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Unverified Commit 2890bcb5 authored by Kai Chen's avatar Kai Chen Committed by GitHub
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Merge pull request #121 from hellock/cascade-rcnn 

Add Cascade R-CNN
parents 559b0558 b466cc57
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MODEL_ZOO.md
View file @ 2890bcb5
...@@ -79,6 +79,22 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m ...@@ -79,6 +79,22 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
| R-50-FPN | pytorch | 1x | | | | | | | R-50-FPN | pytorch | 1x | | | | | |
| R-50-FPN | pytorch | 2x | | | | | | | R-50-FPN | pytorch | 2x | | | | | |
### Cascade R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 5.0 | 0.592 | 8.1 | 40.3 | - |
| R-50-FPN | pytorch | 1x | 5.5 | 0.622 | 8.0 | 40.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20181123-b1987c4a.pth) |
| R-50-FPN | pytorch | 20e | 5.5 | 0.622 | 8.0 | 41.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth) |
### Cascade Mask R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | caffe | 1x | 7.5 | 0.880 | 5.8 | 41.0 | 35.6 | - |
| R-50-FPN | pytorch | 1x | 7.6 | 0.910 | 5.7 | 41.3 | 35.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) |
| R-50-FPN | pytorch | 20e | 7.6 | 0.910 | 5.7 | 42.4 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth) |
## Comparison with Detectron ## Comparison with Detectron
......
configs/cascade_mask_rcnn_r50_fpn_1x.py 0 → 100644
View file @ 2890bcb5
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
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),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
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=81))
# model training and testing settings
train_cfg = dict(
rpn=dict(
assigner=dict(
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False,
pos_balance_sampling=False,
neg_balance_thr=0),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rcnn=[
dict(
assigner=dict(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
mask_size=28,
pos_weight=-1,
debug=False)
],
stage_loss_weights=[1, 0.5, 0.25])
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, max_per_img=100, nms_thr=0.5, mask_thr_binary=0.5),
keep_all_stages=False)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=True,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=True,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=True,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/cascade_mask_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/cascade_rcnn_r50_fpn_1x.py 0 → 100644
View file @ 2890bcb5
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='modelzoo://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
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),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=True),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=True),
dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067],
reg_class_agnostic=True)
])
# model training and testing settings
train_cfg = dict(
rpn=dict(
assigner=dict(
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False,
pos_balance_sampling=False,
neg_balance_thr=0),
allowed_border=0,
pos_weight=-1,
smoothl1_beta=1 / 9.0,
debug=False),
rcnn=[
dict(
assigner=dict(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
ignore_iof_thr=-1),
sampler=dict(
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
pos_balance_sampling=False,
neg_balance_thr=0),
pos_weight=-1,
debug=False)
],
stage_loss_weights=[1, 0.5, 0.25])
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(score_thr=0.05, max_per_img=100, nms_thr=0.5),
keep_all_stages=False)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0.5,
with_mask=False,
with_crowd=True,
with_label=True),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_crowd=True,
with_label=True),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
img_scale=(1333, 800),
img_norm_cfg=img_norm_cfg,
size_divisor=32,
flip_ratio=0,
with_mask=False,
with_label=False,
test_mode=True))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/cascade_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
mmdet/models/bbox_heads/bbox_head.py
View file @ 2890bcb5
import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
...@@ -122,3 +123,73 @@ class BBoxHead(nn.Module): ...@@ -122,3 +123,73 @@ class BBoxHead(nn.Module):
nms_cfg.max_per_img) nms_cfg.max_per_img)
return det_bboxes, det_labels return det_bboxes, det_labels
def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
"""Refine bboxes during training.
Args:
rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
and bs is the sampled RoIs per image.
labels (Tensor): Shape (n*bs, ).
bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
is a gt bbox.
img_metas (list[dict]): Meta info of each image.
Returns:
list[Tensor]: Refined bboxes of each image in a mini-batch.
"""
img_ids = rois[:, 0].long().unique(sorted=True)
assert img_ids.numel() == len(img_metas)
bboxes_list = []
for i in range(len(img_metas)):
inds = torch.nonzero(rois[:, 0] == i).squeeze()
num_rois = inds.numel()
bboxes_ = rois[inds, 1:]
label_ = labels[inds]
bbox_pred_ = bbox_preds[inds]
img_meta_ = img_metas[i]
pos_is_gts_ = pos_is_gts[i]
bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
img_meta_)
# filter gt bboxes
pos_keep = 1 - pos_is_gts_
keep_inds = pos_is_gts_.new_ones(num_rois)
keep_inds[:len(pos_is_gts_)] = pos_keep
bboxes_list.append(bboxes[keep_inds])
return bboxes_list
def regress_by_class(self, rois, label, bbox_pred, img_meta):
"""Regress the bbox for the predicted class. Used in Cascade R-CNN.
