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README.md

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-<<<<<<< HEAD
-# cvdm
-=======
 # Conditional Variational Diffusion Models
 
 This code implements the Conditional Variational Diffusion Models as described [in the paper](https://arxiv.org/abs/2312.02246).
 
 ## Where to get the data?
 
 The datasets that we are using are available online:
-- [BioSR](https://github.com/qc17-THU/DL-SR)
+- [BioSR](https://github.com/qc17-THU/DL-SR), the data that we are using has been transformed to .npy files
 - [ImageNet from ILSVRC2012](https://www.image-net.org/challenges/LSVRC/2012/)
 - [HCOCO](https://github.com/bcmi/Image-Harmonization-Dataset-iHarmony4?tab=readme-ov-file) - only used in model evaluation
 
+It is assumed that for:
+- BioSR super-resolution task, data can be found in the directory specified as dataset_path in configs/biosr.yaml, in two files, x.npy (input) and y.npy (ground truth)
+- BioSR phase task, data can be found in the directory specified as dataset_path in configs/biosr_phase.yaml, in one file, y.npy (ground truth). Input to the model will be generated based on the ground truth.
+- ImageNet super-resolution task, data can be found in the directory specified as dataset_path in configs/imagenet_sr.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.
+- ImageNet phase task, data can be found in the directory specified as dataset_path in configs/imagenet_phase.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.
+- HCOCO phase evaluation task, data can be found in the directory specified as dataset_path in configs/hcoco_phase_eval.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.
 
 ## How to prepare environment?
 
+Run the following code:
 ```
 conda create -n myenv python=3.10
 conda activate myenv
 pip install -r requirements.txt
 pip install -e .
 ```
 
-## How to run the code?
-1. Modify the config in `configs/` directory with the path to the data you want to use and the directory for outputs
-2. Run the code from the root directory: `python scripts/train.py --config-path $PATH_TO_CONFIG --neptune-token $NEPTUNE_TOKEN`
->>>>>>> 9d9308e (Package CVDM)
+## How to run the training code?
+
+1. Download the data. 
+1. Modify the config in `configs/` directory with the path to the data you want to use and the directory for outputs.
+2. Run the code from the root directory: `python scripts/train.py --config-path $PATH_TO_CONFIG --neptune-token $NEPTUNE_TOKEN`.
+
+`--neptune-token` argument is optional.
+
+## How to run the training code?
+
+1. Download the data. 
+1. Modify the config in `configs/` directory with the path to the data you want to use and the directory for outputs.
+2. Run the code from the root directory: `python scripts/eval.py --config-path $PATH_TO_CONFIG --neptune-token $NEPTUNE_TOKEN`.
+
+`--neptune-token` argument is optional.
+
+## License
+This repository is released under the MIT License (refer to the LICENSE file for details).
+

configs/hcoco_phase_eval.yaml

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+task: "hcoco_phase"
+
+model:
+  noise_model_type: "unet"
+  alpha: 0.001
+  load_weights: null
+  snr_expansion_n: 1
+
+training:
+  lr: 0.0001
+  epochs: 100
+
+eval:
+  output_path: "outputs/hcoco"
+  generation_timesteps: 1000
+  checkpoint_freq: 1000
+  log_freq: 10
+  image_freq: 100
+  val_freq: 200
+  val_len: 100
+
+data:
+  dataset_path: "/bigdata/casus/MLID/maria/hcoco_sample"
+  n_samples: 100
+  batch_size: 1
+  im_size: 256
+
+neptune:
+  name: "Virtual_Stain"
+  project: "mlid/test"