我在Tensorflow中构建了一个Yolo V3 Tiny模型，我想加载Yolo本身提供的权重。我发现here并阅读了Yolo的官方代码，我可以阅读yolov3-tiny.weights丢弃前16个字节，然后读取其余字节，将它们转换为float32。
现在，yolov3-tiny.weights具有35.434.956字节，因此（35.434.956-16）/4=8.858.735 float32数字，所以我应该具有8.858.735权重。
无论如何，我的yolov3-tiny网络的摘要如下：
>>> model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Input (InputLayer) [(None, 416, 416, 3) 0
__________________________________________________________________________________________________
conv_1 (Conv2D) (None, 416, 416, 16) 448 Input[0][0]
__________________________________________________________________________________________________
norm_1 (BatchNormalizationV1) (None, 416, 416, 16) 64 conv_1[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0 norm_1[0][0]
__________________________________________________________________________________________________
conv_2 (Conv2D) (None, 208, 208, 32) 4640 max_pooling2d[0][0]
__________________________________________________________________________________________________
norm_2 (BatchNormalizationV1) (None, 208, 208, 32) 128 conv_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 104, 104, 32) 0 norm_2[0][0]
__________________________________________________________________________________________________
conv_3 (Conv2D) (None, 104, 104, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
norm_3 (BatchNormalizationV1) (None, 104, 104, 64) 256 conv_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 52, 52, 64) 0 norm_3[0][0]
__________________________________________________________________________________________________
conv_4 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
norm_4 (BatchNormalizationV1) (None, 52, 52, 128) 512 conv_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 128) 0 norm_4[0][0]
__________________________________________________________________________________________________
conv_5 (Conv2D) (None, 26, 26, 256) 295168 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
norm_5 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 256) 0 norm_5[0][0]
__________________________________________________________________________________________________
conv_6 (Conv2D) (None, 13, 13, 512) 1180160 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
norm_6 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 13, 13, 512) 0 norm_6[0][0]
__________________________________________________________________________________________________
conv_7 (Conv2D) (None, 13, 13, 1024) 4719616 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
norm_7 (BatchNormalizationV1) (None, 13, 13, 1024) 4096 conv_7[0][0]
__________________________________________________________________________________________________
conv_8 (Conv2D) (None, 13, 13, 256) 262400 norm_7[0][0]
__________________________________________________________________________________________________
norm_8 (BatchNormalizationV1) (None, 13, 13, 256) 1024 conv_8[0][0]
__________________________________________________________________________________________________
conv_11 (Conv2D) (None, 13, 13, 128) 32896 norm_8[0][0]
__________________________________________________________________________________________________
norm_10 (BatchNormalizationV1) (None, 13, 13, 128) 512 conv_11[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 26, 26, 128) 0 norm_10[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 26, 26, 384) 0 lambda_1[0][0]
norm_5[0][0]
__________________________________________________________________________________________________
conv_9 (Conv2D) (None, 13, 13, 512) 1180160 norm_8[0][0]
__________________________________________________________________________________________________
conv_12 (Conv2D) (None, 26, 26, 256) 884992 concatenate[0][0]
__________________________________________________________________________________________________
norm_9 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_9[0][0]
__________________________________________________________________________________________________
norm_11 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_12[0][0]
__________________________________________________________________________________________________
conv_10 (Conv2D) (None, 13, 13, 255) 130815 norm_9[0][0]
__________________________________________________________________________________________________
conv_13 (Conv2D) (None, 26, 26, 255) 65535 norm_11[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (None, 507, 85) 0 conv_10[0][0]
__________________________________________________________________________________________________
lambda_2 (Lambda) (None, 2028, 85) 0 conv_13[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 2535, 85) 0 lambda[0][0]
lambda_2[0][0]
==================================================================================================
Total params: 8,861,918
Trainable params: 8,855,550
Non-trainable params: 6,368
__________________________________________________________________________________________________
，并且具有8.861.918的权重。与yolov3-tiny.weights中包含的参数相比，有（8.861.918-8.858.735）= 3183个参数更多。我在建立网络时出现任何错误还是我错过了什么？
谢谢。
答案 0 :(得分：0)
对于每个具有批量归一化功能的conv layer，您都会错误地使用bias(b)。在yolo中，conv layer后跟batchnorm没有偏见。例如，对于conv_1 layer，正确的段号应该是3*3*3*16=432，而在模型中，正确的段号应该是432+16=448。