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[Docs] Inferencer docs (#1744)
* [Enhancement] Support batch visualization & dumping in Inferencer * fix empty det output * Update mmocr/apis/inferencers/base_mmocr_inferencer.py Co-authored-by: liukuikun <24622904+Harold-lkk@users.noreply.github.com> * [Docs] Inferencer docs * fix * Support weight_list * add req * improve md * inferencers.md * update * add tab * refine * polish * add cn docs * js * js * js * fix ch docs * translate * translate * finish * fix * fix * fix * update * standard inferencer * update docs * update docs * update docs * update docs * update docs * update docs * en * update * update * update * update * fix * apply sugg --------- Co-authored-by: liukuikun <24622904+Harold-lkk@users.noreply.github.com>
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‎.gitignore

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# Sphinx documentation
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docs/en/_build/
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docs/zh_cn/_build/
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docs/*/api/generated/
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# PyBuilder
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target/

‎configs/kie/sdmgr/metafile.yml

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Metrics:
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macro_f1: 0.931
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micro_f1: 0.940
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edgee_micro_f1: 0.792
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edge_micro_f1: 0.792
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Weights: https://download.openmmlab.com/mmocr/kie/sdmgr/sdmgr_novisual_60e_wildreceipt-openset/sdmgr_novisual_60e_wildreceipt-openset_20220831_200807-dedf15ec.pth

‎configs/textdet/drrg/metafile.yml

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Metrics:
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hmean-iou: 0.8467
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Weights: https://download.openmmlab.com/mmocr/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500/drrg_resnet50_fpn-unet_1200e_ctw1500_20220827_105233-d5c702dd.pth
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- Name: drrg_resnet50-oclip_fpn-unet_1200e_ctw1500
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In Collection: DRRG
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Config: configs/textdet/drrg/drrg_resnet50-oclip_fpn-unet_1200e_ctw1500.py
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Metadata:
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Training Data: CTW1500
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Results:
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- Task: Text Detection
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Dataset: CTW1500
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Metrics:
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hmean-iou:
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Weights:

‎configs/textdet/maskrcnn/metafile.yml

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- Task: Text Detection
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Dataset: CTW1500
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Metrics:
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hmean: 0.7458
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hmean-iou: 0.7458
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Weights: https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500/mask-rcnn_resnet50_fpn_160e_ctw1500_20220826_154755-ce68ee8e.pth
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- Name: mask-rcnn_resnet50-oclip_fpn_160e_ctw1500
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- Task: Text Detection
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Dataset: CTW1500
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Metrics:
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hmean: 0.7562
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hmean-iou: 0.7562
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Weights: https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500_20221101_154448-6e9e991c.pth
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- Name: mask-rcnn_resnet50_fpn_160e_icdar2015
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- Task: Text Detection
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Dataset: ICDAR2015
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Metrics:
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hmean: 0.8182
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hmean-iou: 0.8182
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Weights: https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015/mask-rcnn_resnet50_fpn_160e_icdar2015_20220826_154808-ff5c30bf.pth
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- Name: mask-rcnn_resnet50-oclip_fpn_160e_icdar2015
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- Task: Text Detection
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Dataset: ICDAR2015
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Metrics:
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hmean: 0.8513
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hmean-iou: 0.8513
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Weights: https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015_20221101_131357-a19f7802.pth

‎configs/textrecog/master/README.md

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```bibtex
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@article{Lu2021MASTER,
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title={{MASTER}: Multi-Aspect Non-local Network for Scene Text Recognition},
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title={MASTER: Multi-Aspect Non-local Network for Scene Text Recognition},
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author={Ning Lu and Wenwen Yu and Xianbiao Qi and Yihao Chen and Ping Gong and Rong Xiao and Xiang Bai},
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journal={Pattern Recognition},
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year={2021}

‎docs/en/_static/js/table.js

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$(document).ready(function () {
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table = $('.model-summary').DataTable({
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"stateSave": false,
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"lengthChange": false,
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"pageLength": 10,
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"order": [],
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"scrollX": true,
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"columnDefs": [
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{ "type": "summary", targets: '_all' },
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]
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});
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// Override the default sorting for the summary columns, which
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// never takes the "-" character into account.
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jQuery.extend(jQuery.fn.dataTableExt.oSort, {
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"summary-asc": function (str1, str2) {
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if (str1 == "<p>-</p>")
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return 1;
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if (str2 == "<p>-</p>")
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return -1;
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return ((str1 < str2) ? -1 : ((str1 > str2) ? 1 : 0));
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},
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"summary-desc": function (str1, str2) {
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if (str1 == "<p>-</p>")
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return 1;
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return -1;
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return ((str1 < str2) ? 1 : ((str1 > str2) ? -1 : 0));
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}
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});
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})

‎docs/en/basic_concepts/structures.md

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| | | |
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| ----------- | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Field | Type | Description |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, x2, y1, y2]` with the shape `(N, 4)`. |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, y1, x2, y2]` with the shape `(N, 4)`. |
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| labels | `torch.LongTensor` | Instance label with the shape `(N, )`. By default, MMOCR uses `0` to represent the "text" class. |
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| polygons | `list[np.array(dtype=np.float32)]` | Polygonal bounding boxes with the shape `(N, )`. |
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| scores | `torch.Tensor` | Confidence scores of the predictions of bounding boxes. `(N, )`. |
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| | | |
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| -------- | ---------------------------------- | ------------------------------------------------------------------------------------------------ |
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| Field | Type | Description |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, x2, y1, y2]` with the shape `(N, 4)`. |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, y1, x2, y2]` with the shape `(N, 4)`. |
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| labels | `torch.LongTensor` | Instance label with the shape `(N, )`. By default, MMOCR uses `0` to represent the "text" class. |
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| polygons | `list[np.array(dtype=np.float32)]` | Polygonal bounding boxes with the shape `(N, )`. |
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| scores | `torch.Tensor` | Confidence scores of the predictions of bounding boxes. `(N, )`. |
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| ----------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Field | Type | Description |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, x2, y1, y2]` with the shape `(N, 4)`. |
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| bboxes | `torch.FloatTensor` | Bounding boxes `[x1, y1, x2, y2]` with the shape `(N, 4)`. |
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| labels | `torch.LongTensor` | Instance label with the shape `(N, )`. |
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| texts | `list[str]` | The text content of each instance with the shape `(N, )`，used for e2e text spotting or KIE task. |
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| edge_labels | `torch.IntTensor` | The node adjacency matrix with the shape `(N, N)`. In the KIE task, the optional values for the state between nodes are `-1` (ignored, not involved in loss calculation)，`0` (disconnected) and `1`(connected). |

‎docs/en/conf.py

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'sphinx.ext.autodoc.typehints',
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'sphinx.ext.autosummary',
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'sphinx.ext.autosectionlabel',
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'sphinx_tabs.tabs',
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myst_enable_extensions = ['colon_fence']
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templates_path = ['_templates']
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# relative to this directory. They are copied after the builtin static files,
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html_js_files = ['js/collapsed.js']
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html_css_files = [
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'https://cdn.datatables.net/1.13.2/css/dataTables.bootstrap5.min.css',
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'css/readthedocs.css'
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]
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html_js_files = [
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'https://cdn.datatables.net/1.13.2/js/jquery.dataTables.min.js',
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'https://cdn.datatables.net/1.13.2/js/dataTables.bootstrap5.min.js',
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'js/collapsed.js',
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'js/table.js',
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‎docs/en/get_started/install.md

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**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
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On GPU platforms:
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````{tabs}
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```{code-tab} shell GPU Platform
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## Installation Steps
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We recommend that users follow our best practices to install MMOCR. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information.
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### Best Practices
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**Step 0.** Install [MMEngine](https://github.com/open-mmlab/mmengine) and [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
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**Step 0.** Install [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection) using [MIM](https://github.com/open-mmlab/mim).
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mim install 'mmdet>=3.0.0rc0'
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**Step 1.** Install MMOCR.
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If you wish to run and develop MMOCR directly, install it from **source** (recommended).
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**Step 2.** Install MMOCR.
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If you use MMOCR as a dependency or third-party package, install it with **MIM**.
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`````{tabs}
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````{group-tab} Install from Source
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````
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**Step 3. (Optional)** If you wish to use any transform involving `albumentations` (For example, `Albu` in ABINet's pipeline), install the dependency using the following command:
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````
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`````
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**Step 2. (Optional)** If you wish to use any transform involving `albumentations` (For example, `Albu` in ABINet's pipeline), install the dependency using the following command:
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`````{tabs}
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````{group-tab} Install from Source
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```
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````
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````{group-tab} Install via MIM
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````
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`````
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```{note}
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We recommend checking the environment after installing `albumentations` to
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ensure that `opencv-python` and `opencv-python-headless` are not installed together, otherwise it might cause unexpected issues. If that's unfortunately the case, please uninstall `opencv-python-headless` to make sure MMOCR's visualization utilities can work.
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to [albumentations's official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) for more details.
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### Verify the installation
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We provide a method to verify the installation via inference demo, depending on your installation method. You should be able to see a pop-up image and the inference result upon successful verification.
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You may verify the installation via this inference demo.
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`````{tabs}
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````{tab} Python
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Run the following code in a Python interpreter:
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```python
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>>> from mmocr.apis import MMOCRInferencer
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>>> ocr = MMOCRInferencer(det='DBNet', rec='CRNN')
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>>> ocr('demo/demo_text_ocr.jpg', show=True, print_result=True)
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```
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````
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````{tab} Shell
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python tools/infer.py demo/demo_text_ocr.jpg --det DBNet --rec CRNN --show --print-result
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```
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````
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`````
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You should be able to see a pop-up image and the inference result printed out in the console upon successful verification.
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<div align="center">
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<img src="https://user-images.githubusercontent.com/24622904/187825445-d30cbfa6-5549-4358-97fe-245f08f4ed94.jpg" height="250"/>
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</div>
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<br />
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```bash
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# Inference result
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{'rec_texts': ['cbanke', 'docece', 'sroumats', 'chounsonse', 'doceca', 'c', '', 'sond', 'abrandso', 'sretane', '1', 'tosl', 'roundi', 'slen', 'yet', 'ally', 's', 'sue', 'salle', 'v'], 'rec_scores': [...], 'det_polygons': [...], 'det_scores': tensor([...])}
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{'predictions': [{'rec_texts': ['cbanks', 'docecea', 'grouf', 'pwate', 'chobnsonsg', 'soxee', 'oeioh', 'c', 'sones', 'lbrandec', 'sretalg', '11', 'to8', 'round', 'sale', 'year',
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'ally', 'sie', 'sall'], 'rec_scores': [...], 'det_polygons': [...], 'det_scores':
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[...]}]}
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ocr.readtext('demo_text_ocr.jpg', show=True)
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If you are running MMOCR on a server without GUI or via SSH tunnel with X11 forwarding disabled, you may not see the pop-up window.
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## Customize Installation

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