{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "prwiIBb1lin_" }, "source": [ "# Fine-tune Pix2Struct using Hugging Face `transformers` and `datasets` 🤗\n", "\n", "This tutorial is largely based from the [GiT tutorial](https://colab.research.google.com/drive/1HLxgrG7xZJ9FvXckNG61J72FkyrbqKAA?usp=sharing) on how to fine-tune GiT on a custom image captioning dataset. Here we will use a dummy dataset of [football players](https://huggingface.co/datasets/ybelkada/football-dataset) ⚽ that is uploaded on the Hub. The images have been manually selected together with the captions. \n", "Check the 🤗 [documentation](https://huggingface.co/docs/datasets/image_dataset) on how to create and upload your own image-text dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "VYVMeWxjxQ73" }, "source": [ "## Model overview\n", "\n", "In this tutorial, we will load an architecture called Pix2Struct recently released by Google and made them available on 🤗 Hub! This architecture differs from other models from its pretraining procedure and the way the model extract patches from the image by using the aspect-ratio preserving patch extraction method.\n", "\n", "![Fig5 paper](https://user-images.githubusercontent.com/49240599/224268204-f97d76b4-54e4-470a-aa44-9b6c086c8369.png)\n", "\n", "The release came with no more than 20 checkpoints! \n", "\n", "![Screenshot 2023-03-10 at 09 42 19](https://user-images.githubusercontent.com/49240599/224267136-389b8300-6497-4321-a6cc-d85b4f8e55d7.png)\n", "\n", "As each checkpoint has been finetuned on specific domain, let's finetune our own Pix2Struct to our target domain: Football players! For that we will use the [`google/pix2struct-base`](https://huggingface.co/ybelkada/pix2struct-base) which corresponds to a general usecase model that you can load to fine-tune your model." ] }, { "cell_type": "markdown", "metadata": { "id": "4KNaYcxCqn8t" }, "source": [ "## Set-up environment" ] }, { "cell_type": "markdown", "metadata": { "id": "tPUTVo06yL28" }, "source": [ "Run the cells below to setup the environment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FqrW65lfqhhJ", "outputId": "383b08d3-a3e2-4ebf-edb0-b1846c4e6d73" }, "outputs": [], "source": [ "!pip install -q git+https://github.com/huggingface/transformers.git" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lTI8wKxgql9i", "outputId": "bb14d3c7-a60e-4b7e-b569-86bf442e0dab" }, "outputs": [], "source": [ "!pip install -q datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "mmUzlJNHq0CK" }, "source": [ "## Load the image captioning dataset\n", "\n", "Let's load the image captioning dataset, you just need few lines of code for that. The dataset only consists of 6 images that we have manually labeled for the sake of the tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 201, "referenced_widgets": [ "1d15a70612fc4dfb96d847e3ad2fdea9", "64934a8d8f8c41849cffd91c93102885", "859e972d58b549e1a79d8794b423f056", "439437c102b74788bb7f2904cc1be0c0", "4843ca871b1645ffa5cf1208f63c99e0", "a95f6506d439444a8fb8a7aebe1784f5", "17b54731890b4c3ab7e38a497c2c2481", "ac9f8fbab3194e8fadec5ef3f67c520a", "609d797968064f7db0be56ed0c9f2f93", "bc75e053cf024fd4b3ee4856ea6f6a07", "90578280bdf74e26b3b39e2e397ad68b", "955a0f7a5f9b435fbf73deb5e184292f", "c3c630323c7244ecbe540a82be9c5e65", "37ffd3db29e94f10b539ca997d81d4ba", "655627bcf0f44b119624200d9c70ee60", "e2b4a3fc314a4c92b39e2155b04450a8", "1acf11262df94c34bc771f15a42eebf4", "60cae91f55d54a9aadb7d8253f4012c5", "b2a89240aa6a4003be52b59c9091050c", "a4d62753050a47e298a