def __init__(self, cfg, output_dir):
self.cfg = cfg
self.output_dir = output_dir
self.DETECTOR = Registry()
self.DETECTOR.register("gradient-edge", dm_gradient_edge_detector)
self.DETECTOR.register("entropy", dm_entropy_detector)
self.DETECTOR.register("dev", dev_detector)
def get_faceTrackNoIR_path():
"""Get the path to FaceTrackNoIR installation."""
fake_path = devmode.get_facetracknoir_path()
if fake_path:
return fake_path
try:
key = 'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\FaceTrackNoIR_is1'
reg_val = Registry.ReadValueUserAndMachine(key, 'InstallLocation',
True)
Logger.info('FaceTrackNoIR: Install location: {}'.format(reg_val))
path = os.path.join(reg_val, 'FaceTrackNoIR.exe')
if not os.path.isfile(path):
Logger.info(
'FaceTrackNoIR: Found install location but no expected exe file found: {}'
.format(path))
raise FaceTrackNoIRNotInstalled()
return path
except Registry.Error:
raise FaceTrackNoIRNotInstalled()
def get_opentrack_path():
"""Get the path to Opentrack installation."""
fake_path = devmode.get_opentrack_path()
if fake_path:
return fake_path
try:
key = 'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\{63F53541-A29E-4B53-825A-9B6F876A2BD6}_is1'
reg_val = Registry.ReadValueUserAndMachine(key, 'InstallLocation',
True)
Logger.info('Opentrack: Install location: {}'.format(reg_val))
path = os.path.join(reg_val, 'opentrack.exe')
if not os.path.isfile(path):
Logger.info(
'Opentrack: Found install location but no expected exe file found: {}'
.format(path))
raise OpentrackNotInstalled()
return path
except Registry.Error:
raise OpentrackNotInstalled()
def get_installation_path():
"""Return the folder where Arma is installed.
1) Check local directory
2) Search the registry entry
3) Browse steam libraries in search for Arma
Raises ArmaNotInstalled if the required registry keys cannot be found."""
if Arma.installation_path_cached:
return Arma.installation_path_cached
if devmode.get_arma_path():
Arma.installation_path_cached = devmode.get_arma_path()
return Arma.installation_path_cached
# 1) Check local directory
path = paths.get_external_executable_dir()
if os.path.isfile(os.path.join(path, 'Arma3.exe')):
Logger.info('Arma: Arma3.exe found in launcher directory: {}'.format(path))
Arma.installation_path_cached = path
return Arma.installation_path_cached
Logger.error('Arma: Could not find Arma3.exe in launcher directory')
# 2) Search the registry entry
try:
path = Registry.ReadValueUserAndMachine(Arma._arma_registry_path, 'main', check_both_architectures=True)
if os.path.isfile(os.path.join(path, 'Arma3.exe')):
Logger.info('Arma: Arma3.exe found through registry: {}'.format(path))
Arma.installation_path_cached = path
return Arma.installation_path_cached
else:
Logger.error('Arma: Could not find Arma3.exe at the location pointed by the registry: {}'.format(path))
except Registry.Error:
Logger.error('Arma: Could not find registry entry for installation path')
# 3) Browse steam libraries in search for Arma
steam_libraries = steam.find_steam_libraries()
for library in steam_libraries:
path = os.path.join(library, 'steamapps', 'common', 'Arma 3')
if os.path.isfile(os.path.join(path, 'Arma3.exe')):
Logger.info('Arma: Arma3.exe found in Steam libraries: {}'.format(path))
Arma.installation_path_cached = path
return Arma.installation_path_cached
# All failed :(
raise ArmaNotInstalled()
def get_install_location():
"""Return the path to where teamspeak executables are installed."""
if devmode.get_ts_install_location():
return devmode.get_ts_install_location()
try:
key = 'SOFTWARE\\TeamSpeak 3 Client'
reg_val = Registry.ReadValueMachineAndUser(key, '', True)
return reg_val
except Registry.Error:
raise TeamspeakNotInstalled('Could not get the TS install location')
def get_steam_exe_path():
"""Return the path to the steam executable.
Raises SteamNotInstalled if steam is not installed."""
if devmode.get_steam_executable():
return devmode.get_steam_executable()
try:
# Optionally, there is also SteamPath
return Registry.ReadValueUserAndMachine(
_steam_registry_path, 'SteamExe',
check_both_architectures=True) # SteamPath
except Registry.Error:
raise SteamNotInstalled()
def _get_config_location():
"""Return the value meaning where the user configuration is stored.
0 - C:/Users/<username>/AppData/Roaming/TS3Client
1 - Installation folder (get_install_location()/config).
