Merge pull request #38 from cuhk-hbsun/hbsun/rename

rename datasets.md

docker/Dockerfile

@@ -15,11 +15,7 @@ RUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 lib
 RUN conda clean --all
 RUN pip install mmcv-full==1.2.6+torch1.5.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html
 
-RUN git clone https://github.com/open-mmlab/mmdetection.git /mmdet
-WORKDIR /mmdet
-RUN git checkout -b v2.9.0 v2.9.0
-RUN pip install -r requirements.txt
-RUN pip install .
+RUN pip install mmdet==2.9.0
 
 RUN git clone https://github.com/open-mmlab/mmocr.git /mmocr
 WORKDIR /mmocr

docs/api.rst

@@ -23,19 +23,102 @@ mmocr.utils
 
 mmocr.models
 ---------------
-common
+common_backbones
 ^^^^^^^^^^^
-.. automodule:: mmocr.models.common
+.. automodule:: mmocr.models.common.backbones
     :members:
 
-textdet
-^^^^^^^^^^^
-.. automodule:: mmocr.models.textdet
+.. automodule:: mmocr.models.common.losses
     :members:
 
-textrecog
+textdet_dense_heads
 ^^^^^^^^^^^
-.. automodule:: mmocr.models.textrecog
+.. automodule:: mmocr.models.textdet.dense_heads
+    :members:
+
+textdet_necks
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textdet.necks
+    :members:
+
+textdet_detectors
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textdet.detectors
+    :members:
+
+textdet_losses
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textdet.losses
+    :members:
+
+textdet_postprocess
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textdet.postprocess
+    :members:
+
+textrecog_recognizer
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.recognizer
+    :members:
+
+textrecog_backbones
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.backbones
+    :members:
+
+textrecog_necks
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.necks
+    :members:
+
+textrecog_heads
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.heads
+    :members:
+
+textrecog_convertors
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.convertors
+    :members:
+
+textrecog_encoders
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.encoders
+    :members:
+
+textrecog_decoders
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.decoders
+    :members:
+
+textrecog_losses
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.losses
+    :members:
+
+textrecog_backbones
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.backbones
+    :members:
+
+textrecog_layers
+^^^^^^^^^^^
+.. automodule:: mmocr.models.textrecog.layers
+    :members:
+
+kie_extractors
+^^^^^^^^^^^
+.. automodule:: mmocr.models.kie.extractors
+    :members:
+
+kie_heads
+^^^^^^^^^^^
+.. automodule:: mmocr.models.kie.heads
+    :members:
+
+kie_losses
+^^^^^^^^^^^
+.. automodule:: mmocr.models.kie.losses
     :members:
 
 
@@ -49,20 +132,23 @@ datasets
 .. automodule:: mmocr.datasets.base_dataset
     :members:
 
+.. automodule:: mmocr.datasets.icdar_dataset
+    :members:
+
+.. automodule:: mmocr.datasets.ocr_dataset
+    :members:
+
+.. automodule:: mmocr.datasets.ocr_seg_dataset
+    :members:
+
+.. automodule:: mmocr.datasets.text_det_dataset
+    :members:
+
+.. automodule:: mmocr.datasets.kie_dataset
+    :members:
+
 
 pipelines
 ^^^^^^^^^^^
 .. automodule:: mmocr.datasets.pipelines
     :members:
-
-.. automodule:: mmocr.datasets.pipelines.transforms
-    :members:
-
-.. automodule:: mmocr.datasets.pipelines.ocr_transforms
-    :members:
-
-
-targets
-^^^^^^^^^^^
-.. automodule:: mmocr.datasets.pipelines.textdet_targets
-    :members:

docs/conf.py

@@ -50,7 +50,6 @@ autodoc_mock_imports = [
     'matplotlib',
     'Polygon',
     'cv2',
-    'mmcv',
     'numpy',
     'pyclipper',
     'pycocotools',

docs/getting_started.md

@@ -37,7 +37,7 @@ It will save both the prediction results and visualized images to `${RESULTS_DIR
 
 ### Test a Dataset
 
-MMOCR implements **distributed** testing with `MMDistributedDataParallel`. (Please refer to [datasets.md](datasets.md) to prepare your datasets)
+MMOCR implements **distributed** testing with `MMDistributedDataParallel`. (Please refer to [dataset.md](dataset.md) to prepare your datasets)
 
 #### Test with Single/Multiple GPUs
 
@@ -78,7 +78,7 @@ You can check [slurm_test.sh](https://github.com/open-mmlab/mmocr/blob/master/to
 
 ## Train a Model
 
-MMOCR implements **distributed** training with `MMDistributedDataParallel`. (Please refer to [datasets.md](datasets.md) to prepare your datasets)
+MMOCR implements **distributed** training with `MMDistributedDataParallel`. (Please refer to [dataset.md](dataset.md) to prepare your datasets)
 
 All outputs (log files and checkpoints) will be saved to a working directory specified by `work_dir` in the config file.
 

docs/index.rst

@@ -23,7 +23,7 @@ Welcome to MMOCR's documentation!
    :maxdepth: 2
    :caption: Datasets
 
-   datasets.md
+   dataset.md
 
 .. toctree::
    :maxdepth: 2

docs/install.md

@@ -129,7 +129,7 @@ docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmocr/data mmocr
 
 ## Prepare Datasets
 
-It is recommended to symlink the dataset root to `mmocr/data`. Please refer to [datasets.md](datasets.md) to prepare your datasets.
+It is recommended to symlink the dataset root to `mmocr/data`. Please refer to [dataset.md](dataset.md) to prepare your datasets.
 If your folder structure is different, you may need to change the corresponding paths in config files.
 
 The `mmocr` folder is organized as follows:

docs/stats.py

@@ -85,7 +85,7 @@ modelzoo = f"""
 * Number of papers: {len(allpapers)}
 {countstr}
 
-For supported datasets, see [datasets overview](datasets.md).
+For supported datasets, see [datasets overview](dataset.md).
 
 {msglist}
 """

requirements/docs.txt

@@ -1,4 +1,4 @@
 recommonmark
-sphinx
+sphinx==v2.4.4
 sphinx_markdown_tables
 sphinx_rtd_theme