[BUG] TensorRT optimised model is detecting less objects compared to pytorch model, most likely some difference in post processing.

[himansh1314]

Checklist

• I have searched related issues but cannot get the expected help.
• 2. I have read the FAQ documentation but cannot get the expected help.
• 3. The bug has not been fixed in the latest version.

Describe the bug

So I have managed to train one model on non square input sizes - height-1216, width - 1920. I optimised this model using mmdeploy and converted the model to tensorrt with FP16 precision using the tools/deploy.py script. However, when visualising the sample result, there are less number of objects detected by the TensorRT model as compared to PyTorch model. I believe this is not a problem with optimisation or quantisation, as the objects that have been correctly detected by TensorRT model have the exact same location and confidence as PyTorch model. Moreover, the TensorRT model is only missing objects in places where the objects are closely and densely located, which leads me to believe that there is discrepancy with the post processing pipeline. Please help me in identifying the problem and fixing this. I'm attaching all the config files below for your reference.

Model config file

default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=10),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/media/chetan/Project/Projects/rtmdet_train/mmdetection/work_dirs/config_corrected_det/epoch_160.pth'
resume = True
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=300,
    val_interval=1,
    dynamic_intervals=[(80, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='CosineAnnealingLR',
        eta_min=0.0002,
        begin=150,
        end=300,
        T_max=100,
        by_epoch=True,
        convert_to_iter_based=True)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.001, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=96)
dataset_type = 'CocoDataset'
data_root = '/home/chetan/Desktop/rtmdet_training/coco_finetuning_data'
backend_args = None
train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=False,
        poly2mask=False),
    dict(type='CachedMosaic', img_scale=(1920, 1216), pad_val=114.0, prob=0.2),
    dict(
        type='RandomResize',
        scale=(1920, 1216),
        ratio_range=(0.8, 1.2),
        keep_ratio=True,
        prob=0.1),
    dict(
        type='RandomCrop',
        crop_size=(1920, 1216),
        recompute_bbox=True,
        allow_negative_crop=True,
        prob=0.1),
    dict(type='YOLOXHSVRandomAug', prob=0.1),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(1920, 1216), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='CachedMixUp',
        img_scale=(1920, 1216),
        ratio_range=(1.0, 1.0),
        max_cached_images=20,
        prob=0.1,
        pad_val=(114, 114, 114)),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='Resize', scale=(1920, 1216), keep_ratio=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=96,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=None,
    dataset=dict(
        type='CocoDataset',
        data_root='/home/chetan/Desktop/rtmdet_training/coco_finetuning_data',
        ann_file='train/coco_annotations.json',
        data_prefix=dict(img='train/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=False,
                poly2mask=False),
            dict(
                type='CachedMosaic',
                img_scale=(1920, 1216),
                pad_val=114.0,
                prob=0.2),
            dict(
                type='RandomResize',
                scale=(1920, 1216),
                ratio_range=(0.8, 1.2),
                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_size=(1920, 1216),
                recompute_bbox=True,
                allow_negative_crop=True),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(1920, 1216),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='CachedMixUp',
                img_scale=(1920, 1216),
                ratio_range=(1.0, 1.0),
                max_cached_images=20,
                pad_val=(114, 114, 114),
                prob=0.2),
            dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
            dict(type='PackDetInputs')
        ],
        backend_args=None,
        metainfo=dict(
            classes=('Neoplastic', 'Inflammatory', 'Stroma',
                     'Necrosis/Dead Cells', 'Normal Epithelial'),
            palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
                     (0, 255, 255)])),
    pin_memory=True)
val_dataloader = dict(
    batch_size=32,
    num_workers=8,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='/home/chetan/Desktop/rtmdet_training/coco_finetuning_data',
        ann_file='val/coco_annotations.json',
        data_prefix=dict(img='val/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='Resize', scale=(1920, 1216), keep_ratio=True),
            dict(
                type='Pad', size=(1920, 1216),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        backend_args=None,
        metainfo=dict(
            classes=('Neoplastic', 'Inflammatory', 'Stroma',
                     'Necrosis/Dead Cells', 'Normal Epithelial'),
            palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
                     (0, 255, 255)])))
test_dataloader = dict(
    batch_size=64,
    num_workers=8,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='/home/chetan/Desktop/rtmdet_training/coco_finetuning_data',
