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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import unittest | |
| from detectron2.layers import ShapeSpec | |
| from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector | |
| try: | |
| import mmdet.models # noqa | |
| HAS_MMDET = True | |
| except ImportError: | |
| HAS_MMDET = False | |
| class TestMMDetWrapper(unittest.TestCase): | |
| def test_backbone(self): | |
| MMDetBackbone( | |
| backbone=dict( | |
| type="DetectoRS_ResNet", | |
| conv_cfg=dict(type="ConvAWS"), | |
| sac=dict(type="SAC", use_deform=True), | |
| stage_with_sac=(False, True, True, True), | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type="BN", requires_grad=True), | |
| norm_eval=True, | |
| style="pytorch", | |
| ), | |
| neck=dict( | |
| type="FPN", | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| num_outs=5, | |
| ), | |
| # skip pretrained model for tests | |
| # pretrained_backbone="torchvision://resnet50", | |
| output_shapes=[ShapeSpec(channels=256, stride=s) for s in [4, 8, 16, 32, 64]], | |
| output_names=["p2", "p3", "p4", "p5", "p6"], | |
| ) | |
| def test_detector(self): | |
| # a basic R50 Mask R-CNN | |
| MMDetDetector( | |
| detector=dict( | |
| type="MaskRCNN", | |
| backbone=dict( | |
| type="ResNet", | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type="BN", requires_grad=True), | |
| norm_eval=True, | |
| style="pytorch", | |
| # skip pretrained model for tests | |
| # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')) | |
| ), | |
| neck=dict( | |
| type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5 | |
| ), | |
| rpn_head=dict( | |
| type="RPNHead", | |
| in_channels=256, | |
| feat_channels=256, | |
| anchor_generator=dict( | |
| type="AnchorGenerator", | |
| scales=[8], | |
| ratios=[0.5, 1.0, 2.0], | |
| strides=[4, 8, 16, 32, 64], | |
| ), | |
| bbox_coder=dict( | |
| type="DeltaXYWHBBoxCoder", | |
| target_means=[0.0, 0.0, 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=256, | |
| featmap_strides=[4, 8, 16, 32], | |
| ), | |
| bbox_head=dict( | |
| type="Shared2FCBBoxHead", | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| roi_feat_size=7, | |
| num_classes=80, | |
| bbox_coder=dict( | |
| type="DeltaXYWHBBoxCoder", | |
| target_means=[0.0, 0.0, 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), | |
| ), | |
| mask_roi_extractor=dict( | |
| type="SingleRoIExtractor", | |
| roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0), | |
| out_channels=256, | |
| featmap_strides=[4, 8, 16, 32], | |
| ), | |
| mask_head=dict( | |
| type="FCNMaskHead", | |
| num_convs=4, | |
| in_channels=256, | |
| conv_out_channels=256, | |
| num_classes=80, | |
| loss_mask=dict(type="CrossEntropyLoss", use_mask=True, 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=2000, | |
| max_per_img=1000, | |
| 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=True, | |
| ignore_iof_thr=-1, | |
| ), | |
| sampler=dict( | |
| type="RandomSampler", | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True, | |
| ), | |
| mask_size=28, | |
| pos_weight=-1, | |
| debug=False, | |
| ), | |
| ), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=1000, | |
| 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, | |
| mask_thr_binary=0.5, | |
| ), | |
| ), | |
| ), | |
| pixel_mean=[1, 2, 3], | |
| pixel_std=[1, 2, 3], | |
| ) | |