def _get_config_module(fname):
"""Load a configuration as a python module."""
from xdoctest.utils import import_module_from_path
config_dpath = _get_config_directory()
config_fpath = join(config_dpath, fname)
config_mod = import_module_from_path(config_fpath)
return config_mod
def _import_module(self):
"""
After this point we are in dynamic analysis mode, in most cases
xdoctest should have been in static-analysis-only mode.
"""
if self.module is None:
if not self.modname.startswith('<'):
# self.module = utils.import_module_from_path(self.modpath, index=0)
self.module = utils.import_module_from_path(self.modpath, index=-1)
def parse_dynamic_calldefs(modpath=None):
"""
Dynamic parsing of module doctestable items.
While this does execute module code it is needed for testing extension
libraries.
CommandLine:
python -m xdoctest.dynamic_analysis parse_dynamic_calldefs
Example:
>>> from xdoctest import dynamic_analysis
>>> module = dynamic_analysis
>>> calldefs = parse_dynamic_calldefs(module.__file__)
>>> for key, calldef in sorted(calldefs.items()):
... print('key = {!r}'.format(key))
... print(' * calldef.callname = {}'.format(calldef.callname))
... if calldef.docstr is None:
... print(' * len(calldef.docstr) = {}'.format(calldef.docstr))
... else:
... print(' * len(calldef.docstr) = {}'.format(len(calldef.docstr)))
"""
from xdoctest import static_analysis as static
from xdoctest import utils # NOQA
# Possible option for dynamic parsing
module = utils.import_module_from_path(modpath)
calldefs = {}
if getattr(module, '__doc__'):
calldefs['__doc__'] = static.CallDefNode(
callname='__doc__',
docstr=module.__doc__,
lineno=0,
doclineno=1,
doclineno_end=1,
args=None
)
for key, val in iter_module_doctestables(module):
# if hasattr(val, '__doc__'):
if hasattr(val, '__doc__') and hasattr(val, '__name__'):
calldefs[key] = static.CallDefNode(
callname=val.__name__,
docstr=val.__doc__,
lineno=0,
doclineno=1,
doclineno_end=1,
args=None
)
return calldefs
def _import_module(self):
"""
After this point we are in dynamic analysis mode, in most cases
xdoctest should have been in static-analysis-only mode.
"""
if self.module is None:
if not self.modname.startswith('<'):
# self.module = utils.import_module_from_path(self.modpath, index=0)
try:
self.module = utils.import_module_from_path(self.modpath,
index=-1)
except RuntimeError as ex:
msg_parts = [('XDoctest failed to pre-import the module '
'containing the doctest.')]
msg_parts.append(str(ex))
new_exc = RuntimeError('\n'.join(msg_parts))
# new_exc = ex
# Remove traceback before this line
new_exc.__traceback__ = None
# Backwards syntax compatible raise exc from None
# https://www.python.org/dev/peps/pep-3134/#explicit-exception-chaining
new_exc.__cause__ = None
raise new_exc
def test_config_build_detector():
"""
Test that all detection models defined in the configs can be initialized.
