Merge branch 'main' into anomaly_detection_porting

detection/generate_transforms.py

@@ -148,7 +148,7 @@ def generate_detection_train_transform(
                 spatial_axes=(0, 1),
             ),
             # apply the same affine matrix which already applied on the images to the points
-            ApplyTransformToPointsd(keys=[point_key], refer_key=image_key, affine_lps_to_ras=affine_lps_to_ras),
+            ApplyTransformToPointsd(keys=[point_key], refer_keys=image_key, affine_lps_to_ras=affine_lps_to_ras),
             # convert points back to boxes
             ConvertPointsToBoxesd(keys=[point_key]),
             ClipBoxToImaged(

generation/maisi/README.md

@@ -68,6 +68,14 @@ The information for the inference input, like body region and anatomy to generat
 
 To generate images with substantial dimensions, such as 512 × 512 × 512 or larger, using GPUs with 80GB of memory, it is advisable to configure the `"num_splits"` parameter in [the auto-encoder configuration](./configs/config_maisi.json#L11-L37) to 16. This adjustment is crucial to avoid out-of-memory issues during inference.
 
+#### Recommended spacing for different output sizes:
+
+|`output_size`| Recommended `"spacing"`|
+|:-----:|:-----:|
+[256, 256, 256]  | [1.5, 1.5, 1.5] |
+[512, 512, 128]  | [0.8, 0.8, 2.5] |
+[512, 512, 512]  | [1.0, 1.0, 1.0] |
+
 #### Execute Inference:
 To run the inference script, please run:
 ```bash

generation/maisi/scripts/infer_controlnet.py

@@ -23,7 +23,7 @@
 from monai.transforms import SaveImage
 from monai.utils import RankFilter
 
-from .sample import ldm_conditional_sample_one_image
+from .sample import check_input, ldm_conditional_sample_one_image
 from .utils import define_instance, prepare_maisi_controlnet_json_dataloader, setup_ddp
 
 
@@ -150,10 +150,13 @@ def main():
         top_region_index_tensor = batch["top_region_index"].to(device)
         bottom_region_index_tensor = batch["bottom_region_index"].to(device)
         spacing_tensor = batch["spacing"].to(device)
+        out_spacing = tuple((batch["spacing"].squeeze().numpy() / 100).tolist())
         # get target dimension
         dim = batch["dim"]
         output_size = (dim[0].item(), dim[1].item(), dim[2].item())
         latent_shape = (args.latent_channels, output_size[0] // 4, output_size[1] // 4, output_size[2] // 4)
+        # check if output_size and out_spacing are valid.
+        check_input(None, None, None, output_size, out_spacing, None)
         # generate a single synthetic image using a latent diffusion model with controlnet.
         synthetic_images, _ = ldm_conditional_sample_one_image(
             autoencoder,