[Refactor] union to MODELS

demo/ner_demo.py

@@ -3,7 +3,6 @@ from argparse import ArgumentParser
 
 from mmocr.apis import init_detector
 from mmocr.apis.inference import text_model_inference
-from mmocr.models import build_detector  # NOQA
 from mmocr.registry import DATASETS  # NOQA
 
 

demo/webcam_demo.py

@@ -5,7 +5,6 @@ import cv2
 import torch
 
 from mmocr.apis import init_detector, model_inference
-from mmocr.models import build_detector  # noqa: F401
 from mmocr.registry import DATASETS  # noqa: F401
 
 

mmocr/apis/inference.py

@@ -11,7 +11,7 @@ from mmdet.core import get_classes
 from mmdet.datasets import replace_ImageToTensor
 from mmdet.datasets.pipelines import Compose
 
-from mmocr.models import build_detector
+from mmocr.registry import MODELS
 from mmocr.utils import is_2dlist
 from .utils import disable_text_recog_aug_test
 
@@ -40,7 +40,7 @@ def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
     if config.model.get('pretrained'):
         config.model.pretrained = None
     config.model.train_cfg = None
-    model = build_detector(config.model, test_cfg=config.get('test_cfg'))
+    model = MODELS.build(config.model, test_cfg=config.get('test_cfg'))
     if checkpoint is not None:
         checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
         if 'CLASSES' in checkpoint.get('meta', {}):

mmocr/core/deployment/deploy_utils.py

@@ -5,7 +5,6 @@ from typing import Any, Iterable
 
 import numpy as np
 import torch
-from mmdet.models.builder import DETECTORS
 
 from mmocr.models.textdet.detectors.single_stage_text_detector import \
     SingleStageTextDetector
@@ -13,6 +12,7 @@ from mmocr.models.textdet.detectors.text_detector_mixin import \
     TextDetectorMixin
 from mmocr.models.textrecog.recognizer.encode_decode_recognizer import \
     EncodeDecodeRecognizer
+from mmocr.registry import MODELS
 
 
 def inference_with_session(sess, io_binding, input_name, output_names,
@@ -34,7 +34,7 @@ def inference_with_session(sess, io_binding, input_name, output_names,
     return pred
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class ONNXRuntimeDetector(TextDetectorMixin, SingleStageTextDetector):
     """The class for evaluating onnx file of detection."""
 
@@ -110,7 +110,7 @@ class ONNXRuntimeDetector(TextDetectorMixin, SingleStageTextDetector):
         return boundaries
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class ONNXRuntimeRecognizer(EncodeDecodeRecognizer):
     """The class for evaluating onnx file of recognition."""
 
@@ -201,7 +201,7 @@ class ONNXRuntimeRecognizer(EncodeDecodeRecognizer):
         return results
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class TensorRTDetector(TextDetectorMixin, SingleStageTextDetector):
     """The class for evaluating TensorRT file of detection."""
 
@@ -257,7 +257,7 @@ class TensorRTDetector(TextDetectorMixin, SingleStageTextDetector):
         return boundaries
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class TensorRTRecognizer(EncodeDecodeRecognizer):
     """The class for evaluating TensorRT file of recognition."""
 

mmocr/datasets/pipelines/ner_transforms.py

@@ -1,8 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from mmocr.models.builder import build_convertor
+from mmocr.registry import MODELS, TRANSFORMS
-from mmocr.registry import TRANSFORMS
 
 
 @TRANSFORMS.register_module()
@@ -18,7 +17,7 @@ class NerTransform:
     """
 
     def __init__(self, label_convertor, max_len):
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
         self.max_len = max_len
 
     def __call__(self, results):

mmocr/datasets/pipelines/ocr_seg_targets.py

@@ -4,8 +4,7 @@ import numpy as np
 from mmdet.core import BitmapMasks
 
 import mmocr.utils.check_argument as check_argument
-from mmocr.models.builder import build_convertor
+from mmocr.registry import MODELS, TRANSFORMS
-from mmocr.registry import TRANSFORMS
 
 
 @TRANSFORMS.register_module()
@@ -41,7 +40,7 @@ class OCRSegTargets:
 
         self.attn_shrink_ratio = attn_shrink_ratio
         self.seg_shrink_ratio = seg_shrink_ratio
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
         self.box_type = box_type
         self.pad_val = pad_val
 

mmocr/models/__init__.py

@@ -1,19 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from . import common, kie, textdet, textrecog
-from .builder import (BACKBONES, CONVERTORS, DECODERS, DETECTORS, ENCODERS,
-                      HEADS, LOSSES, NECKS, PREPROCESSOR, build_backbone,
-                      build_convertor, build_decoder, build_detector,
-                      build_encoder, build_loss, build_preprocessor)
 from .common import *  # NOQA
 from .kie import *  # NOQA
 from .ner import *  # NOQA
 from .textdet import *  # NOQA
 from .textrecog import *  # NOQA
 
-__all__ = [
+__all__ = common.__all__ + kie.__all__ + textdet.__all__ + textrecog.__all__
-    'BACKBONES', 'DETECTORS', 'HEADS', 'LOSSES', 'NECKS', 'build_backbone',
-    'build_detector', 'build_loss', 'CONVERTORS', 'ENCODERS', 'DECODERS',
-    'PREPROCESSOR', 'build_convertor', 'build_encoder', 'build_decoder',
-    'build_preprocessor'
-]
-__all__ += common.__all__ + kie.__all__ + textdet.__all__ + textrecog.__all__

mmocr/models/builder.py

@@ -1,115 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
 import torch.nn as nn
+from mmcv.cnn import ACTIVATION_LAYERS as MMCV_ACTIVATION_LAYERS
+from mmcv.cnn import UPSAMPLE_LAYERS as MMCV_UPSAMPLE_LAYERS
+from mmcv.utils import Registry, build_from_cfg
 
