def __init__(self, block, layers, num_classes):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
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=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,1,1)) # we do not apply multi-grid method here
# extra added layers
self.head = nn.Sequential(
nn.Conv2d(2048, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512),
NonLocal2d(inplanes=512, ratio=256, downsample=False),
nn.Dropout2d(0.05)
)
self.cls = nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512),
nn.Dropout2d(0.05),
nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
def __init__(self, block, layers, num_classes):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3,
stride=2,
padding=1,
ceil_mode=True) # change
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=1,
dilation=2)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=1,
dilation=4,
multi_grid=(1, 1, 1))
# extra added layers
self.context = nn.Sequential(
nn.Conv2d(2048, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512),
BaseOC_Module(in_channels=512,
out_channels=512,
key_channels=256,
value_channels=256,
dropout=0.05,
sizes=([1])))
self.cls = nn.Conv2d(512,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512), nn.Dropout2d(0.05),
nn.Conv2d(512,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
def __init__(self, block, layers, num_classes):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3,
stride=2,
padding=1,
ceil_mode=True) # change
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=1,
dilation=2)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=1,
dilation=4,
multi_grid=(1, 2, 1))
self.layer5 = nn.Sequential(
nn.Conv2d(2048, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512), nn.Dropout2d(0.05))
self.layer6 = nn.Conv2d(512,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)
def __init__(self, block, layers, num_classes):
print("model.py")
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(
kernel_size=3, stride=2, padding=1, ceil_mode=True
) # NOTE! (ceil_mode=True will do that x (batch_size, 128, h/4, w/4) e.g. has shape (batch_size, 128, 33, 33) instead of (batch_size, 128, 32, 32) if h == w == 256)
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=1,
dilation=2)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=1,
dilation=4,
multi_grid=(1, 1, 1))
self.aspp = ASPP()
self.cls = nn.Conv2d(512,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)
