Merge remote-tracking branch 'origin/viame/master' into viame/master-old

* origin/viame/master: (28 commits) Fix FPN upscale Extra compiler args VIAME-specific build parameters Bump version to 1.0.0 (open-mmlab#2029) Fix the incompatibility of the latest numpy and pycocotools (open-mmlab#2024) format configs with yapf (open-mmlab#2023) options for FCNMaskHead during testtime (open-mmlab#2013) Enhance AssignResult and SamplingResult (open-mmlab#1995) Fix typo activatation -> activation (open-mmlab#2007) Reorganize requirements, make albumentations optional (open-mmlab#1969) Encapsulate DCN into a ConvModule & Conv_layers (open-mmlab#1894) Code for Paper "Bridging the Gap Between Anchor-based and Anchor-free… (open-mmlab#1872) Non color images (open-mmlab#1976) Fix albu mask format bug (open-mmlab#1818) Fix CI by limiting the version of torchvision (open-mmlab#2005) Add ability to overwite existing module in Registry (open-mmlab#1982) bug for distributed training (open-mmlab#1985) Update Libra RetinaNet config with the latest code (open-mmlab#1975) Fix issue in refine_bboxes and add doctest (open-mmlab#1962) add link to official repo (open-mmlab#1971) ...
VIAME · Mar 13, 2020 · 3eaa2a7 · 3eaa2a7
2 parents 9410824 + 31600ea
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diff --git a/.isort.cfg b/.isort.cfg
@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet
-known_third_party = Cython,albumentations,asynctest,cv2,imagecorruptions,matplotlib,mmcv,numpy,pycocotools,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
+known_third_party = Cython,asynctest,cv2,matplotlib,mmcv,numpy,pycocotools,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
diff --git a/.travis.yml b/.travis.yml
@@ -24,14 +24,15 @@ before_install:
   - PATH=${CUDA_HOME}/bin:${PATH}
 
 install:
-  - pip install Cython torch==1.2
+  - pip install Pillow==6.2.2  # remove this line when torchvision>=0.5
+  - pip install Cython torch==1.2 torchvision==0.4.0  # TODO: fix CI for pytorch>1.2
+  - pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
   - pip install -r requirements.txt
-  - pip install -r tests/requirements.txt
 
 before_script:
   - flake8 .
   - isort -rc --check-only --diff mmdet/ tools/ tests/
-  - yapf -r -d --style .style.yapf mmdet/ tools/ tests/
+  - yapf -r -d --style .style.yapf mmdet/ tools/ tests/ configs/
 
 script:
   - python setup.py check -m -s

diff --git a/README.md b/README.md
@@ -40,8 +40,8 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v1.0rc1 was released in 13/12/2019, with more than 20 bug fixes and 30 improvements and new features.
-Please refer to [CHANGELOG.md](docs/CHANGELOG.md) for details and history versions.
+v1.0.0 was released in 30/1/2020, with more than 20 fixes and improvements.
+Please refer to [CHANGELOG.md](docs/CHANGELOG.md) for details and release history.
 
 ## Benchmark and model zoo
 
@@ -70,6 +70,7 @@ Results and models are available in the [Model zoo](docs/MODEL_ZOO.md).
 | Foveabox           | ✓        | ✓        | ☐        | ✗        | ✓     |
 | FreeAnchor         | ✓        | ✓        | ☐        | ✗        | ✓     |
 | NAS-FPN            | ✓        | ✓        | ☐        | ✗        | ✓     |
+| ATSS               | ✓        | ✓        | ☐        | ✗        | ✓     |
 
 Other features
 - [x] DCNv2
@@ -80,6 +81,7 @@ Other features
 - [x] Generalized Attention
 - [x] GCNet
 - [x] Mixed Precision (FP16) Training
+- [x] [InstaBoost](configs/instaboost/README.md)
 
 
 ## Installation

diff --git a/configs/atss/README.md b/configs/atss/README.md
@@ -0,0 +1,20 @@
+# Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
+
+
+## Introduction
+
+```
+@article{zhang2019bridging,
+  title   =  {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection},
+  author  =  {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.},
+  journal =  {arXiv preprint arXiv:1912.02424},
+  year    =  {2019}
+}
+```
+
+
+## Results and Models
+
+| Backbone  | Style   | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
+|:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
+| R-50      | pytorch | 1x      | 3.6      | 0.357               | 12.8           |  39.2  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/atss/atss_r50_fpn_1x_20200113-a7aa251e.pth)|
diff --git a/configs/atss/atss_r50_fpn_1x.py b/configs/atss/atss_r50_fpn_1x.py
@@ -0,0 +1,127 @@
+# model settings
+model = dict(
+    type='ATSS',
+    pretrained='torchvision://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,
+        start_level=1,
+        add_extra_convs=True,
+        extra_convs_on_inputs=False,
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=81,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        octave_base_scale=8,
+        scales_per_octave=1,
+        anchor_ratios=[1.0],
+        anchor_strides=[8, 16, 32, 64, 128],
+        target_means=[.0, .0, .0, .0],
+        target_stds=[0.1, 0.1, 0.2, 0.2],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
+# training and testing settings
+train_cfg = dict(
+    assigner=dict(type='ATSSAssigner', topk=9),
+    allowed_border=-1,
+    pos_weight=-1,
+    debug=False)
+test_cfg = dict(
+    nms_pre=1000,
+    min_bbox_size=0,
+    score_thr=0.05,
+    nms=dict(type='nms', iou_thr=0.6),
+    max_per_img=100)
+# 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(
+    imgs_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))
+# optimizer
+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=[8, 11])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 12
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/atss_r50_fpn_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,8 +10,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=False, deformable_groups=1, fallback_on_stride=False),
+        dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,8 +10,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=False, deformable_groups=1, fallback_on_stride=False),
+        dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -9,8 +9,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=False, deformable_groups=1, fallback_on_stride=False),
+        dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py b/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py
@@ -11,11 +11,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=False,
-            groups=32,
-            deformable_groups=1,
-            fallback_on_stride=False),
+        dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
@@ -9,8 +9,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=True, deformable_groups=4, fallback_on_stride=False),
+        dcn=dict(type='DCNv2', deformable_groups=4, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
@@ -9,8 +9,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=True, deformable_groups=1, fallback_on_stride=False),
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

diff --git a/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -9,8 +9,7 @@
         out_indices=(0, 1, 2, 3),
         frozen_stages=1,
         style='pytorch',
-        dcn=dict(
-            modulated=False, deformable_groups=1, fallback_on_stride=False),
+        dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',