* [Refactor] Use MMOCR's registry
1. Define MMOCR's registries as a child of MMDet's
2. Register all models to MMOCR's own registries
3. Modify some model configs so that some models in MMDet can be
correctly located
4. Remove some outdated demo scripts
* add detectors
* support batch inference during testing
* fix unittest
* update docs using url
* set cfg for train, val and test
* update docs
* update docs and test.py
* samples_per_gpu as global setting
* changes revert
* add deployment evaluation
* fix lint
* remove cpu unit tests for trt and onnx
* use pytest.mark to skip cpu unit test
* move to mmocr/core
* emm... renamed to wrappers
* renamed to deploy_utils
* renamed unit test to test_deploy_utils
* fix lint
* using pytest.mark.importorskip
* update ner standard code format
* add pytest
* fix pre-commit
* Annotate the dataset section
* fix pre-commit for dataset
* rm big files and add comments in dataset
* rename configs for ner task
* minor changes if metric
* Note modification
* fix pre-commit
* detail modification
* rm transform
* rm magic number
* fix warnings in pylint
* fix pre-commit
* correct help info
* rename model files
* rename err fixed
* 428_tag
* Adjust to more general pipline
* update unit test rate
* update
* Unit test coverage over 90% and add Readme
* modify details
* fix precommit
* update
* fix pre-commit
* update
* update
* update
* update result
* update readme
* update baseline config
* update config and small minor changes
* minor changes in readme and etc.
* back to original
* update toy config
* upload model and log
* fix pytest
* Modify the notes.
* fix readme
* Delete Chinese punctuation
* add demo and fix some logic and naming problems
* add To_tensor transformer for ner and load pretrained model in config
* delete extra lines
* split ner loss to MaskedCrossEntropyLoss and MaskedFocalLoss
* update config
* fix err
* updata
* modify noqa
* update new model report
* fix err in ner demo
* Update ner_dataset.py
* Update test_ner_dataset.py
* Update ner_dataset.py
* Update ner_transforms.py
* rm toy config and data
* add comment
* add empty
* fix conflict
* fix precommit
* fix pytest
* fix pytest err
* Update ner_dataset.py
* change dataset name to cluener2020
* move the postprocess in metric to convertor
* rm __init__ etc.
* precommit
* add discription in loss
* add auto download
* add http
* update
* remove some 'issert'
* replace unsqueeze
* update config
* update doc and bert.py
* update
* update demo code
Co-authored-by: weihuaqiang <weihuaqiang@sensetime.com>
Co-authored-by: Hongbin Sun <hongbin306@gmail.com>
* Add support for numpy arrays in model_inference
* Add test for numpy ndarray inference
* Fix linting problems
* Add support for batch inference
* Add batch inference demo script
* Fix comment
* Test batch inference with paths and arrays
* lint code
* Update model_inference docstring
* Refactor model inference tests
* Change inference function to make text detectors and recognizers use the same input data types
* Change single state text detector model to support batch inference
* Lint code
* simplify inference tests
* Remove psenet from batch inference test cases to prevent the pytest being killed
* Update batch_image_demo.py
* fix bug when test with dataset
fix bug when test with dataset, for example, `./tools/dist_test.sh configs/textrecog/sar/sar_r31_parallel_decoder_academic.py <checkpoint> 1 --eval acc`
Co-authored-by: Hongbin Sun <hongbin306@gmail.com>
* Improve KIE
* allow data do not contain label
* Make relation float32
Signed-off-by: lizz <lizz@sensetime.com>
* Add test
Signed-off-by: lizz <lizz@sensetime.com>
* fix#122: textsnake targets adaptation
* fix#122: textsnake targets adaptation
* add unittest
* fix format
* fix textsnake unittest on cpu
* fix unit test coverage
* add unit test
* Add TPS
* Update tps_preprocessor.py
* Add licence, change variable name and format
* renamed some parameters and add tests of ocr preprocessor
* renamed params
* Update tps_preprocessor.py
* add config file and readme for TPS
* Add support for numpy arrays in model_inference
* Add test for numpy ndarray inference
* Fix linting problems
* Simplify assertion in model_inference
