[Bug] RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead

### Branch

main branch (mmpretrain version)

### Describe the bug

Hi. I am currently using mmdetection custom cascade rcnn model with custom dataset. While working with it I have faced with two bugs, first of which was running train.py on M1 CPU, which resulted in multiple errors but got fixed by changing library files to .cpu(). As I fixed it I have faced new issue, which is described in title. Error appears after 10 images getting processed. I have searched it up and saw that people already had it before, and fixed it py changing accuracy.py view() to reshape() and it worked for them, but now as I looked through the file I have this error though I am using reshape in accuracy.py. If anyone can help I will be really grateful.

# mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco_200e.py
_base_ = [
    '../_base_/models/cascade_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_detection.py', 
    '../_base_/schedules/schedule_1x.py',
    '../_base_/default_runtime.py'
]

# Now you can simply rely on the default data paths:
# data/coco/train2017/ + data/coco/annotations/instances_train2017.json, etc.

# But override training epochs, optimizer, etc.:
custom_imports = dict(
    imports=['configs.cascade_rcnn.debug_pipeline'],
    allow_failed_imports=False
)

dataset_type = 'CocoDataset'
classes = ('handwrittentext', 'handwritten_text')
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    # Insert the debug pipeline step here
    dict(type='DebugPipeline', prefix='[TRAIN DATA]'),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    dict(type='LoadAnnotations', with_bbox=True),
    # Insert the debug pipeline step for validation/test
    dict(type='DebugPipeline', prefix='[VAL/TEST DATA]'),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')
    )
]

train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    dataset=dict(
        type=dataset_type,
        metainfo=dict(classes=classes),
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        pipeline=train_pipeline
        )
    )

val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    dataset=dict(
        type=dataset_type,
        metainfo=dict(classes=classes),
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        pipeline=test_pipeline
        )
    )

test_dataloader = dict(
    batch_size=1,
    num_workers=2,
    dataset=dict(
        type=dataset_type,
        metainfo=dict(classes=classes),
        ann_file='annotations/instances_test2017.json',
        data_prefix=dict(img='test2017/')
        )
    )

model = dict(
    roi_head=dict(
        bbox_head=[
            dict(
                type='Shared2FCBBoxHead',
                num_classes=len(classes)
                ),
            dict(
                type='Shared2FCBBoxHead',
                num_classes=len(classes)
                ),
            dict(
                type='Shared2FCBBoxHead',
                num_classes=len(classes)
                ),
        ]
    )
    
)


optim_wrapper = dict(
    optimizer=dict(
        type='SGD',
        lr=0.001,
        momentum=0.9,
        weight_decay=0.0001,
    )
)
param_scheduler = [
    dict(
        type='MultiStepLR',
        begin=0,
        end=200,
        by_epoch=True,
        milestones=[120, 160],
        gamma=0.1
    )
]
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=200, val_interval=10)
default_hooks = dict(checkpoint=dict(interval=10))

Here is my code for custom model and here is error

(openmmlab) (base) kirottu@nas-10-240-117-236 mmdetection % python tools/train.py configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco_200e.py

/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.
  from torch.distributed.optim import \
01/18 20:10:41 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: darwin
    Python: 3.11.7 (main, Dec 15 2023, 12:09:56) [Clang 14.0.6 ]
    CUDA available: False
    MUSA available: False
    numpy_random_seed: 476397440
    GCC: Apple clang version 16.0.0 (clang-1600.0.26.4)
    PyTorch: 2.5.1
    PyTorch compiling details: PyTorch built with:
  - GCC 4.2
  - C++ Version: 201703
  - clang 14.0.3
  - OpenMP 201811
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: NO AVX
  - Build settings: BLAS_INFO=accelerate, BUILD_TYPE=Release, CXX_COMPILER=/Applications/Xcode_14.3.1.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_PYTORCH_QNNPACK -DAT_BUILD_ARM_VEC256_WITH_SLEEF -DUSE_XNNPACK -DUSE_PYTORCH_METAL_EXPORT -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DUSE_COREML_DELEGATE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -Wno-unused-but-set-variable -fno-math-errno -fno-trapping-math -Werror=format -DUSE_MPS -Wno-unused-private-field -Wno-missing-braces, LAPACK_INFO=accelerate, TORCH_VERSION=2.5.1, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 

