{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "gCMycQ_2U8SA" }, "source": [ "
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\n", " OpenMMLab website\n", " \n", " \n", " HOT\n", " \n", " \n", "     \n", " OpenMMLab platform\n", " \n", " \n", " TRY IT OUT\n", " \n", " \n", "
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\n", "\n", "\"Open\n", "\n", "[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)\n", "[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)\n", "[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)\n", "[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)\n", "[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE)\n", "[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)\n", "[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)\n", "\n", "[πŸ“˜Documentation](https://mmdetection.readthedocs.io/en/3.x/) |\n", "[πŸ› οΈInstallation](https://mmdetection.readthedocs.io/en/3.x/get_started.html) |\n", "[πŸ‘€Model Zoo](https://mmdetection.readthedocs.io/en/3.x/model_zoo.html) |\n", "[πŸ†•Update News](https://mmdetection.readthedocs.io/en/3.x/notes/changelog.html) |\n", "[πŸš€Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |\n", "[πŸ€”Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)\n", "\n", "
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" ] }, { "cell_type": "markdown", "metadata": { "id": "aGYwt_UjIrqp" }, "source": [ "# Inferencer\n", "\n", "In this tutorial, you will learn how to perform inference with a MMDetection `DetInferencer`.\n", "\n", "Let's start!\n", "\n", "```{note}\n", "The commands in this tutorial are mainly for Colab.\n", "You can click the button above, `Open in Colab`, to run this notebook in Colab.\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "tJxJHruNLb7Y" }, "source": [ "## Install MMDetection" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Wi4LPmsR66sy", "outputId": "0077a9a3-0183-4002-fe7a-2a12f020cf69" }, "outputs": [], "source": [ "# Check nvcc version\n", "!nvcc -V\n", "# Check GCC version\n", "!gcc --version" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gkGnB9WyHSXB", "outputId": "5945fe0b-13a5-4f1b-dff9-8beb1df67ab0" }, "outputs": [], "source": [ "# install dependencies\n", "%pip install -U openmim\n", "!mim install \"mmengine>=0.7.0\"\n", "!mim install \"mmcv>=2.0.0rc4\"\n", "\n", "# Install mmdetection\n", "!rm -rf mmdetection\n", "!git clone https://github.com/open-mmlab/mmdetection.git -b dev-3.x\n", "%cd mmdetection\n", "\n", "%pip install -e ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_YeUiqAoCaoV", "outputId": "06e4c803-ac46-49e6-b8fa-1a85c23fa482" }, "outputs": [], "source": [ "from mmengine.utils import get_git_hash\n", "from mmengine.utils.dl_utils import collect_env as collect_base_env\n", "\n", "import mmdet\n", "\n", "\n", "def collect_env():\n", " \"\"\"Collect the information of the running environments.\"\"\"\n", " env_info = collect_base_env()\n", " env_info['MMDetection'] = f'{mmdet.__version__}+{get_git_hash()[:7]}'\n", " return env_info\n", "\n", "\n", "if __name__ == '__main__':\n", " for name, val in collect_env().items():\n", " print(f'{name}: {val}')" ] }, { "cell_type": "markdown", "metadata": { "id": "fLgFRMtP91ue" }, "source": [ "## `DetInferencer`\n", "\n", "### Basic Usage\n", "\n", "We use the high-level API `DetInferencer` implemented in the MMDetection. This API is created to ease the inference process. The details of the codes can be found [here](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/apis/det_inferencer.py)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "6fa2cda48fda43f9bf53a0f533392eba", "0226fedc26044ab2abdccc4fcbe226f8" ] }, "id": "WJHpC402p2w9", "outputId": "c2326326-d198-4fce-ec0e-a9cc2e35ba09" }, "outputs": [], "source": [ "from mmdet.apis import DetInferencer\n", "\n", "# Initialize the DetInferencer\n", "inferencer = DetInferencer('rtmdet_tiny_8xb32-300e_coco')\n", "\n", "# Perform inference\n", "inferencer('demo/demo.jpg', out_dir='./output')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 444 }, "id": "34JfPWRRSlNh", "outputId": "8eec8bc4-4824-47ac-b10f-41538422fb28" }, "outputs": [], "source": [ "# Show the output image\n", "from PIL import Image\n", "Image.open('./output/vis/demo.jpg')" ] }, { "cell_type": "markdown", "metadata": { "id": "-53WPeyBqRHe" }, "source": [ "### Initialization\n", "\n", "Each Inferencer must be initialized with a model. You can also choose the inference device during initialization.