CodeCamp #1555[Feature] Support Mapillary Vistas Dataset

projects/mapillary_dataset/README.md

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+# Mapillary Vistas Dataset
+
+Support **`Mapillary Vistas Dataset`**
+
+## Description
+
+Author: AI-Tianlong
+
+This project implements **`Mapillary Vistas Dataset`**
+
+### Dataset preparing
+
+Preparing `Mapillary Vistas Dataset` dataset following [Mapillary Vistas Dataset Preparing Guide](https:/open-mmlab/mmsegmentation/blob/63df18c6efe2cdfeb21ced3159490395fe62acf7/projects/mapillary_dataset/docs/en/user_guides/2_dataset_prepare.md)
+
+```none
+ mmsegmentation
+ ├── mmseg
+ ├── tools
+ ├── configs
+ ├── data
+ │ ├── mapillary
+ │ │ ├── training
+ │ │ │ ├── images
+ │ │ │ ├── v1.2
+ | │ │ │ ├── instances
+ | │ │ │ ├── labels
+ | │ │ │ ├── labels_mask
+ | │   │   │ └── panoptic
+ │ │ │ ├── v2.0
+ | │ │ │ ├── instances
+ | │ │ │ ├── labels
+ | │ │ │ ├── labels_mask
+ | │ │ │ ├── panoptic
+ | │   │   │ └── polygons
+ │ │ ├── validation
+ │ │ │ ├── images
+ | │ │ │ ├── instances
+ | │ │ │ ├── labels
+ | │ │ │ ├── labels_mask
+ | │   │   │ └── panoptic
+ │ │ │ ├── v2.0
+ | │ │ │ ├── instances
+ | │ │ │ ├── labels
+ | │ │ │ ├── labels_mask
+ | │ │ │ ├── panoptic
+ | │   │   │ └── polygons
+```
+
+### Training commands with `deeplabv3plus_r101-d8_4xb2-240k_mapillay-512x1024.py`
+
+```bash
+# Dataset train commands
+# at `mmsegmentation` folder
+bash tools/dist_train.sh projects/mapillary_dataset/configs/deeplabv3plus_r101-d8_4xb2-240k_mapillay-512x1024.py 4
+```
+
+## Checklist
+
+- [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+ - [ ] Finish the code
+
+ - [ ] Basic docstrings & proper citation
+
+ - [ ] Test-time correctness
+
+ - [ ] A full README
+
+- [ ] Milestone 2: Indicates a successful model implementation.
+
+ - [ ] Training-time correctness
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+ - [ ] Type hints and docstrings
+
+ - [ ] Unit tests
+
+ - [ ] Code polishing
+
+ - [ ] Metafile.yml
+
+- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
+
+- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.

projects/mapillary_dataset/configs/_base_/datasets/mapillary_v1_2.py

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+# dataset settings
+dataset_type = 'MapillaryDataset_v1_2'
+data_root = 'data/mapillary/'
+crop_size = (512, 1024)
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations'),
+ dict(
+ type='RandomResize',
+ scale=(2048, 1024),
+ ratio_range=(0.5, 2.0),
+ keep_ratio=True),
+ dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type='RandomFlip', prob=0.5),
+ dict(type='PhotoMetricDistortion'),
+ dict(type='PackSegInputs')
+]
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+ # add loading annotation after ``Resize`` because ground truth
+ # does not need to do resize data transform
+ dict(type='LoadAnnotations'),
+ dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(
+ type='TestTimeAug',
+ transforms=[
+ [
+ dict(type='Resize', scale_factor=r, keep_ratio=True)
+ for r in img_ratios
+ ],
+ [
+ dict(type='RandomFlip', prob=0., direction='horizontal'),
+ dict(type='RandomFlip', prob=1., direction='horizontal')
+ ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+ ])
+]
+train_dataloader = dict(
+ batch_size=2,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type='InfiniteSampler', shuffle=True),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='training/images',
+ seg_map_path='training/v1.2/labels_mask'),
+ pipeline=train_pipeline))
+val_dataloader = dict(
+ batch_size=1,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='validation/images',
+ seg_map_path='validation/v1.2/labels_mask'),
+ pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