        ann_file='val/coco_annotations.json',
        data_prefix=dict(img='val/'),
        test_mode=True,
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(type='Resize', scale=(1920, 1216), keep_ratio=True),
            dict(
                type='Pad', size=(1920, 1216),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ],
        backend_args=None,
        metainfo=dict(
            classes=('Neoplastic', 'Inflammatory', 'Stroma',
                     'Necrosis/Dead Cells', 'Normal Epithelial'),
            palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
                     (0, 255, 255)])))
val_evaluator = dict(
    type='CocoMetric',
    ann_file=
    '/home/chetan/Desktop/rtmdet_training/coco_finetuning_data/val/coco_annotations.json',
    metric='bbox',
    format_only=False,
    backend_args=None,
    proposal_nums=(3000, 1, 10))
test_evaluator = dict(
    type='CocoMetric',
    ann_file=
    '/home/chetan/Desktop/rtmdet_training/coco_finetuning_data/val/coco_annotations.json',
    metric='bbox',
    format_only=False,
    backend_args=None,
    proposal_nums=(3000, 1, 10))
tta_model = dict(
    type='DetTTAModel',
    tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=3000))
img_scales = [(1920, 1216), (256, 256)]
tta_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='TestTimeAug',
        transforms=[[{
            'type': 'Resize',
            'scale': (1920, 1216),
            'keep_ratio': True,
            'prob': 0.0
        }, {
            'type': 'Resize',
            'scale': (144, 128),
            'keep_ratio': True,
            'prob': 0.0
        }, {
            'type': 'Resize',
            'scale': (576, 512),
            'keep_ratio': True,
            'prob': 0.0
        }],
                    [{
                        'type': 'RandomFlip',
                        'prob': 0.0
                    }, {
                        'type': 'RandomFlip',
                        'prob': 0.0
                    }],
                    [{
                        'type': 'Pad',
                        'size': (1920, 1216),
                        'pad_val': {
                            'img': (114, 114, 114)
                        },
                        'prob': 0.0
                    }],
                    [{
                        'type':
                        'PackDetInputs',
                        'meta_keys':
                        ('img_id', 'img_path', 'ori_shape', 'img_shape',
                         'scale_factor', 'flip', 'flip_direction')
                    }]])
]
model = dict(
    type='RTMDet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[179.92, 149.48, 198.26],
        std=[14.06, 11.88, 11.06],
        bgr_to_rgb=True,
        batch_augments=None),
    backbone=dict(
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=0.67,
        widen_factor=0.75,
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    neck=dict(
        type='CSPNeXtPAFPN',
        in_channels=[192, 384, 768],
        out_channels=192,
        num_csp_blocks=2,
        expand_ratio=0.5,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='RTMDetSepBNHead',
        num_classes=5,
        in_channels=192,
        stacked_convs=2,
        feat_channels=192,
        anchor_generator=dict(
            type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        with_objectness=False,
        exp_on_reg=True,
        share_conv=True,
        pred_kernel_size=1,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU', inplace=True)),
    train_cfg=dict(
        assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=30000,
        min_bbox_size=0,
        score_thr=0.001,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=3000))
train_pipeline_stage2 = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=False,
        poly2mask=False),
    dict(
        type='RandomResize',
        scale=(1920, 1216),
        ratio_range=(0.8, 1.2),
        keep_ratio=True),
    dict(
        type='RandomCrop',
        crop_size=(1920, 1216),
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(1920, 1216), pad_val=dict(img=(114, 114, 114))),
    dict(type='PackDetInputs')
]
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.001
interval = 10
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type='PipelineSwitchHook',
        switch_epoch=280,
        switch_pipeline=[
            dict(type='LoadImageFromFile', backend_args=None),
            dict(
                type='LoadAnnotations',
                with_bbox=True,
                with_mask=False,
                poly2mask=False),
            dict(
                type='RandomResize',
                scale=(1920, 1216),
                ratio_range=(0.8, 1.2),
                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_size=(1920, 1216),
                recompute_bbox=True,
                allow_negative_crop=True),
            dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
            dict(type='YOLOXHSVRandomAug', prob=0.1),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(1920, 1216),
                pad_val=dict(img=(114, 114, 114))),
            dict(type='PackDetInputs')
        ])
]
metainfo = dict(
    classes=('Neoplastic', 'Inflammatory', 'Stroma', 'Necrosis/Dead Cells',
             'Normal Epithelial'),
    palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
             (0, 255, 255)])
launcher = 'none'
work_dir = './work_dirs/config_corrected_det_finetune'

