diff --git a/deployment/Triton/client/client.py b/deployment/Triton/client/client.py index 868a8bb7f5..411a01fc61 100644 --- a/deployment/Triton/client/client.py +++ b/deployment/Triton/client/client.py @@ -51,7 +51,7 @@ from monai.apps.utils import download_and_extract model_name = "monai_covid" -gdrive_path = "https://drive.google.com/uc?id=1GYvHGU2jES0m_msin-FFQnmTOaHkl0LN" +gdrive_path = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/covid19_compressed.tar.gz" covid19_filename = "covid19_compress.tar.gz" md5_check = "cadd79d5ca9ccdee2b49cd0c8a3e6217" diff --git a/deployment/Triton/client/client_mednist.py b/deployment/Triton/client/client_mednist.py index c525bdab33..aea2de8304 100644 --- a/deployment/Triton/client/client_mednist.py +++ b/deployment/Triton/client/client_mednist.py @@ -55,7 +55,7 @@ model_name = "mednist_class" -gdrive_path = "https://drive.google.com/uc?id=1HQk4i4vXKUX_aAYR4wcZQKd-qk5Lcm_W" +gdrive_path = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/MedNIST_demo.tar.gz" mednist_filename = "MedNIST_demo.tar.gz" md5_check = "3f24a5833bb0455a7815c4e0ecc8a810" diff --git a/deployment/Triton/models/mednist_class/1/model.py b/deployment/Triton/models/mednist_class/1/model.py index 5857171159..7e420f9de6 100644 --- a/deployment/Triton/models/mednist_class/1/model.py +++ b/deployment/Triton/models/mednist_class/1/model.py @@ -74,7 +74,7 @@ logger = logging.getLogger(__name__) -gdrive_url = "https://drive.google.com/uc?id=1c6noLV9oR0_mQwrsiQ9TqaaeWFKyw46l" +gdrive_url = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/MedNist_model.tar.gz" model_filename = "MedNIST_model.tar.gz" md5_check = "a4fb9d6147599e104b5d8dc1809ed034" diff --git a/deployment/Triton/models/monai_covid/1/model.py b/deployment/Triton/models/monai_covid/1/model.py index 6b5f9a1ecc..34595f1bec 100644 --- a/deployment/Triton/models/monai_covid/1/model.py +++ b/deployment/Triton/models/monai_covid/1/model.py @@ -64,7 +64,7 @@ logger = logging.getLogger(__name__) -gdrive_url = "https://drive.google.com/uc?id=1U9Oaw47SWMJeDkg1FSTY1W__tQOY1nAZ" +gdrive_url = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/covid19_model.tar.gz" model_filename = "covid19_model.tar.gz" md5_check = "571046a25659515bf7abee4266f14435" diff --git a/generation/2d_ldm/README.md b/generation/2d_ldm/README.md index 914c392065..18b8b522ca 100644 --- a/generation/2d_ldm/README.md +++ b/generation/2d_ldm/README.md @@ -26,12 +26,9 @@ python download_brats_data.py -e ./config/environment.json Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! -### 2. Installation -Please refer to the [Installation of MONAI Generative Model](../README.md) +### 2. Run the example -### 3. Run the example - -#### [3.1 2D Autoencoder Training](./train_autoencoder.py) +#### [2.1 2D Autoencoder Training](./train_autoencoder.py) The network configuration files are located in [./config/config_train_32g.json](./config/config_train_32g.json) for 32G GPU and [./config/config_train_16g.json](./config/config_train_16g.json) for 16G GPU. You can modify the hyperparameters in these files to suit your requirements. @@ -74,7 +71,7 @@ An example reconstruction result is shown below: Autoencoder reconstruction result -#### [3.2 2D Latent Diffusion Training](./train_diffusion.py) +#### [2.2 2D Latent Diffusion Training](./train_diffusion.py) The training script uses the batch size and patch size defined in the configuration files. If you have a different GPU memory size, you should adjust the `"batch_size"` and `"patch_size"` parameters in the `"diffusion_train"` to match your GPU. Note that the `"patch_size"` needs to be divisible by 16 and no larger than 256. To train with single 32G GPU, please run: @@ -97,7 +94,7 @@ torchrun \ latent diffusion validation curve

-#### [3.3 Inference](./inference.py) +#### [2.3 Inference](./inference.py) To generate one image during inference, please run the following command: ```bash python inference.py -c ./config/config_train_32g.json -e ./config/environment.json --num 1 @@ -115,7 +112,7 @@ An example output is shown below.

