def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.downsample = downsample
self.stride = stride
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 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = NN.BatchNorm2d(planes * self.expansion) #NN.BatchNorm2d
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = NN.BatchNorm2d(64) #NN.BatchNorm2d
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, inchannels, outchannels, upfactor=2):
super(AO, self).__init__()
self.inchannels = inchannels
self.outchannels = outchannels
self.upfactor = upfactor
self.adapt_conv = nn.Sequential(nn.Conv2d(in_channels=self.inchannels, out_channels=int(self.inchannels/2), kernel_size=3, padding=1, stride=1, bias=True),\
NN.BatchNorm2d(num_features=int(self.inchannels/2)),\
nn.ReLU(inplace=True),\
nn.Conv2d(in_channels=int(self.inchannels/2), out_channels=self.outchannels, kernel_size=3, padding=1, stride=1, bias=True),\
nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True))
self.init_params()
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), #NN.BatchNorm2d
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def __init__(self, inchannels, midchannels=512):
super(FTB, self).__init__()
self.in1 = inchannels
self.mid = midchannels
self.conv1 = nn.Conv2d(in_channels=self.in1,
out_channels=self.mid,
kernel_size=3,
padding=1,
stride=1,
bias=True)
# NN.BatchNorm2d
self.conv_branch = nn.Sequential(nn.ReLU(inplace=True),\
nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3, padding=1, stride=1, bias=True),\
NN.BatchNorm2d(num_features=self.mid),\
nn.ReLU(inplace=True),\
nn.Conv2d(in_channels=self.mid, out_channels= self.mid, kernel_size=3, padding=1, stride=1, bias=True))
self.relu = nn.ReLU(inplace=True)
self.init_params()
if num_gpu < 2:
print(
"No multi-gpu found. NN.BatchNorm2d will act as normal nn.BatchNorm2d")
m1 = nn.Sequential(
nn.Conv2d(3, 3, 1, 1, bias=False),
nn.BatchNorm2d(3),
nn.ReLU(inplace=True),
nn.Conv2d(3, 3, 1, 1, bias=False),
nn.BatchNorm2d(3),
).cuda()
torch.manual_seed(123)
init_weight(m1)
m2 = nn.Sequential(
nn.Conv2d(3, 3, 1, 1, bias=False),
NN.BatchNorm2d(3),
nn.ReLU(inplace=True),
nn.Conv2d(3, 3, 1, 1, bias=False),
NN.BatchNorm2d(3),
).cuda()
torch.manual_seed(123)
init_weight(m2)
m2 = nn.DataParallel(m2, device_ids=range(num_gpu))
o1 = torch.optim.SGD(m1.parameters(), 1e-3)
o2 = torch.optim.SGD(m2.parameters(), 1e-3)
y = torch.ones(num_gpu).float().cuda()
torch.manual_seed(123)
for _ in range(100):
x = torch.rand(num_gpu, 3, 2, 2).cuda()
o1.zero_grad()
z1 = m1(x)