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 debug(self, batch, output, iter_id):
opt = self.opt
reg = output['reg'] if opt.reg_offset else None
dets = ctdet_decode(output['hm'],
output['wh'],
reg=reg,
cat_spec_wh=opt.cat_spec_wh,
K=opt.K)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])
dets[:, :, :4] *= opt.down_ratio
dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2])
dets_gt[:, :, :4] *= opt.down_ratio
for i in range(1):
debugger = Debugger(dataset=opt.dataset,
ipynb=(opt.debug == 3),
theme=opt.debugger_theme)
img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * opt.std + opt.mean) * 255.), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(
output['hm'][i].detach().cpu().numpy())
gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hm')
debugger.add_blend_img(img, gt, 'gt_hm')
debugger.add_img(img, img_id='out_pred')
for k in range(len(dets[i])):
if dets[i, k, 4] > opt.center_thresh:
# add_coco_bbox(bbox, cat, conf=1, show_txt=True, img_id='default'):
debugger.add_coco_bbox(dets[i, k, :4],
dets[i, k, -1],
dets[i, k, 4],
img_id='out_pred')
debugger.add_img(img, img_id='out_gt')
for k in range(len(dets_gt[i])):
if dets_gt[i, k, 4] > opt.center_thresh:
# add_coco_bbox(bbox, cat, conf=1, show_txt=True, img_id='default'):
debugger.add_coco_bbox(dets_gt[i, k, :4],
dets_gt[i, k, -1],
dets_gt[i, k, 4],
img_id='out_gt')
if opt.debug == 4:
debugger.save_all_imgs(opt.debug_dir,
prefix='{}'.format(iter_id))
else:
debugger.show_all_imgs(pause=True)
def process(self, images, return_time=False):
with torch.no_grad():
output = self.model(images)[-1]
hm = output['hm'].sigmoid_()
wh = output['wh']
reg = output['reg'] if self.opt.reg_offset else None
if self.opt.flip_test:
hm = (hm[0:1] + flip_tensor(hm[1:2])) / 2
wh = (wh[0:1] + flip_tensor(wh[1:2])) / 2
reg = reg[0:1] if reg is not None else None
torch.cuda.synchronize()
forward_time = time.time()
dets = ctdet_decode(hm, wh, reg=reg, K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def run(self, image_or_path_or_tensor):
"""
Args:
image_or_path_or_tensor:
Returns:
dets: (x1,y1,x2,y2,conf,c)
"""
pre_processed = False
if isinstance(image_or_path_or_tensor, np.ndarray):
image = image_or_path_or_tensor
elif type(image_or_path_or_tensor) == type(''):
image = cv2.imread(image_or_path_or_tensor)
else:
assert len(image_or_path_or_tensor.size()) == 4
image = image_or_path_or_tensor
meta = {
'c': (image.size(3) / 2, image.size(2) / 2),
's': max(image.shape[-2:]),
'out_height': image.size(2) // self.cfg.down_ratio,
'out_width': image.size(3) // self.cfg.down_ratio
}
pre_processed = True
if not pre_processed:
image, meta = self.preprocess(image)
image = image.to(self.device)
with torch.no_grad():
output = self.model(image)[-1]
reg = output['reg'] if self.cfg.reg_offset else None
obj = output['obj'] if self.cfg.reg_obj else None
dets = ctdet_decode(output['hm'].sigmoid_(),
output['wh'],
reg=reg,
obj=obj,
cat_spec_wh=self.cfg.cat_spec_wh,
K=self.cfg.K)
dets = self.postprocess(dets, meta)
return dets
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]
