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flow_2p1d_resnets.py
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flow_2p1d_resnets.py
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"""
Please use the kinetics_2p1d_model.py file for representation flow.
This file is outdated, included for replicating HMDB experiments and needs to be used with the old rep_flow_layer.py (https://github.com/piergiaj/representation-flow-cvpr19/commit/787564a99adabb41bf739c7f4b8edf7c89ace6f0#diff-1e2177049dfe6727b914853184ee3d40)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import torch.utils.model_zoo as model_zoo
from rep_flow_layer import FlowLayer
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
def conv3x3(in_planes, out_planes, stride=1,T=1):
""" 3D convolution with padding"""
return nn.Conv3d(in_planes, out_planes, kernel_size=[T,3,3], stride=stride,
padding=(1 if (T>1 or stride>1) else 0, 1,1), bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm3d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes,T=3)
self.bn2 = nn.BatchNorm3d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=[3,1,1], bias=False, padding=(1,0,0))
self.bn1 = nn.BatchNorm3d(planes)
self.conv2 = nn.Conv3d(planes, planes, kernel_size=[1,3,3], stride=stride,
padding=((1 if stride > 1 else 0),1,1), bias=False)
self.bn2 = nn.BatchNorm3d(planes)
self.conv3 = nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=150, in_channels=3, input_size=112, input_len=16, n_iter=3, learnable=[1,1,1,1,1], config=0, dropout=0.3):
self.inplanes = 64
self.in_channels = in_channels
super(ResNet, self).__init__()
self.config = config
self.flow_cmp = nn.Conv3d(128*block.expansion, 32, kernel_size=1, stride=1, padding=0, bias=False)
self.flow_layer = FlowLayer(channels=32, n_iter=n_iter, params=learnable)
self.flow_conv = nn.Conv3d(64, 64, kernel_size=(1,3,3), stride=1, padding=(0,1,1), bias=False)
# Flow-of-flow
self.flow_cmp2 = nn.Conv3d(64, 32, kernel_size=1, stride=1, padding=0, bias=False)
self.flow_layer2 = FlowLayer(channels=32, n_iter=n_iter, params=learnable)
self.flow_conv2 = nn.Conv3d(64, 128*block.expansion, kernel_size=(1,3,3), stride=1, padding=(0,1,1), bias=False)
self.bnf = nn.BatchNorm3d(128*block.expansion)
self.conv1 = nn.Conv3d(in_channels, 64, kernel_size=7, stride=(1,2,2), padding=(0,3,3),
bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
size = int(math.ceil(input_size/32))
length = int(math.ceil(input_len/16))
self.avgpool = nn.AvgPool3d((length,size,size), stride=1)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
torch.nn.init.zeros_(self.bnf.weight)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv3d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, padding=((1 if stride > 1 else 0),0,0), bias=False),
nn.BatchNorm3d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
# x is BxCxTxHxW
b,c,t,h,w = x.size()
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
_,ci,t,h,w = x.size()
res = F.avg_pool3d(x,(3,1,1),1,0)#x[:,:,1:-1].contiguous()
x = self.flow_cmp(x)
x = self.flow_layer.norm_img(x)
# handle time
_,c,t,h,w = x.size()
t = t-1
# compute flow for 0,1,...,T-1
# and 1,2,...,T
u,v = self.flow_layer(x[:,:,:-1].permute(0,2,1,3,4).contiguous().view(-1,c,h,w), x[:,:,1:].permute(0,2,1,3,4).contiguous().view(-1,c,h,w))
x = torch.cat([u,v], dim=1)
x = x.view(b,t,c*2,h,w).permute(0,2,1,3,4).contiguous()
x = self.flow_conv(x)
# Flow-of-flow
_,ci,t,h,w = x.size()
x = self.flow_cmp2(x)
x = self.flow_layer.norm_img(x)
# handle time
_,c,t,h,w = x.size()
t = t-1
# compute flow for 0,1,...,T-1
# and 1,2,...,T
u,v = self.flow_layer2(x[:,:,:-1].permute(0,2,1,3,4).contiguous().view(-1,c,h,w), x[:,:,1:].permute(0,2,1,3,4).contiguous().view(-1,c,h,w))
x = torch.cat([u,v], dim=1)
x = x.view(b,t,c*2,h,w).permute(0,2,1,3,4).contiguous()
x = self.flow_conv2(x)
x = self.bnf(x)
x = x+res
x = self.relu(x)
x = self.layer3(x)
x = self.layer4(x)
# average pool BxC'xT'xH'xW' to be BxC
# mean-pooling over T, H, and W
# may want to look into this for better fully-conv support (i.e., per-frame classification)
# or (temporal) pool after classifying
x = self.avgpool(x)
x = torch.mean(x, dim=2)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
# return BxClasses
return x
def load_2d_state_dict(self, state_dict, strict=True):
# slightly hacky trick to inflate 2d kernel to 3d by repeating over time axis
state_dict = {k:v for k,v in state_dict.items() if 'fc' not in k}
md = self.state_dict()
for k,v in state_dict.items():
if 'conv' in k or 'downsample.0' in k:
if isinstance(v, nn.Parameter):
v = v.data
if self.in_channels != 3 and k == 'conv1.weight':
v = torch.mean(v, dim=1).unsqueeze(1).repeat(1, self.in_channels, 1, 1)
# CxKxHxW -> CxKxTxHxW
D = md[k].size(2)
v = v.unsqueeze(2).repeat(1,1,D,1,1)
state_dict[k] = v
md.update(state_dict)
super(ResNet, self).load_state_dict(md, strict)
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_2d_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, mode='rgb', **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_2d_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, mode='rgb', **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if mode == 'rgb':
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
else:
model = ResNet(Bottleneck, [3, 4, 6, 3], in_channels=2, **kwargs)
if pretrained:
model.load_2d_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_2d_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_2d_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
if __name__ == '__main__':
# test resnet 50
import torch
d = torch.device('cuda')
net = nn.DataParallel(resnet34(pretrained=True, mode='rgb'))
net.to(d)
vid = torch.rand((12,3,16,112,112)).to(d)
print(net(vid).size())