Spaces:
Runtime error
Runtime error
File size: 3,536 Bytes
b334e29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
# 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)
|