def post_process(opt, dets, meta):
dets = dets.detach().cpu().numpy()
dets = dets.reshape(1, -1, dets.shape[2])
dets = ctdet_post_process(dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'],
opt.num_classes)
for j in range(1, opt.num_classes + 1):
dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)
return dets[0]
def save_result1(self, outputs, batch,
results): # 这里的outputs包含model输出的所有特征图（3个特征图）
# 对每一个output特征图都计算dets_out，然后将3个dets_out整合起来，根据score来排序，然后用NMS或者其他方法来过滤！！！
dets_list = []
dets_score_list = []
for idx in range(len(outputs)):
output = outputs[idx]
reg = output['reg'] if self.opt.reg_offset else None
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=self.opt.cat_spec_wh,
K=self.opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
# print(type(dets), dets.shape)
dets_list.append(dets)
dets_300 = np.concatenate(
(dets_list[0], dets_list[1], dets_list[2]),
axis=1) # 将3个dets:array[1,100,6]连接起来成为dets_300:array[1,300,6]
dets_300_sort = dets_300.copy()
array_for_sort = dets_300.reshape(
-1, dets.shape[2])[:, :5] # 将dets_300的最后一列改变成score，方便下面排序
sort_idx = np.lexsort(
-array_for_sort.T) # sort_idx是dets_300按照score从大到小排序的索引
# print(array_for_sort.shape, sort_idx)
for key, item in enumerate(sort_idx):
dets_300_sort[0][key] = dets_300[0][
item] # 将排序后的numpy数组保存到 dets_300_sort
# print(dets_300_sort.shape, dets_300_sort[0][0][4],dets_300_sort[0][101][4])
# dets_300_sort = dets_300_sort[:,:100,:] # 检测结果只取前100？？
dets = dets_300_sort # 这个命名只是为了不改下面的两个语句中的变量名
dets_outs = ctdet_post_process(dets.copy(),
batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2],
output['hm'].shape[3],
output['hm'].shape[1])
for j in range(1, self.opt.num_classes +
1): # 给数组reshape一下，要不然不符合NMS需要输入2维数组的输入要求
dets_outs[0][j] = np.array(dets_outs[0][j],
dtype=np.float32).reshape(-1, 5)
# print(type(dets_outs),len(dets_outs[0]))
results_nms = {}
for j in range(1, self.opt.num_classes + 1):
results_nms[j] = np.concatenate(
[dets_out[j] for dets_out in dets_outs],
axis=0).astype(np.float32)
# print(j, results_nms[j])
soft_nms(results_nms[j], Nt=0.5, method=2)
# print(111, type(dets_outs[0]), dets_outs[0].keys())
# print(222, type(results_nms), results_nms.keys()) # results_nms是dets_outs[0]经过NMS之后得到的结果
# results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_outs[0]
results[batch['meta']['img_id'].cpu().numpy()[0]] = results_nms
def save_result(self, output, batch, results):
reg = output['reg'] if self.opt.reg_offset else None
dets = ctdet_decode(
output['hm'], output['wh'], reg=reg,
cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets_out = ctdet_post_process(
dets.copy(), batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2], output['hm'].shape[3], output['hm'].shape[1])
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
def save_result(self, output, batch, results):
output = output[-1]
reg = output['reg'] if self.opt.reg_offset else None
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=self.opt.cat_spec_wh,
K=self.opt.K)
# print(type(dets), dets.size()) #<class 'torch.Tensor'>, [1, 100, 6] ，其中的6代表了 [tlx, tly, brx, bry, scores, clses]， 按照score的高低排序
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
# dets = dets[:,:30,:]
# print(type(dets), dets.shape) #<class 'numpy.ndarray'>, (1, 100, 6)
dets_out = ctdet_post_process(dets.copy(),
batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2],
output['hm'].shape[3],
output['hm'].shape[1])
# print(len(dets_out), dets_out) # len(dets_out) = 1 ； dets_out = [{1: [...], 2: [], 3: [[130.917, 491.190, 285.635, 504.33, 0.797]], 4: [...],...}, 20: [...]]
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
def save_result(self, output, batch,
results): # 能不能尽量不改这里面的小函数，只改变针对函数的输入？？？？？
# print(output['hm'].size(), output['wh'].size(), output['reg'].size())
# [1, 20, 128, 128] [1, 2, 128, 128] [1, 2, 128, 128]
reg = output[
'reg'] if self.opt.reg_offset else None # self.opt.reg_offset = True
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=self.opt.cat_spec_wh,
K=self.opt.K)
# print(dets.size()) #[1, 100, 6] ，其中的6代表了 [tlx, tly, brx, bry, scores, clses]， 按照score的高低排序
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2]) # detach()用来切断梯度的传播
# print(dets.shape) #(1, 100, 6)
dets_out = ctdet_post_process(dets.copy(),
batch['meta']['c'].cpu().numpy(),
batch['meta']['s'].cpu().numpy(),
output['hm'].shape[2],
output['hm'].shape[3],
output['hm'].shape[1])
# print(len(dets_out), dets_out) # len(dets_out) = 1 ； dets_out = [{1: [...], 2: [], 3: [[130.917, 491.190, 285.635, 504.33, 0.797]], 4: [...],...}, 20: [...]]
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