    TorchVision: 0.20.1
    OpenCV: 4.10.0
    MMEngine: 0.10.5

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 476397440
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

01/18 20:10:41 - mmengine - INFO - Config:
auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
classes = (
    'handwrittentext',
    'handwritten_text',
)
custom_imports = dict(
    allow_failed_imports=False,
    imports=[
        'configs.cascade_rcnn.debug_pipeline',
    ])
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
    checkpoint=dict(interval=10, type='CheckpointHook'),
    logger=dict(interval=50, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
model = dict(
    backbone=dict(
        depth=50,
        frozen_stages=1,
        init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'),
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=True,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        style='pytorch',
        type='ResNet'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_size_divisor=32,
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='DetDataPreprocessor'),
    neck=dict(
        in_channels=[
            256,
            512,
            1024,
            2048,
        ],
        num_outs=5,
        out_channels=256,
        type='FPN'),
    roi_head=dict(
        bbox_head=[
            dict(num_classes=2, type='Shared2FCBBoxHead'),
            dict(num_classes=2, type='Shared2FCBBoxHead'),
            dict(num_classes=2, type='Shared2FCBBoxHead'),
        ],
        bbox_roi_extractor=dict(
            featmap_strides=[
                4,
                8,
                16,
                32,
            ],
            out_channels=256,
            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
            type='SingleRoIExtractor'),
        num_stages=3,
        stage_loss_weights=[
            1,
            0.5,
            0.25,
        ],
        type='CascadeRoIHead'),
    rpn_head=dict(
        anchor_generator=dict(
            ratios=[
                0.5,
                1.0,
                2.0,
            ],
            scales=[
                8,
            ],
            strides=[
                4,
                8,
                16,
                32,
                64,
            ],
            type='AnchorGenerator'),
        bbox_coder=dict(
            target_means=[
                0.0,
                0.0,
                0.0,
                0.0,
            ],
            target_stds=[
                1.0,
                1.0,
                1.0,
                1.0,
            ],
            type='DeltaXYWHBBoxCoder'),
        feat_channels=256,
        in_channels=256,
        loss_bbox=dict(
            beta=0.1111111111111111, loss_weight=1.0, type='SmoothL1Loss'),
        loss_cls=dict(
            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
        type='RPNHead'),
    test_cfg=dict(
        rcnn=dict(
            max_per_img=100,
            nms=dict(iou_threshold=0.5, type='nms'),
            score_thr=0.05),
        rpn=dict(
            max_per_img=1000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.7, type='nms'),
            nms_pre=1000)),
    train_cfg=dict(
        rcnn=[
            dict(
                assigner=dict(
                    ignore_iof_thr=-1,
                    match_low_quality=False,
                    min_pos_iou=0.5,
                    neg_iou_thr=0.5,
                    pos_iou_thr=0.5,
                    type='MaxIoUAssigner'),
                debug=False,
                pos_weight=-1,
                sampler=dict(
                    add_gt_as_proposals=True,
                    neg_pos_ub=-1,
                    num=512,
                    pos_fraction=0.25,
                    type='RandomSampler')),
            dict(
                assigner=dict(
                    ignore_iof_thr=-1,
                    match_low_quality=False,
                    min_pos_iou=0.6,
                    neg_iou_thr=0.6,
                    pos_iou_thr=0.6,
                    type='MaxIoUAssigner'),
                debug=False,
                pos_weight=-1,
                sampler=dict(
                    add_gt_as_proposals=True,
                    neg_pos_ub=-1,
                    num=512,
                    pos_fraction=0.25,
                    type='RandomSampler')),
            dict(
                assigner=dict(
                    ignore_iof_thr=-1,
                    match_low_quality=False,