projects/mapillary_dataset/configs/_base_/datasets/mapillary_v2_0.py

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+# dataset settings
+dataset_type = 'MapillaryDataset_v2_0'
+data_root = 'data/mapillary/'
+crop_size = (512, 1024)
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations'),
+ dict(
+ type='RandomResize',
+ scale=(2048, 1024),
+ ratio_range=(0.5, 2.0),
+ keep_ratio=True),
+ dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(type='RandomFlip', prob=0.5),
+ dict(type='PhotoMetricDistortion'),
+ dict(type='PackSegInputs')
+]
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+ # add loading annotation after ``Resize`` because ground truth
+ # does not need to do resize data transform
+ dict(type='LoadAnnotations'),
+ dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
+ dict(
+ type='TestTimeAug',
+ transforms=[
+ [
+ dict(type='Resize', scale_factor=r, keep_ratio=True)
+ for r in img_ratios
+ ],
+ [
+ dict(type='RandomFlip', prob=0., direction='horizontal'),
+ dict(type='RandomFlip', prob=1., direction='horizontal')
+ ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+ ])
+]
+train_dataloader = dict(
+ batch_size=2,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type='InfiniteSampler', shuffle=True),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='training/images',
+ seg_map_path='training/v2.0/labels_mask'),
+ pipeline=train_pipeline))
+val_dataloader = dict(
+ batch_size=1,
+ num_workers=4,
+ persistent_workers=True,
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ data_prefix=dict(
+ img_path='validation/images',
+ seg_map_path='validation/v2.0/labels_mask'),
+ pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

projects/mapillary_dataset/configs/deeplabv3plus_r101-d8_4xb2-240k_mapillay-512x1024.py

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+_base_ = ['./_base_/datasets/mapillary_v1_2.py'] # v 1.2 labels
+# _base_ = ['./_base_/datasets/mapillary_v2_0.py'] # v2.0 labels
+custom_imports = dict(imports=[
+ 'projects.mapillary_dataset.mmseg.datasets.mapillary_v1_2',
+ 'projects.mapillary_dataset.mmseg.datasets.mapillary_v2_0',
+])
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+ type='SegDataPreProcessor',
+ mean=[123.675, 116.28, 103.53],
+ std=[58.395, 57.12, 57.375],
+ bgr_to_rgb=True,
+ pad_val=0,
+ seg_pad_val=255,
+ size=(512, 1024))
+
+model = dict(
+ type='EncoderDecoder',
+ data_preprocessor=data_preprocessor,
+ pretrained=None,
+ backbone=dict(
+ type='ResNet',
+ depth=101,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ dilations=(1, 1, 2, 4),
+ strides=(1, 2, 1, 1),
+ norm_cfg=norm_cfg,
+ norm_eval=False,
+ style='pytorch',
+ contract_dilation=True),
+ decode_head=dict(
+ type='DepthwiseSeparableASPPHead',
+ in_channels=2048,
+ in_index=3,
+ channels=512,
+ dilations=(1, 12, 24, 36),
+ c1_in_channels=256,
+ c1_channels=48,
+ dropout_ratio=0.1,
+ num_classes=66, # v1.2
+ # num_classes=124, # v2.0
+ norm_cfg=norm_cfg,
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+ auxiliary_head=dict(
+ type='FCNHead',
+ in_channels=1024,
+ in_index=2,
+ channels=256,
+ num_convs=1,
+ concat_input=False,
+ dropout_ratio=0.1,
+ num_classes=66, # v1.2
+ # num_classes=124, # v2.0
+ norm_cfg=norm_cfg,
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ train_cfg=dict(),
+ test_cfg=dict(mode='whole'))
+default_scope = 'mmseg'
+env_cfg = dict(
+ cudnn_benchmark=True,
+ mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+ dist_cfg=dict(backend='nccl'))
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+ type='SegLocalVisualizer',
+ vis_backends=[dict(type='LocalVisBackend')],
+ name='visualizer')
+log_processor = dict(by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = False
+tta_model = dict(type='SegTTAModel')
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optim_wrapper = dict(
+ type='OptimWrapper',
+ optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001),
+ clip_grad=None)
+param_scheduler = [
+ dict(
+ type='PolyLR',
+ eta_min=0.0001,
+ power=0.9,
+ begin=0,
+ end=240000,
+ by_epoch=False)
+]
+train_cfg = dict(
+ type='IterBasedTrainLoop', max_iters=240000, val_interval=24000)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+default_hooks = dict(
+ timer=dict(type='IterTimerHook'),
+ logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
+ param_scheduler=dict(type='ParamSchedulerHook'),
+ checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=24000),
+ sampler_seed=dict(type='DistSamplerSeedHook'),
+ visualization=dict(type='SegVisualizationHook'))