149 lines
5.3 KiB
Python
149 lines
5.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from mmcv import ConfigDict
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from mmcv.utils import get_logger
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from mmfewshot.detection.models.builder import build_detector
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def test_attention_rpn_detector_forward():
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cfg = ConfigDict(
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type='TFA',
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backbone=dict(
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type='ResNet',
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depth=101,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=4,
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norm_cfg=dict(type='BN', requires_grad=False),
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norm_eval=True,
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style='caffe'),
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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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init_cfg=[
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dict(
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type='Caffe2Xavier',
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override=dict(type='Caffe2Xavier', name='lateral_convs')),
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dict(
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type='Caffe2Xavier',
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override=dict(type='Caffe2Xavier', name='fpn_convs'))
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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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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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loss_cls=dict(
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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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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(
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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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bbox_head=dict(
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type='CosineSimBBoxHead',
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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=20,
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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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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0),
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init_cfg=[
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dict(
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type='Caffe2Xavier',
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override=dict(type='Caffe2Xavier', name='shared_fcs')),
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dict(
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type='Normal',
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override=dict(type='Normal', name='fc_cls', std=0.01)),
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dict(
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type='Normal',
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override=dict(type='Normal', name='fc_reg', std=0.001))
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],
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num_shared_fcs=2,
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scale=20)),
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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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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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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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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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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=False,
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ignore_iof_thr=-1),
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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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pos_weight=-1,
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debug=False)),
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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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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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frozen_parameters=['backbone', 'neck'])
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model = build_detector(cfg, logger=get_logger('test'))
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for parameter in model.backbone.parameters():
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assert parameter.requires_grad is False
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for parameter in model.neck.parameters():
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assert parameter.requires_grad is False
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for parameter in model.rpn_head.parameters():
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assert parameter.requires_grad is True
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for parameter in model.roi_head.parameters():
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assert parameter.requires_grad is True
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