def res50_train(mean_value, list_file, is_train=True):
# setup the python data layer
net = caffe.NetSpec()
net.data, net.label \
= L.ReidData(transform_param=dict(mirror=True,crop_size=224,mean_value=mean_value),
reid_data_param=dict(source=list_file,batch_size=16,new_height=256,new_width=256,
pos_fraction=1,neg_fraction=1,pos_limit=1,neg_limit=4,pos_factor=1,neg_factor=1.01),
ntop = 2)
net, final = res50_body(net, 'data', '', is_train)
net['score'] = fc_relu(net[final],
nout=751,
is_train=is_train,
has_relu=False)
net['euclidean'], net['label_dif'] = L.PairEuclidean(net[final],
net['label'],
ntop=2)
net['score_dif'] = fc_relu(net['euclidean'],
nout=2,
is_train=is_train,
has_relu=False)
net['loss'] = L.SoftmaxWithLoss(net['score'],
net['label'],
propagate_down=[1, 0],
loss_weight=0.5)
net['loss_dif'] = L.SoftmaxWithLoss(net['score_dif'],
net['label_dif'],
propagate_down=[1, 0],
loss_weight=1)
return str(net.to_proto())
def vgg16_dev(data_param = dict(shape=dict(dim=[2, 3, 224, 224])), label_param = dict(shape=dict(dim=[2]))): # setup the python data layer net = caffe.NetSpec() net['data'] = L.Input(input_param = data_param) net['label'] = L.Input(input_param = label_param) net, final = vgg16_body(net, 'data', '', False) net['score'] = fc_relu(net[final], nout=751, is_train=False, has_relu=False) net['euclidean'], net['label_dif'] = L.PairEuclidean(net[final], net['label'], ntop = 2) net['score_dif'] = fc_relu(net['euclidean'], nout=2, is_train=False, has_relu=False) return str(net.to_proto())
