upload htc++

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+ignore = E501, F403, C901, W504, W605, E251, E122, E126, E127
+select = E1, E3, E502, E7, E9, W1, W5, W6
+max-line-length = 180
+exclude=*.egg/*,build,dist

.isort.cfg

@@ -0,0 +1,26 @@
+[isort]
+line_length = 180
+multi_line_output = 0
+extra_standard_library = setuptools
+known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,six,terminaltables,torch,ts,yaml
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY
+
+[yapf]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+
+[codespell]
+skip = *.ipynb
+quiet-level = 3
+ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood
+© 2022 GitHub, Inc.
+Terms
+Privacy
+Security
+Status
+Docs
+Contact GitHub
+Pricing
+API

.pre-commit-config.yaml

@@ -0,0 +1,41 @@
+repos:
+  - repo: https://gitlab.com/pycqa/flake8.git
+    rev: 3.8.3
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.30.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.1.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/markdownlint/markdownlint
+    rev: v0.11.0
+    hooks:
+      - id: markdownlint
+        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
+              "-t", "allow_different_nesting"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.1.0
+    hooks:
+      - id: codespell
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]

README.md

@@ -12,7 +12,7 @@ The official implementation of the paper "[Vision Transformer Adapter for Dense
 ## News
 
 (2022/05/17) ViT-Adapter-L yields 60.1 box AP and 52.1 mask AP on COCO test-dev.\
-(2022/05/12) ViT-Adapter-L reachs 85.2 mIoU on Cityscapes test set without coarse data.\
+(2022/05/12) ViT-Adapter-L reaches 85.2 mIoU on Cityscapes test set without coarse data.\
 (2022/05/05) ViT-Adapter-L achieves the SOTA on ADE20K val set with 60.5 mIoU!
 
 ## Abstract

detection/configs/_base_/datasets/cityscapes_detection.py

@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# 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)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=[(2048, 800), (2048, 1024)],
+         keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(2048, 1024),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(type=dataset_type,
+                     ann_file=data_root +
+                     'annotations/instancesonly_filtered_gtFine_train.json',
+                     img_prefix=data_root + 'leftImg8bit/train/',
+                     pipeline=train_pipeline)),
+    val=dict(type=dataset_type,
+             ann_file=data_root +
+             'annotations/instancesonly_filtered_gtFine_val.json',
+             img_prefix=data_root + 'leftImg8bit/val/',
+             pipeline=test_pipeline),
+    test=dict(type=dataset_type,
+              ann_file=data_root +
+              'annotations/instancesonly_filtered_gtFine_test.json',
+              img_prefix=data_root + 'leftImg8bit/test/',
+              pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')

detection/configs/_base_/datasets/cityscapes_instance.py

@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# 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)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=[(2048, 800), (2048, 1024)],
+         keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(2048, 1024),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(type=dataset_type,
+                     ann_file=data_root +
+                     'annotations/instancesonly_filtered_gtFine_train.json',
+                     img_prefix=data_root + 'leftImg8bit/train/',
+                     pipeline=train_pipeline)),
+    val=dict(type=dataset_type,
+             ann_file=data_root +
+             'annotations/instancesonly_filtered_gtFine_val.json',
+             img_prefix=data_root + 'leftImg8bit/val/',
+             pipeline=test_pipeline),
+    test=dict(type=dataset_type,
+              ann_file=data_root +
+              'annotations/instancesonly_filtered_gtFine_test.json',
+              img_prefix=data_root + 'leftImg8bit/test/',
+              pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])

detection/configs/_base_/datasets/coco_detection.py

@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+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=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(1333, 800),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(type=dataset_type,
+               ann_file=data_root + 'annotations/instances_train2017.json',
+               img_prefix=data_root + 'train2017/',
+               pipeline=train_pipeline),
+    val=dict(type=dataset_type,
+             ann_file=data_root + 'annotations/instances_val2017.json',
+             img_prefix=data_root + 'val2017/',
+             pipeline=test_pipeline),
+    test=dict(type=dataset_type,
+              ann_file=data_root + 'annotations/instances_val2017.json',
+              img_prefix=data_root + 'val2017/',
+              pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')

