本文整理汇总了Python中yaml.load方法的典型用法代码示例。如果您正苦于以下问题:Python yaml.load方法的具体用法?Python yaml.load怎么用?Python yaml.load使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类yaml
的用法示例。
在下文中一共展示了yaml.load方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __read_settings
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def __read_settings(self, yaml_file):
if yaml_file is None:
yaml_file = os.path.join(ciftify.config.find_ciftify_global(),
'ciftify_workflow_settings.yaml')
if not os.path.exists(yaml_file):
logger.critical("Settings yaml file {} does not exist"
"".format(yaml_file))
sys.exit(1)
try:
with open(yaml_file, 'r') as yaml_stream:
config = yaml.load(yaml_stream, Loader=yaml.SafeLoader)
except:
logger.critical("Cannot read yaml config file {}, check formatting."
"".format(yaml_file))
sys.exit(1)
return config
示例2: setup
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
self._layer_params = layer_params
# default batch_size = 256
self._batch_size = int(layer_params.get('batch_size', 256))
self._resize = layer_params.get('resize', -1)
self._mean_file = layer_params.get('mean_file', None)
self._source_type = layer_params.get('source_type', 'CSV')
self._shuffle = layer_params.get('shuffle', False)
# read image_mean from file and preload all data into memory
# will read either file or array into self._mean
self.set_mean()
self.preload_db()
self._compressed = self._layer_params.get('compressed', True)
if not self._compressed:
self.decompress_data()
示例3: set_mean
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def set_mean(self):
if self._mean_file:
if type(self._mean_file) is str:
# read image mean from file
try:
# if it is a pickle file
self._mean = np.load(self._mean_file)
except (IOError):
blob = caffe_pb2.BlobProto()
blob_str = open(self._mean_file, 'rb').read()
blob.ParseFromString(blob_str)
self._mean = np.array(caffe.io.blobproto_to_array(blob))[0]
else:
self._mean = self._mean_file
self._mean = np.array(self._mean)
else:
self._mean = None
示例4: load_yaml_file
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def load_yaml_file(filename, label='config file', loader=yaml.Loader):
"""Load a yaml config file.
"""
try:
with open(filename, "r") as file:
res = yaml.load(file.read(), Loader=loader) or {}
if not isinstance(res, dict):
msg = f"Please ensure that {label} consists of key value pairs."
raise VergeMLError(f"Invalid {label}: {filename}", msg)
return res
except yaml.YAMLError as err:
if hasattr(err, 'problem_mark'):
mark = getattr(err, 'problem_mark')
problem = getattr(err, 'problem')
message = f"Could not read {label} {filename}:"
message += "\n" + display_err_in_file(filename, mark.line, mark.column, problem)
elif hasattr(err, 'problem'):
problem = getattr(err, 'problem')
message = f"Could not read {label} {filename}: {problem}"
else:
message = f"Could not read {label} {filename}: YAML Error"
suggestion = f"There is a syntax error in your {label} - please fix it and try again."
raise VergeMLError(message, suggestion)
except OSError as err:
msg = "Please ensure the file exists and you have the required access privileges."
raise VergeMLError(f"Could not open {label} {filename}: {err.strerror}", msg)
示例5: __init__
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def __init__(self, spec_url):
"""Create a new URLLoader.
Keyword arguments:
spec_url -- URL where the specification YAML file is located."""
headers = {'Accept' : 'text/yaml'}
resp = requests.get(spec_url, headers=headers)
if resp.status_code == 200:
self.spec = yaml.load(resp.text)
self.spec['url'] = spec_url
self.spec['files'] = {}
for queryUrl in self.spec['queries']:
queryNameExt = path.basename(queryUrl)
queryName = path.splitext(queryNameExt)[0] # Remove extention
item = {
'name': queryName,
'download_url': queryUrl
}
self.spec['files'][queryNameExt] = item
del self.spec['queries']
else:
raise Exception(resp.text)
示例6: make_model_yaml
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def make_model_yaml(template_yaml, model_json, output_yaml_path):
#
with open(template_yaml, 'r') as f:
model_yaml = yaml.load(f)
#
# get the model config:
json_file = open(model_json, 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = keras.models.model_from_json(loaded_model_json)
#
model_yaml["schema"]["targets"] = []
for oname, oshape in zip(loaded_model.output_names, loaded_model.output_shape):
append_el ={"name":oname , "shape":str(oshape)#replace("None,", "")
, "doc":"Methylation probability for %s"%oname}
model_yaml["schema"]["targets"].append(append_el)
#
with open(output_yaml_path, 'w') as f:
yaml.dump(model_yaml, f, default_flow_style=False)
示例7: make_secondary_dl_yaml
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def make_secondary_dl_yaml(template_yaml, model_json, output_yaml_path):
with open(template_yaml, 'r') as f:
model_yaml = yaml.load(f)
#
# get the model config:
json_file = open(model_json, 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = keras.models.model_from_json(loaded_model_json)
#
model_yaml["output_schema"]["targets"] = []
for oname, oshape in zip(loaded_model.output_names, loaded_model.output_shape):
append_el ={"name":oname , "shape":str(oshape)#replace("None,", "")
, "doc":"Methylation probability for %s"%oname}
model_yaml["output_schema"]["targets"].append(append_el)
#
with open(output_yaml_path, 'w') as f:
yaml.dump(model_yaml, f, default_flow_style=False)
示例8: import_files
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def import_files(self):
print("self.manifest_path", self.manifest_path)
self.manifest = yaml.load(open("{}/manifest.yml".format(self.manifest_path)), Loader=yaml.FullLoader)
sys.path.append(self.manifest_path)
"""
don't remove the import below, this will be the cti_transformations.py,
which is one of the required file to run the job. This file will be provided by the
user during the run.
"""
try:
import ib_functions
except Exception as e:
ib_functions = None
self.ib_functions = ib_functions
print ("self.ib_functions is {}".format(ib_functions))
# print("manifest is {}".format(self.manifest))
# print("ib_functions is {}".format(self.ib_functions))
示例9: get_remote_symptom_codes
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def get_remote_symptom_codes(group):
"""
Remote lookup for symptom codes
"""
symptoms = {}
cache = caches['comptia']
# First, try to load from global cache (updated every 24h)
data = cache.get('codes') or {}
if not data:
# ... then try to fetch from GSX
GsxAccount.fallback()
data = gsxws.comptia.fetch()
cache.set('codes', data)
for k, v in data.get(group):
symptoms[k] = v
return symptoms
示例10: symptom_codes
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def symptom_codes(group):
"""
Returns CompTIA symptom codes for component group
"""
if group == '':
return
try:
symptoms = get_remote_symptom_codes(group)
except Exception as e:
# ... finally fall back to local static data
# @FIXME: How do we keep this up to date?
data = yaml.load(open("servo/fixtures/comptia.yaml", "r"))
symptoms = data[group]['symptoms']
codes = [(k, "%s - %s " % (k, symptoms[k])) for k in sorted(symptoms)]
return codes
示例11: readYaml
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def readYaml(env=None):
"""Load the skelebot.yaml, with environment overrride if present, into the Config object"""
yamlData = None
cwd = os.getcwd()
cfgFile = FILE_PATH.format(path=cwd)
if os.path.isfile(cfgFile):
with open(cfgFile, 'r') as stream:
yamlData = yaml.load(stream, Loader=yaml.FullLoader)
if (env is not None):
envFile = ENV_FILE_PATH.format(path=cwd, env=env)
if os.path.isfile(envFile):
with open(envFile, 'r') as stream:
overrideYaml = yaml.load(stream, Loader=yaml.FullLoader)
yamlData = override(yamlData, overrideYaml)
else:
raise RuntimeError("Environment Not Found")
return yamlData
示例12: load_test_checkpoints
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def load_test_checkpoints(model, save_path, logger, use_best=False):
#try:
if use_best:
print(save_path.EXPS+save_path.NAME+save_path.BESTMODEL)
states= torch.load(save_path.EXPS+save_path.NAME+save_path.BESTMODEL) if torch.cuda.is_available() \
else torch.load(save_path.EXPS+save_path.NAME+save_path.BESTMODEL, map_location=torch.device('cpu'))
else:
states= torch.load(save_path.EXPS+save_path.NAME+save_path.MODEL) if torch.cuda.is_available() \
else torch.load(save_path.EXPS+save_path.NAME+save_path.MODEL, map_location=torch.device('cpu'))
#logger.debug("success")
#try:
model.load_state_dict(states['model_state'])
# except:
# states_no_module = OrderedDict()
# for k, v in states['model_state'].items():
# name_no_module = k[7:]
# states_no_module[name_no_module] = v
# model.load_state_dict(states_no_module)
logger.info('loading checkpoints success')
# except:
# logger.error("no checkpoints")
示例13: get_config
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def get_config(config_file, exp_dir=None):
""" Construct and snapshot hyper parameters """
config = edict(yaml.load(open(config_file, 'r')))
# create hyper parameters
config.run_id = str(os.getpid())
config.exp_name = '_'.join([
config.model.name, config.dataset.name,
time.strftime('%Y-%b-%d-%H-%M-%S'), config.run_id
])
if exp_dir is not None:
config.exp_dir = exp_dir
config.save_dir = os.path.join(config.exp_dir, config.exp_name)
# snapshot hyperparameters
mkdir(config.exp_dir)
mkdir(config.save_dir)
save_name = os.path.join(config.save_dir, 'config.yaml')
yaml.dump(edict2dict(config), open(save_name, 'w'), default_flow_style=False)
return config
示例14: __read_mode
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def __read_mode(self, mode):
logger = logging.getLogger(__name__)
ciftify_data = config.find_ciftify_global()
qc_settings = os.path.join(ciftify_data, 'qc_modes.yaml')
try:
with open(qc_settings, 'r') as qc_stream:
qc_modes = yaml.load(qc_stream, Loader=yaml.SafeLoader)
except:
logger.error("Cannot read qc_modes file: {}".format(qc_settings))
sys.exit(1)
try:
settings = qc_modes[mode]
except KeyError:
logger.error("qc_modes file {} does not define mode {}"
"".format(qc_settings, mode))
sys.exit(1)
return settings
示例15: config
# 需要导入模块: import yaml [as 别名]
# 或者: from yaml import load [as 别名]
def config(monkeypatch):
yaml_string = """
wasp:
setting1: 1 # int
foo: bar # string
database:
username: normal_user
migration:
username: migration_user
flat: true # boolean
flat_with_underscores: hello
"""
def my_load_config(self):
self.default_options = yaml.load(io.StringIO(yaml_string))
monkeypatch.setattr(Config, '_load_config', my_load_config)
config_ = Config()
config_.load()
return config_