Closed CodeXiaoLingYun closed 4 years ago
ZwwWayne
commented
4 years ago Hi @CodeXiaoLingYun ,
Could you try reinstall the pycocotools first?
Use the following command:
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
mikaelkvist
commented
4 years ago Have you solved it? You have to specify the segmentation as a list of lists, i.e. 'segmentation': [[171, 170, 174, 179, 175, 189, 171, 198, 158, 202, 149, 203, 140, 198, 137, 190, 139, 179, 146, 171, 155, 166, 162, 165]].
CodeXiaoLingYun
commented
4 years ago Have you solved it? You have to specify the segmentation as a list of lists, i.e. 'segmentation': [[171, 170, 174, 179, 175, 189, 171, 198, 158, 202, 149, 203, 140, 198, 137, 190, 139, 179, 146, 171, 155, 166, 162, 165]].
i am try this method, please wait a moment. Thank you
CodeXiaoLingYun
commented
4 years ago Have you solved it? You have to specify the segmentation as a list of lists, i.e. 'segmentation': [[171, 170, 174, 179, 175, 189, 171, 198, 158, 202, 149, 203, 140, 198, 137, 190, 139, 179, 146, 171, 155, 166, 162, 165]].
Thank you, i solved all question. And it run
when i python tools/train.py configs/pn_test.py, pn_test.py is similar to mask-r-cnn.
2020-03-08 16:46:36,861 - mmdet - INFO - Environment info:
sys.platform: linux Python: 3.6.10 |Anaconda, Inc.| (default, Jan 7 2020, 21:14:29) [GCC 7.3.0] CUDA available: True CUDA_HOME: /usr/local/cuda-9.0 NVCC: Cuda compilation tools, release 9.0, V9.0.176 GPU 0: GeForce GTX 1070 GCC: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609 PyTorch: 1.4.0 PyTorch compiling details: PyTorch built with:
TorchVision: 0.5.0 OpenCV: 4.2.0 MMCV: 0.3.1 MMDetection: 1.0.0+1f4177b MMDetection Compiler: GCC 5.4 MMDetection CUDA Compiler: 9.0
2020-03-08 16:46:36,861 - mmdet - INFO - Distributed training: False 2020-03-08 16:46:36,862 - mmdet - INFO - Config:
model settings
model = dict( type='MaskRCNN', pretrained='open-mmlab://resnext101_64x4d', backbone=dict( type='ResNeXt', depth=101, groups=64, base_width=4, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch'), 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_scales=[8], anchor_ratios=[0.5, 1.0, 2.0], anchor_strides=[4, 8, 16, 32, 64], target_means=[.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=1.0 / 9.0, loss_weight=1.0)), bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='SharedFCBBoxHead', num_fcs=2, in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=2, target_means=[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='SmoothL1Loss', beta=1.0, loss_weight=1.0)), mask_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2), 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=2, loss_mask=dict( type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
model training and testing settings
train_cfg = dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, 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_across_levels=False, nms_pre=2000, nms_post=2000, max_num=2000, nms_thr=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, 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_across_levels=False, nms_pre=1000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100, mask_thr_binary=0.5))
dataset settings
dataset_type = 'PnDataset' 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), 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/pn_train_coco.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/pn_val_coco.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/pn_test_coco.json', img_prefix=data_root + 'test2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric=['bbox', 'segm'])
optimizer 这里默认的是8核GPU的学习率，0.02/8 = 0.0025
optimizer = dict(type='SGD', lr=0.0025, 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=1.0 / 3, step=[8, 11]) 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 = 12 dist_params = dict(backend='nccl') log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_x101_64x4d_fpn_1x'
work_dir = './checkpoints/pn_mask_rcnn_x101_64x4d_fpn_1x' load_from = None resume_from = None workflow = [('train', 1)]
2020-03-08 16:46:37,912 - mmdet - INFO - load model from: open-mmlab://resnext101_64x4d loading annotations into memory... Done (t=0.03s) creating index... index created! 2020-03-08 16:46:40,004 - mmdet - INFO - Start running, host: xly@xly-Ubuntu, work_dir: /home/xly/mmdetection/checkpoints/pn_mask_rcnn_x101_64x4d_fpn_1x 2020-03-08 16:46:40,004 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs Traceback (most recent call last): File "tools/train.py", line 142, in
main()
File "tools/train.py", line 138, in main
meta=meta)
File "/home/xly/mmdetection/mmdet/apis/train.py", line 111, in train_detector
meta=meta)
File "/home/xly/mmdetection/mmdet/apis/train.py", line 305, in _non_dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/mmcv/runner/runner.py", line 371, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/mmcv/runner/runner.py", line 271, in train
for i, data_batch in enumerate(data_loader):
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in next
data = self._next_data()
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
return self._process_data(data)
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
data.reraise()
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/_utils.py", line 394, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/xly/anaconda3/envs/envtest/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/xly/mmdetection/mmdet/datasets/custom.py", line 132, in getitem
data = self.prepare_train_img(idx)
File "/home/xly/mmdetection/mmdet/datasets/custom.py", line 145, in prepare_train_img
return self.pipeline(results)
File "/home/xly/mmdetection/mmdet/datasets/pipelines/compose.py", line 24, in call
data = t(data)
File "/home/xly/mmdetection/mmdet/datasets/pipelines/loading.py", line 106, in call
results = self._load_masks(results)
File "/home/xly/mmdetection/mmdet/datasets/pipelines/loading.py", line 86, in _load_masks
gt_masks = [self._poly2mask(mask, h, w) for mask in gt_masks]
File "/home/xly/mmdetection/mmdet/datasets/pipelines/loading.py", line 86, in
gt_masks = [self._poly2mask(mask, h, w) for mask in gt_masks]
File "/home/xly/mmdetection/mmdet/datasets/pipelines/loading.py", line 71, in _poly2mask
rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)
File "pycocotools/_mask.pyx", line 292, in pycocotools._mask.frPyObjects
TypeError: object of type 'int' has no len()
i do not look for mask.pyx, so i am not know where the error is .
The type of my dataset is coco. And the example is here. images {'id': 0, 'width': 296, 'height': 310, 'file_name': 'D2019.07.17_S00046_I0874_D_WELL02_RUN027.JPG', 'license': 1, 'date_captured': ''} annotations {'id': 0, 'image_id': 0, 'category_id': 1, 'segmentation': [171, 170, 174, 179, 175, 189, 171, 198, 158, 202, 149, 203, 140, 198, 137, 190, 139, 179, 146, 171, 155, 166, 162, 165], 'area': 1444, 'bbox': [137, 165, 38, 38], 'iscrowd': 0} licenses [{'id': 1, 'name': 'Unknown', 'url': ''}] categories [{'id': 1, 'name': 'pn', 'supercategory': 'Type'}]