remove duplicate link and modify default dataset dir

Signed-off-by: Yiheng Wang <[email protected]>

models/brats_mri_generative_diffusion/configs/train_autoencoder.json

@@ -8,7 +8,7 @@
     "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
     "ckpt_dir": "$@bundle_root + '/models'",
     "tf_dir": "$@bundle_root + '/eval'",
-    "dataset_dir": "/workspace/data/medical/Task01_BrainTumour",
+    "dataset_dir": "/workspace/data/medical",
     "pretrained": false,
     "perceptual_loss_model_weights_path": null,
     "train_batch_size": 2,

models/brats_mri_generative_diffusion/docs/README.md

@@ -7,7 +7,7 @@ This model is trained on BraTS 2016 and 2017 data from [Medical Decathlon](http:
 
 This model is a generator for creating images like the Flair MRIs based on BraTS 2016 and 2017 data. It was trained as a 3d latent diffusion model and accepts Gaussian random noise as inputs to produce an image output. The `train_autoencoder.json` file describes the training process of the variational autoencoder with GAN loss. The `train_diffusion.json` file describes the training process of the 3D latent diffusion model.
 
-In this bundle, the autoencoder uses perceptual loss, which is based on ResNet50 with pre-trained weights (The network is frozen and will not be trained in the bundle). In default, the `pretrained` parameter is specified as `False` in `train_autoencoder.json`. To ensure correct training, changing the default settings is necessary. There are two ways to utilize pretrained weights:
+In this bundle, the autoencoder uses perceptual loss, which is based on ResNet50 with pre-trained weights (the network is frozen and will not be trained in the bundle). In default, the `pretrained` parameter is specified as `False` in `train_autoencoder.json`. To ensure correct training, changing the default settings is necessary. There are two ways to utilize pretrained weights:
 1. if set `pretrained` to `True`, ImageNet pretrained weights from [torchvision](https://pytorch.org/vision/stable/_modules/torchvision/models/resnet.html#ResNet50_Weights) will be used. However, the weights are for non-commercial use only.
 2. if set `pretrained` to `True` and specifies the `perceptual_loss_model_weights_path` parameter, users are able to load weights from a local path. This is the way this bundle used to train, and the pre-trained weights are from some internal data.
 
@@ -31,7 +31,7 @@ cd ..
 ```
 
 ## Data
-The training data is BraTS 2016 and 2017 from [Medical Decathlon](http://medicaldecathlon.com/). Users can find more details on the datasets (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
+The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.
 
 - Target: Image Generation
 - Task: Synthesis
@@ -107,7 +107,7 @@ For more details usage instructions, visit the [MONAI Bundle Configuration Page]
 python -m monai.bundle run --config_file configs/train_autoencoder.json
 ```
 
-Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:
+Please note that if the default dataset path is not modified with the actual path (it should be the path that contains `Task01_BrainTumour`) in the bundle config files, you can also override it by using `--dataset_dir`:
 
 ```
 python -m monai.bundle run --config_file configs/train_autoencoder.json --dataset_dir <actual dataset path>