Args:
rois (Tensor): shape (n, 4) or (n, 5)
label (Tensor): shape (n, )
bbox_pred (Tensor): shape (n, 4*(#class+1)) or (n, 4)
img_meta (dict): Image meta info.
Returns:
Tensor: Regressed bboxes, the same shape as input rois.
"""
assert rois.size(1) == 4 or rois.size(1) == 5
if not self.reg_class_agnostic:
label = label * 4
inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
bbox_pred = torch.gather(bbox_pred, 1, inds)
assert bbox_pred.size(1) == 4
if rois.size(1) == 4:
new_rois = delta2bbox(rois, bbox_pred, self.target_means,
self.target_stds, img_meta['img_shape'])
else:
bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
self.target_stds, img_meta['img_shape'])
new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
return new_rois
mmdet/models/detectors/__init__.py
View file @ 2890bcb5
...@@ -4,8 +4,9 @@ from .rpn import RPN ...@@ -4,8 +4,9 @@ from .rpn import RPN
from .fast_rcnn import FastRCNN from .fast_rcnn import FastRCNN
from .faster_rcnn import FasterRCNN from .faster_rcnn import FasterRCNN
from .mask_rcnn import MaskRCNN from .mask_rcnn import MaskRCNN
from .cascade_rcnn import CascadeRCNN
__all__ = [ __all__ = [
'BaseDetector', 'TwoStageDetector', 'RPN', 'FastRCNN', 'FasterRCNN', 'BaseDetector', 'TwoStageDetector', 'RPN', 'FastRCNN', 'FasterRCNN',
'MaskRCNN' 'MaskRCNN', 'CascadeRCNN'
] ]
mmdet/models/detectors/cascade_rcnn.py 0 → 100644
View file @ 2890bcb5
from __future__ import division
import torch
import torch.nn as nn
from .base import BaseDetector
from .test_mixins import RPNTestMixin
from .. import builder
from mmdet.core import (assign_and_sample, bbox2roi, bbox2result, multi_apply,
merge_aug_masks)
class CascadeRCNN(BaseDetector, RPNTestMixin):
def __init__(self,
num_stages,
backbone,
neck=None,
rpn_head=None,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
assert bbox_roi_extractor is not None
assert bbox_head is not None
super(CascadeRCNN, self).__init__()
self.num_stages = num_stages
self.backbone = builder.build_backbone(backbone)
if neck is not None:
self.neck = builder.build_neck(neck)
else:
raise NotImplementedError
if rpn_head is not None:
self.rpn_head = builder.build_rpn_head(rpn_head)
if bbox_head is not None:
self.bbox_roi_extractor = nn.ModuleList()
self.bbox_head = nn.ModuleList()
if not isinstance(bbox_roi_extractor, list):
bbox_roi_extractor = [
bbox_roi_extractor for _ in range(num_stages)
]
if not isinstance(bbox_head, list):
bbox_head = [bbox_head for _ in range(num_stages)]
assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages
for roi_extractor, head in zip(bbox_roi_extractor, bbox_head):
self.bbox_roi_extractor.append(
builder.build_roi_extractor(roi_extractor))
self.bbox_head.append(builder.build_bbox_head(head))
if mask_head is not None:
self.mask_roi_extractor = nn.ModuleList()
self.mask_head = nn.ModuleList()
if not isinstance(mask_roi_extractor, list):
mask_roi_extractor = [
mask_roi_extractor for _ in range(num_stages)
]
if not isinstance(mask_head, list):
mask_head = [mask_head for _ in range(num_stages)]
assert len(mask_roi_extractor) == len(mask_head) == self.num_stages
for roi_extractor, head in zip(mask_roi_extractor, mask_head):
self.mask_roi_extractor.append(
builder.build_roi_extractor(roi_extractor))
self.mask_head.append(builder.build_mask_head(head))
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.init_weights(pretrained=pretrained)
@property
def with_rpn(self):
return hasattr(self, 'rpn_head') and self.rpn_head is not None
def init_weights(self, pretrained=None):
super(CascadeRCNN, self).init_weights(pretrained)
self.backbone.init_weights(pretrained=pretrained)
if self.with_neck:
if isinstance(self.neck, nn.Sequential):
for m in self.neck:
