Merge pull request #20 from open-mmlab/dev

Initial public release

mmdet/models/mask_heads/__init__.py

+from .fcn_mask_head import FCNMaskHead
+
+__all__ = ['FCNMaskHead']

mmdet/models/necks/__init__.py

+from .fpn import FPN
+
+__all__ = ['FPN']

mmdet/models/roi_extractors/__init__.py

+from .single_level import SingleRoIExtractor
+
+__all__ = ['SingleRoIExtractor']

mmdet/models/rpn_heads/__init__.py

+from .rpn_head import RPNHead
+
+__all__ = ['RPNHead']

mmdet/models/utils/__init__.py

+from .conv_module import ConvModule
+from .norm import build_norm_layer
+from .weight_init import xavier_init, normal_init, uniform_init, kaiming_init
+
+__all__ = [
+    'ConvModule', 'build_norm_layer', 'xavier_init', 'normal_init',
+    'uniform_init', 'kaiming_init'
+]

mmdet/models/utils/norm.py

+import torch.nn as nn
+
+norm_cfg = {'BN': nn.BatchNorm2d, 'SyncBN': None, 'GN': None}
+
+
+def build_norm_layer(cfg, num_features):
+    assert isinstance(cfg, dict) and 'type' in cfg
+    cfg_ = cfg.copy()
+    cfg_.setdefault('eps', 1e-5)
+    layer_type = cfg_.pop('type')
+
+    if layer_type not in norm_cfg:
+        raise KeyError('Unrecognized norm type {}'.format(layer_type))
+    elif norm_cfg[layer_type] is None:
+        raise NotImplementedError
+
+    return norm_cfg[layer_type](num_features, **cfg_)

mmdet/models/utils/weight_init.py

+import torch.nn as nn
+
+
+def xavier_init(module, gain=1, bias=0, distribution='normal'):
+    assert distribution in ['uniform', 'normal']
+    if distribution == 'uniform':
+        nn.init.xavier_uniform_(module.weight, gain=gain)
+    else:
+        nn.init.xavier_normal_(module.weight, gain=gain)
+    if hasattr(module, 'bias'):
+        nn.init.constant_(module.bias, bias)
+
+
+def normal_init(module, mean=0, std=1, bias=0):
+    nn.init.normal_(module.weight, mean, std)
+    if hasattr(module, 'bias'):
+        nn.init.constant_(module.bias, bias)
+
+
+def uniform_init(module, a=0, b=1, bias=0):
+    nn.init.uniform_(module.weight, a, b)
+    if hasattr(module, 'bias'):
+        nn.init.constant_(module.bias, bias)
+
+
+def kaiming_init(module,
+                 mode='fan_out',
+                 nonlinearity='relu',
+                 bias=0,
+                 distribution='normal'):
+    assert distribution in ['uniform', 'normal']
+    if distribution == 'uniform':
+        nn.init.kaiming_uniform_(
+            module.weight, mode=mode, nonlinearity=nonlinearity)
+    else:
+        nn.init.kaiming_normal_(
+            module.weight, mode=mode, nonlinearity=nonlinearity)
+    if hasattr(module, 'bias'):
+        nn.init.constant_(module.bias, bias)

mmdet/ops/nms/.gitignore

+*.cpp