-### 4. Questions and bugs +### 3. Questions and bugs - For questions relating to the use of MONAI, please use our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI. - For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues). diff --git a/generation/3d_ldm/README.md b/generation/3d_ldm/README.md index b34956ce55..3bb741757c 100644 --- a/generation/3d_ldm/README.md +++ b/generation/3d_ldm/README.md @@ -26,12 +26,9 @@ python download_brats_data.py -e ./config/environment.json Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! -### 2. Installation -Please refer to the [Installation of MONAI Generative Model](../README.md) +### 2. Run the example -### 3. Run the example - -#### [3.1 3D Autoencoder Training](./train_autoencoder.py) +#### [2.1 3D Autoencoder Training](./train_autoencoder.py) The network configuration files are located in [./config/config_train_32g.json](./config/config_train_32g.json) for 32G GPU and [./config/config_train_16g.json](./config/config_train_16g.json) for 16G GPU. @@ -73,7 +70,7 @@ torchrun \ With eight DGX1V 32G GPUs, it took around 55 hours to train 1000 epochs. -#### [3.2 3D Latent Diffusion Training](./train_diffusion.py) +#### [2.2 3D Latent Diffusion Training](./train_diffusion.py) The training script uses the batch size and patch size defined in the configuration files. If you have a different GPU memory size, you should adjust the `"batch_size"` and `"patch_size"` parameters in the `"diffusion_train"` to match your GPU. Note that the `"patch_size"` needs to be divisible by 16. To train with single 32G GPU, please run: @@ -96,7 +93,7 @@ torchrun \ latent diffusion validation curve

-#### [3.3 Inference](./inference.py) +#### [2.3 Inference](./inference.py) To generate one image during inference, please run the following command: ```bash python inference.py -c ./config/config_train_32g.json -e ./config/environment.json --num 1 @@ -112,7 +109,7 @@ An example output is shown below.

-### 4. Questions and bugs +### 3. Questions and bugs - For questions relating to the use of MONAI, please use our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI. - For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues). diff --git a/modules/developer_guide.ipynb b/modules/developer_guide.ipynb index 2fa23bdf3d..0ac767db09 100644 --- a/modules/developer_guide.ipynb +++ b/modules/developer_guide.ipynb @@ -717,7 +717,7 @@ "id": "kvn_6mf9gZoA" }, "source": [ - "The following commands will start a `SupervisedTrainer` instance. As an extension of Pytorch ignite's facilities, it combines all the elements mentioned before. Calling `trainer.run()` will train the network for two epochs and compute `MeadDice` metric based on the training data at the end of every epoch.\n", + "The following commands will start a `SupervisedTrainer` instance. As an extension of Pytorch ignite's facilities, it combines all the elements mentioned before. Calling `trainer.run()` will train the network for two epochs and compute `MeanDice` metric based on the training data at the end of every epoch.\n", "\n", "The `key_train_metric` is used to track the progress of model quality improvement. Additional handlers could be set to do early stopping and learning rate scheduling.\n", "\n", diff --git a/modules/interpretability/class_lung_lesion.ipynb b/modules/interpretability/class_lung_lesion.ipynb index 33f4167517..b16c9354cd 100644 --- a/modules/interpretability/class_lung_lesion.ipynb +++ b/modules/interpretability/class_lung_lesion.ipynb @@ -29,7 +29,7 @@ "\n", "For the demo data:\n", "- Please see the `bbox_gen.py` script for generating the patch classification data from MSD task06_lung (available via `monai.apps.DecathlonDataset`);\n", - "- Alternatively, the patch dataset (~130MB) is available for direct downloading at: https://drive.google.com/drive/folders/1pQdzdkkC9c2GOblLgpGlG3vxsSK9NtDx\n", + "- Alternatively, the patch dataset (~130MB) is available for direct downloading at: https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/lung_lesion_patches.tar.gz\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/interpretability/class_lung_lesion.ipynb)" ] diff --git a/modules/tcia_csv_processing.ipynb b/modules/tcia_csv_processing.ipynb index 68e5d89901..deee2473d3 100644 --- a/modules/tcia_csv_processing.ipynb +++ b/modules/tcia_csv_processing.ipynb @@ -247,8 +247,8 @@ "metadata": {}, "source": [ "## Download and Load the CSV file with `TCIADataset`\n", - "Here we use the demo data in Google drive:\n", - "https://drive.google.com/file/d/1HQ7BZvBr1edmi8HIwdG5KBweXWms5Uzk/view?usp=sharing \n", + "Here we use the demo data located here:\n", + "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/ISPY1_Combined.csv \n", "\n", "Expect the first row of CSV file to be titles of columns. we only use the first 8 rows to execute demo processing." ]