你好，想问一下聚合教师的代码在哪一块呀

[lzx101]

No description provided.

[lzx101]

是不是ema.py文件

[lzx101]

if not self.every_n_iters(runner, self.interval):
大佬好，我想知道every_n_iters是聚合教师的关键代码吗，但是我找不到位置

[chenbinghui1]

@lzx101 聚合教师请看这个issue
#7 (comment)

[lzx101]

非常感谢您的回复，我还有一个疑问，EMAHook是原本的EMA吗，然后resnet_rla.py关于聚合教师的代码在哪一块呀？感谢大佬

[chenbinghui1]

@lzx101
[1]是的
[2]resnet_rla.py:
def _forward_impl(self, x):

[lzx101]

感谢大佬，我再去细看一下

[lzx101]

大佬你好，我有一个疑问，聚合教师的公式是这样的：xl+1=θl+1[xl + hl] + xl，hl+1=g2[g1[θl+1[xl + hl] + hl]。RLA_Bottleneck返回out, y, h。out是对应xl+1，y是对应xl，这个理解对吗

[lzx101]

for layers, bns, conv_out, recurrent_conv in zip(self.stages, self.stage_bns, self.conv_outs, self.recurrent_convs):
for layer, bn in zip(layers, bns):
#返回三个输出 x残差表示残差块的输出特征，h是隐藏状态
x, y, h = layer(x, h)
# RLA module updates
#conv_out=g1
y_out = conv_out(y)

            h = h + y_out
            h = bn(h)
            h = self.tanh(h)
            h = recurrent_conv(h)
             
        # outs.append(torch.cat((x, h), dim=1))
        outs.append(x)

return tuple(outs)
还有就是最后返回的时候只使用了x，那么y和h的作用是什么呀

[chenbinghui1]

@lzx101
h 用于下一次的x计算

DSL/mmdet/models/backbones/resnet_rla.py

Line 305 in f06a411

x, y, h = layer(x, h)

[lzx101]

@lzx101 h 用于下一次的x计算

DSL/mmdet/models/backbones/resnet_rla.py

Line 305 in f06a411

x, y, h = layer(x, h)

明白了，謝謝大佬

[lzx101]

大佬，我想将聚合教师用于denseteacher（这是一个基于FCOS的半监督目标检测），只将模型的backbone改为resnet_rla.py，10/17 17:04:07 - mmengine - INFO - Iter(train) [ 5900/180000] lr: 2.5000e-03 eta: 1 day, 12:11:09 time: 0.7321 data_time: 0.0055 memory: 8939 loss: 1.9870 sup_loss_cls: 0.8551 sup_loss_bbox: 0.4710 sup_loss_centerness: 0.6610
10/17 17:04:43 - mmengine - INFO - Iter(train) [ 5950/180000] lr: 2.5000e-03 eta: 1 day, 12:10:04 time: 0.7296 data_time: 0.0055 memory: 8259 loss: 2.0174 sup_loss_cls: 0.8731 sup_loss_bbox: 0.4792 sup_loss_centerness: 0.6650
10/17 17:05:21 - mmengine - INFO - Exp name: rla_r50-caffe_fpn_gn_180k_semi-0.1-coco_20231017_153616
10/17 17:05:21 - mmengine - INFO - Iter(train) [ 6000/180000] lr: 2.5000e-03 eta: 1 day, 12:09:30 time: 0.7503 data_time: 0.0057 memory: 7672 loss: 2.0066 sup_loss_cls: 0.8717 sup_loss_bbox: 0.4733 sup_loss_centerness: 0.6615
10/17 17:05:57 - mmengine - INFO - Iter(train) [ 6050/180000] lr: 2.5000e-03 eta: 1 day, 12:08:19 time: 0.7243 data_time: 0.0055 memory: 8334 loss: 1.9943 sup_loss_cls: 0.8642 sup_loss_bbox: 0.4669 sup_loss_centerness: 0.6632
10/17 17:06:33 - mmengine - INFO - Iter(train) [ 6100/180000] lr: 2.5000e-03 eta: 1 day, 12:07:14 time: 0.7289 data_time: 0.0057 memory: 8661 loss: 1.9826 sup_loss_cls: 0.8393 sup_loss_bbox: 0.4815 sup_loss_centerness: 0.6618
10/17 17:07:10 - mmengine - INFO - Iter(train) [ 6150/180000] lr: 2.5000e-03 eta: 1 day, 12:06:28 time: 0.7417 data_time: 0.0062 memory: 8070 loss: 2.0134 sup_loss_cls: 0.8736 sup_loss_bbox: 0.4757 sup_loss_centerness: 0.6640
10/17 17:07:49 - mmengine - INFO - Iter(train) [ 6200/180000] lr: 2.5000e-03 eta: 1 day, 12:06:26 time: 0.7726 data_time: 0.0065 memory: 8546 loss: 1.9929 sup_loss_cls: 0.8551 sup_loss_bbox: 0.4741 sup_loss_centerness: 0.6636
但是我发现cls损失很高，然后结果不行。请问我还需要在哪里做改进吗