The config file base_static.py

_base_ = ['../../_base_/onnx_config.py']

onnx_config = dict(output_names=['dets', 'labels'], input_shape=None)
codebase_config = dict(
    type='mmdet',
    task='ObjectDetection',
    model_type='end2end',
    post_processing=dict(
        score_threshold=0.05,
        confidence_threshold=0.005,  # for YOLOv3
        iou_threshold=0.6,
        max_output_boxes_per_class=3000,
        pre_top_k=5000,
        keep_top_k=3000,
        background_label_id=-1,
    ))

The tensorrt static optimisation

_base_ = ['./base_static.py', '../../_base_/backends/tensorrt.py']

onnx_config = dict(input_shape=(1920, 1216))

backend_config = dict(
    common_config=dict(max_workspace_size=1 << 30),
    model_inputs=[
        dict(
            input_shapes=dict(
                input=dict(
                    min_shape=[1, 3, 1216, 1920],
                    opt_shape=[1, 3, 1216, 1920],
                    max_shape=[1, 3, 1216, 1920])))
    ])

Below are the detail.json, pipeline.json and deploy.json

deploy.json

{
    "version": "1.0.0",
    "task": "Detector",
    "models": [
        {
            "name": "rtmdet",
            "net": "end2end.engine",
            "weights": "",
            "backend": "tensorrt",
            "precision": "FP16",
            "batch_size": 1,
            "dynamic_shape": false
        }
    ],
    "customs": []
}

detail.json

{
    "version": "1.0.0",
    "codebase": {
        "task": "ObjectDetection",
        "codebase": "mmdet",
        "version": "3.0.0",
        "pth": "/root/workspace/data/finetune_checkpoint_static/epoch_240.pth",
        "config": "/root/workspace/data/finetune_checkpoint_static/config_corrected_det_finetune.py"
    },
    "codebase_config": {
        "type": "mmdet",
        "task": "ObjectDetection",
        "model_type": "end2end",
        "post_processing": {
            "score_threshold": 0.05,
            "confidence_threshold": 0.005,
            "iou_threshold": 0.6,
            "max_output_boxes_per_class": 3000,
            "pre_top_k": 5000,
            "keep_top_k": 3000,
            "background_label_id": -1
        }
    },
    "onnx_config": {
        "type": "onnx",
        "export_params": true,
        "keep_initializers_as_inputs": false,
        "opset_version": 11,
        "save_file": "end2end.onnx",
        "input_names": [
            "input"
        ],
        "output_names": [
            "dets",
            "labels"
        ],
        "input_shape": [
            1920,
            1216
        ],
        "optimize": true
    },
    "backend_config": {
        "type": "tensorrt",
        "common_config": {
            "fp16_mode": true,
            "max_workspace_size": 1073741824
        },
        "model_inputs": [
            {
                "input_shapes": {
                    "input": {
                        "min_shape": [
                            1,
                            3,
                            1216,
                            1920
                        ],
                        "opt_shape": [
                            1,
                            3,
                            1216,
                            1920
                        ],
                        "max_shape": [
                            1,
                            3,
                            1216,
                            1920
                        ]
                    }
                }
            }
        ]
    },
    "calib_config": {}
}

