docs/inference.md

All the configurations for inference is stored in inference.json, change those parameters:

input_dict

input_dict defines the image to segment and the prompt for segmentation.

"input_dict": "$[{'image': '/data/Task09_Spleen/imagesTs/spleen_15.nii.gz', 'label_prompt':[1]}]",
"input_dict": "$[{'image': '/data/Task09_Spleen/imagesTs/spleen_15.nii.gz', 'points':[[138,245,18], [271,343,27]], 'point_labels':[1,0]}]"

• The input_dict must include the key image which contain the absolute path to the nii image file, and includes prompt keys of label_prompt, points and point_labels.
• The label_prompt is a list of length B, which can perform B foreground objects segmentation, e.g. [2,3,4,5]. If B>1, Point prompts must NOT be provided.
• The points is of shape [N, 3] like [[x1,y1,z1],[x2,y2,z2],...[xN,yN,zN]], representing N point coordinates IN THE ORIGINAL IMAGE SPACE of a single foreground object. point_labels is a list of length [N] like [1,1,0,-1,...], which matches the points. 0 means background, 1 means foreground, -1 means ignoring this point. points and point_labels must pe provided together and match length.
• B must be 1 if label_prompt and points are provided together. The inferer only supports SINGLE OBJECT point click segmentatation.
• If no prompt is provided, the model will use everything_labels to segment 117 classes:

list(set([i+1 for i in range(132)]) - set([2,16,18,20,21,23,24,25,26,27,128,129,130,131,132]))

• The points together with label_prompts for "Kidney", "Lung", "Bone" (class index [2, 20, 21]) are not allowed since those prompts will be divided into sub-categories (e.g. left kidney and right kidney). Use points for the sub-categories as defined in the inference.json.
• To specify a new class for zero-shot segmentation, set the label_prompt to a value between 133 and 254. Ensure that points and point_labels are also provided; otherwise, the inference result will be a tensor of zeros.

label_prompt and label_dict

The label_dict defined in configs/metadata.json has in total 132 classes. However, there are 5 we do not support and we keep them due to legacy issue. So in total VISTA3D support 127 classes.

"16, # prostate or uterus" since we already have "prostate" class,
"18, # rectum", insufficient data or dataset excluded.
"130, # liver tumor" already have hepatic tumor.
"129, # kidney mass" insufficient data or dataset excluded.
"131, # vertebrae L6", insufficient data or dataset excluded.

These 5 are excluded in the everything_labels. Another 7 tumor and vessel classes are also removed since they will overlap with other organs and make the output messy. To segment those 7 classes, we recommend users to directly set label_prompt to those indexes and avoid using them in everything_labels. For "Kidney", "Lung", "Bone" (class index [2, 20, 21]), VISTA3D did not directly use the class index for segmentation, but instead convert them to their subclass indexes as defined by subclass dict. For example, "2-Kidney" is converted to "14-Left Kidney" + "5-Right Kidney" since "2" is defined in subclasss dict.

resample_spacing

The optimal inference resample spacing should be changed according to the task. For monkey data, a high resolution of [1,1,1] showed better automatic inference results. This spacing applies to both automatic and interactive segmentation. For zero-shot interactive segmentation for non-human CTs e.g. mouse CT or even rock/stone CT, using original resolution (set resample_spacing to [-1,-1,-1]) may give better interactive results.

use_point_window

When user click a point, there is no need to perform whole image sliding window inference. Set "use_point_window" to true in the inference.json to enable this function. A window centered at the clicked points will be used for inference. All values outside of the window will set to be "NaN" unless "prev_mask" is passed to the inferer (255 is used to represent NaN). If no point click exists, this function will not be used. Notice if "use_point_window" is true and user provided point clicks, there will be obvious cut-off box artefacts.

Inference GPU benchmarks

Benchmarks on a 16GB V100 GPU with 400G system cpu memory.

Volume size at 1.5x1.5x1.5 mm	333x333x603	512x512x512	512x512x768	1024x1024x512	1024x1024x768
RunTime	1m07s	2m09s	3m25s	9m20s	killed

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"

By default, the argument head_trt_enabled is set to false in configs/inference_trt.json. This means that the class_head module of the network will not be converted into a TensorRT model. Setting this to true may accelerate the process, but there are some limitations:

Since the label_prompt will be converted into a tensor and input into the class_head module, the batch size of this input tensor will equal the length of the original label_prompt list (if no prompt is provided, the length is 117). To make the TensorRT model work on the class_head module, you should set a suitable dynamic batch size range. The maximum dynamic batch size can be configured using the argument max_prompt_size in configs/inference_trt.json. If the length of the label_prompt list exceeds max_prompt_size, the engine will fall back to using the normal PyTorch model for inference. Setting a larger max_prompt_size can cover more input cases but may require more GPU memory (the default value is 4, which requires 16 GB of GPU memory). Therefore, please set it to a reasonable value according to your actual requirements.

TensorRT speedup

The vista3d bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note for 32bit precision models, they are benchmarked with tf32 weight format.

method	torch_tf32(ms)	torch_amp(ms)	trt_tf32(ms)	trt_fp16(ms)	speedup amp	speedup tf32	speedup fp16	amp vs fp16
model computation	108.53	91.9	106.84	60.02	1.18	1.02	1.81	1.53
end2end	6740	5166	5242	3386	1.30	1.29	1.99	1.53

Where:

• model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
• end2end means run the bundle end-to-end with the TensorRT based model.
• torch_tf32 and torch_amp are for the PyTorch models with or without amp mode.
• trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
• speedup amp, speedup tf32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
• amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

• TensorRT: 10.3.0+cuda12.6
• Torch-TensorRT Version: 2.4.0
• CPU Architecture: x86-64
• OS: ubuntu 20.04
• Python version:3.10.12
• CUDA version: 12.6
• GPU models and configuration: A100 80G