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Training error: Target size must be the same as input size

See original GitHub issue

To train Cascade mask rcnn and the config file is following:

   model = dict(
    type='CascadeRCNN',
    num_stages=3,
    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],
        use_sigmoid_cls=True),
    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=True),
        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=[0.05, 0.05, 0.1, 0.1],
            reg_class_agnostic=True),
        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=[0.033, 0.033, 0.067, 0.067],
            reg_class_agnostic=True)
    ],
    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))
# 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,
        smoothl1_beta=1 / 9.0,
        debug=False),
    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),
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.6,
                neg_iou_thr=0.6,
                min_pos_iou=0.6,
                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,
                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)
    ],
    stage_loss_weights=[1, 0.5, 0.25])
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=2000,
        max_num=2000,
        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),
    keep_all_stages=False)
# 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)
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=1,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/train.json',
        img_prefix=data_root + 'train/',
        img_scale=(512, 512),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0.5,
        with_mask=True,
        with_crowd=True,
        with_label=True),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/val.json',
        img_prefix=data_root + 'val/',
        img_scale=(512, 512),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=True,
        with_crowd=True,
        with_label=True),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/test.json',
        img_prefix=data_root + 'test/',
        img_scale=(1024, 1024),
        img_norm_cfg=img_norm_cfg,
        size_divisor=32,
        flip_ratio=0,
        with_mask=True,
        with_label=False,
        test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.005, 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=300,
    warmup_ratio=1.0 / 3,
    step=[8, 10])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=20,
    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/cm_rcnn_10164'
load_from = None
resume_from = None
workflow = [('train', 1)]

Then I got following error about mismatching tensor size:

2019-04-15 17:56:39,451 - INFO - Distributed training: True
2019-04-15 17:56:41,176 - INFO - load model from: open-mmlab://resnext101_64x4d
2019-04-15 17:56:41,399 - WARNING - missing keys in source state_dict: layer3.4.bn3.num_batches_tracked, layer3.16.bn2.num_batches_tracked, layer3.6.bn2.num_batches_tracked, layer1.0.bn3.num_batches_tracked, layer1.0.bn2.num_batches_tracked, layer2.3.bn1.num_batches_tracked, layer3.1.bn3.num_batches_tracked, layer4.0.bn2.num_batches_tracked, layer2.1.bn3.num_batches_tracked, layer3.6.bn1.num_batches_tracked, layer3.21.bn1.num_batches_tracked, layer3.18.bn3.num_batches_tracked, layer3.5.bn3.num_batches_tracked, layer3.8.bn1.num_batches_tracked, layer4.2.bn1.num_batches_tracked, layer3.1.bn2.num_batches_tracked, layer2.2.bn1.num_batches_tracked, layer2.1.bn1.num_batches_tracked, layer3.16.bn1.num_batches_tracked, layer3.0.downsample.1.num_batches_tracked, layer2.0.bn3.num_batches_tracked, layer4.0.bn1.num_batches_tracked, layer3.13.bn3.num_batches_tracked, layer3.7.bn2.num_batches_tracked, layer3.0.bn2.num_batches_tracked, layer3.20.bn2.num_batches_tracked, layer3.9.bn2.num_batches_tracked, layer3.18.bn2.num_batches_tracked, layer3.6.bn3.num_batches_tracked, layer3.17.bn3.num_batches_tracked, layer3.0.bn3.num_batches_tracked, layer4.2.bn3.num_batches_tracked, layer3.9.bn1.num_batches_tracked, layer3.12.bn3.num_batches_tracked, layer4.2.bn2.num_batches_tracked, layer3.20.bn3.num_batches_tracked, bn1.num_batches_tracked, layer3.5.bn2.num_batches_tracked, layer3.22.bn2.num_batches_tracked, layer1.1.bn3.num_batches_tracked, layer3.14.bn3.num_batches_tracked, layer3.1.bn1.num_batches_tracked, layer3.12.bn2.num_batches_tracked, layer3.2.bn1.num_batches_tracked, layer3.14.bn2.num_batches_tracked, layer1.2.bn1.num_batches_tracked, layer4.1.bn2.num_batches_tracked, layer1.2.bn2.num_batches_tracked, layer1.0.bn1.num_batches_tracked, layer3.16.bn3.num_batches_tracked, layer1.2.bn3.num_batches_tracked, layer3.4.bn1.num_batches_tracked, layer2.2.bn3.num_batches_tracked, layer2.0.downsample.1.num_batches_tracked, layer3.17.bn1.num_batches_tracked, layer3.19.bn3.num_batches_tracked, layer3.8.bn3.num_batches_tracked, layer3.14.bn1.num_batches_tracked, layer3.10.bn1.num_batches_tracked, layer2.1.bn2.num_batches_tracked, layer2.0.bn2.num_batches_tracked, layer3.3.bn1.num_batches_tracked, layer3.11.bn3.num_batches_tracked, layer3.15.bn3.num_batches_tracked, layer3.13.bn1.num_batches_tracked, layer3.15.bn2.num_batches_tracked, layer3.21.bn2.num_batches_tracked, layer4.0.downsample.1.num_batches_tracked, layer3.20.bn1.num_batches_tracked, layer3.11.bn2.num_batches_tracked, layer2.3.bn3.num_batches_tracked, layer3.8.bn2.num_batches_tracked, layer3.7.bn3.num_batches_tracked, layer3.2.bn2.num_batches_tracked, layer3.11.bn1.num_batches_tracked, layer3.0.bn1.num_batches_tracked, layer3.22.bn3.num_batches_tracked, layer3.5.bn1.num_batches_tracked, layer3.22.bn1.num_batches_tracked, layer1.1.bn1.num_batches_tracked, layer4.1.bn3.num_batches_tracked, layer3.3.bn3.num_batches_tracked, layer3.17.bn2.num_batches_tracked, layer1.1.bn2.num_batches_tracked, layer3.12.bn1.num_batches_tracked, layer3.10.bn3.num_batches_tracked, layer3.3.bn2.num_batches_tracked, layer3.19.bn2.num_batches_tracked, layer3.2.bn3.num_batches_tracked, layer3.13.bn2.num_batches_tracked, layer1.0.downsample.1.num_batches_tracked, layer4.0.bn3.num_batches_tracked, layer3.7.bn1.num_batches_tracked, layer3.18.bn1.num_batches_tracked, layer3.19.bn1.num_batches_tracked, layer2.2.bn2.num_batches_tracked, layer2.0.bn1.num_batches_tracked, layer3.4.bn2.num_batches_tracked, layer2.3.bn2.num_batches_tracked, layer3.10.bn2.num_batches_tracked, layer3.21.bn3.num_batches_tracked, layer3.9.bn3.num_batches_tracked, layer4.1.bn1.num_batches_tracked, layer3.15.bn1.num_batches_tracked

