def __init__(self, block, layers, num_classes=1000):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = nn.Sequential(
conv3x3(3, 64, stride=2), #conv1.0
mynn.Norm2d(64), #conv1.1
nn.ReLU(inplace=True), #conv1.2
conv3x3(64, 64), #cpnv1.3
mynn.Norm2d(64), #conv1.4
nn.ReLU(inplace=True), #conv1.5
conv3x3(64, 128)) #conv1.6
self.bn1 = mynn.Norm2d(128)
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 = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, 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 __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
multi_grid=1):
super(BottleneckICnet, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = mynn.Norm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=dilation * multi_grid,
dilation=dilation * multi_grid,
bias=False)
self.bn2 = mynn.Norm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = mynn.Norm2d(planes * 4)
self.relu = nn.ReLU(inplace=False)
self.relu_inplace = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = mynn.Norm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = mynn.Norm2d(planes)
self.downsample = downsample
self.stride = stride
def _make_layer(self,
block,
planes,
blocks,
stride=1,
dilation=1,
multi_grid=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), mynn.Norm2d(planes * block.expansion))
layers = []
generate_multi_grid = lambda index, grids: grids[index % len(
grids)] if isinstance(grids, tuple) else 1
layers.append(
block(self.inplanes,
planes,
stride,
dilation=dilation,
downsample=downsample,
multi_grid=generate_multi_grid(0, multi_grid)))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
dilation=dilation,
multi_grid=generate_multi_grid(i, multi_grid)))
return nn.Sequential(*layers)
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = mynn.Norm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = mynn.Norm2d(planes)
self.conv3 = nn.Conv2d(planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.bn3 = mynn.Norm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
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),
mynn.Norm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for index in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def bnrelu(channels):
"""
Single Layer BN and Relui
"""
return nn.Sequential(mynn.Norm2d(channels), nn.ReLU(inplace=True))
