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https://github.com/open-mmlab/mmdeploy.git
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42dc5bc316
* fix lint * fix lint * add mmrotate part * update * update * fix * remove init_detector * success run with bs=1 * nms_rotated support batch * support [batch_id, class_id, box_id] * fix * fix * Create test_mmrotate_core.py * add ut * add ut * Update nms_rotated.py * fix * Revert "fix" This reverts commit f792387fb449ba091c1d932f29d28214805fb6e3. * add mmrotate into requirements * add ut * update doc * update * skip test because mmcv version < 1.4.6 * update * Update rotated-detection_static.py * Update rotated-detection_static.py * Update rotated-detection_static.py * fix bug of memory leak. * Update rotated_detection_model.py
97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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model = dict(
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type='RotatedRetinaNet',
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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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zero_init_residual=False,
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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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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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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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start_level=1,
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add_extra_convs='on_input',
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num_outs=5),
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bbox_head=dict(
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type='RotatedRetinaHead',
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num_classes=15,
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in_channels=256,
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stacked_convs=4,
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feat_channels=256,
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assign_by_circumhbbox=None,
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anchor_generator=dict(
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type='RotatedAnchorGenerator',
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octave_base_scale=4,
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scales_per_octave=3,
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ratios=[1.0, 0.5, 2.0],
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strides=[8, 16, 32, 64, 128]),
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bbox_coder=dict(
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type='DeltaXYWHAOBBoxCoder',
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angle_range='le135',
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norm_factor=1,
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edge_swap=False,
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proj_xy=True,
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target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
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target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
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loss_cls=dict(
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type='FocalLoss',
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use_sigmoid=True,
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gamma=2.0,
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alpha=0.25,
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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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train_cfg=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=0.4,
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min_pos_iou=0,
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ignore_iof_thr=-1,
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iou_calculator=dict(type='RBboxOverlaps2D')),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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test_cfg=dict(
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nms_pre=2000,
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min_bbox_size=0,
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score_thr=0.05,
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nms=dict(iou_thr=0.1),
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max_per_img=2000))
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# dataset settings
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dataset_type = 'DOTADataset'
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data_root = '.'
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1024, 1024),
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flip=False,
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transforms=[
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dict(type='RResize'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img'])
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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test=dict(
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type=dataset_type,
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ann_file='tests/test_codebase/test_mmrotate/data/dota_sample/',
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img_prefix=data_root,
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pipeline=test_pipeline,
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version='le135'))
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