-from mmocr.registry import MODELS
+UPSAMPLE_LAYERS = Registry('upsample layer', parent=MMCV_UPSAMPLE_LAYERS)
-
+ACTIVATION_LAYERS = Registry('activation layer', parent=MMCV_ACTIVATION_LAYERS)
-CONVERTORS = MODELS
-ENCODERS = MODELS
-DECODERS = MODELS
-PREPROCESSOR = MODELS
-POSTPROCESSOR = MODELS
-
-UPSAMPLE_LAYERS = MODELS
-BACKBONES = MODELS
-LOSSES = MODELS
-DETECTORS = MODELS
-ROI_EXTRACTORS = MODELS
-HEADS = MODELS
-NECKS = MODELS
-FUSERS = MODELS
-RECOGNIZERS = MODELS
-
-ACTIVATION_LAYERS = MODELS
-
-
-def build_recognizer(cfg, train_cfg=None, test_cfg=None):
-    """Build recognizer."""
-    warnings.warn('``build_recognizer`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return RECOGNIZERS(
-        cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
-
-
-def build_convertor(cfg):
-    """Build label convertor for scene text recognizer."""
-    warnings.warn('``build_convertor`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return CONVERTORS.build(cfg)
-
-
-def build_encoder(cfg):
-    """Build encoder for scene text recognizer."""
-    warnings.warn('``build_encoder`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return ENCODERS.build(cfg)
-
-
-def build_decoder(cfg):
-    """Build decoder for scene text recognizer."""
-    warnings.warn('``build_decoder`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return DECODERS.build(cfg)
-
-
-def build_preprocessor(cfg):
-    """Build preprocessor for scene text recognizer."""
-    warnings.warn(
-        '``build_preprocessor`` would be deprecated soon, please use '
-        '``mmocr.registry.MODELS.build()`` ')
-    return PREPROCESSOR(cfg)
-
-
-def build_postprocessor(cfg):
-    """Build postprocessor for scene text detector."""
-    warnings.warn(
-        '``build_postprocessor`` would be deprecated soon, please use '
-        '``mmocr.registry.MODELS.build()`` ')
-    return POSTPROCESSOR.build(cfg)
-
-
-def build_roi_extractor(cfg):
-    """Build roi extractor."""
-    warnings.warn(
-        '``build_roi_extractor`` would be deprecated soon, please use '
-        '``mmocr.registry.MODELS.build()`` ')
-    return ROI_EXTRACTORS.build(cfg)
-
-
-def build_loss(cfg):
-    """Build loss."""
-    warnings.warn('``build_loss`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return LOSSES.build(cfg)
-
-
-def build_backbone(cfg):
-    """Build backbone."""
-    warnings.warn('``build_backbone`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return BACKBONES.build(cfg)
-
-
-def build_head(cfg):
-    """Build head."""
-    warnings.warn('``build_head`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return HEADS.build(cfg)
-
-
-def build_neck(cfg):
-    """Build neck."""
-    warnings.warn('``build_neck`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return NECKS.build(cfg)
-
-
-def build_fuser(cfg):
-    """Build fuser."""
-    warnings.warn('``build_fuser`` would be deprecated soon, please use '
-                  '``mmocr.registry.MODELS.build()`` ')
-    return FUSERS.build(cfg)
 
 
 def build_upsample_layer(cfg, *args, **kwargs):
@@ -160,21 +56,4 @@ def build_activation_layer(cfg):
     Returns:
         nn.Module: Created activation layer.
     """
-    warnings.warn(
+    return build_from_cfg(cfg, ACTIVATION_LAYERS)
-        '``build_activation_layer`` would be deprecated soon, please use '
-        '``mmocr.registry.MODELS.build()`` ')
-    return ACTIVATION_LAYERS.build(cfg)
-
-
-def build_detector(cfg, train_cfg=None, test_cfg=None):
-    """Build detector."""
-    if train_cfg is not None or test_cfg is not None:
-        warnings.warn(
-            'train_cfg and test_cfg is deprecated, '
-            'please specify them in model', UserWarning)
-    assert cfg.get('train_cfg') is None or train_cfg is None, \
-        'train_cfg specified in both outer field and model field '
-    assert cfg.get('test_cfg') is None or test_cfg is None, \
-        'test_cfg specified in both outer field and model field '
-    return DETECTORS.build(
-        cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))

mmocr/models/common/backbones/unet.py

@@ -6,8 +6,9 @@ from mmcv.cnn import ConvModule, build_norm_layer
 from mmcv.runner import BaseModule
 from mmcv.utils.parrots_wrapper import _BatchNorm
 
-from mmocr.models.builder import (BACKBONES, UPSAMPLE_LAYERS,
+from mmocr.models.builder import (UPSAMPLE_LAYERS, build_activation_layer,
-                                  build_activation_layer, build_upsample_layer)
+                                  build_upsample_layer)
+from mmocr.registry import MODELS
 
 
 class UpConvBlock(nn.Module):
@@ -317,7 +318,7 @@ class InterpConv(nn.Module):
         return out
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class UNet(BaseModule):
     """UNet backbone.
     U-Net: Convolutional Networks for Biomedical Image Segmentation.

mmocr/models/common/detectors/single_stage.py

@@ -4,11 +4,10 @@ import warnings
 from mmdet.models.detectors import \
     SingleStageDetector as MMDET_SingleStageDetector
 
-from mmocr.models.builder import (DETECTORS, build_backbone, build_head,
+from mmocr.registry import MODELS
-                                  build_neck)
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SingleStageDetector(MMDET_SingleStageDetector):
     """Base class for single-stage detectors.
 
@@ -29,11 +28,11 @@ class SingleStageDetector(MMDET_SingleStageDetector):
             warnings.warn('DeprecationWarning: pretrained is deprecated, '
                           'please use "init_cfg" instead')
             backbone.pretrained = pretrained
-        self.backbone = build_backbone(backbone)
+        self.backbone = MODELS.build(backbone)
         if neck is not None:
-            self.neck = build_neck(neck)
+            self.neck = MODELS.build(neck)
         bbox_head.update(train_cfg=train_cfg)
         bbox_head.update(test_cfg=test_cfg)
-        self.bbox_head = build_head(bbox_head)
+        self.bbox_head = MODELS.build(bbox_head)
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg

mmocr/models/common/losses/dice_loss.py

@@ -2,10 +2,10 @@
 import torch
 import torch.nn as nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class DiceLoss(nn.Module):
 
     def __init__(self, eps=1e-6):

mmocr/models/kie/extractors/sdmgr.py

@@ -7,12 +7,12 @@ from torch import nn
 from torch.nn import functional as F
 
 from mmocr.core import imshow_edge, imshow_node
-from mmocr.models.builder import DETECTORS, build_roi_extractor
 from mmocr.models.common.detectors import SingleStageDetector
+from mmocr.registry import MODELS
 from mmocr.utils import list_from_file
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SDMGR(SingleStageDetector):
     """The implementation of the paper: Spatial Dual-Modality Graph Reasoning
     for Key Information Extraction. https://arxiv.org/abs/2103.14470.
@@ -42,7 +42,7 @@ class SDMGR(SingleStageDetector):
             backbone, neck, bbox_head, train_cfg, test_cfg, init_cfg=init_cfg)
         self.visual_modality = visual_modality
         if visual_modality:
-            self.extractor = build_roi_extractor({
+            self.extractor = MODELS.build({
                 **extractor, 'out_channels':
                 self.backbone.base_channels
             })

mmocr/models/kie/heads/sdmgr_head.py

@@ -4,10 +4,10 @@ from mmcv.runner import BaseModule
 from torch import nn
 from torch.nn import functional as F
 
