TensorFlow对象检测API:使用预训练模型在训练中更改班级数量时的分类权重初始化 [英] TensorFlow object detection api: classification weights initialization when changing number of classes at training using pre-trained models
问题描述
我不仅要使用特征提取器预训练的权重，还要利用特征图图层的分类器/本地化预训练的权重来使用Tensorflow对象检测API微调Tensorflow对象检测模型(SSD).当我的新模型与用于微调检查点的预训练模型具有不同数量的类时，TensorFlow对象检测API将如何处理分类权重张量?
I want to utilize not only the feature-extractor pre-trained weights but also the feature-map layers' classifier/localization pre-trained weights for fine-tuning tensorflow object detection models (SSD) using tensorflow object detection API. When my new model has a different number of classes from the pre-trained model that I'm using for the fine-tuning checkpoint, how would the TensorFlow object detection API handle the classification weight tensors?
在ML对象检测模型(如SSD)中微调预训练的模型时，我不仅可以使用预训练的权重初始化特征提取器权重，还可以初始化特征图的本地化层权重和分类层权重，后者仅选择选择的经过预先训练的班级权重，这样我就可以减少模型最初可以识别的班级数量(例如，从90个MSCOCO班级减少到这90个班级中的任意一个班级，例如仅汽车和行人)等)
https://github.com/pierluigiferrari/ssd_keras/blob/master/weight_sampling_tutorial. ipynb
这是在keras模型中(即在h5文件中)完成的方式，我也想在Tensorflow对象检测API中执行相同的操作.似乎在训练时我可以在config protobuf文件中指定新模型将要拥有的类数，但是由于我是API(和tensorflow)的新手，所以我无法遵循源代码结构并了解如何在微调时处理该数字.我所知道的大多数SSD模型只是忽略并初始化分类权重张量，以防预训练模型的类权重形状不同于新模型的分类权重形状，但是我想保留必要的分类权重并对其进行训练.另外，我将如何在API结构中执行此操作?
谢谢！
When fine-tuning pre-trained models in ML object detection models like SSD, I can initialize not only the feature-extractor weights with the pre-trained weights but also initialize the feature-map's localization layer weights and classification layer weights, with latter only choosing the pre-trained class weights of choice, so that I can decrease the number of classes that the model can initially identify (for example from 90 MSCOCO classes to whichever classes of choice within those 90 classes like cars & pedestrian only etc.)
https://github.com/pierluigiferrari/ssd_keras/blob/master/weight_sampling_tutorial.ipynb
This is how it's done in keras models (ie in h5 files) and I want to do the same in Tensorflow object detection API as well. It seems that at training time I can specify the number of classes the new model is going to have in the config protobuf file, but since I'm new to the API (and tensorflow) I haven't been able to follow the source structure and understand how that number is going to be handled at fine-tuning. Most SSD models I know just ignore and initialize the classification weight tensor in case the pre-trained model's class weight shape is different from the new model's classification weight shape, but I want to retain the necessary classification weights and train upon those. Also, how would I do that within the API structure?
Thanks!
推荐答案
当我通读代码时，我发现了负责的代码，如果新定义的模型之间的层的形状相同，则该代码仅保留预先训练的模型的权重.与预训练的模型匹配.因此，如果我更改班级的数量，分类器层的形状就会更改，并且不会保留预训练的权重.
As I read through the code I found the responsible code, which only retains the pre-trained model's weights if the shape of the layers between the newly-defined model and the pre-trained model match. So if I change the number of the class, the shape of the classifier layers change, and the pre-trained weights are not retained.
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