apdepth/util/batchsize.py

# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# 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.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------


import torch
import math


# Search table for suggested max. inference batch size
bs_search_table = [
    # tested on A100-PCIE-80GB
    {"res": 768, "total_vram": 79, "bs": 35, "dtype": torch.float32},
    {"res": 1024, "total_vram": 79, "bs": 20, "dtype": torch.float32},
    # tested on A100-PCIE-40GB
    {"res": 768, "total_vram": 39, "bs": 15, "dtype": torch.float32},
    {"res": 1024, "total_vram": 39, "bs": 8, "dtype": torch.float32},
    {"res": 768, "total_vram": 39, "bs": 30, "dtype": torch.float16},
    {"res": 1024, "total_vram": 39, "bs": 15, "dtype": torch.float16},
    # tested on RTX3090, RTX4090
    {"res": 512, "total_vram": 23, "bs": 20, "dtype": torch.float32},
    {"res": 768, "total_vram": 23, "bs": 7, "dtype": torch.float32},
    {"res": 1024, "total_vram": 23, "bs": 3, "dtype": torch.float32},
    {"res": 512, "total_vram": 23, "bs": 40, "dtype": torch.float16},
    {"res": 768, "total_vram": 23, "bs": 18, "dtype": torch.float16},
    {"res": 1024, "total_vram": 23, "bs": 10, "dtype": torch.float16},
    # tested on GTX1080Ti
    {"res": 512, "total_vram": 10, "bs": 5, "dtype": torch.float32},
    {"res": 768, "total_vram": 10, "bs": 2, "dtype": torch.float32},
    {"res": 512, "total_vram": 10, "bs": 10, "dtype": torch.float16},
    {"res": 768, "total_vram": 10, "bs": 5, "dtype": torch.float16},
    {"res": 1024, "total_vram": 10, "bs": 3, "dtype": torch.float16},
]


def find_batch_size(ensemble_size: int, input_res: int, dtype: torch.dtype) -> int:
    """
    Automatically search for suitable operating batch size.

    Args:
        ensemble_size (`int`):
            Number of predictions to be ensembled.
        input_res (`int`):
            Operating resolution of the input image.

    Returns:
        `int`: Operating batch size.
    """
    if not torch.cuda.is_available():
        return 1

    total_vram = torch.cuda.mem_get_info()[1] / 1024.0**3
    filtered_bs_search_table = [s for s in bs_search_table if s["dtype"] == dtype]
    for settings in sorted(
        filtered_bs_search_table,
        key=lambda k: (k["res"], -k["total_vram"]),
    ):
        if input_res <= settings["res"] and total_vram >= settings["total_vram"]:
            bs = settings["bs"]
            if bs > ensemble_size:
                bs = ensemble_size
            elif bs > math.ceil(ensemble_size / 2) and bs < ensemble_size:
                bs = math.ceil(ensemble_size / 2)
            return bs

    return 1