-from mmocr.models.builder import HEADS, build_loss
+from mmocr.registry import MODELS
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class SDMGRHead(BaseModule):
 
     def __init__(self,
@@ -45,7 +45,7 @@ class SDMGRHead(BaseModule):
             [GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)])
         self.node_cls = nn.Linear(node_embed, num_classes)
         self.edge_cls = nn.Linear(edge_embed, 2)
-        self.loss = build_loss(loss)
+        self.loss = MODELS.build(loss)
 
     def forward(self, relations, texts, x=None):
         node_nums, char_nums = [], []

mmocr/models/kie/losses/sdmgr_loss.py

@@ -3,10 +3,10 @@ import torch
 from mmdet.models.losses import accuracy
 from torch import nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class SDMGRLoss(nn.Module):
     """The implementation the loss of key information extraction proposed in
     the paper: Spatial Dual-Modality Graph Reasoning for Key Information

mmocr/models/ner/classifiers/ner_classifier.py

@@ -1,10 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import (DETECTORS, build_convertor, build_decoder,
-                                  build_encoder, build_loss)
 from mmocr.models.textrecog.recognizer.base import BaseRecognizer
+from mmocr.registry import MODELS
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class NerClassifier(BaseRecognizer):
     """Base class for NER classifier."""
 
@@ -17,15 +16,15 @@ class NerClassifier(BaseRecognizer):
                  test_cfg=None,
                  init_cfg=None):
         super().__init__(init_cfg=init_cfg)
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
 
-        self.encoder = build_encoder(encoder)
+        self.encoder = MODELS.build(encoder)
 
         decoder.update(num_labels=self.label_convertor.num_labels)
-        self.decoder = build_decoder(decoder)
+        self.decoder = MODELS.build(decoder)
 
         loss.update(num_labels=self.label_convertor.num_labels)
-        self.loss = build_loss(loss)
+        self.loss = MODELS.build(loss)
 
     def extract_feat(self, imgs):
         """Extract features from images."""

mmocr/models/ner/convertors/ner_convertor.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from mmocr.utils import list_from_file
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class NerConvertor:
     """Convert between text, index and tensor for NER pipeline.
 

mmocr/models/ner/decoders/fc_decoder.py

@@ -4,10 +4,10 @@ import torch.nn as nn
 import torch.nn.functional as F
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import DECODERS
+from mmocr.registry import MODELS
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class FCDecoder(BaseModule):
     """FC Decoder class for Ner.
 

mmocr/models/ner/encoders/bert_encoder.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import ENCODERS
 from mmocr.models.ner.utils.bert import BertModel
+from mmocr.registry import MODELS
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class BertEncoder(BaseModule):
     """Bert encoder
     Args:

mmocr/models/ner/losses/masked_cross_entropy_loss.py

@@ -2,10 +2,10 @@
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class MaskedCrossEntropyLoss(nn.Module):
     """The implementation of masked cross entropy loss.
 

mmocr/models/ner/losses/masked_focal_loss.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from torch import nn
 
-from mmocr.models.builder import LOSSES
 from mmocr.models.common.losses.focal_loss import FocalLoss
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class MaskedFocalLoss(nn.Module):
     """The implementation of masked focal loss.
 

mmocr/models/textdet/dense_heads/db_head.py

@@ -5,11 +5,11 @@ import torch
 import torch.nn as nn
 from mmcv.runner import BaseModule, Sequential
 
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 from .head_mixin import HeadMixin
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class DBHead(HeadMixin, BaseModule):
     """The class for DBNet head.
 

mmocr/models/textdet/dense_heads/drrg_head.py

@@ -7,13 +7,13 @@ import torch.nn as nn
 import torch.nn.functional as F
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import HEADS, build_loss
 from mmocr.models.textdet.modules import GCN, LocalGraphs, ProposalLocalGraphs
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 from .head_mixin import HeadMixin
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class DRRGHead(HeadMixin, BaseModule):
     """The class for DRRG head: `Deep Relational Reasoning Graph Network for
     Arbitrary Shape Text Detection <https://arxiv.org/abs/2003.07493>`_.
@@ -118,7 +118,7 @@ class DRRGHead(HeadMixin, BaseModule):
         self.center_region_thr = center_region_thr
         self.center_region_area_thr = center_region_area_thr
         self.local_graph_thr = local_graph_thr
-        self.loss_module = build_loss(loss)
+        self.loss_module = MODELS.build(loss)
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
 

mmocr/models/textdet/dense_heads/fce_head.py

@@ -5,12 +5,12 @@ import torch.nn as nn
 from mmcv.runner import BaseModule
 from mmdet.core import multi_apply
 
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 from ..postprocess.utils import poly_nms
 from .head_mixin import HeadMixin
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class FCEHead(HeadMixin, BaseModule):
     """The class for implementing FCENet head.
 

mmocr/models/textdet/dense_heads/head_mixin.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
 
-from mmocr.models.builder import HEADS, build_loss, build_postprocessor
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class HeadMixin:
     """Base head class for text detection, including loss calcalation and
     postprocess.
@@ -19,8 +19,8 @@ class HeadMixin:
         assert isinstance(loss, dict)
         assert isinstance(postprocessor, dict)
 
-        self.loss_module = build_loss(loss)
+        self.loss_module = MODELS.build(loss)
-        self.postprocessor = build_postprocessor(postprocessor)
+        self.postprocessor = MODELS.build(postprocessor)
 
     def resize_boundary(self, boundaries, scale_factor):
         """Rescale boundaries via scale_factor.

mmocr/models/textdet/dense_heads/pan_head.py

@@ -6,12 +6,12 @@ import torch
 import torch.nn as nn
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 from .head_mixin import HeadMixin
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class PANHead(HeadMixin, BaseModule):
     """The class for PANet head.
 

mmocr/models/textdet/dense_heads/pse_head.py

@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 from . import PANHead
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class PSEHead(PANHead):
     """The class for PSENet head.
 

mmocr/models/textdet/dense_heads/textsnake_head.py

@@ -4,11 +4,11 @@ import warnings
 import torch.nn as nn
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 from .head_mixin import HeadMixin
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class TextSnakeHead(HeadMixin, BaseModule):
     """The class for TextSnake head: TextSnake: A Flexible Representation for
     Detecting Text of Arbitrary Shapes.

mmocr/models/textdet/detectors/dbnet.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class DBNet(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing DBNet text detector: Real-time Scene Text
     Detection with Differentiable Binarization.

mmocr/models/textdet/detectors/drrg.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class DRRG(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing DRRG text detector. Deep Relational Reasoning
     Graph Network for Arbitrary Shape Text Detection.

mmocr/models/textdet/detectors/fcenet.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class FCENet(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing FCENet text detector
     FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text

mmocr/models/textdet/detectors/ocr_mask_rcnn.py

@@ -2,11 +2,11 @@
 from mmdet.models.detectors import MaskRCNN
 
 from mmocr.core import seg2boundary
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class OCRMaskRCNN(TextDetectorMixin, MaskRCNN):
     """Mask RCNN tailored for OCR."""
 

mmocr/models/textdet/detectors/panet.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class PANet(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing PANet text detector:
 

mmocr/models/textdet/detectors/psenet.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class PSENet(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing PSENet text detector: Shape Robust Text
     Detection with Progressive Scale Expansion Network.

mmocr/models/textdet/detectors/single_stage_text_detector.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from mmocr.models.builder import DETECTORS
 from mmocr.models.common.detectors import SingleStageDetector
+from mmocr.registry import MODELS
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class SingleStageTextDetector(SingleStageDetector):
     """The class for implementing single stage text detector."""
 

mmocr/models/textdet/detectors/textsnake.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .single_stage_text_detector import SingleStageTextDetector
 from .text_detector_mixin import TextDetectorMixin
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class TextSnake(TextDetectorMixin, SingleStageTextDetector):
     """The class for implementing TextSnake text detector: TextSnake: A
     Flexible Representation for Detecting Text of Arbitrary Shapes.

mmocr/models/textdet/losses/db_loss.py

@@ -3,11 +3,11 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from mmocr.models.builder import LOSSES
 from mmocr.models.common.losses.dice_loss import DiceLoss
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class DBLoss(nn.Module):
     """The class for implementing DBNet loss.
 

mmocr/models/textdet/losses/drrg_loss.py

@@ -4,11 +4,11 @@ import torch.nn.functional as F
 from mmdet.core import BitmapMasks
 from torch import nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class DRRGLoss(nn.Module):
     """The class for implementing DRRG loss. This is partially adapted from
     https://github.com/GXYM/DRRG licensed under the MIT license.

mmocr/models/textdet/losses/fce_loss.py

@@ -5,10 +5,10 @@ import torch.nn.functional as F
 from mmdet.core import multi_apply
 from torch import nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class FCELoss(nn.Module):
     """The class for implementing FCENet loss.
 

mmocr/models/textdet/losses/pan_loss.py

@@ -8,11 +8,11 @@ import torch.nn.functional as F
 from mmdet.core import BitmapMasks
 from torch import nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class PANLoss(nn.Module):
     """The class for implementing PANet loss. This was partially adapted from
     https://github.com/WenmuZhou/PAN.pytorch.

mmocr/models/textdet/losses/pse_loss.py

@@ -1,12 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmdet.core import BitmapMasks
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 from . import PANLoss
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class PSELoss(PANLoss):
     r"""The class for implementing PSENet loss. This is partially adapted from
     https://github.com/whai362/PSENet.

mmocr/models/textdet/losses/textsnake_loss.py

@@ -4,11 +4,11 @@ import torch.nn.functional as F
 from mmdet.core import BitmapMasks
 from torch import nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 from mmocr.utils import check_argument
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class TextSnakeLoss(nn.Module):
     """The class for implementing TextSnake loss. This is partially adapted
     from https://github.com/princewang1994/TextSnake.pytorch.

mmocr/models/textdet/necks/fpem_ffm.py

@@ -3,7 +3,7 @@ import torch.nn.functional as F
 from mmcv.runner import BaseModule, ModuleList
 from torch import nn
 
-from mmocr.models.builder import NECKS
+from mmocr.registry import MODELS
 
 
 class FPEM(BaseModule):
@@ -72,7 +72,7 @@ class SeparableConv2d(BaseModule):
         return x
 
 
-@NECKS.register_module()
+@MODELS.register_module()
 class FPEM_FFM(BaseModule):
     """This code is from https://github.com/WenmuZhou/PAN.pytorch.
 

mmocr/models/textdet/necks/fpn_cat.py

@@ -5,10 +5,10 @@ import torch.nn.functional as F
 from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule, ModuleList, Sequential, auto_fp16
 
-from mmocr.models.builder import NECKS
+from mmocr.registry import MODELS
 
 
-@NECKS.register_module()
+@MODELS.register_module()
 class FPNC(BaseModule):
     """FPN-like fusion module in Real-time Scene Text Detection with
     Differentiable Binarization.

mmocr/models/textdet/necks/fpn_unet.py

@@ -4,7 +4,7 @@ import torch.nn.functional as F
 from mmcv.runner import BaseModule
 from torch import nn
 
-from mmocr.models.builder import NECKS
+from mmocr.registry import MODELS
 
 
 class UpBlock(BaseModule):
@@ -30,7 +30,7 @@ class UpBlock(BaseModule):
         return x
 
 
-@NECKS.register_module()
+@MODELS.register_module()
 class FPN_UNet(BaseModule):
     """The class for implementing DRRG and TextSnake U-Net-like FPN.
 

mmocr/models/textdet/necks/fpnf.py

@@ -4,10 +4,10 @@ import torch.nn.functional as F
 from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule, ModuleList, auto_fp16
 
-from mmocr.models.builder import NECKS
+from mmocr.registry import MODELS
 
 
-@NECKS.register_module()
+@MODELS.register_module()
 class FPNF(BaseModule):
     """FPN-like fusion module in Shape Robust Text Detection with Progressive
     Scale Expansion Network.

mmocr/models/textdet/postprocess/db_postprocessor.py

@@ -3,12 +3,12 @@ import cv2
 import numpy as np
 
 from mmocr.core import points2boundary
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 from .utils import box_score_fast, unclip
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class DBPostprocessor(BasePostprocessor):
     """Decoding predictions of DbNet to instances. This is partially adapted
     from https://github.com/MhLiao/DB.

