Method _verify_spec

python/paddle/hapi/model.py:2994–3035

(self, specs, shapes=None, dtypes=None, is_input=False)

        return summary(self.network, _input_size, dtypes=dtype)

    def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
        out_specs = []

        if specs is None:
            # Note(Aurelius84): If not specific specs of `Input`, using argument names of `forward` function
            # to generate `Input`. But how can we know the actual shape of each input tensor?

            if is_input:
                arg_names = extract_args(self.network.forward)[1:]
                # While Saving inference model in dygraph, and providing inputs only in running.
                if (
                    shapes is not None
                    and dtypes is not None
                    and in_dynamic_mode()
                ):
                    out_specs = [
                        Input(name=n, dtype=dtypes[i], shape=shapes[i])
                        for i, n in enumerate(arg_names)
                    ]
                else:
                    out_specs = [Input(name=n, shape=[None]) for n in arg_names]
            else:
                out_specs = to_list(specs)
        elif isinstance(specs, dict):
            assert is_input is False
            out_specs = [
                specs[n]
                for n in extract_args(self.network.forward)
                if n != 'self'
            ]
        else:
            out_specs = to_list(specs)
        # Note: checks each element has specified `name`.
        if out_specs is not None:
            for i, spec in enumerate(out_specs):
                assert isinstance(spec, Input)
                if spec.name is None:
                    raise ValueError(
                        f"Requires Input[{i}].name != None, but receive `None` with {spec}."
                    )

        return out_specs

    def _reset_metrics(self):
        for metric in self._metrics: