diff --git a/mmdet/models/anchor_heads/anchor_head.py b/mmdet/models/anchor_heads/anchor_head.py
index d526b3b3beb37b3177c2bb25f3bfcaaa7fe887d4..f4899f9f7926ab5bc992b322872ff35a08d1bc69 100644
--- a/mmdet/models/anchor_heads/anchor_head.py
+++ b/mmdet/models/anchor_heads/anchor_head.py
@@ -19,7 +19,7 @@ class AnchorHead(nn.Module):
num_classes (int): Number of categories including the background
category.
in_channels (int): Number of channels in the input feature map.
- feat_channels (int): Number of channels of the feature map.
+ feat_channels (int): Number of hidden channels. Used in child classes.
anchor_scales (Iterable): Anchor scales.
anchor_ratios (Iterable): Anchor aspect ratios.
anchor_strides (Iterable): Anchor strides.
@@ -47,7 +47,6 @@ class AnchorHead(nn.Module):
loss_bbox=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)):
super(AnchorHead, self).__init__()
- # NOTE: in_channels is only used in child classes (e.g. RetinaHead)
self.in_channels = in_channels
self.num_classes = num_classes
self.feat_channels = feat_channels
@@ -82,9 +81,9 @@ class AnchorHead(nn.Module):
self._init_layers()
def _init_layers(self):
- self.conv_cls = nn.Conv2d(self.feat_channels,
+ self.conv_cls = nn.Conv2d(self.in_channels,
self.num_anchors * self.cls_out_channels, 1)
- self.conv_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
+ self.conv_reg = nn.Conv2d(self.in_channels, self.num_anchors * 4, 1)
def init_weights(self):
normal_init(self.conv_cls, std=0.01)
@@ -231,8 +230,7 @@ class AnchorHead(nn.Module):
Example:
>>> import mmcv
- >>> self = AnchorHead(num_classes=9, in_channels=1,
- >>> feat_channels=1)
+ >>> self = AnchorHead(num_classes=9, in_channels=1)
>>> img_metas = [{'img_shape': (32, 32, 3), 'scale_factor': 1}]
>>> cfg = mmcv.Config(dict(
>>> score_thr=0.00,
diff --git a/mmdet/models/anchor_heads/guided_anchor_head.py b/mmdet/models/anchor_heads/guided_anchor_head.py
index 8c71e85cf6b82f6aa99ce3407a741f6d80b830a8..9e9282509b6bd04a7061594ff8e2acf7f3bcca69 100644
--- a/mmdet/models/anchor_heads/guided_anchor_head.py
+++ b/mmdet/models/anchor_heads/guided_anchor_head.py
@@ -72,7 +72,7 @@ class GuidedAnchorHead(AnchorHead):
Args:
num_classes (int): Number of classes.
in_channels (int): Number of channels in the input feature map.
- feat_channels (int): Number of channels of the feature map.
+ feat_channels (int): Number of hidden channels.
octave_base_scale (int): Base octave scale of each level of
feature map.
scales_per_octave (int): Number of octave scales in each level of
@@ -170,11 +170,10 @@ class GuidedAnchorHead(AnchorHead):
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
- self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
- self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
- 1)
+ self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
+ self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2, 1)
self.feature_adaption = FeatureAdaption(
- self.feat_channels,
+ self.in_channels,
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups)
diff --git a/tests/test_config.py b/tests/test_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebc399ff37c4a4ff89b1472191edac1b466480e6
--- /dev/null
+++ b/tests/test_config.py
@@ -0,0 +1,172 @@
+from os.path import dirname, exists, join
+
+
+def _get_config_directory():
+ """ Find the predefined detector config directory """
+ try:
+ # Assume we are running in the source mmdetection repo
+ repo_dpath = dirname(dirname(__file__))
+ except NameError:
+ # For IPython development when this __file__ is not defined
+ import mmdet
+ repo_dpath = dirname(dirname(mmdet.__file__))
+ config_dpath = join(repo_dpath, 'configs')
+ if not exists(config_dpath):
+ raise Exception('Cannot find config path')
+ return config_dpath
+
+
+def test_config_build_detector():
+ """
+ Test that all detection models defined in the configs can be initialized.
