def __init__(self, output_stride=16, num_classes=21):
super(DeepLab, self).__init__()
BatchNorm = nn.BatchNorm2d
self.ResNet = ResNet18()
self.aspp = build_aspp(output_stride, BatchNorm)
self.decoder = build_decoder(num_classes, BatchNorm)
def __init__(self, backbone='resnet', output_stride=16, num_classes=4,
sync_bn=False, freeze_bn=False):
super(DeepLab, self).__init__()
if backbone == 'drn':
output_stride = 8
if sync_bn == True:
BatchNorm = SynchronizedBatchNorm2d
else:
BatchNorm = nn.BatchNorm2d
self.backbone = build_backbone(backbone, output_stride, BatchNorm)
self.aspp = build_aspp(backbone, output_stride, BatchNorm)
self.decoder = build_decoder(num_classes, backbone, BatchNorm)
self.freeze_bn = freeze_bn
def __init__(self, backbone='resnet', output_stride=8, num_classes=10,
sync_bn=False, freeze_bn=False):
super(DeepLab, self).__init__()
BatchNorm = nn.BatchNorm2d
self.backbone = build_backbone(backbone, output_stride, BatchNorm)
self.aspp = build_aspp(backbone, output_stride, BatchNorm)
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
normalize,
])
self.transform = transform
def __init__(self,
backbone='resnet',
output_stride=16,
num_classes=21,
freeze_bn=False):
super(DeepLab, self).__init__()
if backbone == 'drn':
output_stride = 8
BatchNorm = nn.BatchNorm2d
self.backbone = resnet_deeplab.ResNet101(BatchNorm=BatchNorm,
pretrained=False,
output_stride=8)
self.aspp = build_aspp(backbone, output_stride, BatchNorm)
self.decoder = build_decoder(num_classes, backbone, BatchNorm)
self.freeze_bn = freeze_bn