Spaces:

---

Duplicated from  VikramSingh178/MedicalImagingApplication

epochs-demos

/

MedicalImagingApp

like 4

Runtime error

• "pytorch_lightning.profilers.base.PassThroughProfiler",
• "pytorch_lightning.trainer.progress.DataLoaderProgress",
• "torchmetrics.utilities.data.dim_zero_sum",
• "pytorch_lightning.trainer.connectors.signal_connector.SignalConnector",
• "torch.float32",
• "pytorch_lightning.tuner.tuning.Tuner",
• "torch.nn.modules.conv.Conv2d",
• "timm.models.layers.norm_act.BatchNormAct2d",
• "torch.nn.modules.loss.CrossEntropyLoss",
• "pytorch_lightning.trainer.progress.OptimizerProgress",
• "pytorch_lightning.trainer.progress.OptimizationProgress",
• "torchvision.transforms.transforms.Resize",
• "torch._C._VariableFunctionsClass",
• "pytorch_lightning.loops.batch.training_batch_loop.TrainingBatchLoop",
• "torch.utils.data.sampler.SequentialSampler",
• "__builtin__.set",
• "timm.models.layers.adaptive_avgmax_pool.SelectAdaptivePool2d",
• "lightning_fabric.plugins.environments.lightning.LightningEnvironment",
• "torchvision.datasets.folder.default_loader",
• "timm.models.efficientnet_blocks.DepthwiseSeparableConv",
• "torchvision.transforms.transforms.ToTensor",
• "pytorch_lightning.loops.optimization.manual_loop.ManualOptimization",
• "pytorch_lightning.utilities.enums._FaultTolerantMode",
• "pytorch_lightning.callbacks.model_summary.ModelSummary",
• "torch.utils.data.dataloader.DataLoader",
• "torchvision.transforms.transforms.CenterCrop",
• "lightning_utilities.core.rank_zero.WarningCache",
• "__builtin__.dict",
• "pytorch_lightning.trainer.connectors.logger_connector.logger_connector.LoggerConnector",
• "torch.optim.adam.Adam",
• "torch.nn.modules.flatten.Flatten",
• "torchvision.datasets.folder.ImageFolder",
• "pytorch_lightning.trainer.progress.ReadyCompletedTracker",
• "pytorch_lightning.trainer.connectors.callback_connector.CallbackConnector",
• "torchvision.datasets.vision.StandardTransform",
• "pytorch_lightning.trainer.supporters.CombinedDataset",
• "torch.nn.modules.linear.Identity",
• "torch.utils.data.sampler.RandomSampler",
• "pytorch_lightning.trainer.states.TrainerStatus",
• "torchvision.transforms.transforms.Compose",
• "torch._utils._rebuild_parameter",
• "pytorch_lightning.utilities.parsing.AttributeDict",
• "pytorch_lightning.loops.dataloader.prediction_loop.PredictionLoop",
• "torch._utils._rebuild_tensor_v2",
• "pytorch_lightning.callbacks.progress.tqdm_progress.TQDMProgressBar",
• "torch.distributed._shard.sharded_tensor.state_dict_hook",
• "pytorch_lightning.trainer.connectors.data_connector.DataConnector",
• "pytorch_lightning.loops.optimization.optimizer_loop.OptimizerLoop",
• "pytorch_lightning.loops.fit_loop.FitLoop",
• "pytorch_lightning.utilities.types.LRSchedulerConfig",
• "pytorch_lightning.trainer.connectors.accelerator_connector.AcceleratorConnector",
• "pytorch_lightning.strategies.single_device.SingleDeviceStrategy",
• "pytorch_lightning.trainer.progress.ProcessedTracker",
• "timm.models.efficientnet.EfficientNet",
• "pytorch_lightning.plugins.precision.precision_plugin.PrecisionPlugin",
• "pytorch_lightning.trainer.states.TrainerState",
• "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
• "torch.FloatStorage",
• "torch.utils.data.dataset.Subset",
• "torch.nn.modules.container.Sequential",
• "torch.optim.lr_scheduler.StepLR",
• "torch.distributed._shard.sharded_tensor.pre_load_state_dict_hook",
• "torchmetrics.metric.jit_distributed_available",
• "pytorch_lightning.trainer.supporters.TensorRunningAccum",
• "torchmetrics.classification.f_beta.MulticlassF1Score",
• "torch.utils.data._utils.collate.default_collate",
• "__main__.FineTuneModel",
• "pytorch_lightning.trainer.connectors.logger_connector.result._ResultCollection",
• "fsspec.spec.make_instance",
• "pytorch_lightning.trainer.connectors.data_connector._DataHookSelector",
• "pytorch_lightning.trainer.progress.SchedulerProgress",
• "fsspec.implementations.local.LocalFileSystem",
• "torch.nn.modules.module._WrappedHook",
• "pytorch_lightning.loops.epoch.training_epoch_loop.TrainingEpochLoop",
• "pytorch_lightning.loggers.wandb.WandbLogger",
• "pytorch_lightning.trainer.trainer.Trainer",
• "pytorch_lightning.trainer.progress.BatchProgress",
• "collections.defaultdict",
• "pytorch_lightning.accelerators.cuda.CUDAAccelerator",
• "pytorch_lightning.loops.dataloader.evaluation_loop.EvaluationLoop",
• "lightning_fabric.plugins.io.torch_io.TorchCheckpointIO",
• "torch.LongStorage",
• "torch.nn.modules.pooling.AdaptiveAvgPool2d",
• "torchmetrics.classification.accuracy.MulticlassAccuracy",
• "pytorch_lightning.trainer.progress.StartedTracker",
• "pytorch_lightning.callbacks.gradient_accumulation_scheduler.GradientAccumulationScheduler",
• "torchvision.transforms.functional.InterpolationMode",
• "torchmetrics.classification.precision_recall.MulticlassRecall",
• "collections.OrderedDict",
• "pytorch_lightning.trainer.connectors.checkpoint_connector.CheckpointConnector",
• "torch.nn.modules.activation.ReLU6",
• "__builtin__.getattr",
• "pytorch_lightning.trainer.connectors.data_connector._DataLoaderSource",
• "torchvision.transforms.transforms.Normalize",
• "pytorch_lightning.trainer.supporters.CombinedLoader",
• "torch.utils.data.sampler.BatchSampler",
• "torch.device",
• "torch.nn.modules.linear.Linear",
• "pytorch_lightning.trainer.states.TrainerFn",
• "pytorch_lightning.trainer.progress.Progress",
• "pytorch_lightning.loops.epoch.prediction_epoch_loop.PredictionEpochLoop",
• "pytorch_lightning.loops.epoch.evaluation_epoch_loop.EvaluationEpochLoop",
• "timm.models.efficientnet_blocks.InvertedResidual",
• "pytorch_lightning.trainer.connectors.logger_connector.result._ResultMetric"

How to fix it?