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import torch
import torch.nn as nn
from torch import Tensor
from torchvision.models.video.resnet import BasicBlock, Bottleneck, Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D
from typing import Callable, List, Sequence, Type, Union
# TODO: upload models and load them
model_urls = {
"r2plus1d_34_8_ig65m": "https://github.com/moabitcoin/ig65m-pytorch/releases/download/v1.0.0/r2plus1d_34_clip8_ig65m_from_scratch-9bae36ae.pth", # noqa: E501
"r2plus1d_34_32_ig65m": "https://github.com/moabitcoin/ig65m-pytorch/releases/download/v1.0.0/r2plus1d_34_clip32_ig65m_from_scratch-449a7af9.pth", # noqa: E501
"r2plus1d_34_8_kinetics": "https://github.com/moabitcoin/ig65m-pytorch/releases/download/v1.0.0/r2plus1d_34_clip8_ft_kinetics_from_ig65m-0aa0550b.pth", # noqa: E501
"r2plus1d_34_32_kinetics": "https://github.com/moabitcoin/ig65m-pytorch/releases/download/v1.0.0/r2plus1d_34_clip32_ft_kinetics_from_ig65m-ade133f1.pth", # noqa: E501
"r2plus1d_152_ig65m_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/r2plus1d_152_ig65m_from_scratch_f106380637.pth",
"r2plus1d_152_ig_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/r2plus1d_152_ft_kinetics_from_ig65m_f107107466.pth",
"r2plus1d_152_sports1m_32frms": "",
"r2plus1d_152_sports1m_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/r2plus1d_152_ft_kinetics_from_sports1m_f128957437.pth",
"ir_csn_152_ig65m_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/irCSN_152_ig65m_from_scratch_f125286141.pth",
"ir_csn_152_ig_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/irCSN_152_ft_kinetics_from_ig65m_f126851907.pth",
"ir_csn_152_sports1m_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/irCSN_152_Sports1M_from_scratch_f99918785.pth",
"ir_csn_152_sports1m_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/irCSN_152_ft_kinetics_from_Sports1M_f101599884.pth",
"ip_csn_152_ig65m_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/ipCSN_152_ig65m_from_scratch_f130601052.pth",
"ip_csn_152_ig_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/ipCSN_152_ft_kinetics_from_ig65m_f133090949.pth",
"ip_csn_152_sports1m_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/ipCSN_152_Sports1M_from_scratch_f111018543.pth",
"ip_csn_152_sports1m_ft_kinetics_32frms": "https://github.com/bjuncek/VMZ/releases/download/test_models/ipCSN_152_ft_kinetics_from_Sports1M_f111279053.pth",
}
class VideoResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
conv_makers: Sequence[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]],
layers: List[int],
stem: Callable[..., nn.Module],
num_classes: int = 400,
zero_init_residual: bool = False,
) -> None:
"""Generic resnet video generator.
Args:
block (Type[Union[BasicBlock, Bottleneck]]): resnet building block
conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator
function for each layer
layers (List[int]): number of blocks per layer
stem (Callable[..., nn.Module]): module specifying the ResNet stem.
num_classes (int, optional): Dimension of the final FC layer. Defaults to 400.
zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False.
"""
super().__init__()
self.inplanes = 64
self.stem = stem()
self.layer1 = self._make_layer(block, conv_makers[0], 64, layers[0], stride=1)
self.layer2 = self._make_layer(block, conv_makers[1], 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
# init weights
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[union-attr, arg-type]
def forward(self, x: Tensor) -> Tensor:
x = self.stem(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.fc(x)
return x
def _make_layer(
self,
block: Type[Union[BasicBlock, Bottleneck]],
conv_builder: Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]],
planes: int,
blocks: int,
stride: int = 1,
) -> nn.Sequential:
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
ds_stride = conv_builder.get_downsample_stride(stride)
downsample = nn.Sequential(
nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=1, stride=ds_stride, bias=False),
nn.BatchNorm3d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, conv_builder, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, conv_builder))
return nn.Sequential(*layers)
def _generic_resnet(arch, pretrained=False, progress=False, **kwargs):
model = VideoResNet(**kwargs)
# We need exact Caffe2 momentum for BatchNorm scaling
for m in model.modules():
if isinstance(m, nn.BatchNorm3d):
m.eps = 1e-3
m.momentum = 0.9
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
model_urls[arch], progress=progress
)
model.load_state_dict(state_dict)
return model
class BasicStem_Pool(nn.Sequential):
def __init__(self):
super(BasicStem_Pool, self).__init__(
nn.Conv3d(
3,
64,
kernel_size=(3, 7, 7),
stride=(1, 2, 2),
padding=(1, 3, 3),
bias=False,
),
nn.BatchNorm3d(64),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
)
class R2Plus1dStem_Pool(nn.Sequential):
"""R(2+1)D stem is different than the default one as it uses separated 3D convolution
"""
def __init__(self):
super(R2Plus1dStem_Pool, self).__init__(
nn.Conv3d(
3,
45,
kernel_size=(1, 7, 7),
stride=(1, 2, 2),
padding=(0, 3, 3),
bias=False,
),
nn.BatchNorm3d(45),
nn.ReLU(inplace=True),
nn.Conv3d(
45,
64,
kernel_size=(3, 1, 1),
stride=(1, 1, 1),
padding=(1, 0, 0),
bias=False,
),
nn.BatchNorm3d(64),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
)
class Conv3DDepthwise(nn.Conv3d):
def __init__(self, in_planes, out_planes, midplanes=None, stride=1, padding=1):
assert in_planes == out_planes
super(Conv3DDepthwise, self).__init__(
in_channels=in_planes,
out_channels=out_planes,
kernel_size=(3, 3, 3),
stride=stride,
padding=padding,
groups=in_planes,
bias=False,
)
@staticmethod
def get_downsample_stride(stride):
return (stride, stride, stride)
class IPConv3DDepthwise(nn.Sequential):
def __init__(self, in_planes, out_planes, midplanes, stride=1, padding=1):
assert in_planes == out_planes
super(IPConv3DDepthwise, self).__init__(
nn.Conv3d(in_planes, out_planes, kernel_size=1, bias=False),
nn.BatchNorm3d(out_planes),
# nn.ReLU(inplace=True),
Conv3DDepthwise(out_planes, out_planes, None, stride),
)
@staticmethod
def get_downsample_stride(stride):
return (stride, stride, stride)
class Conv2Plus1D(nn.Sequential):
def __init__(self, in_planes, out_planes, midplanes, stride=1, padding=1):
midplanes = (in_planes * out_planes * 3 * 3 * 3) // (
in_planes * 3 * 3 + 3 * out_planes
)
super(Conv2Plus1D, self).__init__(
nn.Conv3d(
in_planes,
midplanes,
kernel_size=(1, 3, 3),
stride=(1, stride, stride),
padding=(0, padding, padding),
bias=False,
),
nn.BatchNorm3d(midplanes),
nn.ReLU(inplace=True),
nn.Conv3d(
midplanes,
out_planes,
kernel_size=(3, 1, 1),
stride=(stride, 1, 1),
padding=(padding, 0, 0),
bias=False,
),
)
@staticmethod
def get_downsample_stride(stride):
return (stride, stride, stride)
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