Description

A pre-trained model for the endoscopic inbody classification task.

Model Overview

This model is trained using the SEResNet50 structure, whose details can be found in [1]. All datasets are from private samples of Activ Surgical. Samples in training and validation dataset are from the same 4 videos, while test samples are from different two videos. The pytorch model and torchscript model are shared in google drive. Modify the "bundle_root" parameter specified in configs/train.json and configs/inference.json to reflect where models are downloaded. Expected directory path to place downloaded models is "models/" under "bundle_root".

Data

Datasets used in this work were provided by Activ Surgical. Here is a link of 20 samples (10 in-body and 10 out-body) to show what this dataset looks like. Modify the "dataset_dir" parameter specified in configs/train.json and configs/inference.json to reflect where the data is.

The input label json should be a list made up by dicts which includes "image" and "label" keys. An example format is shown below.

[
    {
        "image":"/path/to/image/image_name0.jpg",
        "label": 0
    },
    {
        "image":"/path/to/image/image_name1.jpg",
        "label": 0
    },
    {
        "image":"/path/to/image/image_name2.jpg",
        "label": 1
    },
    ....
    {
        "image":"/path/to/image/image_namek.jpg",
        "label": 0
    },
]

Training configuration

The training was performed with an at least 12GB-memory GPU.

Actual Model Input: 256 x 256 x 3

Input and output formats

Input: 3 channel video frames

Output: probability vector whose length equals to 2: Label 0: in body; Label 1: out body

Scores

This model achieves the following accuracy score on the test dataset:

Accuracy = 0.98

commands example

Execute training:

python -m monai.bundle run training \
    --meta_file configs/metadata.json \
    --config_file configs/train.json \
    --logging_file configs/logging.conf

Override the train config to execute multi-GPU training:

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training \
    --meta_file configs/metadata.json \
    --config_file "['configs/train.json','configs/multi_gpu_train.json']" \
    --logging_file configs/logging.conf

Please note that the distributed training related options depend on the actual running environment, thus you may need to remove --standalone, modify --nnodes or do some other necessary changes according to the machine you used. Please refer to pytorch's official tutorial for more details.

Override the train config to execute evaluation with the trained model:

python -m monai.bundle run evaluating \
    --meta_file configs/metadata.json \
    --config_file "['configs/train.json','configs/evaluate.json']" \
    --logging_file configs/logging.conf

Execute inference:

python -m monai.bundle run evaluating \
    --meta_file configs/metadata.json \
    --config_file configs/inference.json \
    --logging_file configs/logging.conf

Export checkpoint to TorchScript file:

python -m monai.bundle ckpt_export network_def \
    --filepath models/model.ts \
    --ckpt_file models/model.pt \
    --meta_file configs/metadata.json \
    --config_file configs/inference.json

Export checkpoint to onnx file, which has been tested on pytorch 1.12.0:

python scripts/export_to_onnx.py --model models/model.pt --outpath models/model.onnx

Export TensorRT float16 model from the onnx model:

trtexec --onnx=models/model.onnx --saveEngine=models/model.trt --fp16 \
    --minShapes=INPUT__0:1x3x256x256 \
    --optShapes=INPUT__0:16x3x256x256 \
    --maxShapes=INPUT__0:32x3x256x256 \
    --shapes=INPUT__0:8x3x256x256

This command need TensorRT with correct CUDA installed in the environment. For the detail of installing TensorRT, please refer to this link.

References

[1] J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.pdf

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.