loading annotations into memory...
loading annotations into memory...
Done (t=0.03s)
creating index...
Done (t=0.03s)
creating index...
index created!
index created!
loading annotations into memory...
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Done (t=0.00s)
creating index...
index created!
2019-04-15 17:56:44,931 - INFO - Start running, host: forestriveral@HanazawaKana, work_dir: /home/forestriveral/DeepLearning/object-detection/mmdetection/work_dirs/cm_rcnn_10164
2019-04-15 17:56:44,932 - INFO - workflow: [('train', 1)], max: 12 epochs
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=405 error=11 : invalid argument
Traceback (most recent call last):
  File "./tools/train.py", line 90, in <module>
    main()
  File "./tools/train.py", line 86, in main
    logger=logger)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 57, in train_detector
    _dist_train(model, dataset, cfg, validate=validate)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 96, in _dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 355, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 261, in train
    self.model, data_batch, train_mode=True, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 37, in batch_processor
    losses = model(**data)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 50, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/base.py", line 80, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/cascade_rcnn.py", line 125, in forward_train
    *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss
    gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 210, in loss
    cfg=cfg)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/utils/misc.py", line 24, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 155, in loss_single
    cls_score, labels, label_weights, avg_factor=num_total_samples)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/loss/losses.py", line 30, in weighted_binary_cross_entropy
    reduction='sum')[None] / avg_factor
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/functional.py", line 2075, in binary_cross_entropy_with_logits
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([98304])) must be the same as input size (torch.Size([98304, 1]))
Traceback (most recent call last):
  File "./tools/train.py", line 90, in <module>
    main()
  File "./tools/train.py", line 86, in main
    logger=logger)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 57, in train_detector
    _dist_train(model, dataset, cfg, validate=validate)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 96, in _dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 355, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/runner/runner.py", line 261, in train
    self.model, data_batch, train_mode=True, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/apis/train.py", line 37, in batch_processor
    losses = model(**data)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 50, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/base.py", line 80, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/detectors/cascade_rcnn.py", line 125, in forward_train
    *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss
    gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 210, in loss
    cfg=cfg)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/utils/misc.py", line 24, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/models/anchor_heads/anchor_head.py", line 155, in loss_single
    cls_score, labels, label_weights, avg_factor=num_total_samples)
  File "/home/forestriveral/DeepLearning/object-detection/mmdetection/mmdet/core/loss/losses.py", line 30, in weighted_binary_cross_entropy
    reduction='sum')[None] / avg_factor
  File "/home/forestriveral/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/functional.py", line 2075, in binary_cross_entropy_with_logits
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([98304])) must be the same as input size (torch.Size([98304, 1]))

Any ideas or suggestions? Thanks

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Comments:7

github_iconTop GitHub Comments

1reaction
yhcao6commented, Apr 16, 2019

Hi @forestriveral

I just pull from the latest master branch, and I’m also getting the exact same error:

