[Fix] Remove dependency on MMCV registry (#1261)

* [Fix] Remove dependency on MMCV registry

* fix

mmocr/models/builder.py

@@ -1,59 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-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
-
-UPSAMPLE_LAYERS = Registry('upsample layer', parent=MMCV_UPSAMPLE_LAYERS)
-ACTIVATION_LAYERS = Registry('activation layer', parent=MMCV_ACTIVATION_LAYERS)
-
-
-def build_upsample_layer(cfg, *args, **kwargs):
-    """Build upsample layer.
-
-    Args:
-        cfg (dict): The upsample layer config, which should contain:
-
-            - type (str): Layer type.
-            - scale_factor (int): Upsample ratio, which is not applicable to
-                deconv.
-            - layer args: Args needed to instantiate a upsample layer.
-        args (argument list): Arguments passed to the ``__init__``
-            method of the corresponding conv layer.
-        kwargs (keyword arguments): Keyword arguments passed to the
-            ``__init__`` method of the corresponding conv layer.
-
-    Returns:
-        nn.Module: Created upsample layer.
-    """
-    if not isinstance(cfg, dict):
-        raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
-    if 'type' not in cfg:
-        raise KeyError(
-            f'the cfg dict must contain the key "type", but got {cfg}')
-    cfg_ = cfg.copy()
-
-    layer_type = cfg_.pop('type')
-    if layer_type not in UPSAMPLE_LAYERS:
-        raise KeyError(f'Unrecognized upsample type {layer_type}')
-    else:
-        upsample = UPSAMPLE_LAYERS.get(layer_type)
-
-    if upsample is nn.Upsample:
-        cfg_['mode'] = layer_type
-    layer = upsample(*args, **kwargs, **cfg_)
-    return layer
-
-
-def build_activation_layer(cfg):
-    """Build activation layer.
-
-    Args:
-        cfg (dict): The activation layer config, which should contain:
-            - type (str): Layer type.
-            - layer args: Args needed to instantiate an activation layer.
-
-    Returns:
-        nn.Module: Created activation layer.
-    """
-    return build_from_cfg(cfg, ACTIVATION_LAYERS)

mmocr/models/common/backbones/unet.py

@@ -6,8 +6,6 @@ from mmcv.cnn import ConvModule, build_norm_layer
 from mmcv.utils.parrots_wrapper import _BatchNorm
 from mmengine.model import BaseModule
 
-from mmocr.models.builder import (UPSAMPLE_LAYERS, build_activation_layer,
-                                  build_upsample_layer)
 from mmocr.registry import MODELS
 
 
@@ -81,13 +79,14 @@ class UpConvBlock(nn.Module):
             dcn=None,
             plugins=None)
         if upsample_cfg is not None:
-            self.upsample = build_upsample_layer(
+            upsample_cfg.update(
-                cfg=upsample_cfg,
+                dict(
                     in_channels=in_channels,
                     out_channels=skip_channels,
                     with_cp=with_cp,
                     norm_cfg=norm_cfg,
-                act_cfg=act_cfg)
+                    act_cfg=act_cfg))
+            self.upsample = MODELS.build(upsample_cfg)
         else:
             self.upsample = ConvModule(
                 in_channels,
@@ -182,7 +181,7 @@ class BasicConvBlock(nn.Module):
         return out
 
 
-@UPSAMPLE_LAYERS.register_module()
+@MODELS.register_module()
 class DeconvModule(nn.Module):
     """Deconvolution upsample module in decoder for UNet (2X upsample).
 
@@ -231,7 +230,7 @@ class DeconvModule(nn.Module):
             padding=padding)
 
         _, norm = build_norm_layer(norm_cfg, out_channels)
-        activate = build_activation_layer(act_cfg)
+        activate = MODELS.build(act_cfg)
         self.deconv_upsamping = nn.Sequential(deconv, norm, activate)
 
     def forward(self, x):
@@ -244,7 +243,7 @@ class DeconvModule(nn.Module):
         return out
 
 
-@UPSAMPLE_LAYERS.register_module()
+@MODELS.register_module()
 class InterpConv(nn.Module):
     """Interpolation upsample module in decoder for UNet.
 

mmocr/models/common/layers/transformer_layers.py

@@ -35,7 +35,7 @@ class TFEncoderLayer(BaseModule):
                  d_v=64,
                  dropout=0.1,
                  qkv_bias=False,
-                 act_cfg=dict(type='mmcv.GELU'),
+                 act_cfg=dict(type='mmengine.GELU'),
                  operation_order=None):
         super().__init__()
         self.attn = MultiHeadAttention(
@@ -103,7 +103,7 @@ class TFDecoderLayer(nn.Module):
                  d_v=64,
                  dropout=0.1,
                  qkv_bias=False,
-                 act_cfg=dict(type='mmcv.GELU'),
+                 act_cfg=dict(type='mmengine.GELU'),
                  operation_order=None):
         super().__init__()
 

mmocr/models/common/modules/transformer_module.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmocr.models.builder import build_activation_layer
+from mmocr.registry import MODELS
 
 
 class ScaledDotProductAttention(nn.Module):
@@ -115,7 +115,7 @@ class PositionwiseFeedForward(nn.Module):
         super().__init__()
         self.w_1 = nn.Linear(d_in, d_hid)
         self.w_2 = nn.Linear(d_hid, d_in)
-        self.act = build_activation_layer(act_cfg)
+        self.act = MODELS.build(act_cfg)
         self.dropout = nn.Dropout(dropout)
 
     def forward(self, x):

mmocr/models/textrecog/plugins/common.py

@@ -3,11 +3,11 @@ from typing import Tuple, Union
 
 import torch
 import torch.nn as nn
-from mmcv.cnn import PLUGIN_LAYERS
+
+from mmocr.registry import MODELS
 
 
-# TODO: Replace PLUGIN_LAYERS with MODELS
+@MODELS.register_module()
-@PLUGIN_LAYERS.register_module()
 class Maxpool2d(nn.Module):
     """A wrapper around nn.Maxpool2d().
 
@@ -36,7 +36,7 @@ class Maxpool2d(nn.Module):
         return self.model(x)
 
 
-@PLUGIN_LAYERS.register_module()
+@MODELS.register_module()
 class GCAModule(nn.Module):
     """GCAModule in MASTER.
 

tests/test_models/test_textrecog/test_backbones/test_resnet.py

@@ -4,12 +4,14 @@ from unittest import TestCase
 import torch
 
 from mmocr.models.textrecog.backbones import ResNet
+from mmocr.utils import register_all_modules
 
 
 class TestResNet(TestCase):
 
     def setUp(self) -> None:
         self.img = torch.rand(1, 3, 32, 100)
+        register_all_modules()
 
     def test_resnet45_aster(self):
         resnet45_aster = ResNet(