Method AddGradientOperators

caffe2/python/core.py:2025–2057

Add the gradient for operators in the net. Inputs: ys: a list or a dictionary specifying what blobs we want to compute derivatives of. If the input is a list, we will automatically generate their gradients with all-one values; if the input is a

(self, ys, skip=0)

        self._recreate_lookup_tables = False

    def AddGradientOperators(self, ys, skip=0):
        """Add the gradient for operators in the net.

        Inputs:
          ys: a list or a dictionary specifying what blobs we want to compute
              derivatives of. If the input is a list, we will automatically
              generate their gradients with all-one values; if the input is a
              dictionary, for any dictionary entries that are not None, we will
              take the corresponding blobs as their gradients; for all those
              that are None, we will auto-fill them with 1.
          skip: skips the first n operators. This is provided mainly because a
              lot of nets may use the first few operators for data generation
              like stuff which really do not need to have gradients.

        Outputs:
          returns a map from the blob name in the input network to a blob
          containing gradient or a GradientSlice in case of sparse gradient

        Currently, this is hard-coded for float operators if there are branches
        (i.e. a blob is used as input to multiple operators). This is because
        the gradient accumulation (Sum) is float only right now.
        """

        grad_ops, input_to_grad = GradientRegistry.GetBackwardPass(
            self._net.op[skip:], ys)
        # Check if in immediate mode: the grad_ops are actually being produced
        # by C++ and bypasses the CreateOperator() call, so in immediate mode
        # we will have to explicitly run them.
        if workspace.IsImmediate():
            for op in grad_ops:
                workspace.RunOperatorImmediate(op)
        self._ExtendOps(grad_ops)
        return input_to_grad

    def AddArgument(self, arg_name, arg_value):
        self._net.arg.extend([utils.MakeArgument(arg_name, arg_value)])