[Enhancement] Make build_xxx_layer allow accepting a class type

mmcv/cnn/bricks/conv.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import inspect
 from typing import Dict, Optional
 
 from mmengine.registry import MODELS
@@ -35,7 +36,8 @@ def build_conv_layer(cfg: Optional[Dict], *args, **kwargs) -> nn.Module:
         cfg_ = cfg.copy()
 
     layer_type = cfg_.pop('type')
-
+    if inspect.isclass(layer_type):
+        return layer_type(*args, **kwargs, **cfg_)  # type: ignore
     # Switch registry to the target scope. If `conv_layer` cannot be found
     # in the registry, fallback to search `conv_layer` in the
     # mmengine.MODELS.

mmcv/cnn/bricks/norm.py

@@ -98,22 +98,25 @@ def build_norm_layer(cfg: Dict,
 
     layer_type = cfg_.pop('type')
 
-    # Switch registry to the target scope. If `norm_layer` cannot be found
-    # in the registry, fallback to search `norm_layer` in the
-    # mmengine.MODELS.
-    with MODELS.switch_scope_and_registry(None) as registry:
-        norm_layer = registry.get(layer_type)
-    if norm_layer is None:
-        raise KeyError(f'Cannot find {norm_layer} in registry under scope '
-                       f'name {registry.scope}')
+    if inspect.isclass(layer_type):
+        norm_layer = layer_type
+    else:
+        # Switch registry to the target scope. If `norm_layer` cannot be found
+        # in the registry, fallback to search `norm_layer` in the
+        # mmengine.MODELS.
+        with MODELS.switch_scope_and_registry(None) as registry:
+            norm_layer = registry.get(layer_type)
+        if norm_layer is None:
+            raise KeyError(f'Cannot find {norm_layer} in registry under '
+                           f'scope name {registry.scope}')
     abbr = infer_abbr(norm_layer)
 
     assert isinstance(postfix, (int, str))
     name = abbr + str(postfix)
 
     requires_grad = cfg_.pop('requires_grad', True)
     cfg_.setdefault('eps', 1e-5)
-    if layer_type != 'GN':
+    if norm_layer is not nn.GroupNorm:
         layer = norm_layer(num_features, **cfg_)
         if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'):
             layer._specify_ddp_gpu_num(1)

mmcv/cnn/bricks/padding.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import inspect
 from typing import Dict
 
 import torch.nn as nn
@@ -27,7 +28,8 @@ def build_padding_layer(cfg: Dict, *args, **kwargs) -> nn.Module:
 
     cfg_ = cfg.copy()
     padding_type = cfg_.pop('type')
-
+    if inspect.isclass(padding_type):
+        return padding_type(*args, **kwargs, **cfg_)
     # Switch registry to the target scope. If `padding_layer` cannot be found
     # in the registry, fallback to search `padding_layer` in the
     # mmengine.MODELS.

mmcv/cnn/bricks/plugin.py

@@ -79,15 +79,18 @@ def build_plugin_layer(cfg: Dict,
     cfg_ = cfg.copy()
 
     layer_type = cfg_.pop('type')
-
-    # Switch registry to the target scope. If `plugin_layer` cannot be found
-    # in the registry, fallback to search `plugin_layer` in the
-    # mmengine.MODELS.
-    with MODELS.switch_scope_and_registry(None) as registry:
-        plugin_layer = registry.get(layer_type)
-    if plugin_layer is None:
-        raise KeyError(f'Cannot find {plugin_layer} in registry under scope '
-                       f'name {registry.scope}')
+    if inspect.isclass(layer_type):
+        plugin_layer = layer_type
+    else:
+        # Switch registry to the target scope. If `plugin_layer` cannot be
+        # found in the registry, fallback to search `plugin_layer` in the
+        # mmengine.MODELS.
+        with MODELS.switch_scope_and_registry(None) as registry:
+            plugin_layer = registry.get(layer_type)
+        if plugin_layer is None:
+            raise KeyError(
+                f'Cannot find {plugin_layer} in registry under scope '
+                f'name {registry.scope}')
     abbr = infer_abbr(plugin_layer)
 
     assert isinstance(postfix, (int, str))

mmcv/cnn/bricks/upsample.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import inspect
 from typing import Dict
 
 import torch
@@ -76,15 +77,18 @@ def build_upsample_layer(cfg: Dict, *args, **kwargs) -> nn.Module:
 
     layer_type = cfg_.pop('type')
 
+    if inspect.isclass(layer_type):
+        upsample = layer_type
     # Switch registry to the target scope. If `upsample` cannot be found
     # in the registry, fallback to search `upsample` in the
     # mmengine.MODELS.
-    with MODELS.switch_scope_and_registry(None) as registry:
-        upsample = registry.get(layer_type)
-    if upsample is None:
-        raise KeyError(f'Cannot find {upsample} in registry under scope '
-                       f'name {registry.scope}')
-    if upsample is nn.Upsample:
-        cfg_['mode'] = layer_type
+    else:
+        with MODELS.switch_scope_and_registry(None) as registry:
+            upsample = registry.get(layer_type)
+        if upsample is None:
+            raise KeyError(f'Cannot find {upsample} in registry under scope '
+                           f'name {registry.scope}')
+        if upsample is nn.Upsample:
+            cfg_['mode'] = layer_type
     layer = upsample(*args, **kwargs, **cfg_)
     return layer

mmcv/ops/nms.py

@@ -293,8 +293,9 @@ def batched_nms(boxes: Tensor,
                 max_coordinate + torch.tensor(1).to(boxes))
             boxes_for_nms = boxes + offsets[:, None]
 
-    nms_type = nms_cfg_.pop('type', 'nms')
-    nms_op = eval(nms_type)
+    nms_op = nms_cfg_.pop('type', 'nms')
+    if isinstance(nms_op, str):
+        nms_op = eval(nms_op)
 
     split_thr = nms_cfg_.pop('split_thr', 10000)
     # Won't split to multiple nms nodes when exporting to onnx