Repository: open-mmlab/mmdetection

Issue:
Albumentations ValueError: Your 'label_fields' are not valid - them must have same names as params in dict
I added an albumentations pipeline on this config:
```
# VOC Dataset

albu_train_transforms = [
    # dict(
    #     type='ShiftScaleRotate',
    #     shift_limit=0.0625,
    #     scale_limit=0.0,
    #     rotate_limit=0,
    #     interpolation=1,
    #     p=0.5),
    dict(
        type='RandomBrightnessContrast',
        brightness_limit=[0.1, 0.3],
        contrast_limit=[0.1, 0.3],
        p=0.2),
    dict(
        type='OneOf',
        transforms=[
            dict(
                type='RGBShift',
                r_shift_limit=10,
                g_shift_limit=10,
                b_shift_limit=10,
                p=1.0),
            dict(
                type='HueSaturationValue',
                hue_shift_limit=20,
                sat_shift_limit=30,
                val_shift_limit=20,
                p=1.0)
        ],
        p=0.1),
    dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
    dict(type='ChannelShuffle', p=0.1),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Blur', blur_limit=3, p=1.0),
            dict(type='MedianBlur', blur_limit=3, p=1.0)
        ],
        p=0.1),
]


model = dict(
    type='FasterRCNN',
    pretrained='torchvision://resnet50',
    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'),
    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_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 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='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=4,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 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='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                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=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_across_levels=False,
            nms_pre=2000,
            nms_post=1000,
            max_num=1000,
            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,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            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_threshold=0.5),
            max_per_img=100)))
dataset_type = 'VOCDataset'
data_root = 'VOC2012/'
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='Resize', img_scale=(512, 512), keep_ratio=True),
    dict(type='Pad', size_divisor=32),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_labels'],
            min_visibility=0.0,
            filter_lost_elements=True),
        keymap={
            'img': 'image',
            # 'gt_masks': 'masks',
            'gt_bboxes': 'bboxes'
        },
        update_pad_shape=False,
        skip_img_without_anno=True),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(512, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='Pad', size_divisor=32),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    train=dict(
        type='VOCDataset',
        ann_file='VOC2012/ImageSets/Main/train.txt',
        img_prefix='VOC2012/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
            dict(type='Pad', size_divisor=32),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Albu',
                transforms=albu_train_transforms,
                bbox_params=dict(
                    type='BboxParams',
                    format='pascal_voc',
                    label_fields=['gt_labels'],
                    min_visibility=0.0,
                    filter_lost_elements=True),
                keymap={
                    'img': 'image',
                    # 'gt_masks': 'masks',
                    'gt_bboxes': 'bboxes'
                },
                update_pad_shape=False,
                skip_img_without_anno=True),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        classes=('D00', 'D10', 'D20', 'D40')),
    val=dict(
        type='VOCDataset',
        ann_file='VOC2012/ImageSets/Main/val.txt',
        img_prefix='VOC2012/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(512, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('D00', 'D10', 'D20', 'D40')),
    test=dict(
        type='VOCDataset',
        ann_file='VOC2012/ImageSets/Main/test.txt',
        img_prefix='VOC2012/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(512, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('D00', 'D10', 'D20', 'D40'),
        samples_per_gpu=2))
evaluation = dict(interval=1, metric='mAP')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup=None,
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
total_epochs = 10
checkpoint_config = dict(interval=10)
log_config = dict(
    interval=10,
    hooks=[
        dict(type='TensorboardLoggerHook'),
        dict(type='TextLoggerHook'),
        # dict(
        #     type='WandbLoggerHook',
        #     init_kwargs=dict(project='mmdetection-sample', name='exp4'))
    ])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/mmdetection/checkpoint/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'
resume_from = None
workflow = [('train', 1), ('val', 1)]
classes = ('D00', 'D10', 'D20', 'D40')
work_dir = './exps'
gpu_ids = range(0, 1)

```

**ERROR**
```
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/content/mmdetection/mmdet/datasets/custom.py", line 193, in __getitem__
    data = self.prepare_train_img(idx)
  File "/content/mmdetection/mmdet/datasets/custom.py", line 216, in prepare_train_img
    return self.pipeline(results)
  File "/content/mmdetection/mmdet/datasets/pipelines/compose.py", line 40, in __call__
    data = t(data)
  File "/content/mmdetection/mmdet/datasets/pipelines/transforms.py", line 1344, in __call__
    results = self.aug(**results)
  File "/usr/local/lib/python3.6/dist-packages/albumentations/core/composition.py", line 170, in __call__
    p.ensure_data_valid(data)
  File "/usr/local/lib/python3.6/dist-packages/albumentations/augmentations/bbox_utils.py", line 37, in ensure_data_valid
    raise ValueError("Your 'label_fields' are not valid - them must have same names as params in dict")
ValueError: Your 'label_fields' are not valid - them must have same names as params in dict
```


Discussion:
@xvjiarui any particular leads on this?

Resolving patch:
diff --git a/mmdet/datasets/pipelines/transforms.py b/mmdet/datasets/pipelines/transforms.py
--- a/mmdet/datasets/pipelines/transforms.py
+++ b/mmdet/datasets/pipelines/transforms.py
@@ -1,3 +1,4 @@
+import copy
 import inspect
 
 import mmcv
@@ -1239,6 +1240,12 @@ def __init__(self,
         if Compose is None:
             raise RuntimeError('albumentations is not installed')
 
+        # Args will be modified later, copying it will be safer
+        transforms = copy.deepcopy(transforms)
+        if bbox_params is not None:
+            bbox_params = copy.deepcopy(bbox_params)
+        if keymap is not None:
+            keymap = copy.deepcopy(keymap)
         self.transforms = transforms
         self.filter_lost_elements = False
         self.update_pad_shape = update_pad_shape


Test patch: