GitLab

* Fix #1223

#1223  is caused by extra > len(indice), so I add a loop to properly pad the indice list. I also changed the list concatenation from ```+=``` to ```.extend```.

* Update sampler.py

style

* Fixed coco_eval bug where seg length check could be wrong (#1376)

* Added check for segm counts field to check if the decode method is present

* Changed classes length check to more general check

* Fixed coco_utils.py to be more robust condition for mask lasbelling

* Added seg length check to ensure two objects exist

* Added type check over len check

* Check for only tuple

* fix dpool

* add _pair in dpool func

* add tta to HTC and Caccade RCNN

* format file with yapf

* fix import error with isort

* Update htc.py

* Update cascade_rcnn.py

* fix bug

* delete some redundant codes

* fix a bug for Modulated Deformable Convolution

* resolve cases for CI test

* resolve cases for CI test

* resolve cases for CI test

* create a new script

* change dir name in config

* update model url

* fix bug when using coco_eval evaluate

* raise TypeError

* add g++ requirement for INSTALL.md.

* fix bugs for cropping masks in RandomCrop.

* simplify the code to fix bugs for cropping masks in RandomCrop.

* Update deform_conv_cuda.cpp

* Update deform_pool_cuda.cpp

* fix bug of ssd

* clean

This reverts commit ea9b20af.

* add reppoints head, reppoints detector and the config files.

* add reppoints generator, target and the center-based assigner.

* add readme for RepPoints

* define data pipelines

* update two config files

* minor fix for config files

* allow img_scale to be optional and update config

* add some docstrings

* add extra aug to transform

* bug fix for mask resizing

* fix cropping

* add faster rcnn example

* fix imports

* fix robustness testing

* add img_norm_cfg to img_meta

* fix the inference api with the new data pipeline

* fix proposal loading

* delete args of DefaultFormatBundle

* add more configs

* update configs

* bug fix

* add a brief doc

* update gt_labels in RandomCrop

* fix key error for new apis

* bug fix for masks of crowd bboxes

* add argument data_root

* minor fix

* update new hrnet configs

* update docs

* rename MultiscaleFlipAug to MultiScaleFlipAug

* add __repr__ for all transforms

* move DATA_PIPELINE.md to docs/

* fix image url

* add testing in Travis

* add proj. requirements

* install CUDA

* def tested packages

* add missing meta in setup

* add empty test

* restructure travis tests

* add sample doctest

* switch to nose2

* add sample test

* using pytest

* minor fixes

* update model zoo

* update model zoo

* update model links

* update HRNet README

* update HRNet README

* remove .DS_Store

* update HRNet results

* update HRNet results and remove .DS_Store

* update FCOS results

* update HRFPN to support FCOS

* remove blank line

* remove blank line

* update Modelzoo

* update models

* update model url

* fix typo

* fix typo

* change name of backbones

* add 20e to cascade w18

* update url

* add flops counter

* minor fix

* add forward_dummy() for most detectors

* add documentation for some tools

* Fix bug of FCOS when image has no positive sample

* somes changes for fcos

* Allowing validation dataset for computing validation loss

* added validation dataset

* fixing datasets errors

* Fixing linting errors

* Added vertical flipping in bbox_flip

* Fixed linting errors

* Fixed linting errors

* Added a blank line in docstrings

* Fixed linting errors.

* Add robust detection benchmark

* Update readmes

* Changed readmes for pull request

* Ensure pep8 conformity

* fixed formatting

* Fix yapf errors

* minor formatting

* fix imports order

* bug fix for recursive imports

* minor formatting

* fix the imports in __init__.py

* update contribution guide

* add isort config

* use isort to sort imports

* add isort to travis

* add major components for training double head

double head only with two losses and no attention

remove double_head detector for now, merge upchannel to double_head bbox head

change the stype using yapl

remove uncessary comment#

to pass check

to pass v2

to pass v3

line too long and style again

reuse bottlenet

* refactoring

* bug fix

* bug fix for weight initialization

* add reg roi scale factor and modify loss weights

* rescale the roi after mapping to fpn levels

* format the codebase with yapf

* add yapf to travis

* restructure the ops directory

* add some repr strings