218 lines
7.0 KiB
Python
218 lines
7.0 KiB
Python
# Deep Residual Learning for Image Recognition, CVPR 2016
|
|
import torch.nn as nn
|
|
from .initialization import initialize_resnet
|
|
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1, groups=1):
|
|
return nn.Conv2d(
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size=3,
|
|
stride=stride,
|
|
padding=1,
|
|
groups=groups,
|
|
bias=False,
|
|
)
|
|
|
|
|
|
def conv1x1(in_planes, out_planes, stride=1):
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
|
|
|
|
|
class BasicBlock(nn.Module):
|
|
expansion = 1
|
|
|
|
def __init__(
|
|
self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
|
|
):
|
|
super(BasicBlock, self).__init__()
|
|
if groups != 1 or base_width != 64:
|
|
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
|
|
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
|
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
|
self.bn1 = nn.BatchNorm2d(planes)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.conv2 = conv3x3(planes, planes)
|
|
self.bn2 = nn.BatchNorm2d(planes)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
identity = 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:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class Bottleneck(nn.Module):
|
|
expansion = 4
|
|
|
|
def __init__(
|
|
self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
|
|
):
|
|
super(Bottleneck, self).__init__()
|
|
width = int(planes * (base_width / 64.0)) * groups
|
|
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
|
|
self.conv1 = conv1x1(inplanes, width)
|
|
self.bn1 = nn.BatchNorm2d(width)
|
|
self.conv2 = conv3x3(width, width, stride, groups)
|
|
self.bn2 = nn.BatchNorm2d(width)
|
|
self.conv3 = conv1x1(width, planes * self.expansion)
|
|
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
identity = 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:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class ResNet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
block_name,
|
|
layers,
|
|
deep_stem,
|
|
num_classes,
|
|
zero_init_residual,
|
|
groups,
|
|
width_per_group,
|
|
):
|
|
super(ResNet, self).__init__()
|
|
|
|
# planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
|
|
if block_name == "BasicBlock":
|
|
block = BasicBlock
|
|
elif block_name == "Bottleneck":
|
|
block = Bottleneck
|
|
else:
|
|
raise ValueError("invalid block-name : {:}".format(block_name))
|
|
|
|
if not deep_stem:
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
|
|
nn.BatchNorm2d(64),
|
|
nn.ReLU(inplace=True),
|
|
)
|
|
else:
|
|
self.conv = nn.Sequential(
|
|
nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
|
|
nn.BatchNorm2d(32),
|
|
nn.ReLU(inplace=True),
|
|
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
|
|
nn.BatchNorm2d(32),
|
|
nn.ReLU(inplace=True),
|
|
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
|
|
nn.BatchNorm2d(64),
|
|
nn.ReLU(inplace=True),
|
|
)
|
|
self.inplanes = 64
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
self.layer1 = self._make_layer(
|
|
block, 64, layers[0], stride=1, groups=groups, base_width=width_per_group
|
|
)
|
|
self.layer2 = self._make_layer(
|
|
block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group
|
|
)
|
|
self.layer3 = self._make_layer(
|
|
block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group
|
|
)
|
|
self.layer4 = self._make_layer(
|
|
block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group
|
|
)
|
|
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
|
self.message = (
|
|
"block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}".format(
|
|
block, layers, deep_stem, num_classes
|
|
)
|
|
)
|
|
|
|
self.apply(initialize_resnet)
|
|
|
|
# Zero-initialize the last BN in each residual branch,
|
|
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
|
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
|
if zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
nn.init.constant_(m.bn3.weight, 0)
|
|
elif isinstance(m, BasicBlock):
|
|
nn.init.constant_(m.bn2.weight, 0)
|
|
|
|
def _make_layer(self, block, planes, blocks, stride, groups, base_width):
|
|
downsample = None
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
if stride == 2:
|
|
downsample = nn.Sequential(
|
|
nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
|
|
conv1x1(self.inplanes, planes * block.expansion, 1),
|
|
nn.BatchNorm2d(planes * block.expansion),
|
|
)
|
|
elif stride == 1:
|
|
downsample = nn.Sequential(
|
|
conv1x1(self.inplanes, planes * block.expansion, stride),
|
|
nn.BatchNorm2d(planes * block.expansion),
|
|
)
|
|
else:
|
|
raise ValueError("invalid stride [{:}] for downsample".format(stride))
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(self.inplanes, planes, stride, downsample, groups, base_width)
|
|
)
|
|
self.inplanes = planes * block.expansion
|
|
for _ in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes, 1, None, groups, base_width))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def get_message(self):
|
|
return self.message
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
|
|
features = self.avgpool(x)
|
|
features = features.view(features.size(0), -1)
|
|
logits = self.fc(features)
|
|
|
|
return features, logits
|