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# Modified from https://github.com/pytorch/vision/tree/master/torchvision/models/video
import torch
import torch.nn as nn
__all__ = ['unet_18', 'unet_34']
useBias = False
class identity(nn.Module):
def __init__(self , *args , **kwargs):
super().__init__()
def forward(self , x):
return x
class Conv3DSimple(nn.Conv3d):
def __init__(self,
in_planes,
out_planes,
midplanes=None,
stride=1,
padding=1):
super(Conv3DSimple, self).__init__(
in_channels=in_planes,
out_channels=out_planes,
kernel_size=(3, 3, 3),
stride=stride,
padding=padding,
bias=useBias)
@staticmethod
def get_downsample_stride(stride , temporal_stride):
if temporal_stride:
return (temporal_stride, stride, stride)
else:
return (stride , stride , stride)
class BasicStem(nn.Sequential):
"""The default conv-batchnorm-relu stem
"""
def __init__(self):
super().__init__(
nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2),
padding=(1, 3, 3), bias=useBias),
batchnorm(64),
nn.ReLU(inplace=False))
class Conv2Plus1D(nn.Sequential):
def __init__(self,
in_planes,
out_planes,
midplanes,
stride=1,
padding=1):
if not isinstance(stride , int):
temporal_stride , stride , stride = stride
else:
temporal_stride = stride
super(Conv2Plus1D, self).__init__(
nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
stride=(1, stride, stride), padding=(0, padding, padding),
bias=False),
# batchnorm(midplanes),
nn.ReLU(inplace=True),
nn.Conv3d(midplanes, out_planes, kernel_size=(3, 1, 1),
stride=(temporal_stride, 1, 1), padding=(padding, 0, 0),
bias=False))
@staticmethod
def get_downsample_stride(stride , temporal_stride):
if temporal_stride:
return (temporal_stride, stride, stride)
else:
return (stride , stride , stride)
class R2Plus1dStem(nn.Sequential):
"""R(2+1)D stem is different than the default one as it uses separated 3D convolution
"""
def __init__(self):
super().__init__(
nn.Conv3d(3, 45, kernel_size=(1, 7, 7),
stride=(1, 2, 2), padding=(0, 3, 3),
bias=False),
batchnorm(45),
nn.ReLU(inplace=True),
nn.Conv3d(45, 64, kernel_size=(3, 1, 1),
stride=(1, 1, 1), padding=(1, 0, 0),
bias=False),
batchnorm(64),
nn.ReLU(inplace=True))
class SEGating(nn.Module):
def __init__(self , inplanes , reduction=16):
super().__init__()
self.pool = nn.AdaptiveAvgPool3d(1)
self.attn_layer = nn.Sequential(
nn.Conv3d(inplanes , inplanes , kernel_size=1 , stride=1 , bias=True),
nn.Sigmoid()
)
def forward(self , x):
out = self.pool(x)
y = self.attn_layer(out)
return x * y
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
super(BasicBlock, self).__init__()
self.conv1 = nn.Sequential(
conv_builder(inplanes, planes, midplanes, stride),
batchnorm(planes),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
conv_builder(planes, planes, midplanes),
batchnorm(planes)
)
self.fg = SEGating(planes) ## Feature Gating
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.conv2(out)
out = self.fg(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class VideoResNet(nn.Module):
def __init__(self, block, conv_makers, layers,
stem, zero_init_residual=False):
"""Generic resnet video generator.
Args:
block (nn.Module): resnet building block
conv_makers (list(functions)): generator function for each layer
layers (List[int]): number of blocks per layer
stem (nn.Module, optional): Resnet stem, if None, defaults to conv-bn-relu. Defaults to None.
"""
super(VideoResNet, self).__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 , temporal_stride=1)
self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2 , temporal_stride=1)
self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=1, temporal_stride=1)
# init weights
self._initialize_weights()
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
def forward(self, x):
x_0 = self.stem(x)
x_1 = self.layer1(x_0)
x_2 = self.layer2(x_1)
x_3 = self.layer3(x_2)
x_4 = self.layer4(x_3)
return x_0 , x_1 , x_2 , x_3 , x_4
def _make_layer(self, block, conv_builder, planes, blocks, stride=1, temporal_stride=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
ds_stride = conv_builder.get_downsample_stride(stride , temporal_stride)
downsample = nn.Sequential(
nn.Conv3d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=ds_stride, bias=False),
batchnorm(planes * block.expansion)
)
stride = ds_stride
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 _initialize_weights(self):
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)
def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
model = VideoResNet(**kwargs)
## TODO: Other 3D resnet models, like S3D, r(2+1)D.
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model
def unet_18(pretrained=False, bn=False, progress=True, **kwargs):
"""
Construct 18 layer Unet3D model as in
https://arxiv.org/abs/1711.11248
Args:
pretrained (bool): If True, returns a model pre-trained on Kinetics-400
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
nn.Module: R3D-18 encoder
"""
global batchnorm
if bn:
batchnorm = nn.BatchNorm3d
else:
batchnorm = identity
return _video_resnet('r3d_18',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv3DSimple] * 4,
layers=[2, 2, 2, 2],
stem=BasicStem, **kwargs)
def unet_34(pretrained=False, bn=False, progress=True, **kwargs):
"""
Construct 34 layer Unet3D model as in
https://arxiv.org/abs/1711.11248
Args:
pretrained (bool): If True, returns a model pre-trained on Kinetics-400
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
nn.Module: R3D-18 encoder
"""
global batchnorm
# bn = False
if bn:
batchnorm = nn.BatchNorm3d
else:
batchnorm = identity
return _video_resnet('r3d_34',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv3DSimple] * 4,
layers=[3, 4, 6, 3],
stem=BasicStem, **kwargs) |