\n", "\n", "#### Model Initialization\n", "\n", "- To infer with MMDetection's pre-trained model, passing its name to the argument `model` can work. The weights will be automatically downloaded and loaded from OpenMMLab's model zoo." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bbMu3IPtv-cX", "outputId": "2bceb594-06c8-4c18-e8c6-b1816b0acb23" }, "outputs": [], "source": [ "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco')" ] }, { "cell_type": "markdown", "metadata": { "id": "AwKtnol3TQlM" }, "source": [ "There is a very easy to list all model names in MMDetection." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3kYfK3ssTIQE", "outputId": "88c4fbb9-bb92-42af-baaa-14cee5b5bdc1" }, "outputs": [], "source": [ "# models is a list of model names, and them will print automatically\n", "models = DetInferencer.list_models('mmdet')" ] }, { "cell_type": "markdown", "metadata": { "id": "G-25HR9HTZvr" }, "source": [ "You can load another weight by passing its path/url to `weights`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j4doHX4exvS1", "outputId": "54ac0be2-835f-4390-aa0e-3be5236d8cc9" }, "outputs": [], "source": [ "!mkdir ./checkpoints\n", "!mim download mmdet --config rtmdet_tiny_8xb32-300e_coco --dest ./checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8LQB4EC-Tako", "outputId": "2cc0960e-d5b5-4c3a-8a0c-cec23989f6a0" }, "outputs": [], "source": [ "# Setup a checkpoint file to load\n", "checkpoint = './checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'\n", "\n", "# Initialize the DetInferencer\n", "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', weights=checkpoint)" ] }, { "cell_type": "markdown", "metadata": { "id": "Atft9tjcwgeD" }, "source": [ "- To load custom config and weight, you can pass the path to the config file to `model` and the path to the weight to `weights`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eukDD4Rzwp9P", "outputId": "0a34392c-0544-4a90-c844-7628d184efc0" }, "outputs": [], "source": [ "# Choose to use a config\n", "config_path = './configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py'\n", "\n", "# Setup a checkpoint file to load\n", "checkpoint = './checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'\n", "\n", "# Initialize the DetInferencer\n", "inferencer = DetInferencer(model=config_path, weights=checkpoint)" ] }, { "cell_type": "markdown", "metadata": { "id": "FC1je9iiTuMS" }, "source": [ "- By default, [MMEngine](https://github.com/open-mmlab/mmengine/) dumps config to the weight. If you have a weight trained on MMEngine, you can also pass the path to the weight file to `weights` without specifying `model`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Kenyo80RTx63", "outputId": "46fe8219-d1a7-4e45-b5a1-b6d21c30be42" }, "outputs": [], "source": [ "# It will raise an error if the config file cannot be found in the weight. Currently, within the MMDetection model repository, only the weights of ddq-detr-4scale_r50 can be loaded in this manner.\n", "inferencer = DetInferencer(weights='https://download.openmmlab.com/mmdetection/v3.0/ddq/ddq-detr-4scale_r50_8xb2-12e_coco/ddq-detr-4scale_r50_8xb2-12e_coco_20230809_170711-42528127.pth')" ] }, { "cell_type": "markdown", "metadata": { "id": "b-AlYOw4T3AO" }, "source": [ "- Passing config file to `model` without specifying `weight` will result in a randomly initialized model." ] }, { "cell_type": "markdown", "metadata": { "id": "quFQ8abYT6As" }, "source": [ "### Device\n", "\n", "Each Inferencer instance is bound to a device.