153 lines
5.2 KiB
Python
153 lines
5.2 KiB
Python
# An example config to train a mmdetection model using detectron2.
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from ..common.data.coco import dataloader
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from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
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from ..common.optim import SGD as optimizer
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from ..common.train import train
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from ..common.data.constants import constants
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from detectron2.modeling.mmdet_wrapper import MMDetDetector
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from detectron2.config import LazyCall as L
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model = L(MMDetDetector)(
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detector=dict(
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type="MaskRCNN",
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pretrained="torchvision://resnet50",
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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(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
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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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# model training and testing settings
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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=constants.imagenet_rgb256_mean,
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pixel_std=constants.imagenet_rgb256_std,
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)
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dataloader.train.mapper.image_format = "RGB" # torchvision pretrained model
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train.init_checkpoint = None # pretrained model is loaded inside backbone
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