And finally the pipeline.json

{
    "pipeline": {
        "input": [
            "img"
        ],
        "output": [
            "post_output"
        ],
        "tasks": [
            {
                "type": "Task",
                "module": "Transform",
                "name": "Preprocess",
                "input": [
                    "img"
                ],
                "output": [
                    "prep_output"
                ],
                "transforms": [
                    {
                        "type": "LoadImageFromFile",
                        "backend_args": null
                    },
                    {
                        "type": "Resize",
                        "keep_ratio": false,
                        "size": [
                            1920,
                            1216
                        ]
                    },
                    {
                        "type": "Normalize",
                        "to_rgb": true,
                        "mean": [
                            179.92,
                            149.48,
                            198.26
                        ],
                        "std": [
                            14.06,
                            11.88,
                            11.06
                        ]
                    },
                    {
                        "type": "Pad",
                        "size_divisor": 1
                    },
                    {
                        "type": "DefaultFormatBundle"
                    },
                    {
                        "type": "Collect",
                        "meta_keys": [
                            "flip",
                            "img_shape",
                            "scale_factor",
                            "flip_direction",
                            "filename",
                            "img_path",
                            "img_id",
                            "img_norm_cfg",
                            "valid_ratio",
                            "pad_param",
                            "pad_shape",
                            "ori_filename",
                            "ori_shape"
                        ],
                        "keys": [
                            "img"
                        ]
                    }
                ]
            },
            {
                "name": "rtmdet",
                "type": "Task",
                "module": "Net",
                "is_batched": false,
                "input": [
                    "prep_output"
                ],
                "output": [
                    "infer_output"
                ],
                "input_map": {
                    "img": "input"
                },
                "output_map": {}
            },
            {
                "type": "Task",
                "module": "mmdet",
                "name": "postprocess",
                "component": "ResizeBBox",
                "params": {
                    "nms_pre": 30000,
                    "min_bbox_size": 0,
                    "score_thr": 0.001,
                    "nms": {
                        "type": "nms",
                        "iou_threshold": 0.6
                    },
                    "max_per_img": 3000
                },
                "output": [
                    "post_output"
                ],
                "input": [
                    "prep_output",
                    "infer_output"
                ]
            }
        ]
    }
}

Reproduction

python /root/workspace/mmdeploy/tools/deploy.py \
    /root/workspace/mmdeploy/configs/mmdet/detection/detection_tensorrt-fp16_static-AOI.py \
    /root/workspace/data/finetune_checkpoint_static/config_corrected_det_finetune.py \
    /root/workspace/data/finetune_checkpoint_static/epoch_240.pth \
    /root/workspace/data/1105.png \
    --test-img /root/workspace/data/1105.png \
    --work-dir /root/workspace/data/finetune_checkpoint_static \
    --device cuda \
    --log-level INFO \
    --show \
    --dump-info

I had modified the config files to accomodate the resolution of 1216 x 1920. I understood all the changes required and the pytorch model works flawlessly. However, the TensorRT optimised model is unable to predict some objects which are densely located.

Environment

05/11 09:00:49 - mmengine - INFO - 

05/11 09:00:49 - mmengine - INFO - **********Environmental information**********
05/11 09:00:50 - mmengine - INFO - sys.platform: linux
05/11 09:00:50 - mmengine - INFO - Python: 3.8.10 (default, Mar 15 2022, 12:22:08) [GCC 9.4.0]
05/11 09:00:50 - mmengine - INFO - CUDA available: True
05/11 09:00:50 - mmengine - INFO - numpy_random_seed: 2147483648
05/11 09:00:50 - mmengine - INFO - GPU 0: NVIDIA GeForce GTX 1650
05/11 09:00:50 - mmengine - INFO - CUDA_HOME: /usr/local/cuda
05/11 09:00:50 - mmengine - INFO - NVCC: Cuda compilation tools, release 11.6, V11.6.124
05/11 09:00:50 - mmengine - INFO - GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
05/11 09:00:50 - mmengine - INFO - PyTorch: 1.11.0+cu113
05/11 09:00:50 - mmengine - INFO - PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

05/11 09:00:50 - mmengine - INFO - TorchVision: 0.12.0+cu113
05/11 09:00:50 - mmengine - INFO - OpenCV: 4.7.0
05/11 09:00:50 - mmengine - INFO - MMEngine: 0.7.3
05/11 09:00:50 - mmengine - INFO - MMCV: 2.0.0
05/11 09:00:50 - mmengine - INFO - MMCV Compiler: GCC 9.3
05/11 09:00:50 - mmengine - INFO - MMCV CUDA Compiler: 11.3
05/11 09:00:50 - mmengine - INFO - MMDeploy: 1.0.0+
05/11 09:00:50 - mmengine - INFO - 