\n", "By default, the best device is automatically decided by [MMEngine](https://github.com/open-mmlab/mmengine/). You can also alter the device by specifying the `device` argument. For example, you can use the following code to create an Inferencer on GPU 0." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Wi6DRpsQPEmV", "outputId": "9eac2017-cce6-491a-ef51-3e7e2560f107" }, "outputs": [], "source": [ "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cuda:0')" ] }, { "cell_type": "markdown", "metadata": { "id": "h3pgIACHUXEv" }, "source": [ "To create an Inferencer on CPU:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JsAotaiRUXWH", "outputId": "531b1cb0-e986-4e0a-91c9-6d3ad65544e7" }, "outputs": [], "source": [ "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cpu')" ] }, { "cell_type": "markdown", "metadata": { "id": "0a4Zw5plUisX" }, "source": [ "### Inference\n", "\n", "Once the Inferencer is initialized, you can directly pass in the raw data to be inferred and get the inference results from return values.\n", "\n", "#### Input\n", "\n", "Input can be either of these types:\n", "\n", "- str: Path/URL to the image." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2abd7eef6f1f4b9c865a466b3dd5ef24", "8951ec1ee7164f7ca7239a37e80e98ea" ] }, "id": "C4McAmYdUnCL", "outputId": "50bea3e2-a912-497e-cee9-26109dccdc12" }, "outputs": [], "source": [ "inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cuda:0')\n", "inferencer('demo/demo.jpg')" ] }, { "cell_type": "markdown", "metadata": { "id": "3G_TPKrMUp2T" }, "source": [ "- array: Image in numpy array. It should be in BGR order." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "59bfd22c751f4ed4baefa466e7653315", "0164804ae2f842fe8d2a4c5414c4a0c2" ] }, "id": "-M1qGlfaUpha", "outputId": "5a06cfe8-e056-4d56-c8e9-489e8f6633a0" }, "outputs": [], "source": [ "import mmcv\n", "array = mmcv.imread('demo/demo.jpg')\n", "inferencer(array)" ] }, { "cell_type": "markdown", "metadata": { "id": "I45B_CtzUuh2" }, "source": [ "- list: A list of basic types above. Each element in the list will be processed separately." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "a64a6eb038c44236b80579b2bfc4b8e3", "eef25a0509854f98883395a2c0fc2134", "f87f0b153b0342ad99dcd320a1302c92", "f6634888109048069b6844e9f9b4ec13" ] }, "id": "k1IXIWXHUwKP", "outputId": "0af73b0b-d703-4cbc-91ad-052f0b521d50" }, "outputs": [], "source": [ "inferencer(['tests/data/color.jpg', 'tests/data/gray.jpg'])\n", "# You can even mix the types\n", "inferencer(['tests/data/color.jpg', array])" ] }, { "cell_type": "markdown", "metadata": { "id": "hUGrTtxrVBAS" }, "source": [ "- str: Path to the directory. All images in the directory will be processed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "07ed8efcd87a40059af36f0c43ef5147", "69ce7e58e27f4e1186ab0afcb99d37c3" ] }, "id": "JWK10ZD6VDDE", "outputId": "91418597-d9ea-4613-b141-16bc8bcc8caf" }, "outputs": [], "source": [ "inferencer('tests/data/')" ] }, { "cell_type": "markdown", "metadata": { "id": "BQxEVr2pVGen" }, "source": [ "### Output\n", "\n", "By default, each `Inferencer` returns the prediction results in a dictionary format.\n", "\n", "- `visualization` contains the visualized predictions.\n", "\n", "- `predictions` contains the predictions results in a json-serializable format. But it's an empty list by default unless `return_vis=True`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 306, "referenced_widgets": [ "95674a6baa1842d2981fe60b31ab6cad", "7e62816d1f6c441fb98c1f8e942fff1d" ] }, "id": "m6a8T4goU8Sq", "outputId": "6f74098f-a3d3-4897-c58f-68fae88889af" }, "outputs": [], "source": [ "# Show the structure of result dict\n", "from rich.pretty import pprint\n", "\n", "result = inferencer('demo/demo.jpg')\n", "pprint(result, max_length=4)" ] }, { "cell_type": "markdown", "metadata": { "id": "a93hFT0jVkrR" }, "source": [ "If you wish to get the raw outputs from the model, you can set `return_datasamples` to `True` to get the original `DataSample`, which will be stored in `predictions`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "060f510b5bda498583d7212060bb528c", "1bb724cb12c240a18f651dd99842e5b0" ] }, "id": "U5DFI7QAVbnP", "outputId": "effaf3ec-2476-4b64-dcbd-802a18a26479" }, "outputs": [], "source": [ "result = inferencer('demo/demo.jpg', return_datasamples=True)\n", "pprint(result, max_length=4)" ] }, { "cell_type": "markdown", "metadata": { "id": "JHdcUnGzVsk1" }, "source": [ "#### Dumping Results\n", "\n", "Apart from obtaining predictions from the return value, you can also export the predictions/visualizations to files by setting `out_dir` and `no_save_pred`/`no_save_vis` arguments." