Layer Type Description Caffe Equivalent TensorFlow Equivalent CPU GPU DSP Batch normalization (+ Scaling) Batch normalization followed by scaling operation. Batch norm operation can be performed by itself or in combination with scaling. Maps to the combination of batch_norm_layer followed immediately by scale_layer.
batch_norm_layer.cpp
scale_layer.cpp batch_normalization ✔ ✔ ✔ Color space conversion Converts input image color format (encoding type) into SNPE native color space. Color space conversion parameters are provided as an option to the model converter tool. There is no such Caffe layer by itself. This functionality is technically part of the Caffe data provider.
data_layer.cpp n/a ✔ ✔ ✔ Concatenation This layer concatenates multiple inputs into a single output. concat_layer.cpp concat ✔ ✔ ✔ Convolution Computes dot products between the entries of the filter and the input at any position. conv_layer.cpp conv2d ✔ ✔ ✔ Crop Crops one layer to the dimensions of a reference layer. crop_layer.cpp   ✔ ✔ ✔ CrossMap Response Normalization This is an option within LRN layer. lrn_layer.cpp   ✔ ✔ ✔ Deconvolution Performs deconvolution operation. deconv_layer.cpp conv2d_transpose ✔ ✔ ✔ Dropout Layer is used for training only. Converters remove this layer from DLC creation. dropout_layer.cpp dropout n/a n/a n/a Elementwise Supports SUM, PROD, and MAX mode with coefficients. eltwise_layer.cpp add
mul
maximum ✔ ✔ ✔ Flatten Flatten an input to a layer flatten_layer.cpp n/a ✔ ✔ ✘ Fully connected Similar to convolution, but with connections to full input region, i.e., with filter size being exactly the size of the input volume. inner_product_layer.cpp dense ✔ ✔ ✔ Input This is an input layer to the network. input_layer.cpp input ✔ ✔ ✔ Local Response Normalization (LRN) Performs a lateral inhibition by normalizing over local input regions. lrn_layer.cpp   ✔ ✔ ✔ Mean Subtraction Performs image mean subtraction on the input. mean subtraction n/a ✔ ✔ ✔ Output There is no explicit output layer as the results from any layer in the network can be specified as an output when loading a network. n/a n/a n/a n/a n/a Pooling Pooling operation down samples the input volume spatially. Both average and max pooling are supported. pooling_layer.cpp average_pooling2d
max_pooling2d ✔ ✔ ✔ Prelu activation function: prelu [ i.e., y = max(0, x) + a*min(0,x) ] prelu_layer.cpp PReLU ✔ ✔ ✔ Relu activation function: relu [ i.e., y = max(0,x) ] relu_layer.cpp relu ✔ ✔ ✔ Reshape Change dimensions of the input to a layer reshape_layer.cpp reshape ✔ ✔ ✘ Sigmoid activation function: sigmoid [ i.e., y = 1/(1 + exp(-x) ] sigmoid_layer.cpp sigmoid ✔ ✔ ✔ Tanh activation function: tanh [ i.e., y = tanh(x) ] tanh_layer.cpp tanh ✔ ✔ ✔ Scale Input image scaling, maintains aspect ratio. This function is primarily intended for images, but technically any 2D input data can be processed if it makes sense. Scaling parameters are provided as an option to the model converter tool. There is no such Caffe layer by itself. This functionality is technically part of the Caffe data provider.
data_layer.cpp n/a ✔ ✔ ✔ Silence Silence is handled and removed from the model during conversion, similar to Dropout. silence_layer.cpp   n/a n/a n/a Slice Slices an input layer into multiple output layers. slice_layer.cpp split ✔ ✔ ✔ Softmax Supports 1D and 2D modes. softmax_layer.cpp softmax ✔ ✔ ✔