                    min_pos_iou=0.7,
                    neg_iou_thr=0.7,
                    pos_iou_thr=0.7,
                    type='MaxIoUAssigner'),
                debug=False,
                pos_weight=-1,
                sampler=dict(
                    add_gt_as_proposals=True,
                    neg_pos_ub=-1,
                    num=512,
                    pos_fraction=0.25,
                    type='RandomSampler')),
        ],
        rpn=dict(
            allowed_border=0,
            assigner=dict(
                ignore_iof_thr=-1,
                match_low_quality=True,
                min_pos_iou=0.3,
                neg_iou_thr=0.3,
                pos_iou_thr=0.7,
                type='MaxIoUAssigner'),
            debug=False,
            pos_weight=-1,
            sampler=dict(
                add_gt_as_proposals=False,
                neg_pos_ub=-1,
                num=256,
                pos_fraction=0.5,
                type='RandomSampler')),
        rpn_proposal=dict(
            max_per_img=2000,
            min_bbox_size=0,
            nms=dict(iou_threshold=0.7, type='nms'),
            nms_pre=2000)),
    type='CascadeRCNN')
optim_wrapper = dict(
    optimizer=dict(lr=0.001, momentum=0.9, type='SGD', weight_decay=0.0001),
    type='OptimWrapper')
param_scheduler = [
    dict(
        begin=0,
        by_epoch=True,
        end=200,
        gamma=0.1,
        milestones=[
            120,
            160,
        ],
        type='MultiStepLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='annotations/instances_test2017.json',
        backend_args=None,
        data_prefix=dict(img='test2017/'),
        data_root='data/coco/',
        metainfo=dict(classes=(
            'handwrittentext',
            'handwritten_text',
        )),
        pipeline=[
            dict(backend_args=None, type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    ann_file='data/coco/annotations/instances_val2017.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        1333,
        800,
    ), type='Resize'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(prefix='[VAL/TEST DATA]', type='DebugPipeline'),
    dict(
        meta_keys=(
            'img_id',
            'img_path',
            'ori_shape',
            'img_shape',
            'scale_factor',
        ),
        type='PackDetInputs'),
]
train_cfg = dict(max_epochs=200, type='EpochBasedTrainLoop', val_interval=10)
train_dataloader = dict(
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    batch_size=2,
    dataset=dict(
        ann_file='annotations/instances_train2017.json',
        backend_args=None,
        data_prefix=dict(img='train2017/'),
        data_root='data/coco/',
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        metainfo=dict(classes=(
            'handwrittentext',
            'handwritten_text',
        )),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(prefix='[TRAIN DATA]', type='DebugPipeline'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PackDetInputs'),
        ],
        type='CocoDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(prefix='[TRAIN DATA]', type='DebugPipeline'),
    dict(keep_ratio=True, scale=(
        1333,
        800,
    ), type='Resize'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='annotations/instances_val2017.json',
        backend_args=None,
        data_prefix=dict(img='val2017/'),
        data_root='data/coco/',
        metainfo=dict(classes=(
            'handwrittentext',
            'handwritten_text',
        )),
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                1333,
                800,
            ), type='Resize'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(prefix='[VAL/TEST DATA]', type='DebugPipeline'),
            dict(
                meta_keys=(
                    'img_id',
                    'img_path',
                    'ori_shape',
                    'img_shape',
                    'scale_factor',
                ),
                type='PackDetInputs'),
        ],
        test_mode=True,
        type='CocoDataset'),
    drop_last=False,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    ann_file='data/coco/annotations/instances_val2017.json',
    backend_args=None,
    format_only=False,
    metric='bbox',
    type='CocoMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='DetLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/cascade_rcnn_r50_fpn_1x_coco_200e'

