How to use albu data augmentation in HTC algorithm？

Checklist

1. I have searched related issues but cannot get the expected help. yes
2. The bug has not been fixed in the latest version. yes

Describe the bug when i use albu data augmentation in htc_x101_32x4d_fpn without semantic ,like albu example(mask rcnn):

dataset_type = 'CocoDataset'
data_root = '/disk2/data/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
albu_train_transforms = [
    dict(
        type='ShiftScaleRotate',
        shift_limit=0.0625,
        scale_limit=0.0,
        rotate_limit=30,
        interpolation=2,
        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),
]
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True),
    #dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(
        type='Resize',
        img_scale=[(1400, 600), (1400, 1024)],
        multiscale_mode='range',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.0),
    dict(type='Pad', size_divisor=32),
    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', **img_norm_cfg),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]

Reproduction

1. What command or script did you run?

#!/bin/bash
export PYTHONPATH=`pwd`:$PYTHONPATH
CUDA_VISIBLE_DEVICES=2 python tools/train.py configs/htc/zh_htc_x101_32x4d_fpn_20e_mstrain_imgaug_8gpu.py

1. Did you make any modifications on the code or config? Did you understand what you have modified? No change!
2. What dataset did you use? custom datasets (like coco ) Environment

• OS: centos 7.2
• GCC :4.9.4
• PyTorch version : 1.1.0
• How you installed PyTorch : pip
• GPU model : V100 *8
• CUDA and CUDNN version: CUDA9 and cudnn7

Error traceback If applicable, paste the error trackback here.

2019-11-28 14:09:55,816 - INFO - Start running, host: [e-mail]2, work_dir: /disk2/data/suishou.zh/poi_project/temp/mmdetection/work_dirs/htc_without_semantic_x101_32x4d_fpn_20e
2019-11-28 14:09:55,816 - INFO - workflow: [('train', 1)], max: 20 epochs
Traceback (most recent call last):
  File "tools/train.py", line 108, in <module>
    main()
  File "tools/train.py", line 104, in main
    logger=logger)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/apis/train.py", line 60, in train_detector
    _non_dist_train(model, dataset, cfg, validate=validate)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/apis/train.py", line 232, in _non_dist_train
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/home/suishou.zh/open-mmlab/lib/python3.6/site-packages/mmcv/runner/runner.py", line 358, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/home/suishou.zh/open-mmlab/lib/python3.6/site-packages/mmcv/runner/runner.py", line 264, in train
    self.model, data_batch, train_mode=True, **kwargs)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/apis/train.py", line 38, in batch_processor
    losses = model(**data)
  File "/home/suishou.zh/open-mmlab/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/suishou.zh/open-mmlab/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/suishou.zh/open-mmlab/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/core/fp16/decorators.py", line 49, in new_func
    return old_func(*args, **kwargs)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/models/detectors/base.py", line 117, in forward
    return self.forward_train(img, img_meta, **kwargs)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/models/detectors/htc.py", line 296, in forward_train
    semantic_feat)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/models/detectors/htc.py", line 109, in _mask_forward_train
    rcnn_train_cfg)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/models/mask_heads/fcn_mask_head.py", line 111, in get_target
    gt_masks, rcnn_train_cfg)
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/core/mask/mask_target.py", line 12, in mask_target
    mask_targets = torch.cat(list(mask_targets))
  File "/disk2/data/suishou.zh/poi_project/temp/mmdetection/mmdet/core/mask/mask_target.py", line 22, in mask_target_single
    _, maxh, maxw = gt_masks.shape
AttributeError: 'list' object has no attribute 'shape'

[unlabeledData]

How to use albu aug in HTC with gt_semantic_seg?

[yhcao6]

Thanks for reporting the bug, #1818 fix this error.