score_model.py 文件源码

def generate_solver_proto(solver_fn, model_fn, trainOpts):
        from caffe.proto import caffe_pb2
        solver = caffe_pb2.SolverParameter()
        solver.net = model_fn

        if trainOpts.num_lr_decays > 0:
            solver.lr_policy = 'step'
            solver.gamma = trainOpts.lr_decay_factor
            solver.stepsize = int(trainOpts.iters/(trainOpts.num_lr_decays+1))
        else:
            solver.lr_policy = 'fixed'
        solver.base_lr = trainOpts.init_lr
        solver.max_iter = trainOpts.iters
        solver.display = 20
        solver.momentum = 0.9
        solver.weight_decay = trainOpts.paramReg

        solver.test_state.add()
        solver.test_state.add()
        solver.test_state[0].stage.append('TestRecognition')
        solver.test_state[1].stage.append('TestZeroShot')
        solver.test_iter.extend([20, 20])
        solver.test_interval = 100

        solver.snapshot = 5000
        solver.snapshot_prefix = os.path.splitext(model_fn)[0]

        with open(solver_fn, 'w') as f:
            f.write(str(solver))