ControlNet/annotator/uniformer/mmcv/utils/parrots_wrapper.py

# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial

import torch

TORCH_VERSION = torch.__version__


def is_rocm_pytorch() -> bool:
    is_rocm = False
    if TORCH_VERSION != 'parrots':
        try:
            from torch.utils.cpp_extension import ROCM_HOME
            is_rocm = True if ((torch.version.hip is not None) and
                               (ROCM_HOME is not None)) else False
        except ImportError:
            pass
    return is_rocm


def _get_cuda_home():
    if TORCH_VERSION == 'parrots':
        from parrots.utils.build_extension import CUDA_HOME
    else:
        if is_rocm_pytorch():
            from torch.utils.cpp_extension import ROCM_HOME
            CUDA_HOME = ROCM_HOME
        else:
            from torch.utils.cpp_extension import CUDA_HOME
    return CUDA_HOME


def get_build_config():
    if TORCH_VERSION == 'parrots':
        from parrots.config import get_build_info
        return get_build_info()
    else:
        return torch.__config__.show()


def _get_conv():
    if TORCH_VERSION == 'parrots':
        from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin
    else:
        from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin
    return _ConvNd, _ConvTransposeMixin


def _get_dataloader():
    if TORCH_VERSION == 'parrots':
        from torch.utils.data import DataLoader, PoolDataLoader
    else:
        from torch.utils.data import DataLoader
        PoolDataLoader = DataLoader
    return DataLoader, PoolDataLoader


def _get_extension():
    if TORCH_VERSION == 'parrots':
        from parrots.utils.build_extension import BuildExtension, Extension
        CppExtension = partial(Extension, cuda=False)
        CUDAExtension = partial(Extension, cuda=True)
    else:
        from torch.utils.cpp_extension import (BuildExtension, CppExtension,
                                               CUDAExtension)
    return BuildExtension, CppExtension, CUDAExtension


def _get_pool():
    if TORCH_VERSION == 'parrots':
        from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd,
                                             _AdaptiveMaxPoolNd, _AvgPoolNd,
                                             _MaxPoolNd)
    else:
        from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd,
                                              _AdaptiveMaxPoolNd, _AvgPoolNd,
                                              _MaxPoolNd)
    return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd


def _get_norm():
    if TORCH_VERSION == 'parrots':
        from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
        SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
    else:
        from torch.nn.modules.instancenorm import _InstanceNorm
        from torch.nn.modules.batchnorm import _BatchNorm
        SyncBatchNorm_ = torch.nn.SyncBatchNorm
    return _BatchNorm, _InstanceNorm, SyncBatchNorm_


_ConvNd, _ConvTransposeMixin = _get_conv()
DataLoader, PoolDataLoader = _get_dataloader()
BuildExtension, CppExtension, CUDAExtension = _get_extension()
_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm()
_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool()


class SyncBatchNorm(SyncBatchNorm_):

    def _check_input_dim(self, input):
        if TORCH_VERSION == 'parrots':
            if input.dim() < 2:
                raise ValueError(
                    f'expected at least 2D input (got {input.dim()}D input)')
        else:
            super()._check_input_dim(input)