43387260ee5ae", "95fcd0336df34c089fe9c219be390591", "adff4b19c76644a290255974bf4ca738", "984b49287f2c4fa785b5d5ecd2b9e8dd", "1fe4fc57e7224fd6a025900653400136", "fe319d97ed6d4f6c895f79a0f707a89f", "d4b64083091f4b12842e39542eecbadc", "91080125fc254eaa8a0b4cc1c9a44347", "a5ccaf0a8dae40b195b8e4579edf4175", "f3b4d7975cce4a21ba5705f55b950477", "2a479b347d914a49a6dd496e8f22c0bb", "4b6808c3509b466182209399055d286d", "500c22e1364b4038bb3409f74565644c", "c42f8a44009b4001b0dc9e68899089bf", "e0cd98d2a77d4bbb80958792e6e520af", "e8c789b6cc5641aca6c4e405c1702c2a", "83108939dfbd4f43b334fd9d27daba88", "8c5b3d57660843818f4ca70ebb439136", "72f69a9e2f394d75acdc07636d3c1b4f", "7569f9b3862e470aa255d7f513e1f810", "7804d9ae2aa042f4af65e4d52c072b79", "483658e2bd304c728b9db6386dd5f2e8", "ca62d0c4e1fc43dcb99406821f3ab13f", "deac5681c6f042598ec82e8e35851e3f", "fd8fd5b9978c4a75a3df2084a4f8351a", "2f27351b9295497eaf5e273c4f41e8b5", "0faa7c917030482691e6caf0913f1ac1", "a85cf063483849a490714738a1b2de0e", "efc45b8dcbf24e87a266c4dc5c85ffcf", "f09d9d8d53324f028012f9e302f27193", "e1c693c6599f46b6a906df5f4eda2c32", "83bfb50365fc4cd18244b99d52c4cc51", "9cf76bd708f5481ea3503ee1f31a6d7b", "eae457b678394e7eab76603fb430038d", "43182408305a4b4fae0d6255544a9b4b", "4237a298c63b4521b278483262fa8c68" ] }, "id": "QwkaKQKLs8vT", "outputId": "1f57d9af-ffa9-47ab-8006-afdb9ab8fae4" }, "outputs": [], "source": [ "from datasets import load_dataset \n", "\n", "dataset = load_dataset(\"ybelkada/football-dataset\", split=\"train\")" ] }, { "cell_type": "markdown", "metadata": { "id": "y52jgY9-mhvJ" }, "source": [ "Let's retrieve the caption of the first example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "n4WLPzYlutg6", "outputId": "f3215c4e-e844-4c79-bc66-6d1e30941c5b" }, "outputs": [], "source": [ "dataset[0][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "ukZ5x_Sumn1f" }, "source": [ "And the corresponding image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 377 }, "id": "lxoDUv5ymqBJ", "outputId": "a98c7e80-841e-4ac1-a7e2-1421185a9c34" }, "outputs": [], "source": [ "dataset[0][\"image\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "sSWkkqiKqrhv" }, "source": [ "## Create PyTorch Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "LLJ1yB6gyhk9" }, "source": [ "### Understanding `max_patches` argument\n", "\n", "The paper introduces a new paradigm for processing the input image. It takes the image and create `n_patches` aspect-ratio preserving patches, and concatenates the remaining sequence with padding tokens to finally get `max_patches` patches. It appears that this argument is quite crucial for training and evaluation, as the model becomes very sensitive to this parameter.\n", "\n", "For the sake of our example, we will fine-tune a model with `max_patches=1024`.\n", "\n", "Note that most of the `-base` models have been fine-tuned with `max_patches=2048`, and `4096` for `-large` models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "93od71o_qq_V" }, "outputs": [], "source": [ "from torch.utils.data import Dataset, DataLoader\n", "\n", "MAX_PATCHES = 1024\n", "\n", "class ImageCaptioningDataset(Dataset):\n", " def __init__(self, dataset, processor):\n", " self.dataset = dataset\n", " self.processor = processor\n", "\n", " def __len__(self):\n", " return len(self.dataset)\n", "\n", " def __getitem__(self, idx):\n", " item = self.dataset[idx]\n", " encoding = self.processor(images=item[\"image\"], return_tensors=\"pt\", add_special_tokens=True, max_patches=MAX_PATCHES)\n", " \n", " encoding = {k:v.squeeze() for k,v in encoding.items()}\n", " encoding[\"text\"] = item[\"text\"]\n", " return encoding" ] }, { "cell_type": "markdown", "metadata": { "id": "VX6Nv8aEm0yk" }, "source": [ "## Load model and processor" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "5e6df08af89d4f2a81ecdfe3cca562a9", "387ceedb7a014bf6b7ae8fb2f0403410", "ff94038f0e4742f78e8c9c41687508d5", "8130c9fc0464430b82c336de167663e6", "eff38112d774409899092b394b1dea80", "e8f576761a004df2a51c87688b50dcca", "a5c276638093475aa3e3554c718c8848", "c2e3c8ad59154ea1b384db61f5db717e", "59eda31d4fdf4ac3afaeae230c06c574", "2ecd9e0c110c4a11bfcabeb7065bce6d", "fa3cbafbf4a143d09d41f707b39ae287", "7492c912003d41c1abe8357207e5599d", "b2040533a242400986031e6a0b9f5164", "0fff19267ebf4b69ae0dc2d880290751", "07f339a6193b478ea5bd6410711db7db", "ac001b6a99e64265afc027751869bb96", "2522aaab08204eb4a05fa9f87baccbed", "e60a007166344ca8ab9ec85804a3894b", "1291d916e58b49f1915460667ce54c7d", "5fa802fd35c14b70886d9d62402f5831", "910e6baaebbf487790f5e241957a6f60", "6ac7be4757104228885d40ceb440a29c", "6ce3b36254ca436a9d0f9e802167cb19", "2743dc38922b4050888b46189fd7d65d", "f85963e1826c4cf8ab709647f0a838d3", "f5c2e60ed19f4671b60c705626408272", "2f5163985a944d918743877c16d86658", "f274b08e9618482ea9d01a19b3c15d6c", "29553a7a2a0040a58dda9a98bad7a57b", "e3f9b5205fcc401f90e7d604018cf51b", "0a85d9d982fb47e69958409bf72844ac", "13b82fa6436b4e2cab530e21267e0339", "779a84991da54161a0f51a92ef973bf1", "fd28120bfde5452b877522021d154caa", "1e4ee37ed9714ba59abc1de1f520acd2", "8cf92960824f48e0b622a804c8bb6b63", "184dc37e1be94b6f8c15e4c227dfe202", "193d23c0aa0c4c34a2fd37e44dff1688", "4f8ede08a65d45a6b4bc28a5a1bbf628", "5c236930169f4b4f8f79d88e3b4e90bd", "2a8f5de0aa5f4ee7b09d4b4f7aec31dd", "1a8c27bb68bc4a5dbd04036f09d45e3e", "9ca5408344fd4868bdf2c53fddc2c53a", "428e03e816b34a2aa9be76419be95fd9", "cbae77129c7b4b52bfcb3caf84bb0d07", "9825689c591f4526a271cf25f857a5dc", "8d9a80d2a0cf415296e8b066e4d1b2f2", "7a7d649e14b74359be1c6577bd49370c", "a59320a4a3634d43a1a3494019e2efa6", "3463e16d9fdc45edb8294c64e2543fbc", "3c978a8c361346069baadc36847343ac", "012ba941c3cf401ba7567e78ca64f480", "0fb617a1223849968b3448f65e7175bc", "09d06c44739744b89126256eb06f3844", "115b55d1b43244ce8c9d007ad0e269e5", "6206003a757c4317b466febb0398a86a", "32abd59a845f47488b1297fae6d40a18", "ff5c6eb6094542e6a919957603e0f71e", "ef62dd778ac14b2c8046218173fce5dc", "623b9482ee02494dbd52f413ef1606c1", "a3cd0cbfc6a04ee6bacd9321c3c6b6eb", "6149ff7606e14486b699273dd2051a15", "13a0a3409dc04cca9c5d392face5b8b9", "1ebcb532a2ca4ba885c398e3b5334a23", "6fc3338886a24fce810c444691dbf2d9", "aee803cafb954516910821e9f98ab0ad", "4242df0920614fe59dd5fa986bc8be2a", "a6cbb1330bfa4dc9b29174a3b6b0db32", "d135f39ac194443abd53c231b799f1e7", "bab8ead3d71748f6902e4cfc920a208f", "5dc635231cb64bfca5cb921798d73c05", "4900212f5d584698bdf25d8c4ad5466d", "6251202a9dca4506a3d23518905ddc93", "2a60d9c2424c4caca6b03a4fd5cd6e28", "05b681796e2c4654855af719ec59b35a", "baa9083a819e4097a134d005ef7e8230", "3e75eeebe7234ad8a5d677bb962c15a9" ] }, "id": "hLhbdBLNxBuF", "outputId": "d8d6fa9f-96e2-44f4-8334-465f210fa4a0" }, "outputs": [], "source": [ "from transformers import AutoProcessor, Pix2StructForConditionalGeneration\n", "\n", "processor = AutoProcessor.from_pretrained(\"ybelkada/pix2struct-base\")\n", "model = Pix2StructForConditionalGeneration.from_pretrained(\"ybelkada/pix2struct-base\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZXlkVag6nDx3" }, "source": [ "Now that we have loaded the processor, let's load the