mmocr/models/textdet/postprocess/drrg_postprocessor.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 from .utils import (clusters2labels, comps2boundaries, connected_components,
                     graph_propagation, remove_single)
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class DRRGPostprocessor(BasePostprocessor):
     """Merge text components and construct boundaries of text instances.
 

mmocr/models/textdet/postprocess/fce_postprocessor.py

@@ -2,12 +2,12 @@
 import cv2
 import numpy as np
 
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 from .utils import fill_hole, fourier2poly, poly_nms
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class FCEPostprocessor(BasePostprocessor):
     """Decoding predictions of FCENet to instances.
 

mmocr/models/textdet/postprocess/pan_postprocessor.py

@@ -5,11 +5,11 @@ import torch
 from mmcv.ops import pixel_group
 
 from mmocr.core import points2boundary
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class PANPostprocessor(BasePostprocessor):
     """Convert scores to quadrangles via post processing in PANet. This is
     partially adapted from https://github.com/WenmuZhou/PAN.pytorch.

mmocr/models/textdet/postprocess/pse_postprocessor.py

@@ -6,11 +6,11 @@ import torch
 from mmcv.ops import contour_expand
 
 from mmocr.core import points2boundary
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class PSEPostprocessor(BasePostprocessor):
     """Decoding predictions of PSENet to instances. This is partially adapted
     from https://github.com/whai362/PSENet.

mmocr/models/textdet/postprocess/textsnake_postprocessor.py

@@ -5,12 +5,12 @@ import numpy as np
 import torch
 from skimage.morphology import skeletonize
 
-from mmocr.models.builder import POSTPROCESSOR
+from mmocr.registry import MODELS
 from .base_postprocessor import BasePostprocessor
 from .utils import centralize, fill_hole, merge_disks
 
 
-@POSTPROCESSOR.register_module()
+@MODELS.register_module()
 class TextSnakePostprocessor(BasePostprocessor):
     """Decoding predictions of TextSnake to instances. This was partially
     adapted from https://github.com/princewang1994/TextSnake.pytorch.

mmocr/models/textrecog/backbones/nrtr_modality_transformer.py

@@ -2,10 +2,10 @@
 import torch.nn as nn
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import BACKBONES
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class NRTRModalityTransform(BaseModule):
 
     def __init__(self,

mmocr/models/textrecog/backbones/resnet.py

@@ -3,11 +3,11 @@ from mmcv.cnn import ConvModule, build_plugin_layer
 from mmcv.runner import BaseModule, Sequential
 
 import mmocr.utils as utils
-from mmocr.models.builder import BACKBONES
 from mmocr.models.textrecog.layers import BasicBlock
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class ResNet(BaseModule):
     """
     Args:

mmocr/models/textrecog/backbones/resnet31_ocr.py

@@ -3,11 +3,11 @@ import torch.nn as nn
 from mmcv.runner import BaseModule, Sequential
 
 import mmocr.utils as utils
-from mmocr.models.builder import BACKBONES
 from mmocr.models.textrecog.layers import BasicBlock
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class ResNet31OCR(BaseModule):
     """Implement ResNet backbone for text recognition, modified from
       `ResNet <https://arxiv.org/pdf/1512.03385.pdf>`_

mmocr/models/textrecog/backbones/resnet_abi.py

@@ -3,11 +3,11 @@ import torch.nn as nn
 from mmcv.runner import BaseModule, Sequential
 
 import mmocr.utils as utils
-from mmocr.models.builder import BACKBONES
 from mmocr.models.textrecog.layers import BasicBlock
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class ResNetABI(BaseModule):
     """Implement ResNet backbone for text recognition, modified from `ResNet.
 

mmocr/models/textrecog/backbones/shallow_cnn.py

@@ -3,10 +3,10 @@ import torch.nn as nn
 from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import BACKBONES
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class ShallowCNN(BaseModule):
     """Implement Shallow CNN block for SATRN.
 

mmocr/models/textrecog/backbones/very_deep_vgg.py

@@ -2,10 +2,10 @@
 import torch.nn as nn
 from mmcv.runner import BaseModule, Sequential
 
-from mmocr.models.builder import BACKBONES
+from mmocr.registry import MODELS
 
 
-@BACKBONES.register_module()
+@MODELS.register_module()
 class VeryDeepVgg(BaseModule):
     """Implement VGG-VeryDeep backbone for text recognition, modified from
     `VGG-VeryDeep <https://arxiv.org/pdf/1409.1556.pdf>`_

mmocr/models/textrecog/convertors/abi.py

@@ -2,11 +2,11 @@
 import torch
 
 import mmocr.utils as utils
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from .attn import AttnConvertor
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class ABIConvertor(AttnConvertor):
     """Convert between text, index and tensor for encoder-decoder based
     pipeline. Modified from AttnConvertor to get closer to ABINet's original

mmocr/models/textrecog/convertors/attn.py

@@ -2,11 +2,11 @@
 import torch
 
 import mmocr.utils as utils
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from .base import BaseConvertor
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class AttnConvertor(BaseConvertor):
     """Convert between text, index and tensor for encoder-decoder based
     pipeline.

mmocr/models/textrecog/convertors/base.py

@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from mmocr.utils import list_from_file
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class BaseConvertor:
     """Convert between text, index and tensor for text recognize pipeline.
 

mmocr/models/textrecog/convertors/ctc.py

@@ -5,11 +5,11 @@ import torch
 import torch.nn.functional as F
 
 import mmocr.utils as utils
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from .base import BaseConvertor
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class CTCConvertor(BaseConvertor):
     """Convert between text, index and tensor for CTC loss-based pipeline.
 

mmocr/models/textrecog/convertors/seg.py

@@ -4,11 +4,11 @@ import numpy as np
 import torch
 
 import mmocr.utils as utils
-from mmocr.models.builder import CONVERTORS
+from mmocr.registry import MODELS
 from .base import BaseConvertor
 
 
-@CONVERTORS.register_module()
+@MODELS.register_module()
 class SegConvertor(BaseConvertor):
     """Convert between text, index and tensor for segmentation based pipeline.
 

mmocr/models/textrecog/decoders/abinet_language_decoder.py

@@ -6,12 +6,12 @@ import torch.nn as nn
 from mmcv.cnn.bricks.transformer import BaseTransformerLayer
 from mmcv.runner import ModuleList
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.common.modules import PositionalEncoding
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class ABILanguageDecoder(BaseDecoder):
     r"""Transformer-based language model responsible for spell correction.
     Implementation of language model of \

mmocr/models/textrecog/decoders/abinet_vision_decoder.py

@@ -3,12 +3,12 @@ import torch
 import torch.nn as nn
 from mmcv.cnn import ConvModule
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.common.modules import PositionalEncoding
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class ABIVisionDecoder(BaseDecoder):
     """Converts visual features into text characters.
 

