def test_absval(): # type: ()->caffe.NetSpec n = caffe.NetSpec() n.input1 = L.Input(shape=make_shape([6, 4, 64, 64])) n.abs1 = L.AbsVal(n.input1) return n
def gradient_x(bottom): dummy_data = L.DummyData(dummy_data_param=dict( shape=[dict(dim=[1, 1, 100, 99])])) crop_1 = L.Crop(bottom, dummy_data, crop_param=dict(offset=[0, 1])) crop_2 = L.Crop(bottom, dummy_data, crop_param=dict(offset=[0, 0])) diff = L.Eltwise(crop_1, crop_2, eltwise_param=dict(operation=P.Eltwise.SUM, coeff=[1.0, -1.0])) gradient_x = L.AbsVal(diff) return gradient_x
def l1_loss(bottom1, bottom2, l_weight): diff = L.Eltwise(bottom1, bottom2, eltwise_param=dict(operation=P.Eltwise.SUM, coeff=[1, -1])) absval = L.AbsVal(diff) loss = L.Reduction(absval, reduction_param=dict(operation=P.Reduction.SUM), loss_weight=l_weight) return loss
def test_absval(self): n = caffe.NetSpec() n.input1 = L.Input(shape=make_shape([6, 4, 64, 64])) n.abs1 = L.AbsVal(n.input1) self._test_model(*self._netspec_to_model(n, 'absval'))
def net(): n = caffe.NetSpec() n.data = L.Input(input_param=dict(shape=dict(dim=data_shape))) n.dataout = L.AbsVal(n.data) return n.to_proto()