问题描述
TF 版本 - 1.15
这是我做的
- 训练模型并保存模型。
我的模型
model.summary()看起来像这样
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None,300,480,3) 0
__________________________________________________________________________________________________
identity_layer (Lambda) (None,3) 0 input_1[0][0]
__________________________________________________________________________________________________
input_mean_normalization (Lambd (None,3) 0 identity_layer[0][0]
__________________________________________________________________________________________________
input_stddev_normalization (Lam (None,3) 0 input_mean_normalization[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None,32) 2432 input_stddev_normalization[0][0]
__________________________________________________________________________________________________
bn1 (Batchnormalization) (None,32) 128 conv1[0][0]
__________________________________________________________________________________________________
elu1 (ELU) (None,32) 0 bn1[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None,150,240,32) 0 elu1[0][0]
__________________________________________________________________________________________________
conv2 (Conv2D) (None,48) 13872 pool1[0][0]
__________________________________________________________________________________________________
bn2 (Batchnormalization) (None,48) 192 conv2[0][0]
__________________________________________________________________________________________________
elu2 (ELU) (None,48) 0 bn2[0][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None,75,120,48) 0 elu2[0][0]
__________________________________________________________________________________________________
conv3 (Conv2D) (None,64) 27712 pool2[0][0]
__________________________________________________________________________________________________
bn3 (Batchnormalization) (None,64) 256 conv3[0][0]
__________________________________________________________________________________________________
elu3 (ELU) (None,64) 0 bn3[0][0]
__________________________________________________________________________________________________
pool3 (MaxPooling2D) (None,37,60,64) 0 elu3[0][0]
__________________________________________________________________________________________________
conv4 (Conv2D) (None,64) 36928 pool3[0][0]
__________________________________________________________________________________________________
bn4 (Batchnormalization) (None,64) 256 conv4[0][0]
__________________________________________________________________________________________________
elu4 (ELU) (None,64) 0 bn4[0][0]
__________________________________________________________________________________________________
pool4 (MaxPooling2D) (None,18,30,64) 0 elu4[0][0]
__________________________________________________________________________________________________
conv5 (Conv2D) (None,48) 27696 pool4[0][0]
__________________________________________________________________________________________________
bn5 (Batchnormalization) (None,48) 192 conv5[0][0]
__________________________________________________________________________________________________
elu5 (ELU) (None,48) 0 bn5[0][0]
__________________________________________________________________________________________________
pool5 (MaxPooling2D) (None,9,15,48) 0 elu5[0][0]
__________________________________________________________________________________________________
conv6 (Conv2D) (None,48) 20784 pool5[0][0]
__________________________________________________________________________________________________
bn6 (Batchnormalization) (None,48) 192 conv6[0][0]
__________________________________________________________________________________________________
elu6 (ELU) (None,48) 0 bn6[0][0]
__________________________________________________________________________________________________
pool6 (MaxPooling2D) (None,4,7,48) 0 elu6[0][0]
__________________________________________________________________________________________________
conv7 (Conv2D) (None,32) 13856 pool6[0][0]
__________________________________________________________________________________________________
bn7 (Batchnormalization) (None,32) 128 conv7[0][0]
__________________________________________________________________________________________________
elu7 (ELU) (None,32) 0 bn7[0][0]
__________________________________________________________________________________________________
classes4 (Conv2D) (None,24) 13848 elu4[0][0]
__________________________________________________________________________________________________
classes5 (Conv2D) (None,24) 10392 elu5[0][0]
__________________________________________________________________________________________________
classes6 (Conv2D) (None,24) 10392 elu6[0][0]
__________________________________________________________________________________________________
classes7 (Conv2D) (None,24) 6936 elu7[0][0]
__________________________________________________________________________________________________
Boxes4 (Conv2D) (None,16) 9232 elu4[0][0]
__________________________________________________________________________________________________
Boxes5 (Conv2D) (None,16) 6928 elu5[0][0]
__________________________________________________________________________________________________
Boxes6 (Conv2D) (None,16) 6928 elu6[0][0]
__________________________________________________________________________________________________
Boxes7 (Conv2D) (None,16) 4624 elu7[0][0]
__________________________________________________________________________________________________
classes4_reshape (Reshape) (None,None,6) 0 classes4[0][0]
__________________________________________________________________________________________________
classes5_reshape (Reshape) (None,6) 0 classes5[0][0]
__________________________________________________________________________________________________
classes6_reshape (Reshape) (None,6) 0 classes6[0][0]
__________________________________________________________________________________________________
classes7_reshape (Reshape) (None,6) 0 classes7[0][0]
__________________________________________________________________________________________________
anchors4 (AnchorBoxes) (None,8) 0 Boxes4[0][0]