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "38083c2f29604d1d9a7dcf9845dfbf33", "54cdfe55e0f04df9ab844961a089fe2f" ] }, "id": "0dr-ixmfVtng", "outputId": "af22d458-9aed-41e2-f675-e017a0cb588b" }, "outputs": [], "source": [ "inferencer('demo/demo.jpg', out_dir='outputs/', no_save_pred=False)" ] } ], "metadata": { "accelerator": "GPU", "colab": { 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Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "07ed8efcd87a40059af36f0c43ef5147": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_69ce7e58e27f4e1186ab0afcb99d37c3", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5 it/s  \n
\n", "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[35m13.5 it/s\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1bb724cb12c240a18f651dd99842e5b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2abd7eef6f1f4b9c865a466b3dd5ef24": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_8951ec1ee7164f7ca7239a37e80e98ea", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "38083c2f29604d1d9a7dcf9845dfbf33": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_54cdfe55e0f04df9ab844961a089fe2f", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "54cdfe55e0f04df9ab844961a089fe2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "59bfd22c751f4ed4baefa466e7653315": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0164804ae2f842fe8d2a4c5414c4a0c2", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "69ce7e58e27f4e1186ab0afcb99d37c3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6fa2cda48fda43f9bf53a0f533392eba": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_0226fedc26044ab2abdccc4fcbe226f8", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "7e62816d1f6c441fb98c1f8e942fff1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "8951ec1ee7164f7ca7239a37e80e98ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "95674a6baa1842d2981fe60b31ab6cad": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7e62816d1f6c441fb98c1f8e942fff1d", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━   \n
\n", "text/plain": "Inference \u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "a64a6eb038c44236b80579b2bfc4b8e3": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_eef25a0509854f98883395a2c0fc2134", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9.7 it/s  \n
\n", "text/plain": "Inference \u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m \u001b[35m9.7 it/s\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "eef25a0509854f98883395a2c0fc2134": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f6634888109048069b6844e9f9b4ec13": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f87f0b153b0342ad99dcd320a1302c92": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_f6634888109048069b6844e9f9b4ec13", "msg_id": "", "outputs": [ { "data": { "text/html": "
Inference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9.0 it/s  \n
\n", "text/plain": "Inference \u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;249;38;114m━\u001b[0m\u001b[38;2;244;38;112m━\u001b[0m\u001b[38;2;230;39;108m━\u001b[0m\u001b[38;2;209;42;102m━\u001b[0m\u001b[38;2;183;44;94m━\u001b[0m\u001b[38;2;153;48;86m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;58;58;58m━\u001b[0m\u001b[38;2;62;57;59m━\u001b[0m\u001b[38;2;76;56;63m━\u001b[0m\u001b[38;2;97;53;69m━\u001b[0m\u001b[38;2;123;51;77m━\u001b[0m \u001b[35m9.0 it/s\u001b[0m \u001b[36m \u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } } } } }, "nbformat": 4, "nbformat_minor": 0 }