Hi,
A few days ago I asked a question about importing a pretrained keras vgg16 model into Opencv dnn [1].
Now I finetuned the vgg16 for my own application by excluding the existed imagenet head and adding a new head to the model. Below shows the "pseudocode" how it's done:
baseModel = VGG16(input_shape=(224, 224, 3), weights='imagenet', include_top=False)
headModel = baseModel.output
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(256, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(5, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
Subsequently, I train the new model with my own data and export it similar to the answer of my previous question.
However when I try to read the net into opencv, it returns a ImportError:
cv2.error: C:\projects\opencv-python\opencv\modules\dnn\src\tensorflow\tf_importer.cpp:1487: error: (-2) Unknown layer type PlaceholderWithDefault in op dropout_1/keras_learning_phase in function cv::dnn::experimental_dnn_v3::`anonymous-namespace'::TFImporter::populateNet
I've read on github, there is a solution to include dropout layers (https://github.com/opencv/opencv/pull/9673). Do you have any suggestions on how to implement this with keras? Or am I just making it myself difficult using Keras on top of tensorflow.
I have one more additional question: Do you ever plan to implement a readNetFromKeras(...) where a config.json and weights.h5 is given?
Edit:
Pbtxt file before (so flatten and dropout layers are included)
... some stuff before ...
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input: "dense_1/MatMul"
input: "dense_1/bias"
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node {
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op: "Relu"
input: "dense_1/BiasAdd"
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input: "dropout_1/keras_learning_phase/input"
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input: "dropout_1/keras_learning_phase"
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node {
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input: "dropout_1/keras_learning_phase"
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... some stuff after...
Snippet after:
node {
name: "flatten/Reshape"
op: "Flatten"
input: "block5_pool/MaxPool"
}
node {
name: "dense_1/MatMul"
op: "MatMul"
input: "flatten/Reshape"
input: "dense_1/kernel"
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node {
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