__________________________________________________________________________________________________
anchors5 (AnchorBoxes) (None,8) 0 Boxes5[0][0]
__________________________________________________________________________________________________
anchors6 (AnchorBoxes) (None,8) 0 Boxes6[0][0]
__________________________________________________________________________________________________
anchors7 (AnchorBoxes) (None,8) 0 Boxes7[0][0]
__________________________________________________________________________________________________
classes_concat (Concatenate) (None,6) 0 classes4_reshape[0][0]
classes5_reshape[0][0]
classes6_reshape[0][0]
classes7_reshape[0][0]
__________________________________________________________________________________________________
Boxes4_reshape (Reshape) (None,4) 0 Boxes4[0][0]
__________________________________________________________________________________________________
Boxes5_reshape (Reshape) (None,4) 0 Boxes5[0][0]
__________________________________________________________________________________________________
Boxes6_reshape (Reshape) (None,4) 0 Boxes6[0][0]
__________________________________________________________________________________________________
Boxes7_reshape (Reshape) (None,4) 0 Boxes7[0][0]
__________________________________________________________________________________________________
anchors4_reshape (Reshape) (None,8) 0 anchors4[0][0]
__________________________________________________________________________________________________
anchors5_reshape (Reshape) (None,8) 0 anchors5[0][0]
__________________________________________________________________________________________________
anchors6_reshape (Reshape) (None,8) 0 anchors6[0][0]
__________________________________________________________________________________________________
anchors7_reshape (Reshape) (None,8) 0 anchors7[0][0]
__________________________________________________________________________________________________
classes_softmax (Activation) (None,6) 0 classes_concat[0][0]
__________________________________________________________________________________________________
Boxes_concat (Concatenate) (None,4) 0 Boxes4_reshape[0][0]
Boxes5_reshape[0][0]
Boxes6_reshape[0][0]
Boxes7_reshape[0][0]
__________________________________________________________________________________________________
anchors_concat (Concatenate) (None,8) 0 anchors4_reshape[0][0]
anchors5_reshape[0][0]
anchors6_reshape[0][0]
anchors7_reshape[0][0]
__________________________________________________________________________________________________
predictions (Concatenate) (None,18) 0 classes_softmax[0][0]
Boxes_concat[0][0]
anchors_concat[0][0]
==================================================================================================
Total params: 213,904
Trainable params: 213,232
Non-trainable params: 672
- 使用
model.load()加载相同的模型并在模型的卷积层上应用量化。现在量化的model.summary()如下所示
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None,3) 0 identity_layer[1][0]
__________________________________________________________________________________________________
input_stddev_normalization (Lam (None,3) 0 input_mean_normalization[1][0]
__________________________________________________________________________________________________
quant_conv1 (QuantizeWrapper) (None,32) 2499 input_stddev_normalization[1][0]
__________________________________________________________________________________________________
bn1 (Batchnormalization) (None,32) 128 quant_conv1[0][0]
__________________________________________________________________________________________________
elu1 (ELU) (None,32) 0 bn1[1][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None,32) 0 elu1[1][0]
__________________________________________________________________________________________________
quant_conv2 (QuantizeWrapper) (None,48) 13971 pool1[1][0]
__________________________________________________________________________________________________
bn2 (Batchnormalization) (None,48) 192 quant_conv2[0][0]
__________________________________________________________________________________________________
elu2 (ELU) (None,48) 0 bn2[1][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None,48) 0 elu2[1][0]
__________________________________________________________________________________________________
quant_conv3 (QuantizeWrapper) (None,64) 27843 pool2[1][0]
__________________________________________________________________________________________________
bn3 (Batchnormalization) (None,64) 256 quant_conv3[0][0]
__________________________________________________________________________________________________
elu3 (ELU) (None,64) 0 bn3[1][0]
__________________________________________________________________________________________________
pool3 (MaxPooling2D) (None,64) 0 elu3[1][0]
__________________________________________________________________________________________________
quant_conv4 (QuantizeWrapper) (None,64) 37059 pool3[1][0]
__________________________________________________________________________________________________
bn4 (Batchnormalization) (None,64) 256 quant_conv4[0][0]
__________________________________________________________________________________________________
elu4 (ELU) (None,64) 0 bn4[1][0]
__________________________________________________________________________________________________
pool4 (MaxPooling2D) (None,64) 0 elu4[1][0]
__________________________________________________________________________________________________
quant_conv5 (QuantizeWrapper) (None,48) 27795 pool4[1][0]
__________________________________________________________________________________________________
bn5 (Batchnormalization) (None,48) 192 quant_conv5[0][0]
__________________________________________________________________________________________________
elu5 (ELU) (None,48) 0 bn5[1][0]
__________________________________________________________________________________________________
pool5 (MaxPooling2D) (None,48) 0 elu5[1][0]
__________________________________________________________________________________________________
quant_conv6 (QuantizeWrapper) (None,48) 20883 pool5[1][0]
