hnamt

/

RT-DisDINOv3-ViT-Base

like 0

• "torch.nn.modules.sparse.Embedding",
• "torch._utils._rebuild_tensor_v2",
• "torch.nn.modules.activation.ReLU",
• "torch._utils._rebuild_parameter",
• "torch.nn.modules.activation.GELU",
• "torch.nn.modules.pooling.AdaptiveAvgPool2d",
• "torch.nn.modules.linear.Identity",
• "src.zoo.rtdetr.rtdetrv2_decoder.RTDETRTransformerv2",
• "torch.nn.modules.linear.NonDynamicallyQuantizableLinear",
• "src.zoo.rtdetr.hybrid_encoder.ConvNormLayer",
• "torch.nn.modules.activation.SiLU",
• "torch.nn.modules.container.Sequential",
• "torch.nn.modules.container.ModuleList",
• "torch.nn.modules.dropout.Dropout",
• "lightly_train._models.embedding_model.EmbeddingModel",
• "src.zoo.rtdetr.rtdetr.RTDETR",
• "functools.partial",
• "src.zoo.rtdetr.hybrid_encoder.HybridEncoder",
• "src.zoo.rtdetr.utils.deformable_attention_core_func_v2",
• "src.nn.backbone.common.FrozenBatchNorm2d",
• "torch.LongStorage",
• "src.nn.backbone.presnet.Blocks",
• "torch.nn.modules.activation.MultiheadAttention",
• "src.nn.backbone.presnet.ConvNormLayer",
• "__builtin__.set",
• "torch.nn.modules.batchnorm.BatchNorm2d",
• "torch.nn.modules.normalization.LayerNorm",
• "torch.nn.modules.conv.Conv2d",
• "torch.nn.modules.pooling.AvgPool2d",
• "src.zoo.rtdetr.rtdetrv2_decoder.TransformerDecoder",
• "src.zoo.rtdetr.rtdetrv2_decoder.MSDeformableAttention",
• "src.nn.backbone.presnet.PResNet",
• "src.zoo.rtdetr.rtdetrv2_decoder.MLP",
• "src.zoo.rtdetr.hybrid_encoder.RepVggBlock",
• "torch.FloatStorage",
• "collections.OrderedDict",
• "src.nn.backbone.presnet.BottleNeck",
• "src.zoo.rtdetr.hybrid_encoder.CSPRepLayer",
• "src.zoo.rtdetr.rtdetrv2_decoder.TransformerDecoderLayer",
• "src.zoo.rtdetr.hybrid_encoder.TransformerEncoderLayer",
• "torch.nn.modules.linear.Linear",
• "src.zoo.rtdetr.hybrid_encoder.TransformerEncoder",
• "torch.BoolStorage",
• "lightly_train._models.rtdetr.rtdetr.RTDETRModelWrapper"

How to fix it?