def _fully_connected(opr, context):
assert opr.inp_tensors[1].np_data is not None
param_W = opr.inp_tensors[1].np_data
assert not opr.transpose_a
if not opr.transpose_b:
param_W = param_W.T
blobs = [context.gen_blob_proto(param_W)]
bias_term = False
if isinstance(opr, LinearOpr) and opr.has_bias:
bias_term = True
blobs.append(
context.gen_blob_proto(opr.inp_tensors[2].np_data.reshape(-1, )))
param = cp.InnerProductParameter(bias_term=bias_term,
num_output=opr.out_tensors[0].shape[1])
bottom = [context.get_blob_name(opr.inp_tensors[0])]
top = [context.set_blob_name(opr.out_tensors[0], opr.out_tensors[0].name)]
context.add_layer(
cp.LayerParameter(
name=opr.out_tensors[0].name,
type="InnerProduct",
bottom=bottom,
top=top,
inner_product_param=param,
blobs=blobs,
))
def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant', has_bias=True):
if self.type != 'InnerProduct':
raise TypeError('the layer type must be InnerProduct if you want set fc param')
fc_param = pb.InnerProductParameter()
fc_param.num_output = num_output
fc_param.weight_filler.type = weight_filler
fc_param.bias_term = has_bias
if has_bias:
fc_param.bias_filler.type = bias_filler
self.param.inner_product_param.CopyFrom(fc_param)
def create_inner_product_layer(input, out_name, output_number):
inner_product_layer = caffe_pb2.LayerParameter(
name=out_name,
type="InnerProduct",
bottom=[input],
top=[out_name],
param=[
caffe_pb2.ParamSpec(
lr_mult=1.0,
decay_mult=1.0,
),
caffe_pb2.ParamSpec(
lr_mult=2.0,
decay_mult=0.0,
)
],
inner_product_param=caffe_pb2.InnerProductParameter(
num_output=output_number,
bias_term=False,
weight_filler=caffe_pb2.FillerParameter(type="xavier"),
bias_filler=caffe_pb2.FillerParameter(type="constant", value=0.0)))
net = caffe_pb2.NetParameter()
net.layer.extend([inner_product_layer])
return net