mmocr/models/textrecog/decoders/base_decoder.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import DECODERS
+from mmocr.registry import MODELS
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class BaseDecoder(BaseModule):
     """Base decoder class for text recognition."""
 

mmocr/models/textrecog/decoders/crnn_decoder.py

@@ -2,12 +2,12 @@
 import torch.nn as nn
 from mmcv.runner import Sequential
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.textrecog.layers import BidirectionalLSTM
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class CRNNDecoder(BaseDecoder):
     """Decoder for CRNN.
 

mmocr/models/textrecog/decoders/master_decoder.py

@@ -8,8 +8,8 @@ import torch.nn.functional as F
 from mmcv.cnn.bricks.transformer import BaseTransformerLayer
 from mmcv.runner import ModuleList
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.common.modules import PositionalEncoding
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
@@ -30,7 +30,7 @@ class Embeddings(nn.Module):
         return self.lut(x) * math.sqrt(self.d_model)
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class MasterDecoder(BaseDecoder):
     """Decoder module in `MASTER <https://arxiv.org/abs/1910.02562>`_.
 

mmocr/models/textrecog/decoders/nrtr_decoder.py

@@ -6,12 +6,12 @@ import torch.nn as nn
 import torch.nn.functional as F
 from mmcv.runner import ModuleList
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.common import PositionalEncoding, TFDecoderLayer
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class NRTRDecoder(BaseDecoder):
     """Transformer Decoder block with self attention mechanism.
 

mmocr/models/textrecog/decoders/position_attention_decoder.py

@@ -4,13 +4,13 @@ import math
 import torch
 import torch.nn as nn
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.textrecog.layers import (DotProductAttentionLayer,
                                            PositionAwareLayer)
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class PositionAttentionDecoder(BaseDecoder):
     """Position attention decoder for RobustScanner.
 

mmocr/models/textrecog/decoders/robust_scanner_decoder.py

@@ -3,12 +3,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import DECODERS, build_decoder
 from mmocr.models.textrecog.layers import RobustScannerFusionLayer
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class RobustScannerDecoder(BaseDecoder):
     """Decoder for RobustScanner.
 
@@ -72,7 +72,7 @@ class RobustScannerDecoder(BaseDecoder):
         hybrid_decoder.update(encode_value=self.encode_value)
         hybrid_decoder.update(return_feature=True)
 
-        self.hybrid_decoder = build_decoder(hybrid_decoder)
+        self.hybrid_decoder = MODELS.build(hybrid_decoder)
 
         # init position decoder
         position_decoder.update(num_classes=self.num_classes)
@@ -83,7 +83,7 @@ class RobustScannerDecoder(BaseDecoder):
         position_decoder.update(encode_value=self.encode_value)
         position_decoder.update(return_feature=True)
 
-        self.position_decoder = build_decoder(position_decoder)
+        self.position_decoder = MODELS.build(position_decoder)
 
         self.fusion_module = RobustScannerFusionLayer(
             self.dim_model if encode_value else dim_input)

mmocr/models/textrecog/decoders/sar_decoder.py

@@ -6,11 +6,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 import mmocr.utils as utils
-from mmocr.models.builder import DECODERS
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class ParallelSARDecoder(BaseDecoder):
     """Implementation Parallel Decoder module in `SAR.
 
@@ -255,7 +255,7 @@ class ParallelSARDecoder(BaseDecoder):
         return outputs
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class SequentialSARDecoder(BaseDecoder):
     """Implementation Sequential Decoder module in `SAR.
 

mmocr/models/textrecog/decoders/sar_decoder_with_bs.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn.functional as F
 
 import mmocr.utils as utils
-from mmocr.models.builder import DECODERS
+from mmocr.registry import MODELS
 from . import ParallelSARDecoder
 
 
@@ -31,7 +31,7 @@ class DecodeNode:
         return accu_score
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class ParallelSARDecoderWithBS(ParallelSARDecoder):
     """Parallel Decoder module with beam-search in SAR.
 

mmocr/models/textrecog/decoders/sequence_attention_decoder.py

@@ -5,12 +5,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import DECODERS
 from mmocr.models.textrecog.layers import DotProductAttentionLayer
+from mmocr.registry import MODELS
 from .base_decoder import BaseDecoder
 
 
-@DECODERS.register_module()
+@MODELS.register_module()
 class SequenceAttentionDecoder(BaseDecoder):
     """Sequence attention decoder for RobustScanner.
 

mmocr/models/textrecog/encoders/abinet_vision_model.py

@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import ENCODERS, build_decoder, build_encoder
+from mmocr.registry import MODELS
 from .base_encoder import BaseEncoder
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class ABIVisionModel(BaseEncoder):
     """A wrapper of visual feature encoder and language token decoder that
     converts visual features into text tokens.
@@ -23,8 +23,8 @@ class ABIVisionModel(BaseEncoder):
                  init_cfg=dict(type='Xavier', layer='Conv2d'),
                  **kwargs):
         super().__init__(init_cfg=init_cfg)
-        self.encoder = build_encoder(encoder)
+        self.encoder = MODELS.build(encoder)
-        self.decoder = build_decoder(decoder)
+        self.decoder = MODELS.build(decoder)
 
     def forward(self, feat, img_metas=None):
         """

mmocr/models/textrecog/encoders/base_encoder.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import ENCODERS
+from mmocr.registry import MODELS
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class BaseEncoder(BaseModule):
     """Base Encoder class for text recognition."""
 

mmocr/models/textrecog/encoders/channel_reduction_encoder.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch.nn as nn
 
-from mmocr.models.builder import ENCODERS
+from mmocr.registry import MODELS
 from .base_encoder import BaseEncoder
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class ChannelReductionEncoder(BaseEncoder):
     """Change the channel number with a one by one convoluational layer.
 

mmocr/models/textrecog/encoders/nrtr_encoder.py

@@ -4,12 +4,12 @@ import math
 import torch.nn as nn
 from mmcv.runner import ModuleList
 
-from mmocr.models.builder import ENCODERS
 from mmocr.models.common import TFEncoderLayer
+from mmocr.registry import MODELS
 from .base_encoder import BaseEncoder
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class NRTREncoder(BaseEncoder):
     """Transformer Encoder block with self attention mechanism.
 

