Implementing YOLOv3 architecture

configs/yolo/README.md

@@ -0,0 +1,23 @@
+#YOLOv3
+
+## Introduction
+```
+@misc{redmon2018yolov3,
+    title={YOLOv3: An Incremental Improvement},
+    author={Joseph Redmon and Ali Farhadi},
+    year={2018},
+    eprint={1804.02767},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+## Results and Models
+
+Test set: COCO val2017
+
+bbox_mAP: 0.3520
+
+bbox_mAP_50: 0.6100
+
+Checkpoint link: [here](https://drive.google.com/drive/folders/1NzQ5LwBaYPlu1gywnRAViNz70NV9743O?usp=sharing)

configs/yolo/yolov3_ms_aug_273e.py

@@ -0,0 +1,115 @@
+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+
+# model settings
+model = dict(
+    type='YoloNet',
+    pretrained='checkpoints/darknet_state_dict_only.pth',
+    backbone=dict(
+        type='DarkNet53',),
+    neck=dict(
+        type='YoloNeck',),
+    bbox_head=dict(
+        type='YoloHead',))
+# training and testing settings
+train_cfg = dict(
+    one_hot_smoother=0.,
+    ignore_config=0.5,
+    xy_use_logit=False,
+    debug=False)
+test_cfg = dict(
+    nms_pre=1000,
+    min_bbox_size=0,
+    score_thr=0.05,
+    conf_thr=0.005,
+    nms=dict(type='nms', iou_thr=0.45),
+    max_per_img=100)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Expand',
+         mean=img_norm_cfg['mean'],
+         to_rgb=img_norm_cfg['to_rgb'],
+         ratio_range=(1, 2)
+         ),
+    dict(type='MinIoURandomCrop',
+         min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+         min_crop_size=0.3
+         ),
+    dict(type='Resize', img_scale=[(320, 320), (608, 608)], 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=(608, 608),
+        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=8,
+    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,
+    )
+)
+# optimizer
+optimizer = dict(type='SGD', lr=5e-4, momentum=0.9, weight_decay=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=2000,  # same as burn-in in darknet
+    warmup_ratio=0.1,
+    step=[218, 246])
+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 = 273
+device_ids = range(8)
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/retinanet_r50_fpn_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

mmdet/models/anchor_heads/__init__.py

@@ -11,10 +11,11 @@
 from .retina_sepbn_head import RetinaSepBNHead
 from .rpn_head import RPNHead
 from .ssd_head import SSDHead
+from .yolo_head import YoloHead
 
 __all__ = [
     'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption', 'RPNHead',
     'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead', 'SSDHead',
     'FCOSHead', 'RepPointsHead', 'FoveaHead', 'FreeAnchorRetinaHead',
-    'ATSSHead'
+    'ATSSHead', 'YoloHead',
 ]