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OpenGVLab / InternImage

[CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
https://arxiv.org/abs/2211.05778
MIT License
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When evaluating: AttributeError: 'NoneType' object has no attribute 'items' #195

Open imzhaoruijie opened 1 year ago

imzhaoruijie commented 1 year ago

I trained on other dataset,and trained successfully.However,there are some bug when evaluating:

Traceback (most recent call last): File "./train.py", line 247, in main() File "./train.py", line 237, in main train_detector(model, File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmdet/apis/train.py", line 246, in train_detector runner.run(data_loaders, cfg.workflow) File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], **kwargs) File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 54, in train self.call_hook('after_train_epoch') File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 309, in call_hook getattr(hook, fn_name)(self) File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 267, in after_train_epoch self._do_evaluate(runner) File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmdet/core/evaluation/eval_hooks.py", line 135, in _do_evaluate key_score = self.evaluate(runner, results) File "/root/miniconda3/envs/internimage/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 368, in evaluate for name, val in eval_res.items(): AttributeError: 'NoneType' object has no attribute 'items'

And here is my config information:

model = dict( type='CascadeRCNN', backbone=dict( type='InternImage', core_op='DCNv3', channels=160, depths=[5, 5, 22, 5], groups=[10, 20, 40, 80], mlp_ratio=4.0, drop_path_rate=0.4, norm_layer='LN', layer_scale=1.0, offset_scale=2.0, post_norm=True, with_cp=False, out_indices=(0, 1, 2, 3), init_cfg=dict( type='Pretrained', checkpoint= '/mnt/home/VisDrone-Relevant/InternImage-master/detection/cascade_internimage_l_fpn_3x_coco.pth' )), neck=dict( type='FPN', in_channels=[160, 320, 640, 1280], 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='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='CascadeRoIHead', num_stages=3, stage_loss_weights=[1, 0.5, 0.25], 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='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=16, 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, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), dict( type='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=16, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=False, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), dict( type='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=16, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=False, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) ]), 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=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=2000, max_per_img=2000, 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=False, 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), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6, match_low_quality=False, 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), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.7, match_low_quality=False, 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))) dataset_type = 'ZeroShotDataset' data_root = '/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/' classes = ('golffield', 'Expressway-toll-station', 'vehicle', 'trainstation', 'chimney', 'storagetank', 'harbor', 'ship', 'airplane', 'stadium', 'tenniscourt', 'dam', 'bridge', 'baseballfield', 'Expressway-Service-area', 'overpass') img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='AutoAugment', policies=[[{ 'type': 'Resize', 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }], [{ 'type': 'Resize', 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'RandomCrop', 'crop_type': 'absolute_range', 'crop_size': (384, 600), 'allow_negative_crop': True }, { 'type': 'Resize', 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], 'multiscale_mode': 'value', 'override': True, 'keep_ratio': True }]]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=1, workers_per_gpu=1, train=dict( type='ZeroShotDataset', classes=('golffield', 'Expressway-toll-station', 'vehicle', 'trainstation', 'chimney', 'storagetank', 'harbor', 'ship', 'airplane', 'stadium', 'tenniscourt', 'dam', 'bridge', 'baseballfield', 'Expressway-Service-area', 'overpass'), filter_empty_gt=True, ann_file= '/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/train.json', img_prefix= '/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/trainset/JPEGImages/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='AutoAugment', policies=[[{ 'type': 'Resize', 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }], [{ 'type': 'Resize', 'img_scale': [(400, 1333), (500, 1333), (600, 1333)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'RandomCrop', 'crop_type': 'absolute_range', 'crop_size': (384, 600), 'allow_negative_crop': True }, { 'type': 'Resize', 'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], 'multiscale_mode': 'value', 'override': True, 'keep_ratio': True }]]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) ]), val=dict( type='ZeroShotDataset', classes=('golffield', 'Expressway-toll-station', 'vehicle', 'trainstation', 'chimney', 'storagetank', 'harbor', 'ship', 'airplane', 'stadium', 'tenniscourt', 'dam', 'bridge', 'baseballfield', 'Expressway-Service-area', 'overpass'), ann_file='/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/val.json', img_prefix= '/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/trainset/JPEGImages/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='ZeroShotDataset', classes=('golffield', 'Expressway-toll-station', 'vehicle', 'trainstation', 'chimney', 'storagetank', 'harbor', 'ship', 'airplane', 'stadium', 'tenniscourt', 'dam', 'bridge', 'baseballfield', 'Expressway-Service-area', 'overpass'), ann_file='/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/val.json', img_prefix= '/mnt/home/VisDrone-Relevant/VisDrone_data/zero_test/trainset/JPEGImages/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=1, metric='bbox', save_best='auto') optimizer = dict( type='AdamW', lr=0.0002, weight_decay=0.05, constructor='CustomLayerDecayOptimizerConstructor', paramwise_cfg=dict( num_layers=37, layer_decay_rate=0.9, depths=[5, 5, 22, 5], offset_lr_scale=0.01)) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[27, 33]) runner = dict(type='EpochBasedRunner', max_epochs=36) checkpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [ dict( type='ExpMomentumEMAHook', resume_from=None, momentum=0.0001, priority=49) ] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] pretrained = '/mnt/home/VisDrone-Relevant/InternImage-master/detection/cascade_internimage_l_fpn_3x_coco.pth' work_dir = './work_dirs/cascade_internimage_l_fpn_3x_zero' auto_resume = False gpu_ids = range(0, 4)

SuroshAhmadZobair commented 11 months ago

Hi @imzhaoruijie have you managed to solve the issue?

Cheers!