def convert_trained_caffemodel(lasagne_model, caffemodel, prototxt='', caffe_parse = caffe_parsing): ''' PARAMETERS: - lasagne_model: A lasagne model (i.e, BaseModel) to set parameters of. This probably came from a call to convert_model_def. The layers should be named the same as the caffemodel. - caffemodel: /path/to/trained_model.caffemodel file - prototxt: /path/to/deploy.prototxt file. This is not needed if you don't use the caffe parsing, otherwise it's needed. - caffe_parse: don't worry about this, it will be set based on whether or not you have caffe installed. NOTES: sets the params of lasagne_model to be from the trained caffemodel. ''' # load in the caffemodel (this takes a long time without cpp implementation of protobuf) if caffe_parse: layer_params = parse_from_protobuf_caffe.parse_caffemodel(caffemodel, prototxt=prototxt) # prototxt is passed in if caffemodel uses caffe else: layer_params = parse_from_protobuf.parse_caffemodel(caffemodel) # this should be in the same order as was made by the lasagne model, but reversed. we will check that. # todo: maybe just go by names, strictly? for lasagne_layer in lasagne_model.all_layers: if len(lasagne_layer.get_params()) == 0: # no params to set continue lp = layer_params[lasagne_layer.name] Wblob = lp[0] bblob = lp[1] # get arrays of parameters W = array_from_blob(Wblob) b = array_from_blob(bblob) # set parameters set_model_params(lasagne_layer, W,b)
def convert_trained_caffemodel(lasagne_model, caffemodel, prototxt='', caffe_parse = caffe_parsing): ''' sets the params of lasagne_model to be from the trained caffemodel. lasagne_model is a lasagne model from e.g convert_model_def(prototxt). caffemodel is the filepath to the .caffemodel file ''' # load in the caffemodel (this takes a long time without cpp implementation of protobuf) if caffe_parse: layer_params = parse_from_protobuf_caffe.parse_caffemodel(caffemodel, prototxt=prototxt) # prototxt is passed in if caffemodel uses caffe else: layer_params = parse_from_protobuf.parse_caffemodel(caffemodel) # this should be in the same order as was made by the lasagne model, but reversed. we will check that. # todo: maybe just go by names, strictly? for lasagne_layer in lasagne_model.all_layers: if len(lasagne_layer.get_params()) == 0: # no params to set continue lp = layer_params[lasagne_layer.name] Wblob = lp[0] bblob = lp[1] # get arrays of parameters W = array_from_blob(Wblob) b = array_from_blob(bblob) # set parameters set_model_params(lasagne_layer, W,b)
def convert_trained_caffemodel(lasagne_model,
caffemodel,
prototxt='',
caffe_parse=caffe_parsing):
'''
sets the params of lasagne_model to be from the trained caffemodel.
lasagne_model is a lasagne model from e.g convert_model_def(prototxt).
caffemodel is the filepath to the .caffemodel file
'''
# load in the caffemodel (this takes a long time without cpp implementation of protobuf)
if caffe_parse:
layer_params = parse_from_protobuf_caffe.parse_caffemodel(
caffemodel, prototxt=prototxt
) # prototxt is passed in if caffemodel uses caffe
else:
layer_params = parse_from_protobuf.parse_caffemodel(caffemodel)
# this should be in the same order as was made by the lasagne model, but reversed. we will check that.
# todo: maybe just go by names, strictly?
for lasagne_layer in lasagne_model.all_layers:
if len(lasagne_layer.get_params()) == 0:
# no params to set
continue
lp = layer_params[lasagne_layer.name]
Wblob = lp[0]
bblob = lp[1]
# get arrays of parameters
W = array_from_blob(Wblob)
b = array_from_blob(bblob)
# set parameters
set_model_params(lasagne_layer, W, b)
def convert_trained_caffemodel(lasagne_model,
caffemodel,
prototxt='',
caffe_parse=caffe_parsing):
'''
PARAMETERS:
- lasagne_model: A lasagne model (i.e, BaseModel) to set parameters of. This probably came from a call to
convert_model_def. The layers should be named the same as the caffemodel.
- caffemodel: /path/to/trained_model.caffemodel file
- prototxt: /path/to/deploy.prototxt file. This is not needed if you don't use the caffe parsing, otherwise it's needed.
- caffe_parse: don't worry about this, it will be set based on whether or not you have caffe installed.
NOTES:
sets the params of lasagne_model to be from the trained caffemodel.
'''
# load in the caffemodel (this takes a long time without cpp implementation of protobuf)
if caffe_parse:
layer_params = parse_from_protobuf_caffe.parse_caffemodel(
caffemodel, prototxt=prototxt
) # prototxt is passed in if caffemodel uses caffe
else:
layer_params = parse_from_protobuf.parse_caffemodel(caffemodel)
# this should be in the same order as was made by the lasagne model, but reversed. we will check that.
# todo: maybe just go by names, strictly?
for lasagne_layer in lasagne_model.all_layers:
if len(lasagne_layer.get_params()) == 0:
# no params to set
continue
print lasagne_layer.name
lp = layer_params[lasagne_layer.name]
Wblob = lp[0]
bblob = lp[1]
# get arrays of parameters
W = array_from_blob(Wblob)
b = array_from_blob(bblob)
print "W: ", W.shape
print "b: ", b.shape
# set parameters
set_model_params(lasagne_layer, W, b)