__________________________________________________________________________________________________
bn6 (Batchnormalization) (None,48) 192 quant_conv6[0][0]
__________________________________________________________________________________________________
elu6 (ELU) (None,48) 0 bn6[1][0]
__________________________________________________________________________________________________
pool6 (MaxPooling2D) (None,48) 0 elu6[1][0]
__________________________________________________________________________________________________
quant_conv7 (QuantizeWrapper) (None,32) 13923 pool6[1][0]
__________________________________________________________________________________________________
bn7 (Batchnormalization) (None,32) 128 quant_conv7[0][0]
__________________________________________________________________________________________________
elu7 (ELU) (None,32) 0 bn7[1][0]
__________________________________________________________________________________________________
quant_classes4 (QuantizeWrapper (None,24) 13899 elu4[1][0]
__________________________________________________________________________________________________
quant_classes5 (QuantizeWrapper (None,24) 10443 elu5[1][0]
__________________________________________________________________________________________________
quant_classes6 (QuantizeWrapper (None,24) 10443 elu6[1][0]
__________________________________________________________________________________________________
quant_classes7 (QuantizeWrapper (None,24) 6987 elu7[1][0]
__________________________________________________________________________________________________
quant_Boxes4 (QuantizeWrapper) (None,16) 9267 elu4[1][0]
__________________________________________________________________________________________________
quant_Boxes5 (QuantizeWrapper) (None,16) 6963 elu5[1][0]
__________________________________________________________________________________________________
quant_Boxes6 (QuantizeWrapper) (None,16) 6963 elu6[1][0]
__________________________________________________________________________________________________
quant_Boxes7 (QuantizeWrapper) (None,16) 4659 elu7[1][0]
__________________________________________________________________________________________________
classes4_reshape (Reshape) (None,6) 0 quant_classes4[0][0]
__________________________________________________________________________________________________
classes5_reshape (Reshape) (None,6) 0 quant_classes5[0][0]
__________________________________________________________________________________________________
classes6_reshape (Reshape) (None,6) 0 quant_classes6[0][0]
__________________________________________________________________________________________________
classes7_reshape (Reshape) (None,6) 0 quant_classes7[0][0]
__________________________________________________________________________________________________
anchors4 (AnchorBoxes) (None,8) 0 quant_Boxes4[0][0]
__________________________________________________________________________________________________
anchors5 (AnchorBoxes) (None,8) 0 quant_Boxes5[0][0]
__________________________________________________________________________________________________
anchors6 (AnchorBoxes) (None,8) 0 quant_Boxes6[0][0]
__________________________________________________________________________________________________
anchors7 (AnchorBoxes) (None,8) 0 quant_Boxes7[0][0]
__________________________________________________________________________________________________
classes_concat (Concatenate) (None,6) 0 classes4_reshape[1][0]
classes5_reshape[1][0]
classes6_reshape[1][0]
classes7_reshape[1][0]
__________________________________________________________________________________________________
Boxes4_reshape (Reshape) (None,4) 0 quant_Boxes4[0][0]
__________________________________________________________________________________________________
Boxes5_reshape (Reshape) (None,4) 0 quant_Boxes5[0][0]
__________________________________________________________________________________________________
Boxes6_reshape (Reshape) (None,4) 0 quant_Boxes6[0][0]
__________________________________________________________________________________________________
Boxes7_reshape (Reshape) (None,4) 0 quant_Boxes7[0][0]
__________________________________________________________________________________________________
anchors4_reshape (Reshape) (None,8) 0 anchors4[1][0]
__________________________________________________________________________________________________
anchors5_reshape (Reshape) (None,8) 0 anchors5[1][0]
__________________________________________________________________________________________________
anchors6_reshape (Reshape) (None,8) 0 anchors6[1][0]
__________________________________________________________________________________________________
anchors7_reshape (Reshape) (None,8) 0 anchors7[1][0]
__________________________________________________________________________________________________
classes_softmax (Activation) (None,6) 0 classes_concat[1][0]
__________________________________________________________________________________________________
Boxes_concat (Concatenate) (None,4) 0 Boxes4_reshape[1][0]
Boxes5_reshape[1][0]
Boxes6_reshape[1][0]
Boxes7_reshape[1][0]
__________________________________________________________________________________________________
anchors_concat (Concatenate) (None,8) 0 anchors4_reshape[1][0]
anchors5_reshape[1][0]
anchors6_reshape[1][0]
anchors7_reshape[1][0]
__________________________________________________________________________________________________
predictions (Concatenate) (None,18) 0 classes_softmax[1][0]
Boxes_concat[1][0]
anchors_concat[1][0]
==================================================================================================
Total params: 214,941
Trainable params: 213,232
Non-trainable params: 1,709
- 现在我必须将这个
quantized_model.h5文件转换为应用程序的quantized_model.pb文件。 - 我已成功转换为
quantized_model.pb文件，但现在当我尝试加载quantized_model.pb文件时，出现以下错误
Error creating graph: Invalid argument: Input 0 of node quant_conv1/cond/LastValueQuant/AssignMinLast was passed float from quant_conv1/cond/LastValueQuant/AssignMinLast/Switch:1 incompatible with expected resource
有人可以帮助我吗。
解决方法
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