I would like to read out the network parameters from a caffe network defined in a .prototxt
in Python, as the layer object in the layer_dict
only tell me e.g. that its a "Convolution" layer, but not things like kernel_size
, strides
etc. that are well defined in the .prototxt
file.
So lets say I have a model.prototxt
like so:
name: "Model"
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape: {
dim: 64
dim: 1
dim: 28
dim: 28
}
}
}
layer {
name: "conv2d_1"
type: "Convolution"
bottom: "data"
top: "conv2d_1"
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian" # initialize the filters from a Gaussian
std: 0.01 # distribution with stdev 0.01 (default mean: 0)
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "dense_1"
type: "InnerProduct"
bottom: "conv2d_1"
top: "out"
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
I have found that one can parse the model like so:
from caffe.proto import caffe_pb2
import google.protobuf.text_format
net = caffe_pb2.NetParameter()
f = open('model.prototxt', 'r')
net = google.protobuf.text_format.Merge(str(f.read()), net)
f.close()
but I have no idea how to get the fields from the protobuf message out of the resulting object.