import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
__all__ = ['ResNeXt', 'resnet50', 'resnet101', 'get_fine_tuning_parameters']
def conv3x3x3(in_planes, out_planes, stride=1):
# 3x3x3 convolution with padding
return nn.Conv3d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
def downsample_basic_block(x, planes, stride):
out = F.avg_pool3d(x, kernel_size=1, stride=stride)
zero_pads = torch.Tensor(
out.size(0), planes - out.size(1), out.size(2), out.size(3),
out.size(4)).zero_()
if isinstance(out.data, torch.cuda.FloatTensor):
zero_pads = zero_pads.cuda()
out = Variable(torch.cat([out.data, zero_pads], dim=1))
return out
class ResNeXtBottleneck(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, cardinality, stride=1,
downsample=None):
super(ResNeXtBottleneck, self).__init__()
mid_planes = cardinality * int(planes / 32)
self.conv1 = nn.Conv3d(inplanes, mid_planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm3d(mid_planes)
self.conv2 = nn.Conv3d(
mid_planes,
mid_planes,
kernel_size=3,
stride=stride,
padding=1,
groups=cardinality,
bias=False)
self.bn2 = nn.BatchNorm3d(mid_planes)
self.conv3 = nn.Conv3d(
mid_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 ResNeXt(nn.Module):
def __init__(self,
block,
layers,
spatial_size,
sample_duration,
shortcut_type='B',
cardinality=32,
num_classes=400):
self.inplanes = 64
super(ResNeXt, self).__init__()
self.conv1 = nn.Conv3d(
3,
64,
kernel_size=7,
stride=(1, 2, 2),
padding=(3, 3, 3),
bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.layer1 = self._make_layer(block, 128, layers[0], shortcut_type,
cardinality)
self.layer2 = self._make_layer(
block, 256, layers[1], shortcut_type, cardinality, stride=2)
self.layer3 = self._make_layer(
block, 512, layers[2], shortcut_type, cardinality, stride=2)
self.layer4 = self._make_layer(
block, 1024, layers[3], shortcut_type, cardinality, stride=2)
last_duration = int(math.ceil(sample_duration / 16))
last_size = int(math.ceil(spatial_size / 32))
self.avgpool = nn.AvgPool3d(
(last_duration, last_size, last_size), stride=1)
self.fc = nn.Linear(cardinality * 32 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self,
block,
planes,
blocks,
shortcut_type,
cardinality,
stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
if shortcut_type == 'A':
downsample = partial(
downsample_basic_block,
planes=planes * block.expansion,
stride=stride)
else:
downsample = nn.Sequential(
nn.Conv3d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm3d(planes * block.expansion))
layers = []
layers.append(
block(self.inplanes, planes, cardinality, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, cardinality))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
##########################################################################################
##########################################################################################
def get_fine_tuning_parameters(model, ft_begin_index):
assert isinstance(ft_begin_index, int)
if ft_begin_index == 0:
print('WARNING: training full network because --finetune_begin_index=0')
return model.parameters()
ft_module_names = []
for i in range(ft_begin_index, 5):
ft_module_names.append('layer{}'.format(i))
ft_module_names.append('fc')
parameters = []
for k, v in model.named_parameters():
for ft_module in ft_module_names:
if ft_module in k:
parameters.append({'params': v})
break
else:
parameters.append({'params': v, 'lr': 0.0})
return parameters
##########################################################################################
##########################################################################################
def resnet50(**kwargs):
"""Constructs a ResNet-50 model.
"""
model = ResNeXt(ResNeXtBottleneck, [3, 4, 6, 3], **kwargs)
return model
def resnet101(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNeXt(ResNeXtBottleneck, [3, 4, 23, 3], **kwargs)
return model
def resnet152(**kwargs):
"""Constructs a ResNet-101 model.
"""
model = ResNeXt(ResNeXtBottleneck, [3, 8, 36, 3], **kwargs)
return model
