def test_Ceil(tmpdir):
data = np.asarray([0.2, 1.3, 4., 5.5, 0.0], np.float32)
model = C.ceil(data)
verify_no_input(model, tmpdir, 'ceil_0')
x = C.input_variable(data.shape)
model = C.ceil(x)
verify_one_input(model, data, tmpdir, 'ceil_1')
def test_Ceil(tmpdir):
data = np.asarray([0.2, 1.3, 4., 5.5, 0.0], np.float32)
model = C.ceil(data)
verify_no_input(model, tmpdir, 'ceil_0')
x = C.input_variable(data.shape)
model = C.ceil(x)
verify_one_input(model, data, tmpdir, 'ceil_1')
def test_Ceil(tmpdir, dtype):
with C.default_options(dtype=dtype):
data = np.asarray([0.2, 1.3, 4., 5.5, 0.0], dtype)
model = C.ceil(data)
verify_no_input(model, tmpdir, 'ceil_0')
x = C.input_variable(data.shape)
model = C.ceil(x)
verify_one_input(model, data, tmpdir, 'ceil_1')
def test_Ceil(tmpdir, dtype):
with C.default_options(dtype = dtype):
data = np.asarray([0.2, 1.3, 4., 5.5, 0.0], dtype)
model = C.ceil(data)
verify_no_input(model, tmpdir, 'ceil_0')
x = C.input_variable(data.shape)
model = C.ceil(x)
verify_one_input(model, data, tmpdir, 'ceil_1')
def test_save_no_junk(tmpdir):
""" Test for an issue with save() not having O_TRUNC mode
resulting in possible junk at the end of file if it already exists
"""
try:
import onnx
except ImportError:
pytest.skip('ONNX is not installed.')
filename = os.path.join(str(tmpdir), R'no_junk.onnx')
filename_new = os.path.join(str(tmpdir), R'no_junk_new.onnx')
# Save a large model.
g = C.ceil([1, 2, 3, 4, 5, 6, 7, 8] * 100)
g.save(filename, format=C.ModelFormat.ONNX)
# Save a smaller model using the same file name and a new file name.
g = C.ceil([1, 2, 3, 4, 5, 6, 7] * 100)
g.save(filename, format=C.ModelFormat.ONNX)
g.save(filename_new, format=C.ModelFormat.ONNX)
# Check the files can be loaded as standard ONNX files,
# and both result in the same model.
assert onnx.load(filename) == onnx.load(filename_new)
def test_save_no_junk(tmpdir):
""" Test for an issue with save() not having O_TRUNC mode
resulting in possible junk at the end of file if it already exists
"""
try:
import onnx
except ImportError:
pytest.skip('ONNX is not installed.')
filename = os.path.join(str(tmpdir), R'no_junk.onnx')
filename_new = os.path.join(str(tmpdir), R'no_junk_new.onnx')
# Save a large model.
g = C.ceil([1, 2, 3, 4, 5, 6, 7, 8] * 100)
g.save(filename, format=C.ModelFormat.ONNX)
# Save a smaller model using the same file name and a new file name.
g = C.ceil([1, 2, 3, 4, 5, 6, 7] * 100)
g.save(filename, format=C.ModelFormat.ONNX)
g.save(filename_new, format=C.ModelFormat.ONNX)
# Check the files can be loaded as standard ONNX files,
# and both result in the same model.
assert onnx.load(filename) == onnx.load(filename_new)
def ceil(arg, name=''):
'''
The output of this operation is the element wise value rounded to the smallest
integer greater than or equal to the input.
Example:
>>> C.eval(C.ceil([0.2, 1.3, 4., 5.5, 0.0]))
[array([[ 1., 2., 4., 6., 0.]])]
>>> C.eval(C.ceil([[0.6, 3.3], [1.9, 5.6]]))
[array([[[ 1., 4.],
[ 2., 6.]]])]
Args:
arg: input tensor
name (str): the name of the node in the network (optional)
Returns:
:class:`cntk.Function`
'''
from cntk import ceil
left = sanitize_input(left, get_data_type(right))
right = sanitize_input(right, get_data_type(left))
return ceil(arg, name).output()
def ceil(arg, name=''):
'''
The output of this operation is the element wise value rounded to the smallest
integer greater than or equal to the input.
Example:
>>> C.eval(C.ceil([0.2, 1.3, 4., 5.5, 0.0]))
[array([[ 1., 2., 4., 6., 0.]])]
>>> C.eval(C.ceil([[0.6, 3.3], [1.9, 5.6]]))
[array([[[ 1., 4.],
[ 2., 6.]]])]
Args:
arg: input tensor
name (str): the name of the node in the network (optional)
Returns:
:class:`cntk.Function`
'''
from cntk import ceil
left = sanitize_input(left, get_data_type(right))
right = sanitize_input(right, get_data_type(left))
return ceil(arg, name).output()
