Add FID Score Computation

generation/maisi/README.md

@@ -247,13 +247,65 @@ torchrun \
 ```
 Please also check [maisi_train_controlnet_tutorial.ipynb](./maisi_train_controlnet_tutorial.ipynb) for more details about data preparation and training parameters.
 
-### 4. License
+### 4. FID Score Computation
+
+We provide the `compute_fid_2-5d_ct.py` script that calculates the Frechet Inception Distance (FID) between two 3D medical datasets (e.g., **real** vs. **synthetic** images). It uses a **2.5D** feature-extraction approach across three orthogonal planes (XY, YZ, ZX) and leverages **distributed GPU processing** (via PyTorch’s `torch.distributed` and NCCL) for efficient, large-scale computations.
+
+#### Key Features
+
+- **Distributed Processing**
+  Scales to multiple GPUs and larger datasets by splitting the workload across devices.
+
+- **2.5D Feature Extraction**
+  Uses a slice-based technique, applying a 2D model across all slices in each dimension.
+
+- **Flexible Preprocessing**
+  Supports optional center-cropping, padding, and resampling to target shapes or voxel spacings.
+
+#### Usage Example
+
+Suppose your **real** dataset root is `path/to/real_images`, and you have a `real_filelist.txt` that lists filenames line by line, such as:
+```
+case001.nii.gz
+case002.nii.gz
+case003.nii.gz
+```
+You also have a **synthetic** dataset in `path/to/synth_images` with a corresponding `synth_filelist.txt`. You can run the script as follows:
+
+```bash
+torchrun --nproc_per_node=2 compute_fid_2-5d_ct.py \
+  --model_name "radimagenet_resnet50" \
+  --real_dataset_root "path/to/real_images" \
+  --real_filelist "path/to/real_filelist.txt" \
+  --real_features_dir "datasetA" \
+  --synth_dataset_root "path/to/synth_images" \
+  --synth_filelist "path/to/synth_filelist.txt" \
+  --synth_features_dir "datasetB" \
+  --enable_center_slices_ratio 0.4 \
+  --enable_padding True \
+  --enable_center_cropping True \
+  --enable_resampling_spacing "1.0x1.0x1.0" \
+  --ignore_existing True \
+  --num_images 100 \
+  --output_root "./features/features-512x512x512" \
+  --target_shape "512x512x512"
+```
+
+This command will:
+1. Launch a distributed run with 2 GPUs.
+2. Load each `.nii.gz` file from your specified `real_filelist` and `synth_filelist`.
+3. Apply 2.5D feature extraction across the XY, YZ, and ZX planes.
+4. Compute FID to compare **real** vs. **synthetic** feature distributions.
+
+For more details, see the in-code docstring in [`compute_fid_2-5d_ct.py`](./scripts/compute_fid_2-5d_ct.py) or consult our documentation for a deeper dive into function arguments and the underlying implementation.
+
+### 5. License
 
 The code is released under Apache 2.0 License.
 
 The model weight is released under [NSCLv1 License](./LICENSE.weights).
 
-### 5. Questions and Bugs
+### 6. 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).