update license files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/model.ts filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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configs/evaluate.json

@@ -0,0 +1,48 @@
+{
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "AsDiscreted",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "argmax": [
+                    true,
+                    false
+                ],
+                "to_onehot": 2
+            }
+        ]
+    },
+    "validate#handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_accu"
+            ],
+            "metric_details": [
+                "val_accu"
+            ],
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "summary_ops": "*"
+        }
+    ],
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

@@ -0,0 +1,113 @@
+{
+    "imports": [
+        "$import json",
+        "$import os",
+        "$import torch"
+    ],
+    "bundle_root": "/workspace/bundle/endoscopic_inbody_classification",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/endoscopic_inbody_classification",
+    "test_json": "$@dataset_dir+'/test.json'",
+    "test_fp": "$open(@test_json,'r', encoding='utf8')",
+    "test_dict": "$json.load(@test_fp)",
+    "test_close": "$@test_fp.close()",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SEResNet50",
+        "spatial_dims": 2,
+        "in_channels": 3,
+        "num_classes": 2
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image"
+            },
+            {
+                "_target_": "AsChannelFirstd",
+                "keys": "image"
+            },
+            {
+                "_target_": "Resized",
+                "keys": "image",
+                "spatial_size": [
+                    256,
+                    256
+                ],
+                "mode": "bilinear"
+            },
+            {
+                "_target_": "CastToTyped",
+                "dtype": "$torch.float32",
+                "keys": "image"
+            },
+            {
+                "_target_": "NormalizeIntensityd",
+                "nonzero": true,
+                "channel_wise": true,
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "image"
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "@test_dict",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "AsDiscreted",
+                "argmax": true,
+                "to_onehot": 2,
+                "keys": [
+                    "pred"
+                ]
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": true
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers"
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@evaluator.run()"
+    ]
+}

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,77 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.2.0",
+    "changelog": {
+        "0.2.0": "update license files",
+        "0.1.0": "complete the first version model package",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.0.0",
+    "pytorch_version": "1.12.0",
+    "numpy_version": "1.22.4",
+    "optional_packages_version": {
+        "nibabel": "4.0.1",
+        "pytorch-ignite": "0.4.9"
+    },
+    "task": "Endoscopic inbody classification classification",
+    "description": "A pre-trained binary classification model for endoscopic inbody classification task",
+    "authors": "NVIDIA DLMED team",
+    "copyright": "Copyright (c) 2021-2022, NVIDIA CORPORATION",
+    "data_source": "private dataset",
+    "data_type": "RGB",
+    "image_classes": "three channel data, intensity [0-255]",
+    "label_classes": "0: inbody, 1: outbody",
+    "pred_classes": "vector whose length equals to 2, [1,0] means in body, [0,1] means out body",
+    "eval_metrics": {
+        "accuracy": 0.98
+    },
+    "references": [
+        "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"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "magnitude",
+                "format": "RGB",
+                "modality": "regular",
+                "num_channels": 3,
+                "spatial_shape": [
+                    256,
+                    256
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "R",
+                    "1": "G",
+                    "2": "B"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "probabilities",
+                "format": "classes",
+                "num_channels": 2,
+                "spatial_shape": [
+                    1,
+                    2
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "in body",
+                    "1": "out body"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_train.json

@@ -0,0 +1,36 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "train#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@train#dataset",
+        "even_divisible": true,
+        "shuffle": true
+    },
+    "train#dataloader#sampler": "@train#sampler",
+    "train#dataloader#shuffle": false,
+    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
+    "training": [
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/train.json

