File size: 5,511 Bytes
482ab8a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
import math
import torch.nn as nn
from .lib.nn import SynchronizedBatchNorm2d
from .utils import load_url
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ["ResNeXt", "resnext101"] # support resnext 101
model_urls = {
#'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
"resnext101": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth"
}
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
class GroupBottleneck(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
super(GroupBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
groups=groups,
bias=False,
)
self.bn2 = BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
self.bn3 = BatchNorm2d(planes * 2)
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, groups=32, num_classes=1000):
self.inplanes = 128
super(ResNeXt, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(1024 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / n))
elif isinstance(m, BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, groups=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, groups, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=groups))
return nn.Sequential(*layers)
def forward(self, x):
x = self.relu1(self.bn1(self.conv1(x)))
x = self.relu2(self.bn2(self.conv2(x)))
x = self.relu3(self.bn3(self.conv3(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 resnext50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
return model
'''
def resnext101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(load_url(model_urls["resnext101"]), strict=False)
return model
# def resnext152(pretrained=False, **kwargs):
# """Constructs a ResNeXt-152 model.
#
# Args:
# pretrained (bool): If True, returns a model pre-trained on Places
# """
# model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
# if pretrained:
# model.load_state_dict(load_url(model_urls['resnext152']))
# return model
|