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()