@@ -0,0 +1,259 @@
+{
+    "imports": [
+        "$import torch",
+        "$import json",
+        "$import ignite"
+    ],
+    "bundle_root": "/workspace/bundle/endoscopic_inbody_classification",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/endoscopic_inbody_classification",
+    "train_json": "$@dataset_dir+'/train.json'",
+    "val_json": "$@dataset_dir+'/val.json'",
+    "train_fp": "$open(@train_json,'r', encoding='utf8')",
+    "train_dict": "$json.load(@train_fp)",
+    "train_close": "$@train_fp.close()",
+    "val_fp": "$open(@val_json,'r', encoding='utf8')",
+    "val_dict": "$json.load(@val_fp)",
+    "val_interval": 1,
+    "val_close": "$@val_fp.close()",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "SEResNet50",
+        "spatial_dims": 2,
+        "in_channels": 3,
+        "num_classes": 2
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "torch.nn.CrossEntropyLoss",
+        "reduction": "sum"
+    },
+    "optimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@network.parameters()",
+        "lr": 0.001
+    },
+    "train": {
+        "deterministic_transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image"
+            },
+            {
+                "_target_": "ToTensord",
+                "keys": "label"
+            },
+            {
+                "_target_": "AsChannelFirstd",
+                "keys": "image"
+            },
+            {
+                "_target_": "Resized",
+                "keys": "image",
+                "spatial_size": [
+                    256,
+                    256
+                ],
+                "mode": "bilinear"
+            },
+            {
+                "_target_": "CastToTyped",
+                "dtype": "$torch.float32",
+                "keys": "image"
+            },
+            {
+                "_target_": "NormalizeIntensityd",
+                "nonzero": true,
+                "channel_wise": true,
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureTyped",
+                "keys": "image"
+            }
+        ],
+        "random_transforms": [
+            {
+                "_target_": "RandRotated",
+                "range_x": 0.3,
+                "prob": 0.2,
+                "mode": "bilinear",
+                "keys": "image"
+            },
+            {
+                "_target_": "RandScaleIntensityd",
+                "factors": 0.3,
+                "prob": 0.5,
+                "keys": "image"
+            },
+            {
+                "_target_": "RandShiftIntensityd",
+                "offsets": 0.1,
+                "prob": 0.5,
+                "keys": "image"
+            },
+            {
+                "_target_": "RandGaussianNoised",
+                "std": 0.01,
+                "prob": 0.15,
+                "keys": "image"
+            },
+            {
+                "_target_": "RandFlipd",
+                "spatial_axis": 0,
+                "prob": 0.5,
+                "keys": "image"
+            },
+            {
+                "_target_": "RandFlipd",
+                "spatial_axis": 1,
+                "prob": 0.5,
+                "keys": "image"
+            }
+        ],
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "$@train#deterministic_transforms + @train#random_transforms"
+        },
+        "dataset": {
+            "_target_": "Dataset",
+            "data": "@train_dict",
+            "transform": "@train#preprocessing"
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": 64,
+            "shuffle": true,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "handlers": [
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            }
+        ],
+        "key_metric": {
+            "train_accu": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "AsDiscreted",
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": [
+                        2,
+                        2
+                    ],
+                    "keys": [
+                        "pred",
+                        "label"
+                    ]
+                }
+            ]
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": 25,
+            "device": "@device",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "loss_function": "@loss",
+            "optimizer": "@optimizer",
+            "inferer": "@train#inferer",
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers"
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": "%train#deterministic_transforms"
+        },
+        "dataset": {
+            "_target_": "Dataset",
+            "data": "@val_dict",
+            "transform": "@validate#preprocessing"
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 64,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": "%train#postprocessing"
+        },
+        "handlers": [
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "model.pt"
+            }
+        ],
+        "key_metric": {
+            "val_accu": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "val_handlers": "@validate#handlers"
+        }
+    },
+    "training": [
+        "$monai.utils.set_determinism(seed=0)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()"
+    ]
+}

docs/data_license.txt

@@ -0,0 +1 @@
+Datasets used in this work were provided by Activ Surgical.

docs/readme.md

@@ -0,0 +1,129 @@
+# 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](https://www.activsurgical.com/). Samples in training and validation dataset are from the same 4 videos, while test samples are from different two videos.
+The [pytorch model](https://drive.google.com/file/d/14CS-s1uv2q6WedYQGeFbZeEWIkoyNa-x/view?usp=sharing) and [torchscript model](https://drive.google.com/file/d/1fOoJ4n5DWKHrt9QXTZ2sXwr9C-YvVGCM/view?usp=sharing) 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](https://www.activsurgical.com/). Here is a [link](https://drive.google.com/uc?id=1rQQfHcZFs74OG0uJsL4vg7YqMWXV-s4k) 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
+```
+
+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](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html).
+
+# 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
+Copyright (c) MONAI Consortium
+
+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.

