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import unittest |
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from detectron2.layers import ShapeSpec |
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from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector |
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try: |
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import mmdet.models |
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HAS_MMDET = True |
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except ImportError: |
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HAS_MMDET = False |
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@unittest.skipIf(not HAS_MMDET, "mmdet not available") |
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class TestMMDetWrapper(unittest.TestCase): |
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def test_backbone(self): |
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MMDetBackbone( |
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backbone=dict( |
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type="DetectoRS_ResNet", |
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conv_cfg=dict(type="ConvAWS"), |
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sac=dict(type="SAC", use_deform=True), |
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stage_with_sac=(False, True, True, True), |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type="BN", requires_grad=True), |
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norm_eval=True, |
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style="pytorch", |
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), |
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neck=dict( |
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type="FPN", |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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num_outs=5, |
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), |
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output_shapes=[ShapeSpec(channels=256, stride=s) for s in [4, 8, 16, 32, 64]], |
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output_names=["p2", "p3", "p4", "p5", "p6"], |
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) |
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def test_detector(self): |
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MMDetDetector( |
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detector=dict( |
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type="MaskRCNN", |
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backbone=dict( |
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type="ResNet", |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type="BN", requires_grad=True), |
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norm_eval=True, |
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style="pytorch", |
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), |
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neck=dict( |
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type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5 |
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), |
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rpn_head=dict( |
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type="RPNHead", |
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in_channels=256, |
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feat_channels=256, |
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anchor_generator=dict( |
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type="AnchorGenerator", |
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scales=[8], |
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ratios=[0.5, 1.0, 2.0], |
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strides=[4, 8, 16, 32, 64], |
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), |
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bbox_coder=dict( |
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type="DeltaXYWHBBoxCoder", |
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target_means=[0.0, 0.0, 0.0, 0.0], |
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target_stds=[1.0, 1.0, 1.0, 1.0], |
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), |
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loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0), |
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loss_bbox=dict(type="L1Loss", loss_weight=1.0), |
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), |
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roi_head=dict( |
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type="StandardRoIHead", |
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bbox_roi_extractor=dict( |
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type="SingleRoIExtractor", |
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roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0), |
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out_channels=256, |
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featmap_strides=[4, 8, 16, 32], |
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), |
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bbox_head=dict( |
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type="Shared2FCBBoxHead", |
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in_channels=256, |
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fc_out_channels=1024, |
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roi_feat_size=7, |
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num_classes=80, |
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bbox_coder=dict( |
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type="DeltaXYWHBBoxCoder", |
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target_means=[0.0, 0.0, 0.0, 0.0], |
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target_stds=[0.1, 0.1, 0.2, 0.2], |
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), |
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reg_class_agnostic=False, |
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loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0), |
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loss_bbox=dict(type="L1Loss", loss_weight=1.0), |
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), |
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mask_roi_extractor=dict( |
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type="SingleRoIExtractor", |
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roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0), |
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out_channels=256, |
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featmap_strides=[4, 8, 16, 32], |
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), |
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mask_head=dict( |
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type="FCNMaskHead", |
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num_convs=4, |
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in_channels=256, |
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conv_out_channels=256, |
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num_classes=80, |
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loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0), |
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), |
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), |
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train_cfg=dict( |
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rpn=dict( |
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assigner=dict( |
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type="MaxIoUAssigner", |
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pos_iou_thr=0.7, |
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neg_iou_thr=0.3, |
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min_pos_iou=0.3, |
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match_low_quality=True, |
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ignore_iof_thr=-1, |
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), |
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sampler=dict( |
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type="RandomSampler", |
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num=256, |
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pos_fraction=0.5, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=False, |
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), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False, |
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), |
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rpn_proposal=dict( |
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nms_pre=2000, |
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max_per_img=1000, |
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nms=dict(type="nms", iou_threshold=0.7), |
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min_bbox_size=0, |
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), |
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rcnn=dict( |
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assigner=dict( |
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type="MaxIoUAssigner", |
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pos_iou_thr=0.5, |
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neg_iou_thr=0.5, |
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min_pos_iou=0.5, |
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match_low_quality=True, |
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ignore_iof_thr=-1, |
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), |
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sampler=dict( |
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type="RandomSampler", |
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num=512, |
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pos_fraction=0.25, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=True, |
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), |
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mask_size=28, |
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pos_weight=-1, |
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debug=False, |
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), |
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), |
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test_cfg=dict( |
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rpn=dict( |
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nms_pre=1000, |
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max_per_img=1000, |
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nms=dict(type="nms", iou_threshold=0.7), |
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min_bbox_size=0, |
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), |
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rcnn=dict( |
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score_thr=0.05, |
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nms=dict(type="nms", iou_threshold=0.5), |
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max_per_img=100, |
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mask_thr_binary=0.5, |
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), |
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), |
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), |
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pixel_mean=[1, 2, 3], |
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pixel_std=[1, 2, 3], |
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) |
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