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pytorch-image-models/models/resnext.py

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6.1 KiB

import torch.nn as nn
import torch.nn.functional as F
import math
import torch.utils.model_zoo as model_zoo
from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
__all__ = ['ResNeXt', 'resnext50', 'resnext101', 'resnext152']
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 ResNeXtBottleneckC(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, base_width=4):
super(ResNeXtBottleneckC, self).__init__()
width = math.floor(planes * (base_width / 64)) * cardinality
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
padding=1, bias=False, groups=cardinality)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
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, num_classes=1000, cardinality=32, base_width=4,
drop_rate=0., global_pool='avg'):
self.num_classes = num_classes
self.inplanes = 64
self.cardinality = cardinality
self.base_width = base_width
self.drop_rate = drop_rate
super(ResNeXt, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(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)
self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
self.num_features = 512 * block.expansion
self.fc = nn.Linear(self.num_features, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
def _make_layer(self, block, planes, blocks, stride=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),
nn.BatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, 1, None, self.cardinality, self.base_width))
return nn.Sequential(*layers)
def get_classifier(self):
return self.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
self.num_classes = num_classes
del self.fc
if num_classes:
self.fc = nn.Linear(self.num_features, num_classes)
else:
self.fc = None
def forward_features(self, x, pool=True):
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)
if pool:
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.fc(x)
return x
def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
"""Constructs a ResNeXt-50 model.
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
return model
def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
"""Constructs a ResNeXt-101 model.
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
return model
def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
"""Constructs a ResNeXt-152 model.
Args:
cardinality (int): Cardinality of the aggregated transform
base_width (int): Base width of the grouped convolution
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
"""
model = ResNeXt(
ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)
return model