def load_cudanet(self,output_shapes):
#1. data layer
self.add_layers(self.dataset,provides=['data', 'label'])
#2. jeffnet layer
cuda_layers = pickle.load(open(self.jf_opt.net_file))
exclude_list = ['probs']
for cuda_layer in cuda_layers:
if cuda_layer['name'] not in exclude_list:
decaf_layer = translator.translate_layer(cuda_layer, output_shapes)
if not decaf_layer:
continue
needs = []
for idx in cuda_layer['inputs']:
if cuda_layers[idx]['type'] == DATA_TYPENAME:
needs.append(cuda_layers[idx]['name'])
else:
needs.append(cuda_layers[idx]['name'] + OUTPUT_AFFIX)
provide = cuda_layer['name'] + OUTPUT_AFFIX
self.add_layers(decaf_layer, needs=needs, provides=provide)
#3. loss layer
#loss_layer = core_layers.SquaredLossLayer(name='loss')
loss_layer = dw_LossLayer(name='loss')
self.add_layer(loss_layer,needs=['fc8_cudanet_out', 'label'])
self.finish()
self.layers['loss'].set_label(self.layers['input']._sources[1])
def dw_cudacnn(cuda_layers, output_shapes):
decaf_net = dw_Net()
#decaf_net = base.Net()
exclude_list = []
db = 1
if db:
#1. data layer
decaf_net.add_layers(dw_data(),provides=['data', 'label'])
#2. jeffnet layer
exclude_list = ['probs']
for cuda_layer in cuda_layers:
if cuda_layer['name'] not in exclude_list:
decaf_layer = translator.translate_layer(cuda_layer, output_shapes)
if not decaf_layer:
continue
needs = []
for idx in cuda_layer['inputs']:
if cuda_layers[idx]['type'] == DATA_TYPENAME:
needs.append(cuda_layers[idx]['name'])
else:
needs.append(cuda_layers[idx]['name'] + OUTPUT_AFFIX)
provide = cuda_layer['name'] + OUTPUT_AFFIX
decaf_net.add_layers(decaf_layer, needs=needs, provides=provide)
if db:
#3. loss layer
#loss_layer = core_layers.SquaredLossLayer(name='loss')
loss_layer = dw_LossLayer(name='loss')
decaf_net.add_layer(loss_layer,needs=['fc8_cudanet_out', 'label'])
decaf_net.finish()
return decaf_net