detection/configs/_base_/datasets/coco_instance.py

@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+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, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(1333, 800),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=8,
+    train=dict(type=dataset_type,
+               ann_file=data_root + 'annotations/instances_train2017.json',
+               img_prefix=data_root + 'train2017/',
+               pipeline=train_pipeline),
+    val=dict(type=dataset_type,
+             ann_file=data_root + 'annotations/instances_val2017.json',
+             img_prefix=data_root + 'val2017/',
+             pipeline=test_pipeline),
+    test=dict(type=dataset_type,
+              ann_file=data_root + 'annotations/instances_val2017.json',
+              img_prefix=data_root + 'val2017/',
+              pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])

detection/configs/_base_/datasets/coco_panoptic.py

@@ -0,0 +1,56 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# dataset settings
+dataset_type = 'CocoPanopticDataset'
+data_root = 'data/coco/'
+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='LoadPanopticAnnotations',
+         with_bbox=True,
+         with_mask=True,
+         with_seg=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 4),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect',
+         keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks',
+               'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(1333, 800),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(samples_per_gpu=2,
+            workers_per_gpu=2,
+            train=dict(
+                type=dataset_type,
+                ann_file=data_root + 'annotations/panoptic_train2017.json',
+                img_prefix=data_root + 'train2017/',
+                seg_prefix=data_root + 'annotations/panoptic_train2017/',
+                pipeline=train_pipeline),
+            val=dict(type=dataset_type,
+                     ann_file=data_root + 'annotations/panoptic_val2017.json',
+                     img_prefix=data_root + 'val2017/',
+                     seg_prefix=data_root + 'annotations/panoptic_val2017/',
+                     pipeline=test_pipeline),
+            test=dict(type=dataset_type,
+                      ann_file=data_root + 'annotations/panoptic_val2017.json',
+                      img_prefix=data_root + 'val2017/',
+                      seg_prefix=data_root + 'annotations/panoptic_val2017/',
+                      pipeline=test_pipeline))
+evaluation = dict(interval=1, metric=['PQ'])

detection/configs/_base_/datasets/deepfashion.py

@@ -0,0 +1,52 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# dataset settings
+dataset_type = 'DeepFashionDataset'
+data_root = 'data/DeepFashion/In-shop/'
+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, with_mask=True),
+    dict(type='Resize', img_scale=(750, 1101), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='MultiScaleFlipAug',
+         img_scale=(750, 1101),
+         flip=False,
+         transforms=[
+             dict(type='Resize', keep_ratio=True),
+             dict(type='RandomFlip'),
+             dict(type='Normalize', **img_norm_cfg),
+             dict(type='Pad', size_divisor=32),
+             dict(type='ImageToTensor', keys=['img']),
+             dict(type='Collect', keys=['img']),
+         ])
+]
+data = dict(imgs_per_gpu=2,
+            workers_per_gpu=1,
+            train=dict(type=dataset_type,
+                       ann_file=data_root +
+                       'annotations/DeepFashion_segmentation_query.json',
+                       img_prefix=data_root + 'Img/',
+                       pipeline=train_pipeline,
+                       data_root=data_root),
+            val=dict(type=dataset_type,
+                     ann_file=data_root +
+                     'annotations/DeepFashion_segmentation_query.json',
+                     img_prefix=data_root + 'Img/',
+                     pipeline=test_pipeline,
+                     data_root=data_root),
+            test=dict(type=dataset_type,
+                      ann_file=data_root +
+                      'annotations/DeepFashion_segmentation_gallery.json',
+                      img_prefix=data_root + 'Img/',
+                      pipeline=test_pipeline,
+                      data_root=data_root))
+evaluation = dict(interval=5, metric=['bbox', 'segm'])