The actual directory may not exist until the user actually launches Teamspeak!
"""
if devmode.get_ts_config_location():
return devmode.get_ts_config_location()
try:
key = 'SOFTWARE\\TeamSpeak 3 Client'
reg_val = Registry.ReadValueMachineAndUser(key, 'ConfigLocation', True)
return reg_val
except Registry.Error:
raise TeamspeakNotInstalled(
'Could not get the TS config location information')
class DMDetector:
def __init__(self, cfg, output_dir):
self.cfg = cfg
self.output_dir = output_dir
self.DETECTOR = Registry()
self.DETECTOR.register("gradient-edge", dm_gradient_edge_detector)
self.DETECTOR.register("entropy", dm_entropy_detector)
self.DETECTOR.register("dev", dev_detector)
def detect(self, dm_image: DMImage):
return self.DETECTOR[self.cfg.METHOD](dm_image=dm_image,
output_dir=self.output_dir,
**self.cfg.ARG)
def get_TrackIR_path():
"""Get the path to FaceTrackNoIR installation."""
fake_path = devmode.get_trackir_path()
if fake_path:
return fake_path
try:
key = 'Software\\NaturalPoint\\NaturalPoint\\NPClient Location'
reg_val = Registry.ReadValueUserAndMachine(key, 'Path', True)
Logger.info('TrackIR: Install location: {}'.format(reg_val))
path = os.path.join(reg_val, 'TrackIR5.exe')
if not os.path.isfile(path):
Logger.info(
'TrackIR: Found install location but no expected exe file found: {}'
.format(path))
raise TrackIRNotInstalled()
return path
except Registry.Error:
raise TrackIRNotInstalled()
# coding:utf-8 # @Time : 2019/10/27 # @Author : xuyouze # @File Name : registry.py from utils.registry import Registry Model = Registry()
import copy
from utils.registry import Registry
BACKBONE_REGISTRY = Registry("backbone")
LOSS_REGISTRY = Registry("loss")
META_ARCH_REGISTRY = Registry("meta_arch")
def build(cfg, registry, args=None):
"""
Build the module with cfg.
Args:
cfg (dict): the config of the modules
registry(Registry): A registry the module belongs to.
Returns:
The built module.
"""
args = copy.deepcopy(cfg)
name = args.pop("name")
ret = registry.get(name)(**args)
return ret
def build_backbone(cfg):
return build(cfg, BACKBONE_REGISTRY)
def build_loss(cfg):
return build(cfg, LOSS_REGISTRY)
else:
data = pickle.load(f)
if "blobs" in data:
weights = data["blobs"]
else:
weights = data
return weights
_C2_STAGE_NAMES = {
"R-50": ["1.2", "2.3", "3.5", "4.2"],
"R-101": ["1.2", "2.3", "3.22", "4.2"],
"R-152": ["1.2", "2.7", "3.35", "4.2"],
}
C2_FORMAT_LOADER = Registry()
@C2_FORMAT_LOADER.register("R-50-C4")
@C2_FORMAT_LOADER.register("R-50-C5")
@C2_FORMAT_LOADER.register("R-101-C4")
@C2_FORMAT_LOADER.register("R-101-C5")
@C2_FORMAT_LOADER.register("R-50-FPN")
@C2_FORMAT_LOADER.register("R-50-FPN-RETINANET")
@C2_FORMAT_LOADER.register("R-101-FPN")
@C2_FORMAT_LOADER.register("R-101-FPN-RETINANET")
@C2_FORMAT_LOADER.register("R-152-FPN")
def load_resnet_c2_format(cfg, f):
state_dict = _load_c2_pickled_weights(f)
conv_body = cfg.MODEL.BACKBONE.CONV_BODY
arch = conv_body.replace("-C4", "").replace("-C5", "").replace("-FPN", "")
out_channels,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = FrozenBatchNorm2d(out_channels)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu_(x)
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
return x
_TRANSFORMATION_MODULES = Registry({
"BottleneckWithFixedBatchNorm":
BottleneckWithFixedBatchNorm,
"DeformConvBottleneckWithGroupNorm":
DeformConvBottleneckWithGroupNorm
})
_STEM_MODULES = Registry({"StemWithFixedBatchNorm": StemWithFixedBatchNorm})
_STAGE_SPECS = Registry({
"Deform-R-50-C5": DeformResNet50StagesTo5,
"R-50-C4": ResNet50StagesTo4,
"R-50-C5": ResNet50StagesTo5,
"R-50-FPN": ResNet50FPNStagesTo5,
"R-101-FPN": ResNet101FPNStagesTo5
})
"""Define the registry for the net models.""" from utils.registry import Registry MODEL_EXAMPLE = Registry()
from utils.registry import Registry
HEAD = Registry('head')
from utils.registry import Registry """ Feature Extractor. """ # Backbone BACKBONES = Registry() # FPN FPN_BODY = Registry() """ ROI Head. """ # Box Head ROI_CLS_HEADS = Registry() ROI_CLS_OUTPUTS = Registry() ROI_BOX_HEADS = Registry() ROI_BOX_OUTPUTS = Registry() # OPLD Head ROI_OPLD_HEADS = Registry() ROI_OPLD_OUTPUTS = Registry()
from utils.registry import Registry LOSS = Registry()
"""
Copyright 2020 Kartik Sharma
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from utils.registry import Registry
FUNNEL = Registry("Funnels")
from utils.registry import Registry BACKBONES = Registry() ROI_ACTION_FEATURE_EXTRACTORS = Registry() ROI_ACTION_PREDICTORS = Registry() INTERACTION_AGGREGATION_STRUCTURES = Registry()
from utils.registry import Registry
DETECTOR = Registry('detector')
from utils.registry import Registry
MODELS_REGISTRY = Registry("MODELS")
# coding:utf-8 # @Time : 2019/10/27 # @Author : xuyouze # @File Name : registry.py from utils.registry import Registry Network = Registry()
w = d[i]
if len(w.shape) == 4:
#HWCN -> NCHW
w = np.transpose(w, [3, 2, 0, 1])
elif len(w.shape) == 2:
w = w.T
d[i] = torch.from_numpy(w)
def _load_tf_pickled_weights(file_path):
with open(file_path, "rb") as f:
data = pickle.load(f)
return data
EXTERNAL_LOADER = Registry()
@EXTERNAL_LOADER.register("lifting_rot")
def load_lifting_net(path='/home/mscv/hand3d/weights/lifting-proposed.pickle'):
path = cfg.WEIGHTS
weights = _load_tf_pickled_weights(path)
keys = _rename_weights(weights.keys(), basic_pairs + lifting_pairs)
key_map = {k: v for k, v in zip(weights.keys(), keys)}
for i, j in zip(weights, keys):
print(i, j)
print(len(set(keys)))
new_weights = OrderedDict()
for k, w in weights.items():
# coding:utf-8 # @Time : 2019/10/27 # @Author : xuyouze # @File Name : registry.py from utils.registry import Registry Dataset = Registry()
from utils.registry import Registry
import torchvision.transforms as transforms
TRANSFORMS = Registry()
@TRANSFORMS.register("random_resized_crop")
def random_resized_crop(cfg, **kwargs):
size = kwargs[
"input_size"] if kwargs["input_size"] != None else cfg.INPUT_SIZE
return transforms.RandomResizedCrop(
size=size,
scale=cfg.TRANSFORMS.PROCESS_DETAIL.RANDOM_RESIZED_CROP.SCALE,
ratio=cfg.TRANSFORMS.PROCESS_DETAIL.RANDOM_RESIZED_CROP.RATIO,
)
@TRANSFORMS.register("random_crop")
def random_crop(cfg, **kwargs):
size = kwargs[
"input_size"] if kwargs["input_size"] != None else cfg.INPUT_SIZE
return transforms.RandomCrop(
size, padding=cfg.TRANSFORMS.PROCESS_DETAIL.RANDOM_CROP.PADDING)
@TRANSFORMS.register("random_horizontal_flip")
def random_horizontal_flip(cfg, **kwargs):
return transforms.RandomHorizontalFlip(p=0.5)
@TRANSFORMS.register("shorter_resize_for_crop")
from utils.registry import Registry
DATASET_REGISTRY = Registry("dataset")
def build_dataset(cfg, args=None):
"""
Build the module with cfg.
Args:
cfg (dict): the config of the modules
registry(Registry): A registry the module belongs to.
Returns:
The built module.
