def post_process(self, 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'],
self.opt.num_classes)
for j in range(1, self.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 = mot_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):
reg = output['reg'] if self.opt.reg_offset else None
dets = mot_decode(heatmap=output['hm'],
wh=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(), # center
batch['meta']['s'].cpu().numpy(), # scale
output['hm'].shape[2], # height
output['hm'].shape[3], # width
output['hm'].shape[1]) # num_classes
results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0]
def post_process(self, dets, meta):
"""
2D bbox检测结果后处理
:param dets:
:param meta:
:return:
"""
dets = dets.detach().cpu().numpy()
dets = dets.reshape(1, -1, dets.shape[2]) # default: 1×128×6
# 仿射变换到输出分辨率的坐标系
dets = ctdet_post_process(dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'],
self.opt.num_classes)
for j in range(1, self.opt.num_classes + 1):
dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)
return dets[0]
def post_process(self, dets, meta):
"""
2D bbox检测结果后处理
:param dets:
:param meta:
:return:
"""
dets = dets.detach().cpu().numpy()
dets = dets.reshape(1, -1, dets.shape[2]) # default: 1×128×6
# affine transform
dets = ctdet_post_process(dets.copy(), [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'],
self.opt.num_classes)
# detection dict(cls_id as key)
dets = dets[
0] # fetch the first image dets results(batch_size = 1 by default)
return dets
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]
def val(self, epoch, data_loader):
model_with_loss = self.model_with_loss
if len(self.opt.gpus) > 1:
model_with_loss = self.model_with_loss.module
model_with_loss.eval()
torch.cuda.empty_cache()
opt = self.opt
results = {}
data_time, batch_time = AverageMeter(), AverageMeter()
avg_loss_stats = {l: AverageMeter() for l in self.loss_stats}
num_iters = len(data_loader) if opt.num_iters < 0 else opt.num_iters
bar = Bar('{}/{}'.format(opt.task, opt.exp_id), max=num_iters)
end = time.time()
for iter_id, batch in enumerate(data_loader):
if iter_id >= num_iters:
break
data_time.update(time.time() - end)
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=opt.device,
non_blocking=True)
output, loss, loss_stats = model_with_loss(batch)
results.setdefault('img_id', []).extend(batch['meta']['img_id'])
boxes = ctdet_decode(output['hm'],
output['wh'],
output['reg'],
K=self.opt.K)
meta = batch['meta']
c = meta['c'].numpy()
s = meta['s'].numpy()
_, _, out_h, out_w = output['hm'].shape
dets = ctdet_post_process(boxes.cpu().numpy(), [c], [s], out_h,
out_w, opt.num_classes)
# breakpoint()
results.setdefault('detections', []).extend(dets)
loss = loss.mean()
batch_time.update(time.time() - end)
end = time.time()
Bar.suffix = '{phase}: [{0}][{1}/{2}]|Tot: {total:} |ETA: {eta:} '.format(
epoch,
iter_id,
num_iters,
phase='val',
total=bar.elapsed_td,
eta=bar.eta_td)
for l in avg_loss_stats:
# breakpoint()
loss_t = loss_stats[l] or torch.zeros(1)
avg_loss_stats[l].update(loss_t.mean().item(),
batch['input'].size(0))
Bar.suffix += '|{} {:.4f} '.format(l, avg_loss_stats[l].avg)
self.val_writer.add_scalar(l, avg_loss_stats[l].avg,
self.global_steps, 1)
if not opt.hide_data_time:
Bar.suffix = Bar.suffix + '|Data {dt.val:.3f}s({dt.avg:.3f}s) ' \
'|Net {bt.avg:.3f}s'.format(dt=data_time, bt=batch_time)
if opt.print_iter > 0:
if iter_id % opt.print_iter == 0:
print('{}/{}| {}'.format(opt.task, opt.exp_id, Bar.suffix))
else:
bar.next()
if opt.debug > 0:
self.debug(batch, output, iter_id)
if opt.test:
self.save_result(output, batch, results)
del output, loss, loss_stats
# break
ret = {k: v.avg for k, v in avg_loss_stats.items()}
for l in avg_loss_stats:
self.val_writer.add_scalar(l, avg_loss_stats[l].avg,
self.global_steps)
bar.next()
bar.finish()
ret['time'] = bar.elapsed_td.total_seconds() / 60.
return ret, results
