Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,012 Bytes
938e515 |
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 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : [email protected]
@File : resnext.py.py
@Time : 8/11/19 8:58 PM
@Desc :
@License : This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import functools
import torch.nn as nn
import math
from torch.utils.model_zoo import load_url
from modules import InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
__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. / 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 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
|