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "vhvCQpP-isJr" }, "outputs": [], "source": [ "model_checkpoint = \"google/vit-base-patch16-224-in21k\" # pre-trained model from which to fine-tune" ] }, { "cell_type": "markdown", "metadata": { "id": "UKN3rMAsjgEz" }, "source": [ "## Load a dataset\n", "\n", "We're only loading the first 5000 instances from the training set of the [Food-101 dataset](https://huggingface.co/datasets/food101) to keep this example runtime short. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 379, "referenced_widgets": [ "61b957d3b51643f78a921979072fe3b6", "d7136a7b3d0040d580508fc665b9fb00", "5ee5e11191fc46dd92d4c2f1a7d6d9da", "3587d42fa09b4fcdb365956a9bb07c77", "c1ed0b68884c4d4291cd67c0e685ef18", "9102cc38ee9942ac91dc66eda069ddcb", "416c65eedcea4a6ea69dae317de79bca", "128677e1b5b14e63b06b0f81c9cc4df0", "22da54e68b1d48f9b3ba55ac1ca56873", "16c7db587b8e475fa3aa9677385b092a", "23c608994006427caca7975e0d81271f", "71f7296ec9be4d9abe1af581722b40fe", "b98e53eefc1944f193169c4f7a72b799", "1d4a5a5b7d1645a8bf8133935e173082", "d29e3b9102f14f3385e47ae6e27d1ab1", "1e3e374b08964a689cfaac9c826f207b", "b377e94780fb4e1db3b9678717e04fc1", "86103f87819b440b8464f4460f50375e", "c3178221dc074657bc0e585c4cfe326d", "3c6842e0158b4dcc9b93eddfe3279d2a", "fc612aaed5644b84959a1958b0240dda", "36c8300bcbb84627a03b94f0eea86ce9", "f0b0cad40fbd461ca7bdcdbb5f442f57", "76cf84387a7c43608ad018188eef4114", "68ef0c8550ee4c00aa8b284d48572610", "58e7f5c36d8b4836a868ce89838f1896", "9b216287b8694bcc9960a356adf15504", "4d653faaedcd497d863bbf2c429ce925", "d10f2e9c25f2417f9728aa8e43acf677", "7a35d0ddc2da4dd69068214b87bcdd7f", "9aac38a8c8694c67a34b2fced0e1a706", "9d1dff20634a403fb8829469d74301aa", "b2e64f35be2d4fa3bc95c769b78e1dd1", "fdf282b234fe4a1a8ab452ac04511b7d", "59792e1ee7074f998d5d4494c09061c6", "cd5b2433cc404ac7b1bb35c6a55f6874", "7c1b6f271fff4d60be39d291c73bfb75", "074f38bd3a9d49719188e8860fb1b5d3", "3ff84efe0edc491c884898424be4ae71", "5a79a196fd7b49128a9647347f85b364", "851fb5ac25db4bb287a6dbe948278eec", "471d44c8e49e42b89302ef53ab0eb316", "4089323832d04dc2a40e238b5fa256cc", "2d867c65533e482a96db93bc5a09b8cb", "849ac914c3cc49d29d619dd4f532d74c", "4cb3d75f80434b48beb6aa4b07c86dfe", "ff39519704b64e68b69ec06aea02791e", "e0f2599ed04c424f896e503630034e84", "1674d568877048368c842c21ffaac811", "0957e36be17c43fd89462c5d5ddcec1b", "dffe636233c84dcd9d75f34baf40fa1d", "dcefc9ba538e4da2b75f9372a4c5b5bf", "77df794cb4e4491e80ee20bbd2801a89", "7e243f4a30c645b080e688fb706b4548", "db6b68a237cf4e93ae6383448b773e47", "c580d3a6e99e48fab09b3ce799711802", "4afba780d0f244548a7f28db15b41dc9", "4e3d482feec9485590d277dfc1d0b3d3", "23436ea247dd43d8829ca143a49637c5", "9609eaf0792345b2ab457cb7188ee14a", "1839e4ed1d3c4975b34c3c050052693f", "1b31bfb0ef4c404698eb2205414170af", "6350637718344d65a757d2919de8c1ab", "42a16474e41343b2a7d46e5930b41b89", "ce16ac2b3ff244e6bd7dd58daa9f4f7f", "d25f3ebb577749d89e2e6d2a72f6ca5f", "13279e67c4d847e4846e2d34e8aac589", "d7d43177c750412cb1522eb08c01d2d9", "70b04a3579a5446f94acd422c70ac50a", "43940212a87d410c82cc9cd15f38a97e", "19fd7c60287b43bbb6e0b12c25b4b375", "9013fd35e17f44bfb7a068833adaf167", "a849dcc9c7f742d49c874597d8c693c5", "039dd9a4b99e433088a0acd8ba7b519b", "17235d013b7c4cee996d0bbc1cc6c70c", "3db63ba25e7349a785244c367d53813e", "4748461200ae4af883577e2fbb8cb686", "19ec79f5a5174aa3b26861a9662951d3", "57c15c64c2374f06a1e0a36bab953ef7", "06cf9f29b929412a8092044e25861f1c", "c2032e5054ac4604832957cb6e2e69ca", "d1ff50e1b871429a85df8cf10e73ffb1", "10c4f5677d1c4af8b3370b7fb1255065", "603dd1541db345879295edc16ace2b0c", "375ac7a15cea4ce3aa484a806cc82717", "6b6459f123ef4f24a550cd9ec3c9f809", "e3047557ae7f40e2aecccf1afad36f3f", "4fc212af0c9b45ebbe334e3dd7f11b59", "fee4fba960ac41ed97984467da41f319", "ee103846621b4c0e8e1266599b99f6ee", "dcd1c1f4fc014c4aa9ebdaf3c533a061", "a29d758fb7f147c7ad1108f140caf23a", "cb52fa97c659430a8bd71dcd76245a7f", "e7144551e74b46529b00a61f580a183d", "9b1bfa11ee3746c38155c4505abfaa86", "26520bc6555d41d9951ea0219dc4b5d7", "60472b5a360f43e89e39d641dabba57b", "aa9b6ac2785c4a5abd1189edd60