dataset and the dataloader:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-nhDbOCmESlB" }, "outputs": [], "source": [ "def collator(batch):\n", " new_batch = {\"flattened_patches\":[], \"attention_mask\":[]}\n", " texts = [item[\"text\"] for item in batch]\n", " \n", " text_inputs = processor(text=texts, padding=\"max_length\", return_tensors=\"pt\", add_special_tokens=True, max_length=20)\n", " \n", " new_batch[\"labels\"] = text_inputs.input_ids\n", " \n", " for item in batch:\n", " new_batch[\"flattened_patches\"].append(item[\"flattened_patches\"])\n", " new_batch[\"attention_mask\"].append(item[\"attention_mask\"])\n", " \n", " new_batch[\"flattened_patches\"] = torch.stack(new_batch[\"flattened_patches\"])\n", " new_batch[\"attention_mask\"] = torch.stack(new_batch[\"attention_mask\"])\n", "\n", " return new_batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hxajSwc3w-LU" }, "outputs": [], "source": [ "train_dataset = ImageCaptioningDataset(dataset, processor)\n", "train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=2, collate_fn=collator)" ] }, { "cell_type": "markdown", "metadata": { "id": "z_CyLSgBxyL2" }, "source": [ "## Train the model" ] }, { "cell_type": "markdown", "metadata": { "id": "8cVsJdd2nV1S" }, "source": [ "Let's train the model! Run the simply the cell below for training the model. We have observed that finding the best hyper-parameters was quite challenging and required a lot of trials and errors, as the model can easily enter in \"collapse-model\" (always predicting the same output, no matter the input) if the HP are not chosen correctly. In this example, we found out that using `AdamW` optimizer with `lr=1e-5` seemed to be the best approach.\n", "\n", "Let's also print the generation output of the model each 20 epochs!\n", "\n", "Bear in mind that the model took some time to converge, for instance to get decent results we had to let the script run for ~1hour. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6cCVhsmJxxjH" }, "outputs": [], "source": [ "import torch\n", "\n", "EPOCHS = 5000\n", "\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "model.to(device)\n", "\n", "model.train()\n", "\n", "for epoch in range(EPOCHS):\n", " print(\"Epoch:\", epoch)\n", " for idx, batch in enumerate(train_dataloader):\n", " labels = batch.pop(\"labels\").to(device)\n", " flattened_patches = batch.pop(\"flattened_patches\").to(device)\n", " attention_mask = batch.pop(\"attention_mask\").to(device)\n", "\n", " outputs = model(flattened_patches=flattened_patches,\n", " attention_mask=attention_mask,\n", " labels=labels)\n", " \n", " loss = outputs.loss\n", "\n", " print(\"Loss:\", loss.item())\n", "\n", " loss.backward()\n", "\n", " optimizer.step()\n", " optimizer.zero_grad()\n", "\n", " if (epoch + 1) % 20 == 0:\n", " model.eval()\n", "\n", " predictions = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask) \n", " print(\"Predictions:\", processor.batch_decode(predictions, skip_special_tokens=True))\n", "\n", " model.