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import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
using_ckpt = False
def conv3x3(in_planes, out_planes, stride=1, groups=1):
"""3x3 convolution with padding"""
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):
"""1x1 convolution"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
class IBasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(IBasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
self.downsample = downsample
self.stride = stride
def forward_impl(self, x):
identity = x
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
def forward(self, x):
if self.training and using_ckpt:
return checkpoint(self.forward_impl, x)
else:
return self.forward_impl(x)
class IResNet(nn.Module):
def __init__(self,
block, layers, dropout=0.4, num_features=512, zero_init_residual=False,
groups=1, fp16=False):
super(IResNet, self).__init__()
self.extra_gflops = 0.0
self.fp16 = fp16
self.inplanes = 64
self.groups = groups
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
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.bn2 = nn.BatchNorm2d(512, eps=1e-05,)
self.dropout = nn.Dropout(p=dropout, inplace=True)
self.fc = nn.Linear(512 * 7 * 7, num_features)
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
nn.init.constant_(self.features.weight, 1.0)
self.features.weight.requires_grad = False
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0, 0.1)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, IBasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes, stride),
nn.BatchNorm2d(planes, eps=1e-05, ),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes))
return nn.Sequential(*layers)
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x.float() if self.fp16 else x)
x = self.features(x)
return x
def iresnet(arch, pretrained=False, **kwargs):
layer_dict = {"18": [2, 2, 2, 2],
"34": [3, 4, 6, 3],
"50": [3, 4, 14, 3],
"100": [3, 13, 30, 3],
"152": [3, 8, 36, 3],
"200": [3, 13, 30, 3]}
model = IResNet(IBasicBlock, layer_dict[arch], **kwargs)
if pretrained:
raise ValueError()
return model
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