update ONNX-TensorRT descriptions

README.md

@@ -72,7 +72,7 @@ Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducib
 ![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)
 
 #### TensorRT speedup
-The `brats_mri_segmentation` bundle supports the TensorRT acceleration through the ONNX-TensorRT way. The table below shows the speedup ratios benchmarked on an A100 80G GPU.
+The `brats_mri_segmentation` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU.
 
 | method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
@@ -87,7 +87,7 @@ Where:
 - `speedup amp`, `speedup fp32` 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.
 
-Currently, this model can only be accelerated through the ONNX-TensorRT way and the Torch-TensorRT way will come soon.
+Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.
 
 This result is benchmarked under:
  - TensorRT: 8.5.3+cuda11.8

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.4.4",
+    "version": "0.4.5",
     "changelog": {
+        "0.4.5": "update ONNX-TensorRT descriptions",
         "0.4.4": "update error links",
         "0.4.3": "add the ONNX-TensorRT way of model conversion",
         "0.4.2": "fix mgpu finalize issue",
@@ -22,7 +23,7 @@
         "0.1.1": "update for MetaTensor",
         "0.1.0": "complete the model package"
     },
-    "monai_version": "1.2.0rc4",
+    "monai_version": "1.2.0rc5",
     "pytorch_version": "1.13.1",
     "numpy_version": "1.22.2",
     "optional_packages_version": {

docs/README.md

@@ -65,7 +65,7 @@ Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducib
 ![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)
 
 #### TensorRT speedup
-The `brats_mri_segmentation` bundle supports the TensorRT acceleration through the ONNX-TensorRT way. The table below shows the speedup ratios benchmarked on an A100 80G GPU.
+The `brats_mri_segmentation` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU.
 
 | method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
@@ -80,7 +80,7 @@ Where:
 - `speedup amp`, `speedup fp32` 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.
 
-Currently, this model can only be accelerated through the ONNX-TensorRT way and the Torch-TensorRT way will come soon.
+Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.
 
 This result is benchmarked under:
  - TensorRT: 8.5.3+cuda11.8