05/11 09:00:50 - mmengine - INFO - **********Backend information**********
05/11 09:00:50 - mmengine - INFO - tensorrt:	8.2.4.2
05/11 09:00:50 - mmengine - INFO - tensorrt custom ops:	Available
05/11 09:00:50 - mmengine - INFO - ONNXRuntime:	None
05/11 09:00:50 - mmengine - INFO - ONNXRuntime-gpu:	1.8.1
05/11 09:00:50 - mmengine - INFO - ONNXRuntime custom ops:	Available
05/11 09:00:50 - mmengine - INFO - pplnn:	None
05/11 09:00:50 - mmengine - INFO - ncnn:	None
05/11 09:00:50 - mmengine - INFO - snpe:	None
05/11 09:00:50 - mmengine - INFO - openvino:	None
05/11 09:00:50 - mmengine - INFO - torchscript:	1.11.0+cu113
05/11 09:00:50 - mmengine - INFO - torchscript custom ops:	NotAvailable
05/11 09:00:50 - mmengine - INFO - rknn-toolkit:	None
05/11 09:00:50 - mmengine - INFO - rknn-toolkit2:	None
05/11 09:00:50 - mmengine - INFO - ascend:	None
05/11 09:00:50 - mmengine - INFO - coreml:	None
05/11 09:00:50 - mmengine - INFO - tvm:	None
05/11 09:00:50 - mmengine - INFO - vacc:	None
05/11 09:00:50 - mmengine - INFO - 

05/11 09:00:50 - mmengine - INFO - **********Codebase information**********
05/11 09:00:50 - mmengine - INFO - mmdet:	3.0.0
05/11 09:00:50 - mmengine - INFO - mmseg:	None
05/11 09:00:50 - mmengine - INFO - mmpretrain:	None
05/11 09:00:50 - mmengine - INFO - mmocr:	None
05/11 09:00:50 - mmengine - INFO - mmedit:	None
05/11 09:00:50 - mmengine - INFO - mmdet3d:	None
05/11 09:00:50 - mmengine - INFO - mmpose:	None
05/11 09:00:50 - mmengine - INFO - mmrotate:	None
05/11 09:00:50 - mmengine - INFO - mmaction:	None
05/11 09:00:50 - mmengine - INFO - mmrazor:	None

### Error traceback

_No response_

[irexyc]

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(type='Resize', scale=(1920, 1216), keep_ratio=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

Please try to use detection_tensorrt-xxx_dynamic-xx.py and edit the min/opt/max shape according to your need. If you inference by pytorch, the resize strategy in preprocess is keep ratio resize. But if you use static model config, the resize will be replaced by a fix size resize which is not same compared to pytorch preprocess.

[himansh1314]

Thanks for the response, @irexyc , however, in my case, the image I am giving to model is of a static size and will always remain of the same size (1216x1920), so i don't need to use the resize transform at all. Also, I have tried via the dynamic config, and the results were same.

[irexyc]

@himansh1314

There is a bug of static resize (mmdet treat the scale of resize as (w, h), while mmdeploy sdk treat it as (h, w)), this pr will fix it #2063.

You can edit the pipeline.json, swap the two parameter of resize to see if it can help.

[himansh1314]

@irexyc Yes, I noticed the bug. And I swapped the parameters and it worked fine. Infact, I removed the 'Resize' transform completely and it worked fine as well because by default, my image is of the size (1216x1920), and all the images in my pipeline are of static size. However, still the same issue as I mentioned above.

[himansh1314]

@irexyc I think there is some issue with post processing in TensorRT. I'm saying this as the objects detected by torch model and tensorrt have the exact same confidence score. So, it's pretty clear that the preprocessing, inferencing and model optimisation is same, but something is different in post processing(probably nms), which is discarding some detections.

[irexyc]

@himansh1314 We made nms as part of model inference instead of postprocess. However the settings for nms are fixed (won't read your model config) when you convert the model. You can edit some parameters in this file configs/mmdet/_base_/base_static.py before convert the model

[himansh1314]

@irexyc So I understood what is possibly the issue. In the configs/mmdet/_base_/base_static.py file, the parameter pre_top_k is set to 5000. I'm not sure what this parameter means, but my best guess is that indicates the top 5000 predictions based on confidence score, which are then sent to nms for post processing. However, in my config.py file, I had set this to 30000, and hence, the pytorch model was able to detect more objects. However, when I changed this 30000 in configs/mmdet/_base_/base_static.py file, the model was able to convert successfully, however, when the tools/deloy.py was testing the model for visualisation, it crashed and returned the error

05/11 12:42:35 - mmengine - INFO - Successfully loaded tensorrt plugins from /root/workspace/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
05/11 12:42:35 - mmengine - INFO - Successfully loaded tensorrt plugins from /root/workspace/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
#assertion/root/workspace/mmdeploy/csrc/mmdeploy/backend_ops/tensorrt/common_impl/nms/allClassNMS.cu,210
05/11 12:42:52 - mmengine - ERROR - /root/workspace/mmdeploy/tools/deploy.py - create_process - 82 - visualize tensorrt model failed.

