diffusion/model/nets/fastlinear/modules/utils/model.py

# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# SPDX-License-Identifier: Apache-2.0


def val2list(x: list or tuple or any, repeat_time=1) -> list:  # type: ignore
    """Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
    if isinstance(x, (list, tuple)):
        return list(x)
    return [x for _ in range(repeat_time)]


def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple:  # type: ignore
    """Return tuple with min_len by repeating element at idx_repeat."""
    # convert to list first
    x = val2list(x)

    # repeat elements if necessary
    if len(x) > 0:
        x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]

    return tuple(x)


def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
    if isinstance(kernel_size, tuple):
        return tuple([get_same_padding(ks) for ks in kernel_size])
    else:
        assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number"
        return kernel_size // 2