"""
from xdoctest.utils import import_module_from_path
from mmdet.models import build_detector
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
# import glob
# config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
# config_names = [relpath(p, config_dpath) for p in config_fpaths]
# Only tests a representative subset of configurations
config_names = [
# 'dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_dpool_r50_fpn_1x.py',
'dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py',
# 'dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdpool_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py',
# 'dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py',
# ---
# 'htc/htc_x101_32x4d_fpn_20e_16gpu.py',
'htc/htc_without_semantic_r50_fpn_1x.py',
# 'htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py',
# 'htc/htc_x101_64x4d_fpn_20e_16gpu.py',
# 'htc/htc_r50_fpn_1x.py',
# 'htc/htc_r101_fpn_20e.py',
# 'htc/htc_r50_fpn_20e.py',
# ---
'cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
# 'cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py',
# ---
# 'scratch/scratch_faster_rcnn_r50_fpn_gn_6x.py',
# 'scratch/scratch_mask_rcnn_r50_fpn_gn_6x.py',
# ---
# 'grid_rcnn/grid_rcnn_gn_head_x101_32x4d_fpn_2x.py',
'grid_rcnn/grid_rcnn_gn_head_r50_fpn_2x.py',
# ---
'double_heads/dh_faster_rcnn_r50_fpn_1x.py',
# ---
'empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x.py',
# 'empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py',
# ---
# 'ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py',
# 'ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py',
# 'ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py',
# ---
# 'guided_anchoring/ga_faster_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_rpn_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_retinanet_r50_caffe_fpn_1x.py',
# 'guided_anchoring/ga_fast_r50_caffe_fpn_1x.py',
# 'guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x.py',
# 'guided_anchoring/ga_rpn_r101_caffe_rpn_1x.py',
# 'guided_anchoring/ga_faster_r50_caffe_fpn_1x.py',
'guided_anchoring/ga_rpn_r50_caffe_fpn_1x.py',
# ---
'foveabox/fovea_r50_fpn_4gpu_1x.py',
# 'foveabox/fovea_align_gn_ms_r101_fpn_4gpu_2x.py',
# 'foveabox/fovea_align_gn_r50_fpn_4gpu_2x.py',
# 'foveabox/fovea_align_gn_r101_fpn_4gpu_2x.py',
'foveabox/fovea_align_gn_ms_r50_fpn_4gpu_2x.py',
# ---
# 'hrnet/cascade_rcnn_hrnetv2p_w32_20e.py',
# 'hrnet/mask_rcnn_hrnetv2p_w32_1x.py',
# 'hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e.py',
# 'hrnet/htc_hrnetv2p_w32_20e.py',
# 'hrnet/faster_rcnn_hrnetv2p_w18_1x.py',
# 'hrnet/mask_rcnn_hrnetv2p_w18_1x.py',
# 'hrnet/faster_rcnn_hrnetv2p_w32_1x.py',
# 'hrnet/faster_rcnn_hrnetv2p_w40_1x.py',
'hrnet/fcos_hrnetv2p_w32_gn_1x_4gpu.py',
# ---
# 'gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py',
# 'gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py',
'gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py',
# 'gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py',
# ---
# 'wider_face/ssd300_wider_face.py',
# ---
'pascal_voc/ssd300_voc.py',
'pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py',
'pascal_voc/ssd512_voc.py',
# ---
# 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_syncbn_1x.py',
# 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_syncbn_1x.py',
# 'gcnet/mask_rcnn_r4_gcb_c3-c5_r50_fpn_1x.py',
# 'gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x.py',
'gcnet/mask_rcnn_r50_fpn_sbn_1x.py',
# ---
'gn/mask_rcnn_r50_fpn_gn_contrib_2x.py',
# 'gn/mask_rcnn_r50_fpn_gn_2x.py',
# 'gn/mask_rcnn_r101_fpn_gn_2x.py',
# ---
# 'reppoints/reppoints_moment_x101_dcn_fpn_2x.py',
'reppoints/reppoints_moment_r50_fpn_2x.py',
# 'reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py',
'reppoints/reppoints_partial_minmax_r50_fpn_1x.py',
'reppoints/bbox_r50_grid_center_fpn_1x.py',
# 'reppoints/reppoints_moment_r101_dcn_fpn_2x.py',
# 'reppoints/reppoints_moment_r101_fpn_2x_mt.py',
# 'reppoints/reppoints_moment_r50_fpn_2x_mt.py',
'reppoints/reppoints_minmax_r50_fpn_1x.py',
# 'reppoints/reppoints_moment_r50_fpn_1x.py',
# 'reppoints/reppoints_moment_r101_fpn_2x.py',
# 'reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py',
'reppoints/bbox_r50_grid_fpn_1x.py',
# ---
# 'fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py',
# 'fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py',
'fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py',
# ---
'albu_example/mask_rcnn_r50_fpn_1x.py',
# ---
'libra_rcnn/libra_faster_rcnn_r50_fpn_1x.py',
# 'libra_rcnn/libra_retinanet_r50_fpn_1x.py',
# 'libra_rcnn/libra_faster_rcnn_r101_fpn_1x.py',
# 'libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x.py',
# 'libra_rcnn/libra_fast_rcnn_r50_fpn_1x.py',
# ---
# 'ghm/retinanet_ghm_r50_fpn_1x.py',
# ---
# 'fp16/retinanet_r50_fpn_fp16_1x.py',
'fp16/mask_rcnn_r50_fpn_fp16_1x.py',
'fp16/faster_rcnn_r50_fpn_fp16_1x.py'
]
print('Using {} config files'.format(len(config_names)))
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = import_module_from_path(config_fpath)
config_mod.model
config_mod.train_cfg
config_mod.test_cfg
print('Building detector, config_fpath = {!r}'.format(config_fpath))
# Remove pretrained keys to allow for testing in an offline environment
if 'pretrained' in config_mod.model:
config_mod.model['pretrained'] = None
detector = build_detector(config_mod.model,
train_cfg=config_mod.train_cfg,
test_cfg=config_mod.test_cfg)
assert detector is not None
def test_config_data_pipeline():
"""
Test whether the data pipeline is valid and can process corner cases.
CommandLine:
xdoctest -m tests/test_config.py test_config_build_data_pipeline
"""
from xdoctest.utils import import_module_from_path
from mmdet.datasets.pipelines import Compose
import numpy as np
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
# Only tests a representative subset of configurations
# TODO: test pipelines using Albu, current Albu throw None given empty GT
config_names = [
'wider_face/ssd300_wider_face.py',
'pascal_voc/ssd300_voc.py',
'pascal_voc/ssd512_voc.py',
# 'albu_example/mask_rcnn_r50_fpn_1x.py',
'fp16/mask_rcnn_r50_fpn_fp16_1x.py',
]
print('Using {} config files'.format(len(config_names)))
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = import_module_from_path(config_fpath)
# remove loading pipeline
loading_pipeline = config_mod.train_pipeline.pop(0)
config_mod.train_pipeline.pop(0)
config_mod.test_pipeline.pop(0)
train_pipeline = Compose(config_mod.train_pipeline)
test_pipeline = Compose(config_mod.test_pipeline)
print(
'Building data pipeline, config_fpath = {!r}'.format(config_fpath))
print('Test training data pipeline: \n{!r}'.format(train_pipeline))
img = np.random.randint(0, 255, size=(888, 666, 3), dtype=np.uint8)
if loading_pipeline.get('to_float32', False):
img = img.astype(np.float32)
results = dict(
filename='test_img.png',
img=img,
img_shape=img.shape,
ori_shape=img.shape,
gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32),
gt_labels=np.array([1], dtype=np.int64),
gt_masks=[(img[..., 0] == 233).astype(np.uint8)],
)
results['bbox_fields'] = ['gt_bboxes']
results['mask_fields'] = ['gt_masks']
output_results = train_pipeline(results)
assert output_results is not None
print('Test testing data pipeline: \n{!r}'.format(test_pipeline))
results = dict(
filename='test_img.png',
img=img,
img_shape=img.shape,
ori_shape=img.shape,
gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32),
gt_labels=np.array([1], dtype=np.int64),
gt_masks=[(img[..., 0] == 233).astype(np.uint8)],
)
results['bbox_fields'] = ['gt_bboxes']
results['mask_fields'] = ['gt_masks']
output_results = test_pipeline(results)
assert output_results is not None
# test empty GT
print('Test empty GT with training data pipeline: \n{!r}'.format(
train_pipeline))
results = dict(
filename='test_img.png',
img=img,
img_shape=img.shape,
ori_shape=img.shape,
gt_bboxes=np.zeros((0, 4), dtype=np.float32),
gt_labels=np.array([], dtype=np.int64),
gt_masks=[],
)
results['bbox_fields'] = ['gt_bboxes']
results['mask_fields'] = ['gt_masks']
output_results = train_pipeline(results)
assert output_results is not None
print('Test empty GT with testing data pipeline: \n{!r}'.format(
test_pipeline))
results = dict(
filename='test_img.png',
img=img,
img_shape=img.shape,
ori_shape=img.shape,
gt_bboxes=np.zeros((0, 4), dtype=np.float32),
gt_labels=np.array([], dtype=np.int64),
gt_masks=[],
)
results['bbox_fields'] = ['gt_bboxes']
results['mask_fields'] = ['gt_masks']
output_results = test_pipeline(results)
assert output_results is not None
