用release2.2中s2anet_1x_spine.yml训练spine_coco数据集，配置文件如下： BASE: [ '../datasets/spine_coco.yml', '../runtime.yml', 'base/s2anet_optimizer_1x.yml', 'base/s2anet.yml', 'base/s2anet_reader.yml', ]

weights: output/s2anet_1x_spine/model_final

for 4 card

LearningRate: base_lr: 0.00125

S2ANetHead: anchor_strides: [8, 16, 32, 64, 128] anchor_scales: [4] anchor_ratios: [4.0] anchor_assign: RBoxAssigner stacked_convs: 2 feat_in: 256 feat_out: 256 num_classes: 9 align_conv_type: 'AlignConv' # AlignConv Conv align_conv_size: 3 use_sigmoid_cls: True reg_loss_weight: [1.0, 1.0, 1.0, 1.0, 1.05] cls_loss_weight: [1.05, 1.0] reg_loss_type: 'l1'

训练12epoch后：mAP(0.50, 11point) = 9.45%

[08/03 10:48:35] ppdet.engine INFO: Epoch: [11] [200/230] learning_rate: 0.000013 fam_cls_loss: 0.093969 fam_reg_loss: 1.341817 odm_cls_loss: 0.100388 odm_reg_loss: 0.080870 loss: 1.627664 eta: 0:00:15 batch_cost: 0.5041 data_cost: 0.0002 ips: 1.9838 images/s [08/03 10:48:53] ppdet.utils.checkpoint INFO: Save checkpoint: output/s2anet_1x_spine [08/03 10:48:54] ppdet.engine INFO: Eval iter: 0 [08/03 10:49:36] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. [08/03 10:49:36] ppdet.metrics.metrics INFO: Accumulating evaluatation results... [08/03 10:49:36] ppdet.metrics.metrics INFO: mAP(0.50, 11point) = 9.45% [08/03 10:49:36] ppdet.engine INFO: Total sample number: 57, averge FPS: 1.3343229141074622 [08/03 10:49:36] ppdet.engine INFO: Best test bbox ap is 0.094.

训练自己的数据集（通过roLabelImg 来标注旋转矩形框，并转成coco），训练24epoch后：mAP(0.50, 11point) = 3.1%。 问题：1、首先两个数据集没有问题，为何mAP很低？ 2、 其中anchor_scales: [4]和 anchor_ratios: [1.0] 与anchor生成相关，如果自己数据集anchor分布很分散可能多个尺寸更好回归，当anchor_ratios设置多种比例时就会报错（如下），只能固定一种吗？该如何调整以提高mAP？ Traceback (most recent call last): File "tools/train.py", line 139, in main() File "tools/train.py", line 135, in main run(FLAGS, cfg) File "tools/train.py", line 110, in run trainer.train(FLAGS.eval) File "/home/aistudio/PaddleDetection-release-2.2/ppdet/engine/trainer.py", line 357, in train outputs = model(data) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 898, in call outputs = self.forward(*inputs, **kwargs) File "/home/aistudio/PaddleDetection-release-2.2/ppdet/modeling/architectures/meta_arch.py", line 26, in forward out = self.get_loss() File "/home/aistudio/PaddleDetection-release-2.2/ppdet/modeling/architectures/s2anet.py", line 97, in get_loss loss = self._forward() File "/home/aistudio/PaddleDetection-release-2.2/ppdet/modeling/architectures/s2anet.py", line 73, in _forward self.s2anet_head(body_feats) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 898, in call outputs = self.forward(*inputs, **kwargs) File "/home/aistudio/PaddleDetection-release-2.2/ppdet/modeling/heads/s2anet_head.py", line 441, in forward featmap_size, self.anchor_strides[feat_idx]) File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 898, in call outputs = self.forward(*inputs, **kwargs) File "/home/aistudio/PaddleDetection-release-2.2/ppdet/modeling/heads/s2anet_head.py", line 90, in forward all_anchors = self.base_anchors[:, :] + shifts[:, :] File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py", line 250, in impl return math_op(self, other_var, 'axis', axis) ValueError: (InvalidArgument) Broadcast dimension mismatch. Operands could not be broadcast together with the shape of X = [3, 4] and the shape of Y = [13824, 4]. Received [3] in X is not equal to [13824] in Y at i:0. [Hint: Expected x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1 == true, but received x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 || y_dims_array[i] <= 1:0 != true:1.] (at /paddle/paddle/fluid/operators/elementwise/elementwise_op_function.h:169) [operator < elementwise_add > error]