From 1c28e66903b07358ddf239a6c1027ce0d8d5ed4e Mon Sep 17 00:00:00 2001
From: Claudio Michaelis <claudio.michaelis@uni-tuebingen.de>
Date: Sat, 27 Jul 2019 16:11:17 +0200
Subject: [PATCH] Add cityscapes dataset (#1037)
* added cityscapes
* updated configs
* removed wip configs
* Add initial dataset instructions
* Add cityscapes readme
* Add explanation for lr scaling
* Ensure pep8 conformity
* Add CityscapesDataset to the registry
* add benchmark
* rename config, modify README.md
* fix typo
* fix typo in config
* modify INSTALL.md
Update information how to arrange cityscapes data.
* Add cityscapes class names
---
INSTALL.md | 11 +
configs/cityscapes/README.md | 28 +++
.../faster_rcnn_r50_fpn_1x_cityscapes.py | 175 ++++++++++++++++
.../mask_rcnn_r50_fpn_1x_cityscapes.py | 189 ++++++++++++++++++
mmdet/core/evaluation/class_names.py | 10 +-
mmdet/datasets/__init__.py | 8 +-
mmdet/datasets/cityscapes.py | 9 +
7 files changed, 426 insertions(+), 4 deletions(-)
create mode 100644 configs/cityscapes/README.md
create mode 100644 configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
create mode 100644 configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
create mode 100644 mmdet/datasets/cityscapes.py
diff --git a/INSTALL.md b/INSTALL.md
index e48b1312..cc390d2b 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -66,10 +66,21 @@ mmdetection
鈹� 鈹� 鈹溾攢鈹€ train2017
鈹� 鈹� 鈹溾攢鈹€ val2017
鈹� 鈹� 鈹溾攢鈹€ test2017
+鈹� 鈹溾攢鈹€ cityscapes
+鈹� 鈹� 鈹溾攢鈹€ annotations
+鈹� 鈹� 鈹溾攢鈹€ train
+鈹� 鈹� 鈹溾攢鈹€ val
鈹� 鈹溾攢鈹€ VOCdevkit
鈹� 鈹� 鈹溾攢鈹€ VOC2007
鈹� 鈹� 鈹溾攢鈹€ VOC2012
+```
+The cityscapes annotations have to be converted into the coco format using the [cityscapesScripts](https://github.com/mcordts/cityscapesScripts) toolbox.
+We plan to provide an easy to use conversion script. For the moment we recommend following the instructions provided in the
+[maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/tree/master/maskrcnn_benchmark/data) toolbox. When using this script all images have to be moved into the same folder. On linux systems this can e.g. be done for the train images with:
+```shell
+cd data/cityscapes/
+mv train/*/* train/
```
### Scripts
diff --git a/configs/cityscapes/README.md b/configs/cityscapes/README.md
new file mode 100644
index 00000000..f63f8e84
--- /dev/null
+++ b/configs/cityscapes/README.md
@@ -0,0 +1,28 @@
+## Common settings
+
+- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate.
+- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`.
+- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870)
+- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo.
+
+
+## Baselines
+
+Download links and more models with different backbones and training schemes will be added to the model zoo.
+
+
+### Faster R-CNN
+
+| Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
+| :-------------: | :-----: | :-----: | :---: | :------: | :-----------------: | :------------: | :----: | :------: |
+| R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | 0.345 | 8.8 | 36.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/faster_rcnn_r50_fpn_1x_city_20190727-7b9c0534.pth) |
+
+### Mask R-CNN
+
+| Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
+| :-------------: | :-----: | :-----: | :------: | :------: | :-----------------: | :------------: | :----: | :-----: | :------: |
+| R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | 0.609 | 2.5 | 37.4 | 32.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/mask_rcnn_r50_fpn_1x_city_20190727-9b3c56a5.pth) |
+
+**Notes:**
+- In the original paper, the mask AP of Mask R-CNN R-50-FPN is 31.5.