mmocr/models/textrecog/encoders/sar_encoder.py

@@ -6,11 +6,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 import mmocr.utils as utils
-from mmocr.models.builder import ENCODERS
+from mmocr.registry import MODELS
 from .base_encoder import BaseEncoder
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class SAREncoder(BaseEncoder):
     """Implementation of encoder module in `SAR.
 

mmocr/models/textrecog/encoders/satrn_encoder.py

@@ -4,13 +4,13 @@ import math
 import torch.nn as nn
 from mmcv.runner import ModuleList
 
-from mmocr.models.builder import ENCODERS
 from mmocr.models.textrecog.layers import (Adaptive2DPositionalEncoding,
                                            SatrnEncoderLayer)
+from mmocr.registry import MODELS
 from .base_encoder import BaseEncoder
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class SatrnEncoder(BaseEncoder):
     """Implement encoder for SATRN, see `SATRN.
 

mmocr/models/textrecog/encoders/transformer.py

@@ -4,11 +4,11 @@ import copy
 from mmcv.cnn.bricks.transformer import BaseTransformerLayer
 from mmcv.runner import BaseModule, ModuleList
 
-from mmocr.models.builder import ENCODERS
 from mmocr.models.common.modules import PositionalEncoding
+from mmocr.registry import MODELS
 
 
-@ENCODERS.register_module()
+@MODELS.register_module()
 class TransformerEncoder(BaseModule):
     """Implement transformer encoder for text recognition, modified from
     `<https://github.com/FangShancheng/ABINet>`.

mmocr/models/textrecog/fusers/abi_fuser.py

@@ -3,10 +3,10 @@ import torch
 import torch.nn as nn
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import FUSERS
+from mmocr.registry import MODELS
 
 
-@FUSERS.register_module()
+@MODELS.register_module()
 class ABIFuser(BaseModule):
     """Mix and align visual feature and linguistic feature Implementation of
     language model of `ABINet <https://arxiv.org/abs/1910.04396>`_.

mmocr/models/textrecog/heads/seg_head.py

@@ -4,10 +4,10 @@ from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule
 from torch import nn
 
-from mmocr.models.builder import HEADS
+from mmocr.registry import MODELS
 
 
-@HEADS.register_module()
+@MODELS.register_module()
 class SegHead(BaseModule):
     """Head for segmentation based text recognition.
 

mmocr/models/textrecog/losses/ce_loss.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch.nn as nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class CELoss(nn.Module):
     """Implementation of loss module for encoder-decoder based text recognition
     method with CrossEntropy loss.
@@ -63,7 +63,7 @@ class CELoss(nn.Module):
         return losses
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class SARLoss(CELoss):
     """Implementation of loss module in `SAR.
 
@@ -95,7 +95,7 @@ class SARLoss(CELoss):
         return outputs, targets
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class TFLoss(CELoss):
     """Implementation of loss module for transformer.
 

mmocr/models/textrecog/losses/ctc_loss.py

@@ -4,10 +4,10 @@ import math
 import torch
 import torch.nn as nn
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class CTCLoss(nn.Module):
     """Implementation of loss module for CTC-loss based text recognition.
 

mmocr/models/textrecog/losses/mix_loss.py

@@ -3,10 +3,10 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class ABILoss(nn.Module):
     """Implementation of ABINet multiloss that allows mixing different types of
     losses with weights.

mmocr/models/textrecog/losses/seg_loss.py

@@ -3,10 +3,10 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import LOSSES
+from mmocr.registry import MODELS
 
 
-@LOSSES.register_module()
+@MODELS.register_module()
 class SegLoss(nn.Module):
     """Implementation of loss module for segmentation based text recognition
     method.

mmocr/models/textrecog/necks/fpn_ocr.py

@@ -4,10 +4,10 @@ import torch.nn.functional as F
 from mmcv.cnn import ConvModule
 from mmcv.runner import BaseModule, ModuleList
 
-from mmocr.models.builder import NECKS
+from mmocr.registry import MODELS
 
 
-@NECKS.register_module()
+@MODELS.register_module()
 class FPNOCR(BaseModule):
     """FPN-like Network for segmentation based text recognition.
 

mmocr/models/textrecog/preprocessor/base_preprocessor.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmcv.runner import BaseModule
 
-from mmocr.models.builder import PREPROCESSOR
+from mmocr.registry import MODELS
 
 
-@PREPROCESSOR.register_module()
+@MODELS.register_module()
 class BasePreprocessor(BaseModule):
     """Base Preprocessor class for text recognition."""
 

mmocr/models/textrecog/preprocessor/tps_preprocessor.py

@@ -17,11 +17,11 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import PREPROCESSOR
+from mmocr.registry import MODELS
 from .base_preprocessor import BasePreprocessor
 
 
-@PREPROCESSOR.register_module()
+@MODELS.register_module()
 class TPSPreprocessor(BasePreprocessor):
     """Rectification Network of RARE, namely TPS based STN in
     https://arxiv.org/pdf/1603.03915.pdf.

mmocr/models/textrecog/recognizer/abinet.py

@@ -3,13 +3,11 @@ import warnings
 
 import torch
 
-from mmocr.models.builder import (RECOGNIZERS, build_backbone, build_convertor,
+from mmocr.registry import MODELS
-                                  build_decoder, build_encoder, build_fuser,
-                                  build_loss, build_preprocessor)
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class ABINet(EncodeDecodeRecognizer):
     """Implementation of `Read Like Humans: Autonomous, Bidirectional and
     Iterative LanguageModeling for Scene Text Recognition.
@@ -36,21 +34,21 @@ class ABINet(EncodeDecodeRecognizer):
         # Label convertor (str2tensor, tensor2str)
         assert label_convertor is not None
         label_convertor.update(max_seq_len=max_seq_len)
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
 
         # Preprocessor module, e.g., TPS
         self.preprocessor = None
         if preprocessor is not None:
-            self.preprocessor = build_preprocessor(preprocessor)
+            self.preprocessor = MODELS.build(preprocessor)
 
         # Backbone
         assert backbone is not None
-        self.backbone = build_backbone(backbone)
+        self.backbone = MODELS.build(backbone)
 
         # Encoder module
         self.encoder = None
         if encoder is not None:
-            self.encoder = build_encoder(encoder)
+            self.encoder = MODELS.build(encoder)
 
         # Decoder module
         self.decoder = None
@@ -59,11 +57,11 @@ class ABINet(EncodeDecodeRecognizer):
             decoder.update(start_idx=self.label_convertor.start_idx)
             decoder.update(padding_idx=self.label_convertor.padding_idx)
             decoder.update(max_seq_len=max_seq_len)
-            self.decoder = build_decoder(decoder)
+            self.decoder = MODELS.build(decoder)
 