698eb", "cfab815edc1f42898b656c0f4a3b366b", "c5718d031b9942f4b8bf331a8543db29", "35d862a4f00c4493920da3e2eb92b043", "16b464f168d844cba5eb0c91ab4fb91c", "af5231ecf6e2489b80cdcd435b5e3451", "62a0f83cf75d4c59a0601c5ad3a817a7", "b48f685dc91540f38690f39eace724d5", "ce4b6a4b6fec4ceb907fa436ff940bd2", "28f82c8fc9cf46c7858132a77e45834b", "ce18faf7b68140a3a8247330b356e05b", "af6a4a054a5d451b9fe256bf60a09c21", "afb1f0681bce47e1ba718900d0430f34" ] }, "id": "rI0d2_liitUr", "outputId": "4ae986eb-6cbb-4d9f-bb99-1ffbb05ee835" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "61b957d3b51643f78a921979072fe3b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/6.21k [00:00\n", " \n", " \n", " [45/45 04:44, Epoch 5/5]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracy
1No log0.5068710.896000
22.1627000.1891410.946000
30.3451000.1447590.960000
40.2116000.1508860.958000
50.1711000.1497510.958000

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n", "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9\n", "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/config.json\n", "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/pytorch_model.bin\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/preprocessor_config.json\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n", "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18\n", "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/config.json\n", "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/pytorch_model.bin\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/preprocessor_config.json\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n", "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27\n", "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/config.json\n", "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/pytorch_model.bin\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/preprocessor_config.json\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n", "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36\n", "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/config.json\n", "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/pytorch_model.bin\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/preprocessor_config.json\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n", "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45\n", "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/config.json\n", "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/pytorch_model.bin\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/preprocessor_config.json\n", "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "Loading best model from vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27 (score: 0.96).\n" ] } ], "source": [ "trainer = Trainer(\n", " model,\n", " args,\n", " train_dataset=train_ds,\n", " eval_dataset=val_ds,\n", " tokenizer=image_processor,\n", " compute_metrics=compute_metrics,\n", " data_collator=collate_fn,\n", ")\n", "train_results = trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "b2NENHxHCejv" }, "source": [ "In just a few minutes, we have a fine-tuned model with 96% validation accuracy. Also, note that we used a very small subset of the training dataset which is definitely impacting the results. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 198 }, "id": "_MAd2906jQKG", "outputId": "7b825531-4e8d-4666-d6fc-eaa1accf3bb6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 500\n", " Batch size = 128\n" ] }, { "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'eval_loss': 0.14475855231285095,\n", " 'eval_accuracy': 0.96,\n", " 'eval_runtime': 3.5725,\n", " 'eval_samples_per_second': 139.958,\n", " 'eval_steps_per_second': 1.12,\n", " 'epoch': 5.0}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.evaluate(val_ds)" ] }, { "cell_type": "markdown", "metadata": { "id": "qo_scDEyAQER" }, "source": [ "## Sharing your model and inference \n", "\n", "Once the fine-tuning is done, we can share the LoRA parameters with the community like so: " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 