def im_detect_bbox_aug(model, images, device):
# Collect detections computed under different transformations
boxlists_ts = []
for _ in range(len(images)):
boxlists_ts.append([])
def add_preds_t(boxlists_t):
for i, boxlist_t in enumerate(boxlists_t):
if len(boxlists_ts[i]) == 0:
# The first one is identity transform, no need to resize the boxlist
boxlists_ts[i].append(boxlist_t)
else:
# Resize the boxlist as the first one
boxlists_ts[i].append(boxlist_t.resize(boxlists_ts[i][0].size))
# Compute detections for the original image (identity transform)
boxlists_i = im_detect_bbox(
model, images, cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MAX_SIZE_TEST, device
)
add_preds_t(boxlists_i)
# Perform detection on the horizontally flipped image
if cfg.TEST.BBOX_AUG.H_FLIP:
boxlists_hf = im_detect_bbox_hflip(
model, images, cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MAX_SIZE_TEST, device
)
add_preds_t(boxlists_hf)
# Compute detections at different scales
for scale in cfg.TEST.BBOX_AUG.SCALES:
max_size = cfg.TEST.BBOX_AUG.MAX_SIZE
boxlists_scl = im_detect_bbox_scale(
model, images, scale, max_size, device
)
add_preds_t(boxlists_scl)
if cfg.TEST.BBOX_AUG.SCALE_H_FLIP:
boxlists_scl_hf = im_detect_bbox_scale(
model, images, scale, max_size, device, hflip=True
)
add_preds_t(boxlists_scl_hf)
# Merge boxlists detected by different bbox aug params
boxlists = []
for i, boxlist_ts in enumerate(boxlists_ts):
bbox = torch.cat([boxlist_t.bbox for boxlist_t in boxlist_ts])
scores = torch.cat([boxlist_t.get_field('scores') for boxlist_t in boxlist_ts])
boxlist = BoxList(bbox, boxlist_ts[0].size, boxlist_ts[0].mode)
boxlist.add_field('scores', scores)
boxlists.append(boxlist)
# Apply NMS and limit the final detections
results = []
post_processor = make_roi_box_post_processor(cfg)
for boxlist in boxlists:
results.append(post_processor.filter_results(boxlist, cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES))
return results
def __init__(self, cfg, in_channels):
super(ROIBoxHead, self).__init__()
self.feature_extractor = make_roi_box_feature_extractor(
cfg, in_channels)
self.predictor = FastRCNNPredictor(cfg,
self.feature_extractor.out_channels)
# assert cfg.MODEL.CLS_AGNOSTIC_BBOX_REG
self.post_processor = make_roi_box_post_processor(cfg)
# self.loss_evaluator = make_roi_box_loss_evaluator(cfg, allow_low_quality_matches=True)
self.WEAK = cfg.WEAK
def __init__(self, cfg):
super(ROIBoxHead, self).__init__()
self.feature_extractor = make_roi_box_feature_extractor(cfg)
self.predictor = make_roi_box_predictor(cfg)
self.post_processor = make_roi_box_post_processor(cfg)
self.loss_evaluator = make_roi_box_loss_evaluator(cfg)
try:
cfg.FILTERMODE
except AttributeError:
self.filter_mode = False
else:
self.filter_mode = cfg.FILTERMODE