+ """
+ from xdoctest.utils import import_module_from_path
+ from mmdet.models import build_detector
+
+ config_dpath = _get_config_directory()
+ print('Found config_dpath = {!r}'.format(config_dpath))
+
+ # import glob
+ # config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
+ # config_names = [relpath(p, config_dpath) for p in config_fpaths]
+
+ # Only tests a representative subset of configurations
+
+ config_names = [
+ # 'dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py',
+ # 'dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
+ # 'dcn/faster_rcnn_dpool_r50_fpn_1x.py',
+ 'dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
+ # 'dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py',
+ # 'dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py',
+ # 'dcn/faster_rcnn_mdpool_r50_fpn_1x.py',
+ # 'dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py',
+ # 'dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py',
+ # ---
+ # 'htc/htc_x101_32x4d_fpn_20e_16gpu.py',
+ 'htc/htc_without_semantic_r50_fpn_1x.py',
+ # 'htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py',
+ # 'htc/htc_x101_64x4d_fpn_20e_16gpu.py',
+ # 'htc/htc_r50_fpn_1x.py',
+ # 'htc/htc_r101_fpn_20e.py',
+ # 'htc/htc_r50_fpn_20e.py',
+ # ---
+ 'cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
+ # 'cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py',
+ # ---
+ # 'scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py',
+ # 'scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py',
+ # ---
+ # 'grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x.py',
+ 'grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x.py',
+ # ---
+ 'double_heads/dh_faster_rcnn_r50_fpn_1x.py',
+ # ---
+ 'empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py',
+ # 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py',
+ # 'empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py',
+ # 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py',
+ # ---
+ # 'ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py',
+ # 'ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py',
+ # 'ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py',
+ # ---
+ # 'guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py',
+ # 'guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py',
+ # 'guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py',
+ # 'guided_anchoring/ga_fast_r50_caffe_fpn_1x.py',
+ # 'guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py',
+ # 'guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py',
+ # 'guided_anchoring/ga_faster_r50_caffe_fpn_1x.py',
+ 'guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py',
+ # ---
+ 'foveabox/fovea_r50_fpn_4gpu_1x.py',
+ # 'foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x.py',
+ # 'foveabox/fovea_align_gn_r50_fpn_4gpu_2x.py',
+ # 'foveabox/fovea_align_gn_r101_fpn_4gpu_2x.py',
+ 'foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x.py',
+ # ---
+ # 'hrnet/cascade_rcnn_hrnetv2p_w32_20e.py',
+ # 'hrnet/mask_rcnn_hrnetv2p_w32_1x.py',
+ # 'hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e.py',
+ # 'hrnet/htc_hrnetv2p_w32_20e.py',
+ # 'hrnet/faster_rcnn_hrnetv2p_w18_1x.py',
+ # 'hrnet/mask_rcnn_hrnetv2p_w18_1x.py',
+ # 'hrnet/faster_rcnn_hrnetv2p_w32_1x.py',
+ # 'hrnet/faster_rcnn_hrnetv2p_w40_1x.py',
+ 'hrnet/fcos_hrnetv2p_w32_gn_1x_4gpu.py',
+ # ---
+ # 'gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py',
+ # 'gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py',
+ 'gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py',
+ # 'gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py',
+ # ---
+ # 'wider_face/ssd300_wider_face.py',
+ # ---
+ 'pascal_voc/ssd300_voc.py',
+ 'pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py',
+ 'pascal_voc/ssd512_voc.py',
+ # ---
+ # 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py',
+ # 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py',
+ # 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py',
+ # 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py',
+ 'gcnet/mask_rcnn_r50_fpn_sbn_1x.py',
+ # ---
+ 'gn/mask_rcnn_r50_fpn_gn_contrib_2x.py',
+ # 'gn/mask_rcnn_r50_fpn_gn_2x.py',
+ # 'gn/mask_rcnn_r101_fpn_gn_2x.py',
+ # ---
+ # 'reppoints/reppoints_moment_x101_dcn_fpn_2x.py',
+ 'reppoints/reppoints_moment_r50_fpn_2x.py',
+ # 'reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py',
+ 'reppoints/reppoints_partial_minmax_r50_fpn_1x.py',
+ 'reppoints/bbox_r50_grid_center_fpn_1x.py',
+ # 'reppoints/reppoints_moment_r101_dcn_fpn_2x.py',
+ # 'reppoints/reppoints_moment_r101_fpn_2x_mt.py',
+ # 'reppoints/reppoints_moment_r50_fpn_2x_mt.py',
+ 'reppoints/reppoints_minmax_r50_fpn_1x.py',
+ # 'reppoints/reppoints_moment_r50_fpn_1x.py',
+ # 'reppoints/reppoints_moment_r101_fpn_2x.py',
+ # 'reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py',
+ 'reppoints/bbox_r50_grid_fpn_1x.py',
+ # ---
+ # 'fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py',
+ # 'fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py',
+ 'fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py',
+ # ---
+ 'albu_example/mask_rcnn_r50_fpn_1x.py',
+ # ---
+ 'libra_rcnn/libra_faster_rcnn_r50_fpn_1x.py',
+ # 'libra_rcnn/libra_retinanet_r50_fpn_1x.py',
+ # 'libra_rcnn/libra_faster_rcnn_r101_fpn_1x.py',
+ # 'libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x.py',
+ # 'libra_rcnn/libra_fast_rcnn_r50_fpn_1x.py',
+ # ---
+ # 'ghm/retinanet_ghm_r50_fpn_1x.py',
+ # ---
+ # 'fp16/retinanet_r50_fpn_fp16_1x.py',
+ 'fp16/mask_rcnn_r50_fpn_fp16_1x.py',
+ 'fp16/faster_rcnn_r50_fpn_fp16_1x.py'
+ ]
+
+ print('Using {} config files'.format(len(config_names)))
+
+ for config_fname in config_names:
+ config_fpath = join(config_dpath, config_fname)
+ config_mod = import_module_from_path(config_fpath)
+
+ config_mod.model
+ config_mod.train_cfg
+ config_mod.test_cfg
+ print('Building detector, config_fpath = {!r}'.format(config_fpath))
+
+ # Remove pretrained keys to allow for testing in an offline environment
+ if 'pretrained' in config_mod.model:
+ config_mod.model['pretrained'] = None
+
+ detector = build_detector(
+ config_mod.model,
+ train_cfg=config_mod.train_cfg,
+ test_cfg=config_mod.test_cfg)
+ assert detector is not None