m.init_weights()
else:
self.neck.init_weights()
if self.with_rpn:
self.rpn_head.init_weights()
for i in range(self.num_stages):
if self.with_bbox:
self.bbox_roi_extractor[i].init_weights()
self.bbox_head[i].init_weights()
if self.with_mask:
self.mask_roi_extractor[i].init_weights()
self.mask_head[i].init_weights()
def extract_feat(self, img):
x = self.backbone(img)
if self.with_neck:
x = self.neck(x)
return x
def forward_train(self,
img,
img_meta,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
gt_masks=None,
proposals=None):
x = self.extract_feat(img)
losses = dict()
if self.with_rpn:
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
self.train_cfg.rpn)
rpn_losses = self.rpn_head.loss(*rpn_loss_inputs)
losses.update(rpn_losses)
proposal_inputs = rpn_outs + (img_meta, self.test_cfg.rpn)
proposal_list = self.rpn_head.get_proposals(*proposal_inputs)
else:
proposal_list = proposals
for i in range(self.num_stages):
rcnn_train_cfg = self.train_cfg.rcnn[i]
lw = self.train_cfg.stage_loss_weights[i]
# assign gts and sample proposals
assign_results, sampling_results = multi_apply(
assign_and_sample,
proposal_list,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
cfg=rcnn_train_cfg)
# bbox head forward and loss
bbox_roi_extractor = self.bbox_roi_extractor[i]
bbox_head = self.bbox_head[i]
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
rois)
cls_score, bbox_pred = bbox_head(bbox_feats)
bbox_targets = bbox_head.get_target(sampling_results, gt_bboxes,
gt_labels, rcnn_train_cfg)
loss_bbox = bbox_head.loss(cls_score, bbox_pred, *bbox_targets)
for name, value in loss_bbox.items():
losses['s{}.{}'.format(i, name)] = (value * lw if
'loss' in name else value)
# mask head forward and loss
if self.with_mask:
mask_roi_extractor = self.mask_roi_extractor[i]
mask_head = self.mask_head[i]
pos_rois = bbox2roi(
[res.pos_bboxes for res in sampling_results])
mask_feats = mask_roi_extractor(
x[:mask_roi_extractor.num_inputs], pos_rois)
mask_pred = mask_head(mask_feats)
mask_targets = mask_head.get_target(sampling_results, gt_masks,
rcnn_train_cfg)
pos_labels = torch.cat(
[res.pos_gt_labels for res in sampling_results])
loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels)
for name, value in loss_mask.items():
losses['s{}.{}'.format(i, name)] = (value * lw
if 'loss' in name else
value)
# refine bboxes
if i < self.num_stages - 1:
pos_is_gts = [res.pos_is_gt for res in sampling_results]
roi_labels = bbox_targets[0] # bbox_targets is a tuple
with torch.no_grad():
proposal_list = bbox_head.refine_bboxes(
rois, roi_labels, bbox_pred, pos_is_gts, img_meta)
return losses
def simple_test(self, img, img_meta, proposals=None, rescale=False):
x = self.extract_feat(img)
proposal_list = self.simple_test_rpn(
x, img_meta, self.test_cfg.rpn) if proposals is None else proposals
img_shape = img_meta[0]['img_shape']
ori_shape = img_meta[0]['ori_shape']
scale_factor = img_meta[0]['scale_factor']
# "ms" in variable names means multi-stage
ms_bbox_result = {}
ms_segm_result = {}
ms_scores = []
rcnn_test_cfg = self.test_cfg.rcnn
rois = bbox2roi(proposal_list)
for i in range(self.num_stages):
bbox_roi_extractor = self.bbox_roi_extractor[i]
bbox_head = self.bbox_head[i]
bbox_feats = bbox_roi_extractor(
x[:len(bbox_roi_extractor.featmap_strides)], rois)
cls_score, bbox_pred = bbox_head(bbox_feats)
ms_scores.append(cls_score)
if self.test_cfg.keep_all_stages:
det_bboxes, det_labels = bbox_head.get_det_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
nms_cfg=rcnn_test_cfg)
bbox_result = bbox2result(det_bboxes, det_labels,
bbox_head.num_classes)
ms_bbox_result['stage{}'.format(i)] = bbox_result
if self.with_mask:
mask_roi_extractor = self.mask_roi_extractor[i]
