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Commit 57d34592 authored by Jiangmiao Pang's avatar Jiangmiao Pang Committed by Kai Chen
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Add loss evaluator (#678) 

* Fix license in setup.py

* Add code for loss evaluator

* Configs support loss evaluator

* Fix a little bug

* Fix flake8

* return revised bbox to reg

* return revised bbox to reg

* revision according to comments

* fix flake8
parent a99dbae7
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Showing with 250 additions and 83 deletions
configs/htc/htc_r50_fpn_20e.py
View file @ 57d34592
......@@ -26,7 +26,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -42,7 +44,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -52,7 +62,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -62,7 +80,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
......@@ -74,7 +100,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81),
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -107,7 +135,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/htc/htc_without_semantic_r50_fpn_1x.py
View file @ 57d34592
......@@ -26,7 +26,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -42,7 +44,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -52,7 +62,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -62,7 +80,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
......@@ -74,7 +100,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -92,7 +120,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/htc/htc_x101_32x4d_fpn_20e_16gpu.py
View file @ 57d34592
......@@ -28,7 +28,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -44,7 +46,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -54,7 +64,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -64,7 +82,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
......@@ -76,7 +102,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81),
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -109,7 +137,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/htc/htc_x101_64x4d_fpn_20e_16gpu.py
View file @ 57d34592
......@@ -28,7 +28,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -44,7 +46,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -54,7 +64,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0)),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
......@@ -64,7 +82,15 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=1.0,
loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
......@@ -76,7 +102,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81),
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -109,7 +137,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/mask_rcnn_r101_fpn_1x.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -38,7 +40,10 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -49,7 +54,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -67,7 +74,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/mask_rcnn_r50_caffe_c4_1x.py
View file @ 57d34592
......@@ -2,7 +2,7 @@
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnet50_caffe',
# pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
......@@ -32,7 +32,9 @@ model = dict(
anchor_strides=[16],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -46,14 +48,19 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=None,
mask_head=dict(
type='FCNMaskHead',
num_convs=0,
in_channels=2048,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -71,7 +78,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/mask_rcnn_r50_fpn_1x.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -38,7 +40,10 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -49,7 +54,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -67,7 +74,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/mask_rcnn_x101_32x4d_fpn_1x.py
View file @ 57d34592
......@@ -25,7 +25,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -40,7 +42,10 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -51,7 +56,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -69,7 +76,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......@@ -171,7 +177,7 @@ log_config = dict(
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_r50_fpn_1x'
work_dir = './work_dirs/mask_rcnn_x101_32x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/mask_rcnn_x101_64x4d_fpn_1x.py
View file @ 57d34592
......@@ -25,7 +25,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -40,7 +42,10 @@ model = dict(
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
......@@ -51,7 +56,9 @@ model = dict(
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=81))
num_classes=81,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -69,7 +76,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......@@ -171,7 +177,7 @@ log_config = dict(
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/mask_rcnn_r50_fpn_1x'
work_dir = './work_dirs/mask_rcnn_x101_64x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -38,7 +40,10 @@ model = dict(
num_classes=21,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False))
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -56,7 +61,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
configs/retinanet_r101_fpn_1x.py
View file @ 57d34592
......@@ -27,7 +27,14 @@ model = dict(
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]))
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
......@@ -36,9 +43,6 @@ train_cfg = dict(
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
smoothl1_beta=0.11,
gamma=2.0,
alpha=0.25,
allowed_border=-1,
pos_weight=-1,
debug=False)
......
configs/retinanet_r50_fpn_1x.py
View file @ 57d34592
......@@ -27,7 +27,14 @@ model = dict(
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]))
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
......@@ -36,9 +43,6 @@ train_cfg = dict(
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
smoothl1_beta=0.11,
gamma=2.0,
alpha=0.25,
allowed_border=-1,
pos_weight=-1,
debug=False)
......
configs/retinanet_x101_32x4d_fpn_1x.py
View file @ 57d34592
......@@ -29,7 +29,14 @@ model = dict(
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]))
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
......@@ -38,9 +45,6 @@ train_cfg = dict(
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
smoothl1_beta=0.11,
gamma=2.0,
alpha=0.25,
allowed_border=-1,
pos_weight=-1,
debug=False)
......@@ -116,7 +120,7 @@ total_epochs = 12
device_ids = range(8)
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/retinanet_r50_fpn_1x'
work_dir = './work_dirs/retinanet_x101_32x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/retinanet_x101_64x4d_fpn_1x.py
View file @ 57d34592
......@@ -29,7 +29,14 @@ model = dict(
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[8, 16, 32, 64, 128],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]))
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
assigner=dict(
......@@ -38,9 +45,6 @@ train_cfg = dict(
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
smoothl1_beta=0.11,
gamma=2.0,
alpha=0.25,
allowed_border=-1,
pos_weight=-1,
debug=False)
......@@ -116,7 +120,7 @@ total_epochs = 12
device_ids = range(8)
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/retinanet_r50_fpn_1x'
work_dir = './work_dirs/retinanet_x101_64x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/rpn_r101_fpn_1x.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True))
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -41,7 +43,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False))
test_cfg = dict(
rpn=dict(
......
configs/rpn_r50_caffe_c4_1x.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[16],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True))
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -41,7 +43,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False))
test_cfg = dict(
rpn=dict(
......
configs/rpn_r50_fpn_1x.py
View file @ 57d34592
......@@ -23,7 +23,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True))
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -41,7 +43,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False))
test_cfg = dict(
rpn=dict(
......
configs/rpn_x101_32x4d_fpn_1x.py
View file @ 57d34592
......@@ -25,7 +25,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True))
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -43,7 +45,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False))
test_cfg = dict(
rpn=dict(
......@@ -117,7 +118,7 @@ log_config = dict(
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/rpn_r101_fpn_1x'
work_dir = './work_dirs/rpn_x101_32x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/rpn_x101_64x4d_fpn_1x.py
View file @ 57d34592
......@@ -25,7 +25,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True))
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -43,7 +45,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False))
test_cfg = dict(
rpn=dict(
......@@ -117,7 +118,7 @@ log_config = dict(
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/rpn_r101_fpn_1x'
work_dir = './work_dirs/rpn_x101_64x4d_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py
View file @ 57d34592
......@@ -27,7 +27,9 @@ model = dict(
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
use_sigmoid_cls=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
......@@ -45,7 +47,10 @@ model = dict(
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
norm_cfg=norm_cfg))
norm_cfg=norm_cfg,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
......@@ -63,7 +68,6 @@ train_cfg = dict(
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
......
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