Merge pull request #19 from hellock/dev

Update inference APIs

mmdet/apis/__init__.py

 from .env import init_dist, get_root_logger, set_random_seed
 from .train import train_detector
-from .inference import inference_detector
+from .inference import inference_detector, show_result
 
 __all__ = [
     'init_dist', 'get_root_logger', 'set_random_seed', 'train_detector',
-    'inference_detector'
+    'inference_detector', 'show_result'
 ]

mmdet/apis/inference.py

@@ -23,19 +23,29 @@ def _prepare_data(img, img_transform, cfg, device):
     return dict(img=[img], img_meta=[img_meta])
 
 
-def inference_detector(model, imgs, cfg, device='cuda:0'):
+def _inference_single(model, img, img_transform, cfg, device):
+    img = mmcv.imread(img)
+    data = _prepare_data(img, img_transform, cfg, device)
+    with torch.no_grad():
+        result = model(return_loss=False, rescale=True, **data)
+    return result
+
+
+def _inference_generator(model, imgs, img_transform, cfg, device):
+    for img in imgs:
+        yield _inference_single(model, img, img_transform, cfg, device)
+
 
-    imgs = imgs if isinstance(imgs, list) else [imgs]
+def inference_detector(model, imgs, cfg, device='cuda:0'):
     img_transform = ImageTransform(
         size_divisor=cfg.data.test.size_divisor, **cfg.img_norm_cfg)
     model = model.to(device)
     model.eval()
-    for img in imgs:
-        img = mmcv.imread(img)
-        data = _prepare_data(img, img_transform, cfg, device)
-        with torch.no_grad():
-            result = model(return_loss=False, rescale=True, **data)
-        yield result
+
+    if not isinstance(imgs, list):
+        return _inference_single(model, imgs, img_transform, cfg, device)
+    else:
+        return _inference_generator(model, imgs, img_transform, cfg, device)
 
 
 def show_result(img, result, dataset='coco', score_thr=0.3):
@@ -46,6 +56,7 @@ def show_result(img, result, dataset='coco', score_thr=0.3):
     ]
     labels = np.concatenate(labels)
     bboxes = np.vstack(result)
+    img = mmcv.imread(img)
     mmcv.imshow_det_bboxes(
         img.copy(),
         bboxes,

mmdet/models/builder.py

@@ -2,7 +2,7 @@ from mmcv.runner import obj_from_dict
 from torch import nn
 
 from . import (backbones, necks, roi_extractors, rpn_heads, bbox_heads,
-               mask_heads, detectors)
+               mask_heads)
 
 __all__ = [
     'build_backbone', 'build_neck', 'build_rpn_head', 'build_roi_extractor',
@@ -48,4 +48,5 @@ def build_mask_head(cfg):
 
 
 def build_detector(cfg, train_cfg=None, test_cfg=None):
+    from . import detectors
     return build(cfg, detectors, dict(train_cfg=train_cfg, test_cfg=test_cfg))

mmdet/models/rpn_heads/rpn_head.py

@@ -48,8 +48,8 @@ class RPNHead(nn.Module):
         self.anchor_scales = anchor_scales
         self.anchor_ratios = anchor_ratios
         self.anchor_strides = anchor_strides
-        self.anchor_base_sizes = anchor_strides.copy(
-        ) if anchor_base_sizes is None else anchor_base_sizes
+        self.anchor_base_sizes = list(
+            anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
         self.target_means = target_means
         self.target_stds = target_stds
         self.use_sigmoid_cls = use_sigmoid_cls