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ml-bench

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cancer-cell-detection-counting

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Running

• "torch.nn.modules.linear.NonDynamicallyQuantizableLinear",
• "torch.nn.modules.dropout.Dropout",
• "torch._utils._rebuild_parameter",
• "torch.nn.modules.normalization.LayerNorm",
• "ultralytics.nn.modules.transformer.DeformableTransformerDecoderLayer",
• "torch.nn.modules.linear.Linear",
• "torch.nn.modules.activation.SiLU",
• "_codecs.encode",
• "ultralytics.nn.modules.conv.RepConv",
• "ultralytics.nn.modules.conv.LightConv",
• "ultralytics.nn.modules.block.RepC3",
• "ultralytics.nn.modules.conv.Conv",
• "numpy._core.multiarray.scalar",
• "ultralytics.utils.loss.VarifocalLoss",
• "ultralytics.nn.modules.conv.Concat",
• "ultralytics.nn.modules.block.HGStem",
• "__builtin__.set",
• "torch.device",
• "torch.HalfStorage",
• "ultralytics.nn.modules.conv.DWConv",
• "ultralytics.nn.modules.transformer.MSDeformAttn",
• "torch.nn.modules.activation.ReLU",
• "numpy.dtype",
• "torch.nn.modules.container.Sequential",
• "torch.FloatStorage",
• "torch.nn.modules.conv.Conv2d",
• "torch.nn.modules.activation.MultiheadAttention",
• "torch.nn.modules.container.ModuleList",
• "ultralytics.nn.modules.transformer.MLP",
• "ultralytics.utils.loss.FocalLoss",
• "torch.nn.modules.batchnorm.BatchNorm2d",
• "ultralytics.utils.IterableSimpleNamespace",
• "ultralytics.models.utils.loss.RTDETRDetectionLoss",
• "torch.nn.modules.linear.Identity",
• "collections.OrderedDict",
• "ultralytics.nn.modules.head.RTDETRDecoder",
• "torch.nn.modules.pooling.MaxPool2d",
• "torch.nn.modules.upsampling.Upsample",
• "torch._utils._rebuild_tensor_v2",
• "ultralytics.models.utils.ops.HungarianMatcher",
• "torch.nn.modules.sparse.Embedding",
• "ultralytics.nn.modules.transformer.AIFI",
• "ultralytics.nn.tasks.RTDETRDetectionModel",
• "ultralytics.nn.modules.transformer.DeformableTransformerDecoder",
• "ultralytics.nn.modules.block.HGBlock",
• "torch.LongStorage",
• "torch.nn.modules.activation.GELU"

How to fix it?