def detection_target(output_maps, corners_list_512, stage_lvl=4):
"""
:param output_maps: net output size(B, H, W, C) have transform to real coordinate (0-512) NOTICE !!!!!
:param corners_list_512: list(B), tensor(N, 8), here B is batch_size, N is obj number in one image, 512 scale
:param stage_lvl: which level the corners project to
:return: sample_area_target, coordinate_target
"""
device = output_maps.device
num_imgs = len(corners_list_512)
corners_list_32 = [
single_corners / (2**stage_lvl) for single_corners in corners_list_512
]
B, H, W, C = output_maps.shape
# ======================target initial==========================
# positive and negative area maps
sample_area_target = torch.zeros((B, H, W)).long().to(device)
# coordinate target maps, the corresponding gt coordinate
coordinate_target = torch.zeros((B, H, W, 8)).float().to(device)
"""
其他的在loss函数里面计算吧
# distance target maps, ratio of distance
distance_target = torch.zeros((B, H, W)).float()
# size target maps, ratio of w / h, arctan
size_target = torch.zeros((B, H, W)).float()
# Discrete degree target maps, Mean square error
discrete_target = torch.zeros((B, H, W)).float()
"""
# no grad
# output_maps_detach = output_maps.detach()
# each image in batch
for img in range(num_imgs):
# single_map_detach = output_maps_detach[img] # size(12, 32, 32)
single_corners_32 = corners_list_32[img] # size(N, 8)
single_corners_512 = corners_list_512[img]
# calculate pos & neg areas
obj_num = single_corners_32.shape[0]
for obj in range(obj_num):
# which to distribute
dist_idx = gtP.scale_distribute(
single_corners_512[obj], tra_cfg.K_Means_args['split_value'])
# print('distribute', dist_idx)
# e_spatial_map, i_spatial_map are all bool tensors
e_spatial_map, i_spatial_map = get_spatial_idx(
single_corners_32[obj], W, H, dist_idx, device)
# print(i_spatial_map[i_spatial_map == 1].size())
# print(i_spatial_map.shape)
coordinate_target[img,
e_spatial_map == 1] = single_corners_512[obj]
# sample_area_target 非背景部分乘以新的i_spatial_map得到相交部分，i_spatial_map除去相交点再赋值
i_spatial_map = (i_spatial_map.byte() - (
(sample_area_target[img] != 0) * i_spatial_map).byte()).bool()
# print(i_spatial_map[i_spatial_map == 1].size())
# print(i_spatial_map.shape)
sample_area_target[img, e_spatial_map == 1] = 1
sample_area_target[img, i_spatial_map == 1] = -1
return sample_area_target, coordinate_target
gauss_target)
# ===================== total loss =======================
coord_loss = smooth_L1_map
coord_loss = torch.sum(
coord_loss[sample_area_target == 1]) / torch.sum(
sample_area_target == 1)
score_loss = sample_focal_loss
detection_loss = score_loss + coord_loss
# print(detection_loss, coord_loss, score_loss)
return detection_loss, coord_loss, score_loss
if __name__ == '__main__':
corners = torch.tensor(
[[100.0, 100.0, 170.0, 120.0, 170.0, 135.0, 100.0, 115.0]])
distr_idx = gtP.scale_distribute(
corners, splitValue=tra_cfg.K_Means_args['split_value'])
distr_idx = distr_idx[0]
print("idx", distr_idx)
corners = corners / 16
print('corner / 16', corners)
e, i = get_spatial_idx(corners, 32, 32, distr_idx, corners.device)
with open('1.txt', 'w') as f:
for y in range(e.shape[0]):
for x in range(e.shape[1]):
f.write(str(e[y][x].item()))
f.write(' ')
f.write('\n')
f.write('\n=======================\n')
for y in range(i.shape[0]):
for x in range(i.shape[1]):
f.write(str(i[y][x].item()))