(pytorch1.0) arthur@arthur-System-Product-Name:/media/arthur/6E6C80E46C80A883/Work/mmdetection-arthur$ ./tools/dist_train.sh configs/mask_rcnn_r50_fpn_1x.py 1
2019-04-16 13:58:43,289 - INFO - Distributed training: True
2019-04-16 13:58:43,607 - INFO - load model from: modelzoo://resnet50
2019-04-16 13:58:43,925 - WARNING - unexpected key in source state_dict: fc.weight, fc.bias

missing keys in source state_dict: layer2.3.bn2.num_batches_tracked, layer3.3.bn3.num_batches_tracked, layer1.0.bn1.num_batches_tracked, layer1.1.bn3.num_batches_tracked, layer2.3.bn3.num_batches_tracked, layer3.0.downsample.1.num_batches_tracked, layer3.5.bn2.num_batches_tracked, layer2.0.bn1.num_batches_tracked, layer1.1.bn1.num_batches_tracked, layer1.0.bn3.num_batches_tracked, layer4.1.bn2.num_batches_tracked, layer3.4.bn2.num_batches_tracked, layer1.2.bn2.num_batches_tracked, layer2.3.bn1.num_batches_tracked, layer2.1.bn1.num_batches_tracked, layer3.1.bn2.num_batches_tracked, layer1.0.bn2.num_batches_tracked, layer3.1.bn1.num_batches_tracked, layer3.3.bn1.num_batches_tracked, layer1.0.downsample.1.num_batches_tracked, layer3.3.bn2.num_batches_tracked, layer3.2.bn1.num_batches_tracked, layer4.0.bn2.num_batches_tracked, layer3.2.bn3.num_batches_tracked, layer3.0.bn1.num_batches_tracked, layer2.2.bn1.num_batches_tracked, layer1.2.bn1.num_batches_tracked, layer1.2.bn3.num_batches_tracked, layer2.0.downsample.1.num_batches_tracked, layer3.0.bn3.num_batches_tracked, layer4.0.downsample.1.num_batches_tracked, bn1.num_batches_tracked, layer3.0.bn2.num_batches_tracked, layer4.0.bn3.num_batches_tracked, layer2.0.bn2.num_batches_tracked, layer2.2.bn2.num_batches_tracked, layer2.2.bn3.num_batches_tracked, layer4.1.bn1.num_batches_tracked, layer3.2.bn2.num_batches_tracked, layer2.0.bn3.num_batches_tracked, layer2.1.bn2.num_batches_tracked, layer3.1.bn3.num_batches_tracked, layer2.1.bn3.num_batches_tracked, layer4.2.bn1.num_batches_tracked, layer4.1.bn3.num_batches_tracked, layer4.0.bn1.num_batches_tracked, layer3.5.bn1.num_batches_tracked, layer4.2.bn2.num_batches_tracked, layer3.4.bn1.num_batches_tracked, layer3.5.bn3.num_batches_tracked, layer4.2.bn3.num_batches_tracked, layer1.1.bn2.num_batches_tracked, layer3.4.bn3.num_batches_tracked

loading annotations into memory...
Done (t=11.70s)
creating index...
index created!
2019-04-16 13:59:00,123 - INFO - Start running, host: arthur@arthur-System-Product-Name, work_dir: /media/arthur/6E6C80E46C80A883/Work/mmdetection-arthur/work_dirs/mask_rcnn_r50_fpn_1x
2019-04-16 13:59:00,123 - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
  File "./tools/train.py", line 90, in <module>
    main()
  File "./tools/train.py", line 86, in main
    logger=logger)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/apis/train.py", line 57, in train_detector
    _dist_train(model, dataset, cfg, validate=validate)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/apis/train.py", line 96, in _dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmcv/runner/runner.py", line 355, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmcv/runner/runner.py", line 261, in train
    self.model, data_batch, train_mode=True, **kwargs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/apis/train.py", line 37, in batch_processor
    losses = model(**data)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmcv/parallel/distributed.py", line 50, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/models/detectors/base.py", line 80, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/models/detectors/two_stage.py", line 98, in forward_train
    *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss
    gt_bboxes_ignore=gt_bboxes_ignore)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/models/anchor_heads/anchor_head.py", line 210, in loss
    cfg=cfg)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/core/utils/misc.py", line 24, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/models/anchor_heads/anchor_head.py", line 155, in loss_single
    cls_score, labels, label_weights, avg_factor=num_total_samples)
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/mmdet-0.6rc0+d67a2e1-py3.7.egg/mmdet/core/loss/losses.py", line 30, in weighted_binary_cross_entropy
    reduction='sum')[None] / avg_factor
  File "/home/arthur/anaconda3/envs/pytorch1.0/lib/python3.7/site-packages/torch/nn/functional.py", line 2075, in binary_cross_entropy_with_logits
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([364800])) must be the same as input size (torch.Size([364800, 1]))

Are you sure the latest PR solved this problem?

Thanks.

When you pull the latest master, have you setup again? I just try mask r50 and it is okay

0reactions
arthur801031commented, Apr 16, 2019

Hi @yhcao6

Thank you for pointing it out 😃 I didn’t know I would need to set it up again. 😅

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