Spaces:
Runtime error
Runtime error
File size: 5,304 Bytes
5395ccf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
# An example config to train a mmdetection model using detectron2.
from ..common.data.coco import dataloader
from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
from ..common.optim import SGD as optimizer
from ..common.train import train
from detectron2.modeling.mmdet_wrapper import MMDetDetector
from detectron2.config import LazyCall as L
model = L(MMDetDetector)(
detector=dict(
type="MaskRCNN",
pretrained="torchvision://resnet50",
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",
),
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=[123.675, 116.280, 103.530],
pixel_std=[58.395, 57.120, 57.375],
)
dataloader.train.mapper.image_format = "RGB" # torchvision pretrained model
train.init_checkpoint = None # pretrained model is loaded inside backbone
|