def _get_anchor_boxes(self, input_size):
'''Compute anchor boxes for each feature map.
Args:
input_size: (tensor) model input size of (w,h).
Returns:
anchor_boxes: (tensor) anchor boxes for each feature map. Each of size [#anchors,4],
where #anchors = fmw * fmh * #anchors_per_cell
'''
num_fms = len(self.anchor_areas)
anchor_wh = self._get_anchor_wh()
fm_sizes = [(input_size/pow(2.,i+3)).ceil() for i in range(num_fms)] # p3 -> p7 feature map sizes
boxes = []
for i in range(num_fms):
fm_size = fm_sizes[i]
grid_size = input_size / fm_size
fm_w, fm_h = int(fm_size[0]), int(fm_size[1])
xy = meshgrid(fm_w,fm_h) + 0.5 # [fm_h*fm_w, 2]
xy = (xy*grid_size).view(fm_h,fm_w,1,2).expand(fm_h,fm_w,9,2)
wh = anchor_wh[i].view(1,1,9,2).expand(fm_h,fm_w,9,2)
box = torch.cat([xy-wh/2.,xy+wh/2.], 3) # [x,y,x,y]
boxes.append(box.view(-1,4))
return torch.cat(boxes, 0)
def _get_anchor_boxes(self):
'''Compute anchor boxes for each feature map.
Args:
input_size: (tensor) model input size of (w,h).
Returns:
boxes: (list) anchor boxes for each feature map. Each of size [#anchors,4],
where #anchors = fmw * fmh * #anchors_per_cell
'''
num_fms = len(self.fm_sizes)
anchor_wh = self._get_anchor_wh()
# anchor_wh = self._get_manual_anchor_wh()
# fm_sizes = [(input_size/pow(2.,i+3)).ceil() for i in range(num_fms)] # p3 -> p7 feature map sizes
fm_sizes = self.fm_sizes
input_size = self.input_size
boxes = []
for i in range(num_fms):
num_anchor = self.num_anchors[i]
fm_size = fm_sizes[i]
grid_size = [
input_size[0] / fm_size[0], input_size[1] / fm_size[1]
]
assert len(set(grid_size)) == 1
grid_size = grid_size[0]
fm_w, fm_h = int(fm_size[1]), int(fm_size[0])
xy = meshgrid(fm_w, fm_h) + 0.5 # [fm_h*fm_w, 2]
xy = (xy * grid_size).view(fm_h, fm_w, 1,
2).expand(fm_h, fm_w, num_anchor, 2)
wh = anchor_wh[i].view(1, 1, num_anchor,
2).expand(fm_h, fm_w, num_anchor, 2)
xy = xy.float()
box = torch.cat([xy, wh], 3) # [x,y,x,y]
# box = torch.cat([xy-wh/2.,xy+wh/2.], 3) # [x,y,x,y]
# box = torch.cat([xy-wh/2.,wh], 3) # [x,y,w,h]
boxes.append(box.view(-1, 4))
# pdb.set_trace()
aboxes = torch.cat(boxes, 0)
# aboxes[:,0] /= input_size[1]
# aboxes[:,1] /= input_size[0]
# aboxes[:,2] /= self.anchor_ref_size[1]
# aboxes[:,3] /= self.anchor_ref_size[0]
aboxes[:, 0] /= input_size[1]
aboxes[:, 1] /= input_size[0]
aboxes[:, 2] /= input_size[1]
aboxes[:, 3] /= input_size[0]
return aboxes