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "mNZQXZrERyQN" }, "source": [ "## Inference" ] }, { "cell_type": "markdown", "metadata": { "id": "SiWpn-q1oFu0" }, "source": [ "Let's check the results on our train dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 377 }, "id": "uC-Fp480XAt7", "outputId": "7d5157e6-21c0-4caf-a492-7f05c8e1e916" }, "outputs": [], "source": [ "# load image\n", "example = dataset[0]\n", "image = example[\"image\"]\n", "image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6P3-u0eRxmsa" }, "outputs": [], "source": [ "# prepare image for the model\n", "model.eval()\n", "\n", "inputs = processor(images=image, return_tensors=\"pt\", max_patches=512).to(device)\n", "\n", "flattened_patches = inputs.flattened_patches\n", "attention_mask = inputs.attention_mask\n", "\n", "generated_ids = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask, max_length=50)\n", "generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", "print(generated_caption)" ] }, { "cell_type": "markdown", "metadata": { "id": "tQFcdiXlnqqu" }, "source": [ "## Load from the Hub" ] }, { "cell_type": "markdown", "metadata": { "id": "L4oBAW8DnsHe" }, "source": [ "Once trained you can push the model and processor on the Hub to use them later. \n", "Meanwhile you can play with the model that we have fine-tuned!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "83dda20272f1492d9384cb1e10f65eec", "f91184b38b9e49f2b10b1a613852aae0", "36f544ca4e9543aebb94377be3d0442e", 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"725dd806d6644f7aafffce4b2f382d9d", "84da57ef41c54b14b67b9988df53e456", "af6fde567ec94bbeb8ceae53dd34629b", "303dcf08b82941d6b9192418ba64be17", "6bde6f47e14a4d65990b54eb1a6b651a", "8d9c0934a91043a1975918f3f53c412e", "198ed5b2ee22485da3ac8e7db50cf0c7", "df0d092163a044cb8430ccd09d1af1d6", "57c970bae3d446218a23121ab7a3b5d3", "460523097ece4a848b3b6c4176fe8211", "78cddcec515e4a399527a456dda0e275", "24f18d08c17042bab0661a46bd41a014", "f12198d16a8341f3b3d26bc638913543", "36a8e84c86bd4ecf91fc16f9e0117931", "7c66b9cbeec640ef80fe5bbb85114cc6", "d76751ac10e14981bd62f3793f381a75", "2246cf290be1464280b9ad84c0c44293", "74b2a262ade240b380428b20846a1d2e", "993b19eb9035468da091cadb7cc48f19", "e7e6955421bf4d48bfed3277ced4cd0f", "58b4ec8d8af541e09319ac0c5499483f", "08e551f4bff34a1da60fc469ce0a4d37", "c5f787c2cdec4c889000ef2a99282a4a", "68ba5a53d1d1490c919b6d3ce9829a30", "30c09d00f3c24260a6f1b73f7370f615" ] }, "id": "FrGmatasn5-B", "outputId": "a198e227-f0fb-4e1d-c5b2-127c390d9fd5" }, "outputs": [], "source": [ "import torch\n", "from transformers import Pix2StructForConditionalGeneration, AutoProcessor\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "model = Pix2StructForConditionalGeneration.from_pretrained(\"ybelkada/pix2struct-base-football\").to(device)\n", "processor = AutoProcessor.from_pretrained(\"ybelkada/pix2struct-base-football\")" ] }, { "cell_type": "markdown", "metadata": { "id": "mNLKCOK2oNK2" }, "source": [ "Let's check the results on our train dataset!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 847 }, "id": "AFGnjCgDoLIJ", "outputId": "b275faf1-7e07-49bb-c4ee-5a36ab904260" }, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "\n", "fig = plt.figure(figsize=(18, 14))\n", "\n", "# prepare image for the model\n", "for i, example in enumerate(dataset):\n", " image = example[\"image\"]\n", " inputs = processor(images=image, return_tensors=\"pt\", max_patches=1024).to(device)\n", " flattened_patches = inputs.flattened_patches\n", " attention_mask = inputs.attention_mask\n", "\n", " generated_ids = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask, max_length=50)\n", " generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", " fig.add_subplot(2, 3, i+1)\n", " plt.imshow(image)\n", " plt.axis(\"off\")\n", " plt.title(f\"Generated caption: {generated_caption}\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "010ac6caf8f247508bdf2b1eee25d3b4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", 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