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open-mmlab / mmdeploy

OpenMMLab Model Deployment Framework
https://mmdeploy.readthedocs.io/en/latest/
Apache License 2.0
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Convert .pt to onnx #2362

Closed drag0n-flyer closed 1 year ago

drag0n-flyer commented 1 year ago

Hello, Following this tutorial: https://github.com/open-mmlab/playground/tree/1c6cace3f6378eaab6f183c418b1d4027763fb72/label_anything

Trained a model. Trying to convert the trained .pt to onnx

https://mmdeploy.readthedocs.io/en/latest/get_started.html#convert-model

python mmdeploy/tools/deploy.py \
    mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \
    mmdetection/configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py \
    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
    mmdetection/demo/demo.jpg \
    --work-dir mmdeploy_model/faster-rcnn \
    --device cuda \
    --dump-info

Working fine but when i tried the Reproduction code 

line 318, in get_model_name
    assert 'decode_head' in self.model_cfg.model, 'model config contains'
AssertionError: model config contains

Reproduction

python3 tools/deploy.py \
    configs/mmseg/segmentation_onnxruntime_dynamic.py \
    data/my_set/mask-rcnn_r50_fpn.py \
    work_dirs/mask-rcnn_r50_fpn/epoch_12.pth \
    /home/yc2/Desktop/seg/rc/1.jpg \
    --work-dir mmdeploy_models/mmseg/ort \
    --device cpu \
    --show \
    --dump-info

Environment

na

Error traceback

raceback (most recent call last):
  File "/home/yc2/Desktop/seg/mmdeploy/tools/deploy.py", line 335, in <module>
    main()
  File "/home/yc2/Desktop/seg/mmdeploy/tools/deploy.py", line 129, in main
    export2SDK(
  File "/home/yc2/.local/lib/python3.10/site-packages/mmdeploy/backend/sdk/export_info.py", line 352, in export2SDK
    deploy_info = get_deploy(deploy_cfg, model_cfg, work_dir, device)
  File "/home/yc2/.local/lib/python3.10/site-packages/mmdeploy/backend/sdk/export_info.py", line 267, in get_deploy
    _, customs = get_model_name_customs(
  File "/home/yc2/.local/lib/python3.10/site-packages/mmdeploy/backend/sdk/export_info.py", line 63, in get_model_name_customs
    name = task_processor.get_model_name()
  File "/home/yc2/.local/lib/python3.10/site-packages/mmdeploy/codebase/mmseg/deploy/segmentation.py", line 318, in get_model_name
    assert 'decode_head' in self.model_cfg.model, 'model config contains'
AssertionError: model config contains
RunningLeon commented 1 year ago

hi, could you provide your model config ?

drag0n-flyer commented 1 year ago

@RunningLeon Thank you for looking at the issue.

Config was created:

cd path/to/playground/label_anything
python tools/convert_to_rle_mask_coco.py --json_file_path path/to/LS_json --out_dir path/to/output/file --out_config mask-rcnn_r50_fpn
dataset_type = 'CocoDataset'
data_root = './yc2/Desktop/seg/my_set/'
train_ann_file = 'annotations/ann.json'
train_data_prefix = 'images/'
val_ann_file = 'annotations/ann.json'
val_data_prefix = 'images/'
num_classes = 1
class_name = ('fire hydrants',)
metainfo = dict(classes=class_name)

model = dict(
    type='MaskRCNN',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_mask=True,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=num_classes,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
        mask_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        mask_head=dict(
            type='FCNMaskHead',
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=num_classes,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=28,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5)))

file_client_args = dict(backend='disk')
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,
        data_root=data_root,
        ann_file=train_ann_file,
        data_prefix=dict(img=train_data_prefix),
        # filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PackDetInputs')
        ]))
val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        metainfo=metainfo,
        data_root=data_root,
        ann_file=val_ann_file,
        data_prefix=dict(img=val_data_prefix),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
test_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        metainfo=metainfo,
        data_root=data_root,
        ann_file=val_ann_file,
        data_prefix=dict(img=val_data_prefix),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root+val_ann_file,
    metric=['bbox', 'segm'],
    format_only=False)
test_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root+val_ann_file,
    metric=['bbox', 'segm'],
    format_only=False)
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
    dict(
        type='MultiStepLR',
        begin=0,
        end=12,
        by_epoch=True,
        milestones=[8, 11],
        gamma=0.1)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
auto_scale_lr = dict(enable=False, base_batch_size=16)
default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=1),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1),
    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 = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'
resume = False
launcher = 'none'
RunningLeon commented 1 year ago

hi, you are using model from mmdet, you should use deploy config mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py instead of configs/mmseg/segmentation_onnxruntime_dynamic.py \

github-actions[bot] commented 1 year ago

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drag0n-flyer commented 1 year ago

Thank you.