         # Loss
         assert loss is not None
-        self.loss = build_loss(loss)
+        self.loss = MODELS.build(loss)
 
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
@@ -78,7 +76,7 @@ class ABINet(EncodeDecodeRecognizer):
 
         self.fuser = None
         if fuser is not None:
-            self.fuser = build_fuser(fuser)
+            self.fuser = MODELS.build(fuser)
 
     def forward_train(self, img, img_metas):
         """

mmocr/models/textrecog/recognizer/crnn.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class CRNNNet(EncodeDecodeRecognizer):
     """CTC-loss based recognizer."""

mmocr/models/textrecog/recognizer/encode_decode_recognizer.py

@@ -3,13 +3,11 @@ import warnings
 
 import torch
 
-from mmocr.models.builder import (RECOGNIZERS, build_backbone, build_convertor,
+from mmocr.registry import MODELS
-                                  build_decoder, build_encoder, build_loss,
-                                  build_preprocessor)
 from .base import BaseRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class EncodeDecodeRecognizer(BaseRecognizer):
     """Base class for encode-decode recognizer."""
 
@@ -31,21 +29,21 @@ class EncodeDecodeRecognizer(BaseRecognizer):
         # Label convertor (str2tensor, tensor2str)
         assert label_convertor is not None
         label_convertor.update(max_seq_len=max_seq_len)
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
 
         # Preprocessor module, e.g., TPS
         self.preprocessor = None
         if preprocessor is not None:
-            self.preprocessor = build_preprocessor(preprocessor)
+            self.preprocessor = MODELS.build(preprocessor)
 
         # Backbone
         assert backbone is not None
-        self.backbone = build_backbone(backbone)
+        self.backbone = MODELS.build(backbone)
 
         # Encoder module
         self.encoder = None
         if encoder is not None:
-            self.encoder = build_encoder(encoder)
+            self.encoder = MODELS.build(encoder)
 
         # Decoder module
         assert decoder is not None
@@ -53,12 +51,12 @@ class EncodeDecodeRecognizer(BaseRecognizer):
         decoder.update(start_idx=self.label_convertor.start_idx)
         decoder.update(padding_idx=self.label_convertor.padding_idx)
         decoder.update(max_seq_len=max_seq_len)
-        self.decoder = build_decoder(decoder)
+        self.decoder = MODELS.build(decoder)
 
         # Loss
         assert loss is not None
         loss.update(ignore_index=self.label_convertor.padding_idx)
-        self.loss = build_loss(loss)
+        self.loss = MODELS.build(loss)
 
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg

mmocr/models/textrecog/recognizer/master.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import DETECTORS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@DETECTORS.register_module()
+@MODELS.register_module()
 class MASTER(EncodeDecodeRecognizer):
     """Implementation of `MASTER <https://arxiv.org/abs/1910.02562>`_"""

mmocr/models/textrecog/recognizer/nrtr.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class NRTR(EncodeDecodeRecognizer):
     """Implementation of `NRTR <https://arxiv.org/pdf/1806.00926.pdf>`_"""

mmocr/models/textrecog/recognizer/robust_scanner.py

@@ -1,9 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class RobustScanner(EncodeDecodeRecognizer):
     """Implementation of `RobustScanner.
 

mmocr/models/textrecog/recognizer/sar.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class SARNet(EncodeDecodeRecognizer):
     """Implementation of `SAR <https://arxiv.org/abs/1811.00751>`_"""

mmocr/models/textrecog/recognizer/satrn.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
+from mmocr.registry import MODELS
 from .encode_decode_recognizer import EncodeDecodeRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class SATRN(EncodeDecodeRecognizer):
     """Implementation of `SATRN <https://arxiv.org/abs/1910.04396>`_"""

mmocr/models/textrecog/recognizer/seg_recognizer.py

@@ -1,13 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
 
-from mmocr.models.builder import (RECOGNIZERS, build_backbone, build_convertor,
+from mmocr.registry import MODELS
-                                  build_head, build_loss, build_neck,
-                                  build_preprocessor)
 from .base import BaseRecognizer
 
 
-@RECOGNIZERS.register_module()
+@MODELS.register_module()
 class SegRecognizer(BaseRecognizer):
     """Base class for segmentation based recognizer."""
 
@@ -26,29 +24,29 @@ class SegRecognizer(BaseRecognizer):
 
         # Label_convertor
         assert label_convertor is not None
-        self.label_convertor = build_convertor(label_convertor)
+        self.label_convertor = MODELS.build(label_convertor)
 
         # Preprocessor module, e.g., TPS
         self.preprocessor = None
         if preprocessor is not None:
-            self.preprocessor = build_preprocessor(preprocessor)
+            self.preprocessor = MODELS.build(preprocessor)
 
         # Backbone
         assert backbone is not None
-        self.backbone = build_backbone(backbone)
+        self.backbone = MODELS.build(backbone)
 
         # Neck
         assert neck is not None
-        self.neck = build_neck(neck)
+        self.neck = MODELS.build(neck)
 
         # Head
         assert head is not None
         head.update(num_classes=self.label_convertor.num_classes())
-        self.head = build_head(head)
+        self.head = MODELS.build(head)
 
         # Loss
         assert loss is not None
-        self.loss = build_loss(loss)
+        self.loss = MODELS.build(loss)
 
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg

mmocr/utils/ocr.py

@@ -24,9 +24,9 @@ from mmocr.apis.inference import model_inference
 from mmocr.core.visualize import det_recog_show_result
 from mmocr.datasets.kie_dataset import KIEDataset
 from mmocr.datasets.pipelines.crop import crop_img
-from mmocr.models import build_detector
 from mmocr.models.textdet.detectors import TextDetectorMixin
 from mmocr.models.textrecog.recognizer import BaseRecognizer
+from mmocr.registry import MODELS
 from mmocr.utils import is_type_list
 from mmocr.utils.box_util import stitch_boxes_into_lines
 from mmocr.utils.fileio import list_from_file
@@ -427,7 +427,7 @@ class MMOCR:
                     'kie/' + kie_models[self.kie]['ckpt']
 
             kie_cfg = Config.fromfile(kie_config)
-            self.kie_model = build_detector(
+            self.kie_model = MODELS.build(
                 kie_cfg.model, test_cfg=kie_cfg.get('test_cfg'))
             self.kie_model = revert_sync_batchnorm(self.kie_model)
             self.kie_model.cfg = kie_cfg