Also, when I try to inference using the mmdeploy_runtime sdk, the operation gets aborted during inferencing.
I don't understand, how come pytorch nms is able to handle 30000 predictions whereas TensorRT fails? I also tried changing the value to 15000, 10000, and 7500, but nothing worked.
I think this is an important issue and would really appreciate if you could help me with this. @irexyc

[irexyc]

@himansh1314

It seems that you have already modified some content of base_static.py. However, you doesn't modify the score_threshold to 0.001. Not sure if it could help, you can have a try.

The config file base_static.py

_base_ = ['../../_base_/onnx_config.py']
onnx_config = dict(output_names=['dets', 'labels'], input_shape=None)
codebase_config = dict(
    type='mmdet',
    task='ObjectDetection',
    model_type='end2end',
    post_processing=dict(
        score_threshold=0.05,
        confidence_threshold=0.005,  # for YOLOv3
        iou_threshold=0.6,
        max_output_boxes_per_class=3000,
        pre_top_k=5000,
        keep_top_k=3000,
        background_label_id=-1,
    ))

There are some assert in allClassNMS.cu, @grimoire could have a look at this.

[himansh1314]

This is the error message I get, when I convert the model using deploy/tools.py after changing a few configurations in base_static.py file.

 build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized.
05/12 05:05:21 - mmengine - INFO - Successfully loaded tensorrt plugins from /root/workspace/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
05/12 05:05:21 - mmengine - INFO - Successfully loaded tensorrt plugins from /root/workspace/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
#assertion/root/workspace/mmdeploy/csrc/mmdeploy/backend_ops/tensorrt/common_impl/nms/allClassNMS.cu,210
05/12 05:05:38 - mmengine - ERROR - /root/workspace/mmdeploy/tools/deploy.py - create_process - 82 - visualize tensorrt model failed.

The code indeed asserts something.

Here's the base_static.py file

_base_ = ['../../_base_/onnx_config.py']

onnx_config = dict(output_names=['dets', 'labels'], input_shape=None)
codebase_config = dict(
    type='mmdet',
    task='ObjectDetection',
    model_type='end2end',
    post_processing=dict(
        score_threshold=0.001,
        confidence_threshold=0.005,  # for YOLOv3
        iou_threshold=0.6,
        max_output_boxes_per_class=3000,
        pre_top_k=10000,
        keep_top_k=3000,
        background_label_id=-1,
    ))

Note that, in the pytorch code, the predictions before nms is set to 30000, and when in base_static.py, the pre_top_k, is set to 5000. My model is supposed to predict over 1000 objects, which can be densely populated, and hence I tried changing it to larger values like 30000, 10000 etc. I just want my tensorrt model to give correct predictions like pytorch model and not miss any predictions. Please look into this. @irexyc @grimoire

[irexyc]

Please keep pre_top_k = 5000, there are asserts in allClassNMS.cu

  const static int BS = 512;
   ...
  const int t_size = (top_k + BS - 1) / BS;

  ASSERT(t_size <= 10);

[himansh1314]

@irexyc @grimoire I understand that there are some asserts. Is there any other way around this? I think this cap on pre_top_k is causing the huge difference between the performance on tensorrt and pytorch model? Is there any other way where I can do inferencing using TensorRT and the NMS and post process can be done on PyTorch? I don't mind if the latency shoots up a little bit.

[himansh1314]

@irexyc if I comment out the ASSERT part in the repository and build the container again entirely, would it work? or there are some other checks and dependencies as well in some other part of the code?

[irexyc]

I think it will work for nms after comment the assert.

I'm not quite sure why there has assert compared to https://github.com/NVIDIA/TensorRT/blob/master/plugin/common/kernels/allClassNMS.cu @grimoire may explain to you

With score_threshold=0.001, the results are still very different compared to pytorch right?