error, when running inference_demo.py

[wcfzl]

Thank you for the source code contribution.

I download your pretrained model, and inference_demo.py works well, and then I trained my own dataset, training is ok, But when I running inference_demo.py in my model, the model returns None line 14: result = inference_detector(model, img), result is None

my dataset has one class, I convert my dataset to coco format, and changed config as : `model = dict( type='SOLOv2', pretrained='torchvision://resnet101', backbone=dict( type='ResNet', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), # C2, C3, C4, C5 frozen_stages=1, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=0, num_outs=5), bbox_head=dict( type='SOLOv2Head', num_classes=2, in_channels=256, stacked_convs=4, seg_feat_channels=512, strides=[8, 8, 16, 32, 32], scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)), sigma=0.2, num_grids=[40, 36, 24, 16, 12], ins_out_channels=256, loss_ins=dict( type='DiceLoss', use_sigmoid=True, loss_weight=3.0), loss_cate=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0)), mask_feat_head=dict( type='MaskFeatHead', in_channels=256, out_channels=128, start_level=0, end_level=3, num_classes=256, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)), )

training and testing settings

train_cfg = dict() test_cfg = dict( nms_pre=500, score_thr=0.1, mask_thr=0.5, update_thr=0.05, kernel='gaussian', # gaussian/linear sigma=2.0, max_per_img=100)

dataset settings

dataset_type = 'CocoDataset' data_root = 'data/coco/' 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='Resize', img_scale=[(1333, 800), (1333, 768), (1333, 736), (1333, 704), (1333, 672), (1333, 640)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), 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', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( imgs_per_gpu=2, workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline))

optimizer

optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

learning policy

lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.01, step=[27, 33]) checkpoint_config = dict(interval=1)

yapf:disable

log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ])

yapf:enable

runtime settings

total_epochs = 36 device_ids = 0 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/solov2_release_r101_fpn_8gpu_3x' load_from = None resume_from = None workflow = [('train', 1)]`

[danielcrane]

I have the same issue, were you ever able to resolve it?

[chenneng]

have the same problem