def __init__(self, cfg):
super(MaskPyramids, self).__init__()
self.cfg = cfg
# self.r50 = resnet.ResNet(cfg)
self.resnet50 = ResNet50(cfg)
num_classes = cfg.DATALOADER.NUM_CLASSES
self.init_pyramid = nn.Sequential(
resnet.Bottleneck(256, 256, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 256, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 256, 256, 1, True, 1, 1, nn.BatchNorm2d),
)
self.class_logits = nn.Sequential(
resnet.Bottleneck(256, 64, 128, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(128, 64, 64, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(64, 32, 32, 1, True, 2, 1, nn.BatchNorm2d),
)
self.cls_score = nn.Linear(32 * 5 * 5, num_classes)
self.chn256_6 = nn.Conv2d(512 + 2, 256, 1)
self.chn256_5 = nn.Conv2d(256 + 2, 256, 1)
self.chn256_4 = nn.Conv2d(512 + 2, 256, 1)
self.chn256_3 = nn.Conv2d(256 + 2, 256, 1)
self.chn256_2 = nn.Conv2d(512 + 2, 256, 1)
self.chn256_s = [
self.chn256_6, self.chn256_5, self.chn256_4, self.chn256_3,
self.chn256_2
]
self.conv_6 = resnet.Bottleneck(256, 64, 256, 1, True, 1, 1,
nn.BatchNorm2d)
self.conv_5 = resnet.Bottleneck(256, 64, 256, 1, True, 1, 1,
nn.BatchNorm2d)
self.conv_4 = resnet.Bottleneck(256, 64, 256, 1, True, 1, 1,
nn.BatchNorm2d)
self.conv_3 = resnet.Bottleneck(256, 64, 256, 1, True, 1, 1,
nn.BatchNorm2d)
self.conv_2 = resnet.Bottleneck(256, 64, 256, 1, True, 1, 1,
nn.BatchNorm2d)
self.conv_s = [
self.conv_6, self.conv_5, self.conv_4, self.conv_3, self.conv_2
]
self.out_6 = nn.Conv2d(256, 1, 1)
self.out_5 = nn.Conv2d(256, 1, 1)
self.out_4 = nn.Conv2d(256, 1, 1)
self.out_3 = nn.Conv2d(256, 1, 1)
self.out_2 = nn.Conv2d(256, 1, 1)
self.out_s = [
self.out_6, self.out_5, self.out_4, self.out_3, self.out_2
]
self.bg_6 = nn.Conv2d(512, 1, 1)
self.bg_5 = nn.Conv2d(256, 1, 1)
self.bg_4 = nn.Conv2d(512, 1, 1)
self.bg_3 = nn.Conv2d(256, 1, 1)
self.bg_2 = nn.Conv2d(512, 1, 1)
self.bg_s = [self.bg_6, self.bg_5, self.bg_4, self.bg_3, self.bg_2]
self.upmix256_5 = nn.Conv2d(256 + 256 + 2, 256, 1)
self.upmix256_4 = nn.Conv2d(512 + 256 + 2, 256, 1)
self.upmix256_3 = nn.Conv2d(256 + 256 + 2, 256, 1)
self.upmix256_2 = nn.Conv2d(512 + 256 + 2, 256, 1)
self.upmix256_s = [
None, self.upmix256_5, self.upmix256_4, self.upmix256_3,
self.upmix256_2
]
# self.sematic_conv_5 = resnet.Bottleneck(512+256, 64, 256, 1, True, 1, 1, nn.BatchNorm2d)
self.sematic_conv_5 = resnet.Bottleneck(512 + 256, 64, 256, 1, True, 1,
1, nn.BatchNorm2d)
self.sematic_conv_4 = resnet.Bottleneck(512 + 256, 64, 256, 1, True, 1,
1, nn.BatchNorm2d)
self.sematic_conv_3 = resnet.Bottleneck(256 + 256, 64, 256, 1, True, 1,
1, nn.BatchNorm2d)
self.sematic_conv_2 = resnet.Bottleneck(512 + 256, 64, 256, 1, True, 1,
1, nn.BatchNorm2d)
self.sematic_conv_1 = resnet.Bottleneck(256 + 256, 64, 256, 1, True, 1,
1, nn.BatchNorm2d)
self.sematic_final = nn.Conv2d(256, num_classes, 1)
self.cs_criteron = nn.CrossEntropyLoss()
self.class_criteron = nn.CrossEntropyLoss()
self.sematic_criteron = nn.CrossEntropyLoss(ignore_index=255)
self.cs_loss_factor = 1.0
self.miss_loss_factor = 1.0
self.log_dict = {}
self._init_weight()
def __init__(self, cfg):
super(ResNet50, self).__init__()
self.stem = resnet.BaseStem(cfg, nn.BatchNorm2d)
self.res_layer_1 = nn.Sequential(
resnet.Bottleneck(64, 64, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 64, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 64, 256, 1, True, 1, 1, nn.BatchNorm2d),
)
self.res_layer_2 = nn.Sequential(
resnet.Bottleneck(256, 128, 512, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
)
self.res_layer_3 = nn.Sequential(
resnet.Bottleneck(512, 128, 256, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
)
self.res_layer_4 = nn.Sequential(
resnet.Bottleneck(256, 128, 512, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
)
self.res_layer_5 = nn.Sequential(
resnet.Bottleneck(512, 128, 256, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(256, 128, 256, 1, True, 1, 1, nn.BatchNorm2d),
)
self.res_layer_6 = nn.Sequential(
resnet.Bottleneck(256, 128, 512, 1, True, 2, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
resnet.Bottleneck(512, 128, 512, 1, True, 1, 1, nn.BatchNorm2d),
)
