Adamdad
AssertionError: train_cfg specified in both outer field and model field
For the configuration file, I only changed the data path and the number of categories.Run the following code: python tools/train.py configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py --work-dir ./work-dir --gpus 1
Then,
/root/anaconda3/envs/cont2/lib/python3.6/site-packages/mmdet/models/builder.py:53: UserWarning: train_cfg and test_cfg is deprecated, please specify them in model
'please specify them in model', UserWarning)
Traceback (most recent call last):
File "tools/train.py", line 200, in
mmdet_base = "../../../mmdetection/configs/base" base = [ f"{mmdet_base}/datasets/coco_detection.py", f"{mmdet_base}/schedules/schedule_1x.py", f"{mmdet_base}/default_runtime.py", ]
model = dict( type='RetinaNet', 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, start_level=1, add_extra_convs='on_output', num_outs=5), bbox_head=dict( type='FAM3DHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_type='anchor_based', anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], octave_base_scale=8, scales_per_octave=1, strides=[8, 16, 32, 64, 128]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2]), loss_cls=dict( type='FocalLoss', use_sigmoid=True, activated=True, # use probability instead of logit as input gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), train_cfg=dict( assigner=dict(type='DynamicSoftLabelAssigner', topk=13, iou_factor=2.0), alpha=1, beta=6, allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
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), dict( type="Sequential", transforms=[ dict( type="RandResize", img_scale=[(1333, 400), (1333, 1200)], multiscale_mode="range", keep_ratio=True, ), dict(type="RandFlip", flip_ratio=0.5), dict( type="OneOf", transforms=[ dict(type=k) for k in [ "Identity", "AutoContrast", "RandEqualize", "RandSolarize", "RandColor", "RandContrast", "RandBrightness", "RandSharpness", "RandPosterize", ] ], ), ], record=True, ), dict(type="Pad", size_divisor=32), dict(type="Normalize", **img_norm_cfg), dict(type="ExtraAttrs", tag="sup"), dict(type="DefaultFormatBundle"), dict( type="Collect", keys=["img", "gt_bboxes", "gt_labels"], meta_keys=( "filename", "ori_shape", "img_shape", "img_norm_cfg", "pad_shape", "scale_factor", "tag", ), ), ]
strong_pipeline = [ dict( type="Sequential", transforms=[ dict( type="RandResize", img_scale=[(1333, 400), (1333, 1200)], multiscale_mode="range", keep_ratio=True, ), dict(type="RandFlip", flip_ratio=0.5), dict( type="ShuffledSequential", transforms=[ dict( type="OneOf", transforms=[ dict(type=k) for k in [ "Identity", "AutoContrast", "RandEqualize", "RandSolarize", "RandColor", "RandContrast", "RandBrightness", "RandSharpness", "RandPosterize", ] ], ), dict( type="OneOf", transforms=[ dict(type="RandTranslate", x=(-0.1, 0.1)), dict(type="RandTranslate", y=(-0.1, 0.1)), dict(type="RandRotate", angle=(-30, 30)), [ dict(type="RandShear", x=(-30, 30)), dict(type="RandShear", y=(-30, 30)), ], ], ), ], ), dict( type="RandErase", n_iterations=(1, 5), size=[0, 0.2], squared=True, ), ], record=True, ), dict(type="Pad", size_divisor=32), dict(type="Normalize", **img_norm_cfg), dict(type="ExtraAttrs", tag="unsup_student"), dict(type="DefaultFormatBundle"), dict( type="Collect", keys=["img", "gt_bboxes", "gt_labels"], meta_keys=( "filename", "ori_shape", "img_shape", "img_norm_cfg", "pad_shape", "scale_factor", "tag", "transform_matrix", ), ), ] weak_pipeline = [ dict( type="Sequential", transforms=[ dict( type="RandResize", img_scale=[(1333, 400), (1333, 1200)], multiscale_mode="range", keep_ratio=True, ), dict(type="RandFlip", flip_ratio=0.5), ], record=True, ), dict(type="Pad", size_divisor=32), dict(type="Normalize", **img_norm_cfg), dict(type="ExtraAttrs", tag="unsup_teacher"), dict(type="DefaultFormatBundle"), dict( type="Collect", keys=["img", "gt_bboxes", "gt_labels"], meta_keys=( "filename", "ori_shape", "img_shape", "img_norm_cfg", "pad_shape", "scale_factor", "tag", "transform_matrix", ), ), ] unsup_pipeline = [ dict(type="LoadImageFromFile"), # dict(type="LoadAnnotations", with_bbox=True), # generate fake labels for data format compatibility dict(type="PseudoSamples", with_bbox=True), dict( type="MultiBranch", unsup_teacher=strong_pipeline, unsup_student=weak_pipeline ), ]
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"]), ], ), ]
fold = 1 percent = 10 data = dict( samples_per_gpu=5, workers_per_gpu=5, train=dict( delete=True, type="SemiDataset", sup=dict( type="CocoDataset", ann_file="data/coco_semi/semi_supervised/instances_train2017.${fold}@${percent}.json", img_prefix="data/coco/train2017/", pipeline=train_pipeline, ), unsup=dict( type="CocoDataset", ann_file="data/coco_semi/semi_supervised/instances_train2017.${fold}@${percent}-unlabeled.json", img_prefix="data/coco/train2017/", pipeline=unsup_pipeline, filter_empty_gt=False, ), ), val=dict( img_prefix="data/coco/val2017/", ann_file='data/coco/annotations/instances_val2017.json', pipeline=test_pipeline ), test=dict( pipeline=test_pipeline, img_prefix="data/coco/val2017/", ann_file='data/coco/annotations/instances_val2017.json' ), sampler=dict( train=dict( type="SemiBalanceSampler", sample_ratio=[1, 4], by_prob=False, # at_least_one=True, epoch_length=7330, ) ), )
semi_wrapper = dict( type="ConsistentTeacher", model="${model}", train_cfg=dict( num_scores=100, dynamic_ratio=1.0, warmup_step=10000, min_pseduo_box_size=0, unsup_weight=2.0, ), test_cfg=dict(inference_on="teacher"), )
custom_hooks = [ dict(type="NumClassCheckHook"), dict(type="WeightSummary"), dict(type='SetIterInfoHook'), dict(type="MeanTeacher", momentum=0.9995, interval=1, warm_up=0), ] evaluation = dict(type="SubModulesDistEvalHook", interval=4000) optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict( delete=True, grad_clip=dict(max_norm=20, norm_type=2)) lr_config = dict(step=[180000, 180000]) runner = dict(delete=True, type="IterBasedRunner", max_iters=180000) checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=2)
log_config = dict( interval=50, hooks=[ dict(type="TextLoggerHook", by_epoch=False), dict( type="WandbLoggerHook", init_kwargs=dict( project="consistent-teacher", name="${cfg_name}", config=dict( fold="${fold}", percent="${percent}", work_dirs="${work_dir}", total_step="${runner.max_iters}", ), ), by_epoch=False, )
],
) fp16 = None
用的自带的配置文件