"""
args = cfg
name = args.pop("name")
dataset = DATASET_REGISTRY.get(name)(args)
return dataset
from utils.registry import Registry BACKBONES = Registry() BOX_HEADS = Registry() BOX_PREDICTORS = Registry()
def test_registry():
CATS = Registry("cat")
assert CATS.name == "cat"
assert CATS.module_dict == {}
assert len(CATS) == 0
@CATS.register_module()
class BritishShorthair:
pass
assert len(CATS) == 1
assert CATS.get("BritishShorthair") is BritishShorthair
class Munchkin:
pass
CATS.register_module(module=Munchkin)
assert len(CATS) == 2
assert CATS.get("Munchkin") is Munchkin
assert "Munchkin" in CATS
with pytest.raises(KeyError):
CATS.register_module(module=Munchkin)
CATS.register_module(module=Munchkin, force=True)
assert len(CATS) == 2
# force=False
with pytest.raises(KeyError):
@CATS.register_module()
class BritishShorthair:
pass
@CATS.register_module(force=True)
class BritishShorthair:
pass
assert len(CATS) == 2
assert CATS.get("PersianCat") is None
assert "PersianCat" not in CATS
@CATS.register_module(name="Siamese")
class SiameseCat:
pass
assert CATS.get("Siamese").__name__ == "SiameseCat"
class SphynxCat:
pass
CATS.register_module(name="Sphynx", module=SphynxCat)
assert CATS.get("Sphynx") is SphynxCat
# name type
with pytest.raises(TypeError):
CATS.register_module(name=7474741)
# the registered module should be a class
with pytest.raises(TypeError):
CATS.register_module(0)
# can only decorate a class
with pytest.raises(TypeError):
@CATS.register_module()
def some_method():
pass
from utils.registry import Registry """ Feature Extractor. """ # Backbone BACKBONES = Registry() # FPN FPN_BODY = Registry() # SEMSEG ROI_SEMSEG_HEADS = Registry() ROI_SEMSEG_OUTPUTS = Registry() """ ROI Head. """ # Box Head ROI_CLS_HEADS = Registry() ROI_CLS_OUTPUTS = Registry() ROI_BOX_HEADS = Registry() ROI_BOX_OUTPUTS = Registry() # Cascade Head ROI_CASCADE_HEADS = Registry() ROI_CASCADE_OUTPUTS = Registry() # Mask Head ROI_MASK_HEADS = Registry() ROI_MASK_OUTPUTS = Registry() MASKIOU_HEADS = Registry() MASKIOU_OUTPUTS = Registry()
def test_build_from_cfg():
BACKBONES = Registry("backbone")
@BACKBONES.register_module()
class ResNet:
def __init__(self, depth, stages=4):
self.depth = depth
self.stages = stages
@BACKBONES.register_module()
class ResNeXt:
def __init__(self, depth, stages=4):
self.depth = depth
self.stages = stages
cfg = dict(type="ResNet", depth=50)
model = build_from_cfg(cfg, BACKBONES)
assert isinstance(model, ResNet)
assert model.depth == 50 and model.stages == 4
cfg = dict(type="ResNet", depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args={"stages": 3})
assert isinstance(model, ResNet)
assert model.depth == 50 and model.stages == 3
cfg = dict(type="ResNeXt", depth=50, stages=3)
model = build_from_cfg(cfg, BACKBONES)
assert isinstance(model, ResNeXt)
assert model.depth == 50 and model.stages == 3
cfg = dict(type=ResNet, depth=50)
model = build_from_cfg(cfg, BACKBONES)
assert isinstance(model, ResNet)
assert model.depth == 50 and model.stages == 4
# type defined using default_args
cfg = dict(depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args=dict(type="ResNet"))
assert isinstance(model, ResNet)
assert model.depth == 50 and model.stages == 4
cfg = dict(depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args=dict(type=ResNet))
assert isinstance(model, ResNet)
assert model.depth == 50 and model.stages == 4
# not a registry
with pytest.raises(TypeError):
cfg = dict(type="VGG")
model = build_from_cfg(cfg, "BACKBONES")
# non-registered class
with pytest.raises(KeyError):
cfg = dict(type="VGG")
model = build_from_cfg(cfg, BACKBONES)
# default_args must be a dict or None
with pytest.raises(TypeError):
cfg = dict(type="ResNet", depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args=1)
# cfg['type'] should be a str or class
with pytest.raises(TypeError):
cfg = dict(type=1000)
model = build_from_cfg(cfg, BACKBONES)
# cfg should contain the key "type"
with pytest.raises(KeyError, match='must contain the key "type"'):
cfg = dict(depth=50, stages=4)
model = build_from_cfg(cfg, BACKBONES)
# cfg or default_args should contain the key "type"
with pytest.raises(KeyError, match='must contain the key "type"'):
cfg = dict(depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args=dict(stages=4))
# incorrect registry type
with pytest.raises(TypeError):
cfg = dict(type="ResNet", depth=50)
model = build_from_cfg(cfg, "BACKBONES")
# incorrect default_args type
with pytest.raises(TypeError):
cfg = dict(type="ResNet", depth=50)
model = build_from_cfg(cfg, BACKBONES, default_args=0)
# incorrect arguments
with pytest.raises(TypeError):
cfg = dict(type="ResNet", non_existing_arg=50)
model = build_from_cfg(cfg, BACKBONES)