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Merge pull request open-mmlab#20 from open-mmlab/dev
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Initial public release
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hellock authored Oct 12, 2018
2 parents 9378f93 + 888a2e4 commit dbc44c9
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5 changes: 5 additions & 0 deletions .gitignore
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Expand Up @@ -102,3 +102,8 @@ venv.bak/

# mypy
.mypy_cache/

# cython generated cpp
mmdet/ops/nms/*.cpp
mmdet/version.py
data
13 changes: 13 additions & 0 deletions .travis.yml
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dist: trusty
language: python

install:
- pip install flake8

python:
- "2.7"
- "3.5"
- "3.6"

script:
- flake8
22 changes: 22 additions & 0 deletions compile.sh
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#!/usr/bin/env bash

PYTHON=${PYTHON:-"python"}

echo "Building roi align op..."
cd mmdet/ops/roi_align
if [ -d "build" ]; then
rm -r build
fi
$PYTHON setup.py build_ext --inplace

echo "Building roi pool op..."
cd ../roi_pool
if [ -d "build" ]; then
rm -r build
fi
$PYTHON setup.py build_ext --inplace

echo "Building nms op..."
cd ../nms
make clean
make PYTHON=${PYTHON}
132 changes: 132 additions & 0 deletions configs/fast_mask_rcnn_r50_fpn_1x.py
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# model settings
model = dict(
type='FastRCNN',
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),
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='SharedFCRoIHead',
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=False),
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(
rcnn=dict(
mask_size=28,
pos_iou_thr=0.5,
neg_iou_thr=0.5,
crowd_thr=1.1,
roi_batch_size=512,
add_gt_as_proposals=True,
pos_fraction=0.25,
pos_balance_sampling=False,
neg_pos_ub=512,
neg_balance_thr=0,
min_pos_iou=0.5,
pos_weight=-1,
debug=False))
test_cfg = dict(
rcnn=dict(
score_thr=0.05, max_per_img=100, nms_thr=0.5, mask_thr_binary=0.5))
# 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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl',
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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
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/fast_mask_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
118 changes: 118 additions & 0 deletions configs/fast_rcnn_r50_fpn_1x.py
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# model settings
model = dict(
type='FastRCNN',
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),
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='SharedFCRoIHead',
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=False))
# model training and testing settings
train_cfg = dict(
rcnn=dict(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
crowd_thr=1.1,
roi_batch_size=512,
add_gt_as_proposals=True,
pos_fraction=0.25,
pos_balance_sampling=False,
neg_pos_ub=512,
neg_balance_thr=0,
min_pos_iou=0.5,
pos_weight=-1,
debug=False))
test_cfg = dict(rcnn=dict(score_thr=0.05, max_per_img=100, nms_thr=0.5))
# 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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl',
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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
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,
proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
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/fast_rcnn_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
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