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import warnings
import torch.hub
import torch.nn as nn
from torchvision.models.video.resnet import BasicStem, BasicBlock, Bottleneck
from .utils import _generic_resnet, Conv3DDepthwise, BasicStem_Pool, IPConv3DDepthwise
__all__ = ["ir_csn_152", "ip_csn_152"]
def ir_csn_152(pretraining="", use_pool1=True, progress=False, **kwargs):
avail_pretrainings = [
"ig65m_32frms",
"ig_ft_kinetics_32frms",
"sports1m_32frms",
"sports1m_ft_kinetics_32frms",
]
if pretraining in avail_pretrainings:
arch = "ir_csn_152_" + pretraining
pretrained = True
else:
arch = "ir_csn_152"
pretrained = False
model = _generic_resnet(
arch,
pretrained,
progress,
block=Bottleneck,
conv_makers=[Conv3DDepthwise] * 4,
layers=[3, 8, 36, 3],
stem=BasicStem_Pool if use_pool1 else BasicStem,
**kwargs,
)
return model
def ip_csn_152(pretraining="", use_pool1=True, progress=False, **kwargs):
avail_pretrainings = [
"ig65m_32frms",
"ig_ft_kinetics_32frms",
"sports1m_32frms",
"sports1m_ft_kinetics_32frms",
]
if pretraining in avail_pretrainings:
arch = "ip_csn_152_" + pretraining
pretrained = True
else:
arch = "ip_csn_152"
pretrained = False
model = _generic_resnet(
arch,
pretrained,
progress,
block=Bottleneck,
conv_makers=[IPConv3DDepthwise] * 4,
layers=[3, 8, 36, 3],
stem=BasicStem_Pool if use_pool1 else BasicStem,
**kwargs,
)
return model
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