def get_pool_loc(ims,
image_id,
flag_,
size=(7, 7),
spatial_scale=1,
batch_size=1):
spatial_locs = []
union_box_out = []
pers_out = []
objs_out = []
flag = 'train'
max_pool = nn.AdaptiveMaxPool2d(size)
for batch in range(batch_size):
this_image = int(image_id[batch])
if int(flag_[batch][0]) == 0:
flag = 'train'
elif int(flag_[batch][0]) == 2:
flag = 'test'
a = helpers_pre.get_compact_detections(this_image, flag)
roi_pers, roi_objs = a['person_bbx'], a['objects_bbx']
union_box = helpers_pre.get_attention_maps(this_image, flag)
union_box_out.append(torch.tensor(union_box).cuda().float())
W, H, C = ims[batch].size()[1], ims[batch].size()[2], ims[batch].size(
)[0]
spatial_scale = [W, H, W, H]
image_this_batch = ims[batch]
roi_pers = roi_pers * spatial_scale
roi_objs = roi_objs * spatial_scale
##### Pooling Persons ##########
for index, roi_val in enumerate(roi_pers):
x1, y1, x2, y2 = int(roi_val[0]), int(roi_val[1]), int(
roi_val[2]), int(roi_val[3])
sp = [x1, y1, x2, y2, x2 - x1, y2 - y1]
im = image_this_batch.narrow(
0, 0,
image_this_batch.size()[0])[..., y1:(y2 + 1), x1:(x2 + 1)]
pooled = max_pool(im)
pers_out.append((pooled))
spatial_locs.append(sp)
### Pooling Objects #####
for index, roi_val in enumerate(roi_objs):
x1, y1, x2, y2 = int(roi_val[0]), int(roi_val[1]), int(
roi_val[2]), int(roi_val[3])
sp = [x1, y1, x2, y2, x2 - x1, y2 - y1]
im = image_this_batch.narrow(
0, 0,
image_this_batch.size()[0])[..., y1:(y2 + 1), x1:(x2 + 1)]
pooled = max_pool(im)
objs_out.append((pooled))
spatial_locs.append(sp)
#import pdb;pdb.set_trace()
return torch.stack(pers_out), torch.stack(
objs_out), spatial_locs, torch.cat(union_box_out)
def get_pool_loc(ims,
image_id,
flag_,
size=(7, 7),
spatial_scale=1,
batch_size=1):
spatial_locs = []
union_box_out = []
pers_out = []
objs_out = []
flag = 'train'
max_pool = nn.AdaptiveMaxPool2d(size)
#二元自适应均值汇聚层,最大池化，输出特征图尺寸仍为7×7，降维，将2048降为1024
for batch in range(batch_size):
this_image = int(image_id[batch])
if int(flag_[batch][0]) == 0:
flag = 'train'
elif int(flag_[batch][0]) == 2:
flag = 'test'
a = helpers_pre.get_compact_detections(this_image, flag)
#get_compact_detections(this_image,flag) {'person_bbx':persons_np, 'objects_bbx':objects_np,'person_bbx_score':scores_persons,'objects_bbx_score':scores_objects,'class_id_objects':class_id_objects}
roi_pers, roi_objs = a['person_bbx'], a['objects_bbx']
union_box = helpers_pre.get_attention_maps(this_image, flag) #得到人物的联合框
union_box_out.append(torch.tensor(union_box).cuda().float())
W, H, C = ims[batch].size()[1], ims[batch].size()[2], ims[batch].size(
)[0]
spatial_scale = [W, H, W, H]
image_this_batch = ims[batch]
roi_pers = roi_pers * spatial_scale #框匹配到原始图像
roi_objs = roi_objs * spatial_scale
##### Pooling Persons ##########
for index, roi_val in enumerate(roi_pers):
x1, y1, x2, y2 = int(roi_val[0]), int(roi_val[1]), int(
roi_val[2]), int(roi_val[3])
sp = [x1, y1, x2, y2, x2 - x1, y2 - y1]
im = image_this_batch.narrow(
0, 0,
image_this_batch.size()[0])[..., y1:(y2 + 1), x1:(
x2 + 1)] #一个batch的图像特征图，每个特征图的宽和高（y1:(y2+1), x1:(x2+1)）
pooled = max_pool(im)
pers_out.append((pooled))
spatial_locs.append(sp)
### Pooling Objects #####
for index, roi_val in enumerate(roi_objs):
x1, y1, x2, y2 = int(roi_val[0]), int(roi_val[1]), int(
roi_val[2]), int(roi_val[3])
sp = [x1, y1, x2, y2, x2 - x1, y2 - y1]
im = image_this_batch.narrow(
0, 0,
image_this_batch.size()[0])[..., y1:(y2 + 1), x1:(x2 + 1)]
pooled = max_pool(im)
objs_out.append((pooled))
spatial_locs.append(sp)
#import pdb;pdb.set_trace()
return torch.stack(pers_out), torch.stack(
objs_out), spatial_locs, torch.cat(union_box_out)