def __init__(self,
num_classes,
num_channels=3,
is_deconv=False,
decoder_kernel_size=3,
pretrained=True):
super().__init__()
filters = [256, 512, 1024, 2048]
resnet = models.resnet152(pretrained=pretrained)
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
# self.firstconv = resnet.conv1
# assert num_channels == 3, "num channels not used now. to use changle first conv layer to support num channels other then 3"
# try to use 8-channels as first input
if num_channels == 3:
self.firstconv = resnet.conv1
else:
self.firstconv = nn.Conv2d(num_channels,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3))
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
# Decoder
self.decoder4 = DecoderBlock(in_channels=filters[3],
n_filters=filters[2],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder3 = DecoderBlock(in_channels=filters[2],
n_filters=filters[1],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder2 = DecoderBlock(in_channels=filters[1],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder1 = DecoderBlock(in_channels=filters[0],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
# Final Classifier
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2)
self.finalrelu1 = nonlinearity(inplace=True)
self.finalconv2 = nn.Conv2d(32, 32, 3)
self.finalrelu2 = nonlinearity(inplace=True)
self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)
def __init__(self,
num_classes,
num_channels=3,
is_deconv=False,
decoder_kernel_size=4,
pretrained=True):
super().__init__()
filters = [64, 128, 256, 512]
resnet = models.resnet18(pretrained=pretrained)
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
if num_channels == 3:
self.firstconv = resnet.conv1
else:
self.firstconv = nn.Conv2d(num_channels,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3))
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
# Decoder
self.decoder4 = DecoderBlock(in_channels=filters[3],
n_filters=filters[2],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder3 = DecoderBlock(in_channels=filters[2],
n_filters=filters[1],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder2 = DecoderBlock(in_channels=filters[1],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder1 = DecoderBlock(in_channels=filters[0],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
# Final Classifier
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2)
self.finalrelu1 = nonlinearity(inplace=True)
self.finalconv2 = nn.Conv2d(32, 32, 3)
self.finalrelu2 = nonlinearity(inplace=True)
self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)
def __init__(self,
num_classes,
num_channels=3,
is_deconv=False,
decoder_kernel_size=4,
pretrained=True):
super().__init__()
filters = [256, 512, 1024, 2048]
# only pretrained
resnet = resnext101_32x4d(num_classes=1000, pretrained='imagenet')
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
self.stem = resnet.stem
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
# Decoder
self.decoder4 = DecoderBlock(in_channels=filters[3],
n_filters=filters[2],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder3 = DecoderBlock(in_channels=filters[2],
n_filters=filters[1],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder2 = DecoderBlock(in_channels=filters[1],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder1 = DecoderBlock(in_channels=filters[0],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
# Final Classifier
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2)
self.finalrelu1 = nonlinearity(inplace=True)
self.finalconv2 = nn.Conv2d(32, 32, 3)
self.finalrelu2 = nonlinearity(inplace=True)
self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)
def __init__(self,
num_classes,
num_channels=3,
is_deconv=False,
decoder_kernel_size=4,
pretrained=True):
super().__init__()
filters = [384, 768, 2112, 2208]
densenet = models.densenet161(pretrained=pretrained)
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
if num_channels == 3:
self.firstconv = densenet.features.conv0
else:
self.firstconv = nn.Conv2d(num_channels,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3))
self.stem = nn.Sequential(
self.firstconv,
densenet.features.norm0,
densenet.features.relu0,
densenet.features.pool0,
)
self.encoder1 = nn.Sequential(densenet.features.denseblock1)
self.encoder2 = nn.Sequential(densenet.features.transition1,
densenet.features.denseblock2)
self.encoder3 = nn.Sequential(densenet.features.transition2,
densenet.features.denseblock3)
self.encoder4 = nn.Sequential(densenet.features.transition3,
densenet.features.denseblock4)
# Decoder
self.decoder4 = DecoderBlock(in_channels=filters[3],
n_filters=filters[2],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder3 = DecoderBlock(in_channels=filters[2],
n_filters=filters[1],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder2 = DecoderBlock(in_channels=filters[1],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
self.decoder1 = DecoderBlock(in_channels=filters[0],
n_filters=filters[0],
kernel_size=decoder_kernel_size,
is_deconv=is_deconv)
# Final Classifier
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3, stride=2)
self.finalrelu1 = nonlinearity(inplace=True)
self.finalconv2 = nn.Conv2d(32, 32, 3)
self.finalrelu2 = nonlinearity(inplace=True)
self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)