+
diff --git a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
new file mode 100644
index 00000000..0ccacd20
--- /dev/null
+++ b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
@@ -0,0 +1,175 @@
+# model settings
+model = dict(
+ type='FasterRCNN',
+ pretrained='modelzoo://resnet50',
+ backbone=dict(
+ type='ResNet',
+ depth=50,
+ 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],
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+ 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=9,
+ target_means=[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='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
+# 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,
+ debug=False),
+ rpn_proposal=dict(
+ nms_across_levels=False,
+ nms_pre=2000,
+ nms_post=2000,
+ max_num=2000,
+ 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,
+ 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_thr=0.5), max_per_img=100)
+ # soft-nms is also supported for rcnn testing
+ # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
+)
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+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=1,
+ workers_per_gpu=2,
+ train=dict(
+ type='RepeatDataset', # to avoid reloading datasets frequently
+ times=8,
+ dataset=dict(
+ type=dataset_type,
+ ann_file=data_root +
+ 'annotations/instancesonly_filtered_gtFine_train.json',
+ img_prefix=data_root + 'train/',
+ img_scale=[(2048, 800), (2048, 1024)],
+ img_norm_cfg=img_norm_cfg,
+ multiscale_mode='range',
+ size_divisor=32,
+ flip_ratio=0.5,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True)),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root +
+ 'annotations/instancesonly_filtered_gtFine_val.json',
+ img_prefix=data_root + 'val/',
+ img_scale=(2048, 1024),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_crowd=True,
+ with_label=True),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root +
+ 'annotations/instancesonly_filtered_gtFine_val.json',
+ img_prefix=data_root + 'val/',
+ img_scale=(2048, 1024),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_label=False,
+ test_mode=True))
+# optimizer
+# lr is set for a batch size of 8
+optimizer = dict(type='SGD', lr=0.01, 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=500,
+ warmup_ratio=1.0 / 3,
+ step=[6])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=100,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 8 # actual epoch = 8 * 8 = 64
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
new file mode 100644
index 00000000..85f32f7e
--- /dev/null
+++ b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
@@ -0,0 +1,189 @@
+# model settings
+model = dict(
+ type='MaskRCNN',
+ pretrained='modelzoo://resnet50',
+ backbone=dict(
+ type='ResNet',
+ depth=50,
+ 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],
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+ 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=9,
+ target_means=[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='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+ 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=9,
+ loss_mask=dict(
+ type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
+# 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,
+ debug=False),
+ rpn_proposal=dict(
+ nms_across_levels=False,
+ nms_pre=2000,
+ nms_post=2000,
+ max_num=2000,
+ 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,
+ 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))
+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_thr=0.5),
+ max_per_img=100,
+ mask_thr_binary=0.5))
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+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=1,
+ workers_per_gpu=2,
+ train=dict(
+ type='RepeatDataset', # to avoid reloading datasets frequently
+ times=8,
+ dataset=dict(
+ type=dataset_type,
+ ann_file=data_root +
+ 'annotations/instancesonly_filtered_gtFine_train.json',
+ img_prefix=data_root + 'train/',
+ img_scale=[(2048, 800), (2048, 1024)],
+ img_norm_cfg=img_norm_cfg,
+ multiscale_mode='range',
+ 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/instancesonly_filtered_gtFine_val.json',
+ img_prefix=data_root + 'val/',
+ img_scale=(2048, 1024),
+ 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/instancesonly_filtered_gtFine_val.json',
+ img_prefix=data_root + 'val/',
+ img_scale=(2048, 1024),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_label=False,
+ test_mode=True))
+# optimizer
+# lr is set for a batch size of 8
+optimizer = dict(type='SGD', lr=0.01, 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=500,
+ warmup_ratio=1.0 / 3,
+ step=[6])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=100,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 8 # actual epoch = 8 * 8 = 64
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/mmdet/core/evaluation/class_names.py b/mmdet/core/evaluation/class_names.py
index 87fb2399..78427734 100644
--- a/mmdet/core/evaluation/class_names.py
+++ b/mmdet/core/evaluation/class_names.py
@@ -82,12 +82,20 @@ def coco_classes():
]
+def cityscapes_classes():
+ return [
+ 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+ 'bicycle'
+ ]
+
+
dataset_aliases = {
'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'],
'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'],
'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'],
'coco': ['coco', 'mscoco', 'ms_coco'],
- 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WDIERFace']
+ 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WDIERFace'],
+ 'cityscapes': ['cityscapes']
}
diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py
index ab3e5490..786b0593 100644
--- a/mmdet/datasets/__init__.py
+++ b/mmdet/datasets/__init__.py
@@ -1,6 +1,7 @@
from .custom import CustomDataset
from .xml_style import XMLDataset
from .coco import CocoDataset
+from .cityscapes import CityscapesDataset
from .voc import VOCDataset
from .wider_face import WIDERFaceDataset
from .loader import GroupSampler, DistributedGroupSampler, build_dataloader
@@ -11,8 +12,9 @@ from .registry import DATASETS
from .builder import build_dataset
__all__ = [
- 'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset', 'GroupSampler',
- 'DistributedGroupSampler', 'build_dataloader', 'to_tensor', 'random_scale',
- 'show_ann', 'ConcatDataset', 'RepeatDataset', 'ExtraAugmentation',
+ 'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',
+ 'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler',
+ 'build_dataloader', 'to_tensor', 'random_scale', 'show_ann',
+ 'ConcatDataset', 'RepeatDataset', 'ExtraAugmentation',
'WIDERFaceDataset', 'DATASETS', 'build_dataset'
]
diff --git a/mmdet/datasets/cityscapes.py b/mmdet/datasets/cityscapes.py
new file mode 100644
index 00000000..fcbd43c1
--- /dev/null
+++ b/mmdet/datasets/cityscapes.py
@@ -0,0 +1,9 @@
+from .coco import CocoDataset
+from .registry import DATASETS
+
+
+@DATASETS.register_module
+class CityscapesDataset(CocoDataset):
+
+ CLASSES = ('person', 'rider', 'car', 'truck', 'bus',
+ 'train', 'motorcycle', 'bicycle')
--
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