{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "R5Z682OrQ6br" }, "source": [ "## Set-up environment\n", "\n", "We install πŸ€— Transformers, which includes many awesome models, including TrOCR by Microsoft Research." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d9JLY5fJ2o4R", "outputId": "754d60ec-5b74-444a-e19d-49ebb6f6a087" }, "outputs": [], "source": [ "!pip install -q transformers" ] }, { "cell_type": "markdown", "metadata": { "id": "JB0lrqtE56xs" }, "source": [ "## Load image\n", "\n", "Here we load an image from the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database), on which TrOCR was fine-tuned." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 117 }, "id": "zcPm6rpT55zE", "outputId": "a2c0e345-be82-4fc6-af56-b21c632aef89" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from PIL import Image\n", "\n", "image = Image.open(\"cendu_ipas/δΈ€δΈ–η³Šε­.gif\").convert(\"RGB\")\n", "image" ] }, { "cell_type": "markdown", "metadata": { "id": "ucNwHh945-D8" }, "source": [ "## Prepare image for model\n", "\n", "Next, we use `TrOCRProcessor` (which wraps a `ViTFeatureExtractor` and a `RobertaTokenizer` into one) to resize + normalize the image for the model." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 195, "referenced_widgets": [ "627bab3808964a598148e17744b7ab91", "c417e180c4904094ade363ecfcbe3ea2", "d740d266c16241acbd272629776d6ab3", "b01e9bca90e64236a3c7524d6d46e992", "ad510101629a49e9910735ddd9e15e88", "7b5ca5f30f2146ecaf18e5c4e3e2a71b", "d57ae66f92b846e5958240b86c1d653f", "a6a516b5018a415687c4adc472c94292", "d187f226fbe9475ead04df8a45e18774", "ff7ac5d8d803474dba61630a90989b50", "e5855eea1680462dacd4cf54614f099b", "a6345df73da94d89a1c2033ee6e9a18c", "3b9d267d05a04aef86b505390bd5f0b3", "e9a39ccf744041d0826ebbed5d8e620a", "137703966f23484d8a66f861ac91702c", "97d2230c01de4b1598ef2f41aabb537b", "1340a88b9a9f409db715b4a10ac246b9", "05c9d29688d1456fb625530cb2157244", "ed89b109c999412696b4f8712f0a717d", "f4b4dcccce97436cbc3454ab8fd4497b", "624e0adec06f4b23a927c5d959d12f1b", "dc13eef4bedd4da091af52bb16a0b859", "d3e7c122c4c2462c9424432199171054", "164e13613bb94212a0bcac4dc382160a", "f2d4eb20fd334f7280f5f6c32d9720a9", "42ced89832764feba4a1180cde5127f5", "35e085ee71684ebabe3b3dd74f75f4c1", "e3d01006c43d4da086a007f9ead308c7", "0a9249cfc19643b89edb1f937605436e", "76601652b13e44d881b082bee2f1f0b5", "749723d13e8646bf91e358e7bf2ba686", "d1f5d6df3b0f4f0384498adad05c5ce7", "8ecf80afbfee4fb495163bf9813187f8", "4bc2dc0b00814a5d90073f90c8aba6ba", "3f365ca3a6c4476ea387fcd31db7018a", "296944e373c64d4691639e8cd69cf7ca", "9727541a237e42fa99c0469ae57394c7", "a4919d3d77c34db6b093dcbf15297251", "9719d40b63424848975aad991d8c8111", "33947acf412443e8aec0c22f737c23b5", "864c437a24cf4a67ae6a3b4919a9fdda", "824bf9a4625e4df5b0113b9c84eef4cc", "98bc4137c5af4b299ea941d49f65ced7", "f2951839b3f14e238d1b080fde050fee", "780b633d091c4bd8a441773c130ff2f9", "a86deba73af541e39fa87b680043ce64", "267e93a0dac54d8fa4b0ba6b86662628", "493ea6c5986141ddbdd7e6e7e77673e5", "f10376a01d744e05805f8a1b76608aa5", "2ab7b572c2c34bc7b901c6c9082848a8", "485344c7385445529f7f5f9f12616ef4", "cf2939562c2d4028ac1738d470553881", "f7935536c8874409b6c9c061b6cb37c9", "d868e3bf58ff40e69a8cae8d6c228f60", "3b08aa87c91d47e18b328859ef4b89c8" ] }, "id": "ut0BMGQm6Khf", "outputId": "75065197-4975-4808-b843-d3a51cf051cf" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 3, 384, 384])\n" ] } ], "source": [ "from transformers import TrOCRProcessor\n", "\n", "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-printed\")\n", "# calling the processor is equivalent to calling the feature extractor\n", "pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n", "print(pixel_values.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "vFbzOsf16UQS" }, "source": [ "## Load model\n", "\n", "Here we load a TrOCR model from the [hub](https://huggingface.co/models?other=trocr). TrOCR models are instances of [`VisionEncoderDecoderModel`](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder), which combine a vision encoder (like ViT, BEiT, DeiT, ...) with a language model as decoder (like BERT, RoBERTa, GPT-2, ...). " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 136, "referenced_widgets": [ "83c00e5e96f541b58c41193273ecc3f4", "fa904b81700f4fe8b60b23092ffcd8cb", "35d5ee909c53439bb0bfd9014ab38ae3", "523bc4ae908d421ea58f11ee5dfc6264", "0c075d3dfbc241b1a925e40330f3f315", "5241dc6ee0cb469983bcd5b7c04546a7", "92eb2886167d45f6b0efea1b52916ae5", "93a663efe36a4e06ada8442b597d0076", "a540ec88f6e348c38a2529d14cfcfa65", "902124e662114511be58d3a4bdea10ce", "73a1e24ce1ba4b508a7d59af479ab726", "c2d794d1ba7e478cb1a775d9e8868762", "5f6622e4a1ea42a08809a9862fd75d66", "20cc95412c524167a1e44b5253d9ee9e", "5c27ee175c67458fa9d4e794a3aa19f1", "2b696e8146714017b75fb6c16809e63b", "e0224e3e928a422a9082189fe86aa0a9", "73ca010ce17d4ffe9556fdc8d9ccbca3", "1cbb5abde3db4997a7a66bb8b30b2324", "fb01081a801d4697a75e3ab081e29357", "eaf7b69f65204c31b71ead1922525e71", "fd08997b6f95449ba30c692705eeafb2" ] }, "id": "v5Yf5Fql6U8Y", "outputId": "a25ec7e2-01e5-421d-f333-67f9fb63b5ed" }, "outputs": [], "source": [ "from transformers import VisionEncoderDecoderModel\n", "\n", "model = VisionEncoderDecoderModel.from_pretrained(\"models/checkpoint-180/\")" ] }, { "cell_type": "markdown", "metadata": { "id": "QKk3c-HG6g87" }, "source": [ "## Generate text \n", "\n", "Finally, we can generate text autoregressively using the `.generate()` method. We use the tokenizer part of the `processor` to decode the generated id's back to text. Note that by default, greedy search is used for generation, but there are more fancy methods like beam search and top-k sampling, which are also supported. You can check out [this blog post](https://huggingface.co/blog/how-to-generate) for details." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4jrTnzzUUPLP", "outputId": "ea9c8c3f-dc02-4e51-a0a1-efcbf4907d9d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yi4 si4 fu2 zi3\n" ] } ], "source": [ "generated_ids = model.generate(pixel_values)\n", "generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", "print(generated_text)" ] }, { "cell_type": "markdown", "metadata": { "id": "P99E4R2vXz9t" }, "source": [ "## Gradio demo\n", "\n", "We can also wrap up everything we wrote above in a quick [Gradio](https://gradio.app/) demo. Gradio is an awesome tool for creating quick ML demos!\n", "\n", "We just need to define a function that takes in an image input and produces a text output (note that Gradio supports many other input & output formats)!" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EzbHm95iX0ia", "outputId": "c8f67dba-dca9-46a1-d1e7-beed3bb6407d" }, "outputs": [], "source": [ "!pip install -q gradio" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 666 }, "id": "BQWsUDw7X4z4", "outputId": "2b9ede5c-481a-4fcd-9f9e-e6c9e68b1667" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/gradio/inputs.py:259: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n", " warnings.warn(value)\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n", "/Users/kevin/.pyenv/versions/3.10.6/lib/python3.10/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (25) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] } ], "source": [ "import gradio as gr\n", "from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n", "\n", "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-printed\")\n", "model = VisionEncoderDecoderModel.from_pretrained(\"models/checkpoint-180/\").to(\"mps\")\n", "\n", "def process_image(image):\n", " # prepare image\n", " pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n", "\n", " # generate (no beam search)\n", " generated_ids = model.generate(pixel_values.to(\"mps\"))\n", "\n", " # decode\n", " generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", "\n", " return generated_text\n", "\n", "title = \"Interactive demo: TrOCR\"\n", "description = \"Demo for Microsoft's TrOCR, an encoder-decoder model consisting of an image Transformer encoder and a text Transformer decoder for state-of-the-art optical character recognition (OCR) on single-text line images. This particular model is fine-tuned on IAM, a dataset of annotated handwritten images. To use it, simply upload an image or use the example image below and click 'submit'. Results will show up in a few seconds.\"\n", "article = \"

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models | Github Repo

\"\n", "examples =[[\"cendu_ipas/δΈ€.gif\"], [\"cendu_ipas/吆.gif\"], [\"cendu_ipas/吹叫叫兒.gif\"], [\"cendu_ipas/加倜班.gif\"], [\"cendu_ipas/ζ‰“ιΊ»ε­ηœΌε…’.gif\"], [\"cendu_ipas/吃混糖鍋盔.gif\"], [\"cendu_ipas/ε’Œε°šι ˜θ‘«θ‘«ε…’.gif\"]]\n", "\n", "iface = gr.Interface(fn=process_image, \n", " inputs=gr.inputs.Image(type=\"pil\"), \n", " outputs=gr.outputs.Textbox(),\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples)\n", "iface.launch(debug=True)" ] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyMcnMD3tmsJbn7+1S7eGB4U", "collapsed_sections": [], "include_colab_link": true, "name": "Inference with TrOCR + Gradio demo.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.10.6" }, 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