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c99b0f2f60b7ceb93fa0ee4e5225aeb1376a109a9993a6327353a370e1c96ca9
+size 104506369

models/model.ts

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f38a4ad17d48414c6a194a6cda11189535ec6263241d3b21f4c56cac579044
+size 104596723

scripts/export_to_onnx.py

@@ -0,0 +1,118 @@
+import argparse
+import os
+
+import numpy as np
+import onnx
+import onnxruntime
+import torch
+from monai.networks.nets import SEResNet50
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def load_model_and_export(modelname, outname, out_channels, height, width, multigpu=False, in_channels=3):
+    """
+    Loading a model by name.
+
+    Args:
+        modelname: a whole path name of the model that need to be loaded.
+        outname: a name for output onnx model.
+        out_channels: output channels, which usually equals to 1 + class_number.
+        height: input images' height.
+        width: input images' width.
+        multigpu: if the pre-trained model trained on a multigpu environment.
+        in_channels: input images' channel number.
+    """
+    isopen = os.path.exists(modelname)
+    if not isopen:
+        raise Exception("The specified model to load does not exist!")
+
+    model = SEResNet50(spatial_dims=2, in_channels=in_channels, num_classes=out_channels)
+
+    if multigpu:
+        model = torch.nn.DataParallel(model)
+    model = model.cuda()
+    model.load_state_dict(torch.load(modelname, map_location=device))  # if the model is trained on multi gpu
+    model = model.eval()
+
+    np.random.seed(0)
+    x = np.random.random((1, 3, width, height))
+    x = torch.tensor(x, dtype=torch.float32)
+    x = x.cuda()
+    torch_out = model(x)
+    input_names = ["INPUT__0"]
+    output_names = ["OUTPUT__0"]
+    # Export the model
+    if multigpu:
+        model_trans = model.module
+    else:
+        model_trans = model
+    torch.onnx.export(
+        model_trans,  # model to save
+        x,  # model input
+        outname,  # model save path
+        export_params=True,
+        verbose=True,
+        do_constant_folding=True,
+        input_names=input_names,
+        output_names=output_names,
+        opset_version=15,
+        dynamic_axes={"INPUT__0": {0: "batch_size"}, "OUTPUT__0": {0: "batch_size"}},
+    )
+    onnx_model = onnx.load(outname)
+    onnx.checker.check_model(onnx_model, full_check=True)
+    ort_session = onnxruntime.InferenceSession(outname)
+
+    def to_numpy(tensor):
+        return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
+
+    # compute ONNX Runtime output prediction
+    ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)}
+    ort_outs = ort_session.run(["OUTPUT__0"], ort_inputs)
+    numpy_torch_out = to_numpy(torch_out)
+    # compare ONNX Runtime and PyTorch results
+    np.testing.assert_allclose(numpy_torch_out, ort_outs[0], rtol=1e-03, atol=1e-05)
+    print("Exported model has been tested with ONNXRuntime, and the result looks good!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # the original model for converting.
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=r"/workspace/bundle/endoscopic_inbody_classification/models/model.pt",
+        help="Input an existing model weight",
+    )
+
+    # path to save the onnx model.
+    parser.add_argument(
+        "--outpath",
+        type=str,
+        default=r"/workspace/bundle/endoscopic_inbody_classification/models/model.onnx",
+        help="A path to save the onnx model.",
+    )
+
+    parser.add_argument("--width", type=int, default=256, help="Width for exporting onnx model.")
+
+    parser.add_argument("--height", type=int, default=256, help="Height for exporting onnx model.")
+
+    parser.add_argument(
+        "--out_channels", type=int, default=2, help="Number of expected out_channels in model for exporting to onnx."
+    )
+
+    parser.add_argument("--multigpu", type=bool, default=False, help="If loading model trained with multi gpu.")
+
+    args = parser.parse_args()
+    modelname = args.model
+    outname = args.outpath
+    out_channels = args.out_channels
+    height = args.height
+    width = args.width
+    multigpu = args.multigpu
+
+    if os.path.exists(outname):
+        raise Exception(
+            "The specified outpath already exists! Change the outpath to avoid overwriting your saved model. "
+        )
+    model = load_model_and_export(modelname, outname, out_channels, height, width, multigpu)