def test_get_package_version_comparison():
package_version = get_package_version('pytest')
current_version = parse_version('4.3.0')
old_version = parse_version('4.2.1')
new_version = parse_version('4.4.1')
assert package_version == current_version
assert not package_version < current_version
assert not package_version > current_version
assert package_version <= new_version
assert package_version >= old_version
def test_get_package_version_comparison():
package_version = get_package_version("pytest")
current_version = parse_version("6.2.3")
old_version = parse_version("6.2.0")
new_version = parse_version("6.2.4")
assert package_version == current_version
assert not package_version < current_version
assert not package_version > current_version
assert package_version <= new_version
assert package_version >= old_version
def test_get_package_version_comparison():
package_version = get_package_version('pytest')
current_version = parse_version('4.3.0')
old_version = parse_version('4.2.1')
new_version = parse_version('4.4.1')
assert package_version == current_version
assert not package_version < current_version
assert not package_version > current_version
assert package_version <= new_version
assert package_version >= old_version
def get_tensorflow():
# Ensures backward and forward compatibility with TensorFlow 1 and 2.
if get_package_version("tensorflow") < parse_version("1.13.1"):
import warnings
warnings.warn(
"Use of TensorFlow 1.12 and older is deprecated. "
"Use Tensorflow 1.13 or newer instead.",
DeprecationWarning,
)
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
return tf
def set_global_seed(seed):
random.seed(seed)
if opt.has_numpy:
opt.np.random.seed(seed)
if module_is_in_cache('tensorflow'):
# Ensures backward and forward compatibility with TensorFlow 1 and 2.
if get_package_version('tensorflow') < parse_version('1.13.1'):
import warnings
warnings.warn("Use of TensorFlow 1.12 and older is deprecated. "
"Use Tensorflow 1.13 or newer instead.",
DeprecationWarning)
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
tf.set_random_seed(seed)
if module_is_in_cache('torch'):
import torch
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def set_global_seed(seed):
random.seed(seed)
if opt.has_numpy:
opt.np.random.seed(seed)
if module_is_in_cache('tensorflow'):
# Ensures backward and forward compatibility with TensorFlow 1 and 2.
if get_package_version('tensorflow') < parse_version('1.13.1'):
import warnings
warnings.warn(
"Use of TensorFlow 1.12 and older is deprecated. "
"Use Tensorflow 1.13 or newer instead.", DeprecationWarning)
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
tf.set_random_seed(seed)
if module_is_in_cache('torch'):
import torch
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def tf():
"""
Creates a simplified tensorflow interface if necessary,
so `tensorflow` is not required during the tests.
"""
from sacred.optional import has_tensorflow
if has_tensorflow:
# Ensures backward and forward compatibility with TensorFlow 1 and 2.
if get_package_version('tensorflow') < parse_version('1.13.1'):
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
return tf
else:
# Let's define a mocked tensorflow
class tensorflow:
class summary:
class FileWriter:
def __init__(self, logdir, graph):
self.logdir = logdir
self.graph = graph
print("Mocked FileWriter got logdir=%s, graph=%s" % (logdir, graph))
class Session:
def __init__(self):
self.graph = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
pass
# Set stflow to use the mock as the test
import sacred.stflow.method_interception
sacred.stflow.method_interception.tf = tensorflow
return tensorflow
def test_get_package_version():
package_version = get_package_version("pytest")
assert str(package_version) == "6.2.3"
def test_get_package_version():
package_version = get_package_version('pytest')
assert str(package_version) == '4.3.0'
from .contextlibbackport import ContextDecorator
from .internal import ContextMethodDecorator
import sacred.optional as opt
from sacred.utils import get_package_version, parse_version
if opt.has_tensorflow:
# Ensures backward and forward compatibility with TensorFlow 1 and 2.
if get_package_version('tensorflow') < parse_version('1.13.1'):
import warnings
warnings.warn("Use of TensorFlow 1.12 and older is deprecated. "
"Use Tensorflow 1.13 or newer instead.",
DeprecationWarning)
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
else:
tf = None
class LogFileWriter(ContextDecorator, ContextMethodDecorator):
"""
Intercept ``logdir`` each time a new ``FileWriter`` instance is created.
:param experiment: Tensorflow experiment.
The state of the experiment must be running when entering the annotated
function / the context manager.
When creating ``FileWriters`` in Tensorflow, you might want to
store the path to the produced log files in the sacred database.
In the scope of ``LogFileWriter``, the corresponding log directory path
def test_get_package_version():
package_version = get_package_version('pytest')
assert str(package_version) == '4.3.0'
