Dimension errors occur when training with MNIST datset.

[danny0628]

I'm using clara sdk using monai.

The following error occurs while doing "Start traning"

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 1, 7, 7], but got 3-dimensional input of size [2, 28, 28] instead

Should I modify it in the train_config.json file or in the densenet.py file? And what should I do?

My dataset size (png) : 28*28

This is my config file

{
  "epochs": 20,
  "multi_gpu": false,
  "learning_rate": 2e-5,
  "num_training_epoch_per_valid": 1,
  "num_interval_per_valid": 1,
  "use_amp": false,
  "train": {
    "model": {
      "name": "DenseNet",
      "args": {
        "spatial_dims": 2,
        "in_channels": 1,
        "out_channels": 2
      }
    },
    "loss": {
      "name": "CrossEntropyLoss"
    },
    "optimizer": {
      "name": "Adam",
      "args":{
          "lr": "{learning_rate}"
      }
    },
    "lr_scheduler":{
        "name":"StepLR",
        "args":{
            "step_size": 5000,
            "gamma": 0.1
        }
    },
    "pre_transforms": [
      {
        "name": "LoadImaged",
        "args": {
          "keys": ["image"]
        },
        "reader":"PILReader",
        "image_only":"True"
      },

      {
        "name": "ToTensord",
        "args": {
          "keys": ["image"]
        }
      }
    ],
    "dataset": {
      "name": "CacheDataset",
      "data_list_file_path": "{DATASET_JSON}",
      "data_file_base_dir": "{DATA_ROOT}",
      "data_list_key": "training",
      "args": {
        "cache_num": 4,
        "cache_rate": 1.0,
        "num_workers": 2
      }
    },
    "dataloader": {
      "name": "DataLoader",
      "args": {
        "batch_size": 2,
        "shuffle": true,
        "num_workers": 4
      }
    },
    "inferer": {
      "name": "SimpleInferer"
    },
    "handlers": [
      {
        "name": "CheckpointLoader",
        "disabled": "{dont_load_ckpt_model}",
        "args": {
          "load_path": "{MMAR_CKPT}",
          "load_dict": ["model"]
        }
      },
      {
        "name": "LrScheduleHandler",
        "args": {
          "print_lr": true
        }
      },
      {
        "name": "ValidationHandler",
        "args": {
          "interval": "{num_interval_per_valid}",
          "epoch_level": true
        }
      },
      {
        "name": "CheckpointSaver",
        "rank": 0,
        "args": {
          "save_dir": "{MMAR_CKPT_DIR}",
          "save_dict": ["model", "optimizer", "lr_scheduler"],
          "save_final": true,
          "save_interval": 0
        }
      },
      {
        "name": "StatsHandler",
        "rank": 0,
        "args": {
          "tag_name": "train_loss",
          "output_transform": "lambda x: x['loss']"
        }
      },
      {
        "name": "TensorBoardStatsHandler",
        "rank": 0,
        "args": {
          "log_dir": "{MMAR_CKPT_DIR}",
          "tag_name": "train_loss",
          "output_transform": "lambda x: x['loss']"
        }
      }
    ],
    "metrics": [
      {
        "name": "Accuracy",
        "log_label": "train_acc",
        "is_key_metric": true,
        "args": {
          "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      }
    ],
    "trainer": {
      "name": "SupervisedTrainer",
      "args": {
        "max_epochs": "{epochs}"
      }
    }
  },
   "validate": {
    "pre_transforms": [
      {
        "ref": "LoadImaged"
      },

      {
        "ref": "ToTensord"
      }
    ],
    "dataset": {
      "name": "CacheDataset",
      "data_list_file_path": "{DATASET_JSON}",
      "data_file_base_dir": "{DATA_ROOT}",
      "data_list_key": "validation",
      "args": {
        "cache_num": 4,
        "cache_rate": 1.0,
        "num_workers": 2
      }
    },
    "dataloader": {
      "name": "DataLoader",
      "args": {
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 4
      }
    },
    "inferer": {
      "name": "SlidingWindowInferer",
      "args": {
        "roi_size": [160, 160, 160],
        "sw_batch_size": 4,
        "overlap": 0.5
      }
    },
    "handlers": [
      {
        "name": "StatsHandler",
        "rank": 0,
        "args": {
          "output_transform": "lambda x: None"
        }
      },
      {
        "name": "TensorBoardStatsHandler",
        "rank": 0,
        "args": {
          "log_dir": "{MMAR_CKPT_DIR}",
          "output_transform": "lambda x: None"
        }
      },
      {
        "name": "CheckpointSaver",
        "rank": 0,
        "args": {
          "save_dir": "{MMAR_CKPT_DIR}",
          "save_dict": ["model","train_conf"],
          "save_key_metric": true
        }
      }
    ],

    "metrics": [
      {
        "name": "MeanDice",
        "log_label": "val_mean_dice",
        "is_key_metric": true,
        "args": {
          "include_background": false,
          "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      },
      {
        "name": "Accuracy",
        "log_label": "val_acc",
        "args": {
          "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      }
    ],
    "evaluator": {
      "name": "SupervisedEvaluator"
    }
  }
}

[Nic-Ma]

Hi @danny0628 ,

Thanks for your interest here.
I think your LoadImaged config is not correct and missed EnsureChannelFirstd transform.
You can refer to: https://github.com/Project-MONAI/tutorials/blob/master/3d_classification/torch/densenet_training_dict.py#L66

Thanks.