[chenbinghui1]

按照readme里
Download ImageNet pre-trained models for initializing DSL
需要下载并加载与训练的resnet_rla模型，不然训练太慢了

[lzx101]

你好，我使用了下载并加载与训练的resnet_rla模型，但是效果还是很不好，我用的数据集是COCO2017，而不是DSL风格的COCO数据集，请问和这个有关系吗
10/18 20:56:45 - mmengine - INFO - Iter(train) [ 4700/180000] lr: 2.5000e-03 eta: 1 day, 11:59:11 time: 0.7001 data_time: 0.0056 memory: 8277 loss: 1.9277 sup_loss_cls: 0.8194 sup_loss_bbox: 0.4496 sup_loss_centerness: 0.6587
10/18 20:57:21 - mmengine - INFO - Iter(train) [ 4750/180000] lr: 2.5000e-03 eta: 1 day, 11:57:54 time: 0.7175 data_time: 0.0054 memory: 8058 loss: 1.9170 sup_loss_cls: 0.8234 sup_loss_bbox: 0.4358 sup_loss_centerness: 0.6578
10/18 20:57:58 - mmengine - INFO - Iter(train) [ 4800/180000] lr: 2.5000e-03 eta: 1 day, 11:57:11 time: 0.7357 data_time: 0.0057 memory: 7729 loss: 1.9380 sup_loss_cls: 0.8245 sup_loss_bbox: 0.4538 sup_loss_centerness: 0.6597
10/18 20:58:35 - mmengine - INFO - Iter(train) [ 4850/180000] lr: 2.5000e-03 eta: 1 day, 11:56:56 time: 0.7509 data_time: 0.0056 memory: 8169 loss: 1.9567 sup_loss_cls: 0.8332 sup_loss_bbox: 0.4615 sup_loss_centerness: 0.6621
10/18 20:59:12 - mmengine - INFO - Iter(train) [ 4900/180000] lr: 2.5000e-03 eta: 1 day, 11:56:01 time: 0.7287 data_time: 0.0055 memory: 8762 loss: 1.9467 sup_loss_cls: 0.8338 sup_loss_bbox: 0.4521 sup_loss_centerness: 0.6608
10/18 20:59:49 - mmengine - INFO - Iter(train) [ 4950/180000] lr: 2.5000e-03 eta: 1 day, 11:55:36 time: 0.7456 data_time: 0.0055 memory: 8337 loss: 1.9383 sup_loss_cls: 0.8332 sup_loss_bbox: 0.4441 sup_loss_centerness: 0.6609
10/18 21:00:26 - mmengine - INFO - Exp name: rla_r50-caffe_fpn_gn_180k_semi-0.1-coco_20231018_195807
10/18 21:00:26 - mmengine - INFO - Iter(train) [ 5000/180000] lr: 2.5000e-03 eta: 1 day, 11:54:51 time: 0.7339 data_time: 0.0057 memory: 7856 loss: 2.0013 sup_loss_cls: 0.8713 sup_loss_bbox: 0.4621 sup_loss_centerness: 0.6679
10/18 21:14:42 - mmengine - INFO - Iter(val) [5000/5000] teacher/coco/bbox_mAP: 0.0010 teacher/coco/bbox_mAP_50: 0.0010 teacher/coco/bbox_mAP_75: 0.0000 teacher/coco/bbox_mAP_s: 0.0000 teacher/coco/bbox_mAP_m: 0.0000 teacher/coco/bbox_mAP_l: 0.0010 student/coco/bbox_mAP: 0.0010 student/coco/bbox_mAP_50: 0.0010 student/coco/bbox_mAP_75: 0.0000 student/coco/bbox_mAP_s: 0.0010 student/coco/bbox_mAP_m: 0.0010 student/coco/bbox_mAP_l: 0.0010 data_time: 0.0095 time: 0.0852

[chenbinghui1]

@lzx101 用我的代码训练的话，需要转成dsl 风格的数据

[lzx101]

好滴感谢大佬的回复，我再去研究一下