ValueError: not enough values to unpack (expected 2, got 1)

[sarmientoj24]

Using the baseline for fully labeled data was training. But then, when I try to train soft-teacher with the following command,

bash tools/dist_train_partially.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_180k.py semi 1 50 1

it is getting this error

Traceback (most recent call last):
  File "tools/train.py", line 210, in <module>
    main()
  File "tools/train.py", line 98, in main
    args = parse_args()
  File "tools/train.py", line 81, in parse_args
    args = parser.parse_args()
  File "/opt/conda/envs/softteacher/lib/python3.6/argparse.py", line 1734, in parse_args
    args, argv = self.parse_known_args(args, namespace)
  File "/opt/conda/envs/softteacher/lib/python3.6/argparse.py", line 1766, in parse_known_args
    namespace, args = self._parse_known_args(args, namespace)
  File "/opt/conda/envs/softteacher/lib/python3.6/argparse.py", line 1972, in _parse_known_args
    start_index = consume_optional(start_index)
  File "/opt/conda/envs/softteacher/lib/python3.6/argparse.py", line 1912, in consume_optional
    take_action(action, args, option_string)
  File "/opt/conda/envs/softteacher/lib/python3.6/argparse.py", line 1840, in take_action
    action(self, namespace, argument_values, option_string)
  File "/opt/conda/envs/softteacher/lib/python3.6/site-packages/mmcv/utils/config.py", line 669, in __call__
    key, val = kv.split('=', maxsplit=1)
ValueError: not enough values to unpack (expected 2, got 1)

Here are my configs:

base.py

mmdet_base = "../../thirdparty/mmdetection/configs/_base_"
_base_ = [
    f"{mmdet_base}/models/faster_rcnn_r50_fpn.py",
    f"{mmdet_base}/datasets/coco_detection.py",
    f"{mmdet_base}/schedules/schedule_1x.py",
    f"{mmdet_base}/default_runtime.py",
]

model = dict(
    backbone=dict(
        norm_cfg=dict(requires_grad=False),
        norm_eval=True,
        style="caffe",
        init_cfg=dict(
            type="Pretrained", checkpoint="open-mmlab://detectron2/resnet50_caffe"
        ),
    )
)

img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)

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"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=None,
    workers_per_gpu=None,
    train=dict(
        _delete_=True,
        type="SemiDataset",
        sup=dict(
            type="CocoDataset",
            ann_file=None,
            img_prefix=None,
            pipeline=train_pipeline,
        ),
        unsup=dict(
            type="CocoDataset",
            ann_file=None,
            img_prefix=None,
            pipeline=unsup_pipeline,
            filter_empty_gt=False,
        ),
    ),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
    sampler=dict(
        train=dict(
            type="SemiBalanceSampler",
            sample_ratio=[1, 4],
            by_prob=True,
            # at_least_one=True,
            epoch_length=7330,
        )
    ),
)

semi_wrapper = dict(
    type="SoftTeacher",
    model="${model}",
    train_cfg=dict(
        use_teacher_proposal=False,
        pseudo_label_initial_score_thr=0.5,
        rpn_pseudo_threshold=0.9,
        cls_pseudo_threshold=0.9,
        reg_pseudo_threshold=0.01,
        jitter_times=10,
        jitter_scale=0.06,
        min_pseduo_box_size=0,
        unsup_weight=4.0,
    ),
    test_cfg=dict(inference_on="student"),
)

custom_hooks = [
    dict(type="NumClassCheckHook"),
    dict(type="WeightSummary"),
    dict(type="MeanTeacher", momentum=0.999, 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)
lr_config = dict(step=[120000, 160000])
runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000)
checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=20)

fp16 = dict(loss_scale="dynamic")

log_config = dict(
    interval=50,
    hooks=[
        dict(type="TextLoggerHook", by_epoch=False),
        dict(
            type="WandbLoggerHook",
            init_kwargs=dict(
                project="pre_release",
                name="${cfg_name}",
                config=dict(
                    work_dirs="${work_dir}",
                    total_step="${runner.max_iters}",
                ),
            ),
            by_epoch=False,
        ),
    ],
)

soft_teacher_faster_rcnn_r50_caffe_fpn_coco_180k.py

_base_ = "base.py"

classes = ('D00', 'D10', 'D20', 'D40')
dataset_type = 'CocoDataset'

data = dict(
    samples_per_gpu=5,
    workers_per_gpu=5,
    train=dict(
        sup=dict(
            type="CocoDataset",
            ann_file="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/annotations/rddc_coco_train_sup_50_perc.json",
            img_prefix="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/train2017/",
            classes=classes
        ),
        unsup=dict(
            type=dataset_type,
            ann_file="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/annotations/rddc_coco_semisup_50_perc.json",
            img_prefix="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/unlabeled2017/",
            classes=classes
        )
    )
    val=dict(
        type=dataset_type,
        ann_file="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/annotations/rddc_coco_test.json",
        img_prefix="/notebooks/pave/semi_supervised_pavement_detector/data/semisup_fifty/val2017/",
        classes=classes
    ),
    sampler=dict(
        train=dict(
            sample_ratio=[1, 4],
        )
    ),
)

fold = 1
percent = 1

work_dir = "work_dirs"
log_config = dict(
    interval=50,
    hooks=[
        dict(type="TextLoggerHook"),
        dict(
            type="WandbLoggerHook",
            init_kwargs=dict(
                project="pre_release",
                name="${cfg_name}",
                config=dict(
                    work_dirs="${work_dir}",
                    total_step="${runner.max_iters}",
                ),
            ),
            by_epoch=False,
        ),
    ],
)

model = dict(
    roi_head=dict(
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            num_classes=4,
        )
    )
)

Have you encountered this error?

[MendelXu]

The reason is that the script tools/dist_train_partially.sh couldn't process the config file argument. If you want to run an experiment with a specified config file, you can run the command bash tools/dist_train.sh {YOUR_CONFIG_FILE} {NUM_GPU}