[MMSIG] MMDet 新 config 体验与适配 #355

mmdet/configs/_base_/models/faster_rcnn_r50_caffe_c4.py

@@ -0,0 +1,145 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.ops import RoIAlign, nms
+from torch.nn import BatchNorm2d
+
+from mmdet.models import BBoxHead, ResLayer
+from mmdet.models.backbones.resnet import ResNet
+from mmdet.models.data_preprocessors.data_preprocessor import \
+    DetDataPreprocessor
+from mmdet.models.dense_heads.rpn_head import RPNHead
+from mmdet.models.detectors.faster_rcnn import FasterRCNN
+from mmdet.models.losses.cross_entropy_loss import CrossEntropyLoss
+from mmdet.models.losses.smooth_l1_loss import L1Loss
+from mmdet.models.roi_heads.roi_extractors.single_level_roi_extractor import \
+    SingleRoIExtractor
+from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
+from mmdet.models.task_modules.assigners.max_iou_assigner import MaxIoUAssigner
+from mmdet.models.task_modules.coders.delta_xywh_bbox_coder import \
+    DeltaXYWHBBoxCoder
+from mmdet.models.task_modules.prior_generators.anchor_generator import \
+    AnchorGenerator
+from mmdet.models.task_modules.samplers.random_sampler import RandomSampler
+
+# model settings
+norm_cfg = dict(type=BatchNorm2d, requires_grad=False)
+model = dict(
+    type=FasterRCNN,
+    data_preprocessor=dict(
+        type=DetDataPreprocessor,
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=3,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        out_indices=(2, ),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        type=RPNHead,
+        in_channels=1024,
+        feat_channels=1024,
+        anchor_generator=dict(
+            type=AnchorGenerator,
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type=DeltaXYWHBBoxCoder,
+            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=L1Loss, loss_weight=1.0)),
+    roi_head=dict(
+        type=StandardRoIHead,
+        shared_head=dict(
+            type=ResLayer,
+            depth=50,
+            stage=3,
+            stride=2,
+            dilation=1,
+            style='caffe',
+            norm_cfg=norm_cfg,
+            norm_eval=True,
+            init_cfg=dict(
+                type='Pretrained',
+                checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+        bbox_roi_extractor=dict(
+            type=SingleRoIExtractor,
+            roi_layer=dict(type=RoIAlign, output_size=14, sampling_ratio=0),
+            out_channels=1024,
+            featmap_strides=[16]),
+        bbox_head=dict(
+            type=BBoxHead,
+            with_avg_pool=True,
+            roi_feat_size=7,
+            in_channels=2048,
+            num_classes=80,
+            bbox_coder=dict(
+                type=DeltaXYWHBBoxCoder,
+                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=L1Loss, 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,
+                match_low_quality=True,
+                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=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=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,
+                match_low_quality=False,
+                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_pre=6000,
+            max_per_img=1000,
+            nms=dict(type=nms, iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type=nms, iou_threshold=0.5),
+            max_per_img=100)))

mmdet/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py

@@ -0,0 +1,133 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.ops import RoIAlign, nms
+from torch.nn import BatchNorm2d
+
+from mmdet.models.backbones.resnet import ResNet
+from mmdet.models.data_preprocessors.data_preprocessor import \
+    DetDataPreprocessor
+from mmdet.models.dense_heads.rpn_head import RPNHead
+from mmdet.models.detectors.faster_rcnn import FasterRCNN
+from mmdet.models.losses.cross_entropy_loss import CrossEntropyLoss
+from mmdet.models.losses.smooth_l1_loss import L1Loss
+from mmdet.models.roi_heads.bbox_heads.convfc_bbox_head import \
+    Shared2FCBBoxHead
+from mmdet.models.roi_heads.roi_extractors.single_level_roi_extractor import \
+    SingleRoIExtractor
+from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
+from mmdet.models.task_modules import RandomSampler
+from mmdet.models.task_modules.assigners.max_iou_assigner import MaxIoUAssigner
+from mmdet.models.task_modules.coders.delta_xywh_bbox_coder import \
+    DeltaXYWHBBoxCoder
+from mmdet.models.task_modules.prior_generators.anchor_generator import \
+    AnchorGenerator
+
+norm_cfg = dict(type=BatchNorm2d, requires_grad=False)
+model = dict(
+    type=FasterRCNN,
+    data_preprocessor=dict(
+        type=DetDataPreprocessor,
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
+    backbone=dict(
+        type=ResNet,
+        depth=50,
+        num_stages=4,
+        strides=(1, 2, 2, 1),
+        dilations=(1, 1, 1, 2),
+        out_indices=(3, ),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        type=RPNHead,
+        in_channels=2048,
+        feat_channels=2048,
+        anchor_generator=dict(
+            type=AnchorGenerator,
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type=DeltaXYWHBBoxCoder,
+            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=L1Loss, loss_weight=1.0)),
+    roi_head=dict(
+        type=StandardRoIHead,
+        bbox_roi_extractor=dict(
+            type=SingleRoIExtractor,
+            roi_layer=dict(type=RoIAlign, output_size=7, sampling_ratio=0),
+            out_channels=2048,
+            featmap_strides=[16]),
+        bbox_head=dict(
+            type=Shared2FCBBoxHead,
+            in_channels=2048,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type=DeltaXYWHBBoxCoder,
+                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='L1Loss', 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,
+                match_low_quality=True,
+                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_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=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,
+                match_low_quality=False,
+                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=dict(type=nms, iou_threshold=0.7),
+            nms_pre=6000,
+            max_per_img=1000,
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type=nms, iou_threshold=0.5),
+            max_per_img=100)))

mmdet/configs/common/lsj_100e_coco_detection.py

@@ -39,6 +39,7 @@
         type=RandomResize,
         scale=image_size,
         ratio_range=(0.1, 2.0),
+        resize_type=Resize,
         keep_ratio=True),
     dict(
         type=RandomCrop,

mmdet/configs/common/ms_3x_coco.py

@@ -48,7 +48,11 @@
 train_pipeline = [
     dict(type=LoadImageFromFile, backend_args=backend_args),
     dict(type=LoadAnnotations, with_bbox=True),
-    dict(type=RandomResize, scale=[(1333, 640), (1333, 800)], keep_ratio=True),
+    dict(
+        type=RandomResize,
+        scale=[(1333, 640), (1333, 800)],
+        resize_type=Resize,
+        keep_ratio=True),
     dict(type=RandomFlip, prob=0.5),
     dict(type=PackDetInputs)
 ]