# Install keras2ncnn (only h5py and numpy is required)
python3 -mpip install --upgrade keras2ncnn
# If you only want to convert the model
python3 -m keras2ncnn -i SOME_H5DF_FILE.h5 -o ./
# You can see the structure of the converted model and the original model(after optimization)
python3 -m keras2ncnn -i SOME_H5DF_FILE.h5 -o DIR_TO_SAVE_NCNN_PARAM --plot_graph/-p
- InputLayer
- Conv2D (Linear, Softmax, ReLU, Sigmoid)
- Conv2DTranspose (Linear, ReLU, Sigmoid)
- DepthwiseConv2D
- SeparableConv2D (Linear, Softmax, ReLU, Sigmoid)
- Add
- Multiply
- Concatenate
- ZeroPadding2D
- ReLU
- LeakyReLU
- Activation (Softmax, ReLU, Sigmoid)
- UpSampling2D
- BilinearUpsampling
- Cropping2D
- GlobalAveragePooling2D
- GlobalMaxPooling2D
- AveragePooling2D
- MaxPooling2D
- BatchNormalization
- Dense (Linear, Softmax, ReLU, Sigmoid)
- Flatten
- Reshape
- Maximum
- TensorFlowOpLayer (Mul with constant)
- Permute (Need more testing)
- Dropout
- Lambda
- TimeDistributed
- InputLayer with inbound nodes