mask_head = self.mask_head[i]
if det_bboxes.shape[0] == 0:
segm_result = [
[] for _ in range(mask_head.num_classes - 1)
]
else:
_bboxes = (det_bboxes[:, :4] * scale_factor
if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)],
mask_rois)
mask_pred = mask_head(mask_feats)
segm_result = mask_head.get_seg_masks(
mask_pred, _bboxes, det_labels, rcnn_test_cfg,
ori_shape, scale_factor, rescale)
ms_segm_result['stage{}'.format(i)] = segm_result
if i < self.num_stages - 1:
bbox_label = cls_score.argmax(dim=1)
rois = bbox_head.regress_by_class(rois, bbox_label, bbox_pred,
img_meta[0])
cls_score = sum(ms_scores) / self.num_stages
det_bboxes, det_labels = self.bbox_head[-1].get_det_bboxes(
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=rescale,
nms_cfg=rcnn_test_cfg)
bbox_result = bbox2result(det_bboxes, det_labels,
self.bbox_head[-1].num_classes)
ms_bbox_result['ensemble'] = bbox_result
if self.with_mask:
if det_bboxes.shape[0] == 0:
segm_result = [
[] for _ in range(self.mask_head[-1].num_classes - 1)
]
else:
_bboxes = (det_bboxes[:, :4] * scale_factor
if rescale else det_bboxes)
mask_rois = bbox2roi([_bboxes])
aug_masks = []
for i in range(self.num_stages):
mask_roi_extractor = self.mask_roi_extractor[i]
mask_feats = mask_roi_extractor(
x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
mask_pred = self.mask_head[i](mask_feats)
aug_masks.append(mask_pred.sigmoid().cpu().numpy())
merged_masks = merge_aug_masks(aug_masks,
[img_meta] * self.num_stages,
self.test_cfg.rcnn)
segm_result = self.mask_head[-1].get_seg_masks(
merged_masks, _bboxes, det_labels, rcnn_test_cfg,
ori_shape, scale_factor, rescale)
ms_segm_result['ensemble'] = segm_result
if not self.test_cfg.keep_all_stages:
if self.with_mask:
results = (ms_bbox_result['ensemble'],
ms_segm_result['ensemble'])
else:
results = ms_bbox_result['ensemble']
else:
if self.with_mask:
results = {
stage: (ms_bbox_result[stage], ms_segm_result[stage])
for stage in ms_bbox_result
}
else:
results = ms_bbox_result
return results
def aug_test(self, img, img_meta, proposals=None, rescale=False):
raise NotImplementedError
def show_result(self, data, result, img_norm_cfg, **kwargs):
# TODO: show segmentation masks
if self.with_mask:
ms_bbox_result, ms_segm_result = result
else:
ms_bbox_result = result
if isinstance(ms_bbox_result, dict):
bbox_result = ms_bbox_result['ensemble']
else:
bbox_result = ms_bbox_result
super(CascadeRCNN, self).show_result(data, bbox_result, img_norm_cfg,
**kwargs)
setup.py
View file @ 2890bcb5
...@@ -12,7 +12,7 @@ def readme(): ...@@ -12,7 +12,7 @@ def readme():
MAJOR = 0 MAJOR = 0
MINOR = 5 MINOR = 5
PATCH = 2 PATCH = 3
SUFFIX = '' SUFFIX = ''
SHORT_VERSION = '{}.{}.{}{}'.format(MAJOR, MINOR, PATCH, SUFFIX) SHORT_VERSION = '{}.{}.{}{}'.format(MAJOR, MINOR, PATCH, SUFFIX)
......
tools/test.py
View file @ 2890bcb5
...@@ -104,10 +104,19 @@ def main(): ...@@ -104,10 +104,19 @@ def main():
print('Starting evaluate {}'.format(' and '.join(eval_types))) print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']: if eval_types == ['proposal_fast']:
result_file = args.out result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else: else:
if not isinstance(outputs[0], dict):
result_file = args.out + '.json' result_file = args.out + '.json'
results2json(dataset, outputs, result_file) results2json(dataset, outputs, result_file)
coco_eval(result_file, eval_types, dataset.coco) coco_eval(result_file, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}.json'.format(name)
results2json(dataset, outputs_, result_file)
coco_eval(result_file, eval_types, dataset.coco)
if __name__ == '__main__': if __name__ == '__main__':
......
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