GitLab

* add logger utils

* replace get_root_logger() and logger.info() with print_log()

* fix a typo

* minor fix for the format of StreamHandler

* Enhance AssignResult and SamplingResult

Add runtime dependency on ubelt (pending approval)

Fix issue in SamplingResult.__init__

Add rng as attribute of RandomSampler

* fix linters

* remove ubelt

* Fix linters

* fix linters again

Changes to be committed:
modified:   mmdet/utils/registry.py

* add GN support for flops computation

* remove useless lines

* modify the flops computation for gn

* async inference support

* implemented concurrent decorator

* fixes for Python versions < 3.7

* async methods depend on python version

* revert changes in forward method

* async_test -> async_simple_test, debug logging is done via logger.debug

* add async test

* add asynctest to test requirements

* async tests are run in Python 3.7

* check CUDA, add docs

* fix device

* run test only if CUDA is available

* fix linting

* custom operators can run on nondefault stream

* set current stream in kernel launch configuration

* example fixes

* add async/sync interface comparison benchmark

* fix linting

* 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

* add flops counter

* minor fix

* add forward_dummy() for most detectors

* add documentation for some tools

* use registry to manage datasets

* bug fix for concat dataset

* update documentation to fit the new api