172, "referenced_widgets": [ "980d2f61332f414d9888b76e78774be4", "cbc0ba8e49a740fcae7b94fe7edb8107", "b3f07ef160a7425880ffe362008d4400", "49ed3330fa9645eda8b5aed0fd7cbafe", "f96302d0c2d849c5b5a0206b65e461ab", "fcc7ad16a0b14d96acd9be9e03ac6af9", "1a08961f063346ccae206a863ab7df6b", "1150e391e753424da7d65bda10463da4", "e6e36d744e1244aeb7eb0c4ce392372d", "b2ad992db5a045668fa55c7393ec7870", "b5fad0f3f2d543ecaed726e52d2d86bb", "e83fd078f467406da0baf26e18b39e89", "9b4b67731a7a4bc59be132b53c24eae8", "e33243b001274d02a25f5940ba41ecf6", "06e4c619e366427a8ff4c358196ecd12", "bacd429b42d843299cb75224db3afb1e", "c53429e699e64b3d8895a355bbd947a6", "2bfd04824f2e4fd6844dd38e46dbbbdf", "40a5a50aeca24f0d8990da97971004d1", "be71e6438e0e49759d2f72feec520cae", "1902fbc4c1da4ffe90f0947e58eb5d48", "12a94351242444d7b0d23b8accc1824a" ] }, "id": "TyQvIcnFzLIV", "outputId": "7ac2819e-080e-4940-9755-15c32832d9a6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Uploading the following files to sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101: adapter_config.json,adapter_model.bin\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "980d2f61332f414d9888b76e78774be4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload 1 LFS files: 0%| | 0/1 [00:00" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from PIL import Image\n", "import requests\n", "\n", "url = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg\"\n", "image = Image.open(requests.get(url, stream=True).raw)\n", "image" ] }, { "cell_type": "markdown", "metadata": { "id": "dFuqZgmCW4cu" }, "source": [ "We first instantiate an `image_processor` from the underlying model repo. " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dN4x_pj-VQx8", "outputId": "b4fddd49-2f10-48e2-de31-0cefd20d405d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file preprocessor_config.json from cache at /root/.cache/huggingface/hub/models--sayakpaul--vit-base-patch16-224-in21k-finetuned-lora-food101/snapshots/fa2503cc7d91e0dd69728c1dc66ed80d7bd3289b/preprocessor_config.json\n", "Image processor ViTImageProcessor {\n", " \"do_normalize\": true,\n", " \"do_rescale\": true,\n", " \"do_resize\": true,\n", " \"image_mean\": [\n", " 0.5,\n", " 0.5,\n", " 0.5\n", " ],\n", " \"image_processor_type\": \"ViTImageProcessor\",\n", " \"image_std\": [\n", " 0.5,\n", " 0.5,\n", " 0.5\n", " ],\n", " \"resample\": 2,\n", " \"rescale_factor\": 0.00392156862745098,\n", " \"size\": {\n", " \"height\": 224,\n", " \"width\": 224\n", " }\n", "}\n", "\n" ] } ], "source": [ "image_processor = AutoImageProcessor.from_pretrained(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "Dc0rCwC5XAaL" }, "source": [ "We then prepare the sample for inference." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "57C6tcdnVYu1", "outputId": "f164dd91-8679-482e-fb10-2f530d4ea9f4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 3, 224, 224])\n" ] } ], "source": [ "# prepare image for the model\n", "encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n", "print(encoding.pixel_values.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "Pn4T1GyTXC47" }, "source": [ "And run inference!" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YyQznW5WViMc", "outputId": "d1b7a77c-68b3-4f6b-a945-4b32e85baabe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted class: beignets\n" ] } ], "source": [ "import torch\n", "\n", "# forward pass\n", "with torch.no_grad():\n", " outputs = inference_model(**encoding)\n", " logits = outputs.logits\n", "\n", "predicted_class_idx = logits.argmax(-1).item()\n", "print(\"Predicted class:\", inference_model.config.id2label[predicted_class_idx])" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "0a_bETbqv4P7", "Y8dSVHoIv7HC", "qo_scDEyAQER" ], "machine_shape": "hm", "provenance": [] }, "gpuClass": "premium", "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", "version": "3.8.2" }, "vscode": { "interpreter": { "hash": "62ba1781de76fc6672ab4d41176558d38a2895b3007f2161f5f79f77fdcaf8cf" } }, "widgets": 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