def c3(pt=cfgs.vgg.model,model=cfgs.vgg.weights):
dcfgs.splitconvrelu=True
cfgs.accname='accuracy@5'
def solve(pt, model):
net = Net(pt, model=model)
net.load_frozen()
WPQ, new_pt = net.R3()
return {"WPQ": WPQ, "new_pt": new_pt}
def stepend(new_pt, model, WPQ):
net = Net(new_pt, model=model)
net.WPQ = WPQ
net.finalmodel(save=False)
net.dis_memory()
#final = net.finalmodel(WPQ, prefix='3r')
new_pt, new_model = net.save(prefix='3c')
print('caffe test -model',new_pt, '-weights',new_model)
return {"final": None}
worker = Worker()
outputs = worker.do(step0, pt=pt, model=model)
printstage("freeze")
pt = outputs['pt']
outputs = worker.do(step1,**outputs)
printstage("speed", dcfgs.dic.keep)
outputs['pt'] = mem_pt(pt)
if 0:
outputs = solve(**outputs)
else:
outputs = worker.do(solve, **outputs)
printstage("saving")
outputs = worker.do(stepend, model=model, **outputs)
def c3(pt=cfgs.vgg.model,model=cfgs.vgg.weights): # TODO: Consider changing cfgs.vgg.model and cfgs.vgg.weights (paths to the .prototxt and .caffemodel files) for a generic model reference -by Mario
dcfgs.splitconvrelu=True
cfgs.accname='accuracy@5' # name of layer in the prototxt -by Mario
def solve(pt, model):
net = Net(pt, model=model)
net.load_frozen() # this method can load images from memory if we pass a feats_dic. For what? -by Mario
WPQ, new_pt = net.R3()
return {"WPQ": WPQ, "new_pt": new_pt}
def stepend(new_pt, model, WPQ):
net = Net(new_pt, model=model)
net.WPQ = WPQ
net.finalmodel(save=False) # load weights into the caffemodel -by Mario
net.dis_memory()
#final = net.finalmodel(WPQ, prefix='3r')
new_pt, new_model = net.save(prefix='3c')
print('caffe test -model',new_pt, '-weights',new_model)
return {"final": None}
worker = Worker()
outputs = worker.do(step0, pt=pt, model=model)
printstage("freeze")
pt = outputs['pt']
outputs = worker.do(step1,**outputs)
printstage("speed", dcfgs.dic.keep)
outputs['pt'] = mem_pt(pt)
if 0:
outputs = solve(**outputs)
else:
outputs = worker.do(solve, **outputs)
printstage("saving")
outputs = worker.do(stepend, model=model, **outputs)
def addbn(pt='../resnet-cifar10-caffe/resnet-56/prb_mem_bn_trainval.prototxt', model="../resnet-cifar10-caffe/resnet-56/snapshot/prb_VH_bn__iter_64000.caffemodel"):
worker=Worker()
def ad(pt, model):
net = Net(pt, model=model, noTF=1)
#net.computation()
pt, WPQ = net.add_bn()
return {'new_pt': pt, 'model':model, 'WPQ':WPQ}
outs = worker.do(ad, pt=pt, model=model)
worker.do(stepend, **outs)
