Question about the autocoder and training data

[Adele0108]

Hi, there is no details about the encoder and decoder in the paper and code. I would like to ask what the specific model structure of the autocoder? And what's the information of training data, do they include the super resolution ground truth? Looking forward to your reply, thanks~

[jueqiw]

Hi, for the model part of this project, I use the model from this Brain LDM paper. You can find the autoencoder architecture here. I use the images from the IXI dataset, which are high resolution, and we create low-resolution images from them.

Sorry for my late reply.

[06Liz]

from pathlib import Path

Use environment variables to auto-detect whether we are running an a Compute Canada cluster:

Thanks to https://github.com/DM-Berger/unet-learn/blob/master/src/train/load.py for this trick.

COMPUTECANADA = False TMP = os.environ.get("SLURM_TMPDIR")

if TMP: COMPUTECANADA = True

if COMPUTECANADA: INPUT_FOLDER = Path(str(TMP)).resolve() / "work" / "inputs" MASK_FOLDER = Path(str(TMP)).resolve() / "work" / "inputs" / "masks" PRETRAINED_MODEL_FOLDER = Path(str(TMP)).resolve() / "work" / "trained_models" PRETRAINED_MODEL_DDPM_PATH = ( Path(str(TMP)).resolve() / "work" / "trained_models" / "ddpm" ) PRETRAINED_MODEL_VAE_PATH = ( Path(str(TMP)).resolve() / "work" / "trained_models" / "vae" ) PRETRAINED_MODEL_DECODER_PATH = ( Path(str(TMP)).resolve() / "work" / "trained_models" / "decoder" ) PRETRAINED_MODEL_VGG_PATH = ( Path(str(TMP)).resolve() / "work" / "trained_models" / "vgg16.pt" ) OUTPUT_FOLDER = Path(str(TMP)).resolve() / "work" / "outputs" else: INPUT_FOLDER = Path(file).resolve().parent.parent.parent / "data" / "IXI" MASK_FOLDER = Path(file).resolve().parent.parent / "masks" OASIS_FOLDER = Path(file).resolve().parent.parent.parent / "data" / "OASIS" PRETRAINED_MODEL_FOLDER = ( Path(file).resolve().parent.parent.parent / "data" / "trained_models" ) 这些预训练模型和数据你是怎么处理和下载的 @Adele0108

[06Liz]

Where the decoder is downloaded from