01/18 20:10:42 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
01/18 20:10:42 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) RuntimeInfoHook                    
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
before_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DistSamplerSeedHook                
 -------------------- 
before_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_train_iter:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) IterTimerHook                      
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DetVisualizationHook               
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) DetVisualizationHook               
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
01/18 20:10:43 - mmengine - INFO - load model from: torchvision://resnet50
01/18 20:10:43 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50
01/18 20:10:43 - mmengine - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

01/18 20:10:44 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
01/18 20:10:44 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
01/18 20:10:44 - mmengine - INFO - Checkpoints will be saved to /Users/kirottu/coding xd/cascade-code/mmdetection/work_dirs/cascade_rcnn_r50_fpn_1x_coco_200e.
[TRAIN DATA]: Now processing image: data/coco/train2017/train_037_jpg.rf.821ecc5bcc5cbafd2ab76f5cee7dda61.jpg
[TRAIN DATA]: Now processing image: data/coco/train2017/train_026_jpg.rf.8b3651f60ad2e63c9aa82831a4a8c79e.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([16, 4])
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([9, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_034_jpg.rf.2c531cc713c34a966db0577316f37966.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([11, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_025_jpg.rf.0cab4128c75baecacefb3bc06d178b55.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([13, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_036_jpg.rf.44951058e657bbe54289998d9201b00b.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([6, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_043_jpg.rf.82a3ccae74be8fa9de88cbda7683396a.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([14, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_035_jpg.rf.3c125ab645edee4782203d1397f3ae96.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([8, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_033_jpg.rf.5b1fc54fce4c35e14dfe114ac8b98532.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([18, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_054_jpg.rf.f4e8cd536e03d3e28473856a13d9978b.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([7, 4])
[TRAIN DATA]: Now processing image: data/coco/train2017/train_041_jpg.rf.7711b2205ae5ced5ba2a874624a47509.jpg
[TRAIN DATA]: Image numpy array shape = (640, 640, 3)
[TRAIN DATA]: Is numpy array contiguous? True
[TRAIN DATA]: Bboxes shape = torch.Size([14, 4])
Traceback (most recent call last):
  File "/Users/kirottu/coding xd/cascade-code/mmdetection/tools/train.py", line 121, in <module>
    main()
  File "/Users/kirottu/coding xd/cascade-code/mmdetection/tools/train.py", line 117, in main
    runner.train()
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/runner/runner.py", line 1777, in train
    model = self.train_loop.run()  # type: ignore
            ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/runner/loops.py", line 98, in run
    self.run_epoch()
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/runner/loops.py", line 115, in run_epoch
    self.run_iter(idx, data_batch)
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/runner/loops.py", line 131, in run_iter
    outputs = self.runner.model.train_step(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/model/base_model/base_model.py", line 116, in train_step
    optim_wrapper.update_params(parsed_losses)
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 196, in update_params
    self.backward(loss)
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/mmengine/optim/optimizer/optimizer_wrapper.py", line 220, in backward
    loss.backward(**kwargs)
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/torch/_tensor.py", line 581, in backward
    torch.autograd.backward(
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/torch/autograd/__init__.py", line 347, in backward
    _engine_run_backward(
  File "/opt/anaconda3/envs/openmmlab/lib/python3.11/site-packages/torch/autograd/graph.py", line 825, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

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[Bug] RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead #1967

Description

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Describe the bug

mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco_200e.py

Now you can simply rely on the default data paths:

data/coco/train2017/ + data/coco/annotations/instances_train2017.json, etc.

But override training epochs, optimizer, etc.:

Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 476397440 Distributed launcher: none Distributed training: False GPU number: 1

before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook

before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook

before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook

after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook

after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook

before_val: (VERY_HIGH ) RuntimeInfoHook

before_val_epoch: (NORMAL ) IterTimerHook

before_val_iter: (NORMAL ) IterTimerHook

after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook

after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook

after_val: (VERY_HIGH ) RuntimeInfoHook

after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook

before_test: (VERY_HIGH ) RuntimeInfoHook

before_test_epoch: (NORMAL ) IterTimerHook

before_test_iter: (NORMAL ) IterTimerHook

after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook

after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook

after_test: (VERY_HIGH ) RuntimeInfoHook

after_run: (BELOW_NORMAL) LoggerHook

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Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 476397440
Distributed launcher: none
Distributed training: False
GPU number: 1

before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook

before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook

before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook

after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

before_val:
(VERY_HIGH ) RuntimeInfoHook

before_val_epoch:
(NORMAL ) IterTimerHook

before_val_iter:
(NORMAL ) IterTimerHook

after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook

after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_val:
(VERY_HIGH ) RuntimeInfoHook

after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook

before_test:
(VERY_HIGH ) RuntimeInfoHook

before_test_epoch:
(NORMAL ) IterTimerHook

before_test_iter:
(NORMAL ) IterTimerHook

after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook

after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_test:
(VERY_HIGH ) RuntimeInfoHook

after_run:
(BELOW_NORMAL) LoggerHook