def make_node(self, x, scale, bias, epsilon=1e-4,
running_average_factor=0.1,
running_mean=None, running_var=None):
x = as_tensor_variable(x)
scale = as_tensor_variable(scale)
bias = as_tensor_variable(bias)
epsilon = as_tensor_variable(epsilon)
running_average_factor = as_tensor_variable(running_average_factor)
if running_mean is not None:
running_mean = as_tensor_variable(running_mean)
if running_var is not None:
running_var = as_tensor_variable(running_var)
assert x.ndim == scale.ndim == bias.ndim
assert ((running_mean is None and running_var is None) or
(running_mean is not None and running_var is not None))
assert (running_mean is None or running_mean.ndim == x.ndim)
assert (running_var is None or running_var.ndim == x.ndim)
# Upcast to common dtype on the non-scalar
# Keep as is dtype of scalar (epsilon and running_average_factor)
if running_mean:
x, scale, bias, running_mean, running_var = as_common_dtype(
x, scale, bias, running_mean, running_var)
else:
x, scale, bias = as_common_dtype(x, scale, bias)
inputs = [x, scale, bias, epsilon, running_average_factor]
output_types = [x.type(), scale.type(), scale.type()]
if running_mean is not None and running_var is not None:
inputs.append(running_mean)
inputs.append(running_var)
output_types.append(scale.type())
output_types.append(scale.type())
return Apply(self, inputs, output_types)
def make_node(
self, x, scale, bias, estimated_mean, estimated_variance, epsilon=1e-4
):
x = as_tensor_variable(x)
scale = as_tensor_variable(scale)
bias = as_tensor_variable(bias)
estimated_mean = as_tensor_variable(estimated_mean)
estimated_variance = as_tensor_variable(estimated_variance)
epsilon = as_tensor_variable(epsilon)
# Upcast to common dtype on the non-scalar
# Keep as is dtype of scalar (epsilon)
x, scale, bias, estimated_mean, estimated_variance = as_common_dtype(
x, scale, bias, estimated_mean, estimated_variance
)
assert (
x.ndim
== scale.ndim
== bias.ndim
== estimated_mean.ndim
== estimated_variance.ndim
)
return Apply(
self,
[x, scale, bias, estimated_mean, estimated_variance, epsilon],
[x.type()],
)
def make_node(self, x, scale, bias, estimated_mean, estimated_variance, epsilon=1e-4):
x = as_tensor_variable(x)
scale = as_tensor_variable(scale)
bias = as_tensor_variable(bias)
estimated_mean = as_tensor_variable(estimated_mean)
estimated_variance = as_tensor_variable(estimated_variance)
epsilon = as_tensor_variable(epsilon)
# Upcast to common dtype on the non-scalar
# Keep as is dtype of scalar (epsilon)
x, scale, bias, estimated_mean, estimated_variance = as_common_dtype(
x, scale, bias, estimated_mean, estimated_variance)
assert x.ndim == scale.ndim == bias.ndim == estimated_mean.ndim == estimated_variance.ndim
return Apply(self, [x, scale, bias, estimated_mean, estimated_variance, epsilon], [x.type()])
def make_node(self, x, dy, scale, x_mean, x_invstd, epsilon=1e-4):
x = as_tensor_variable(x)
dy = as_tensor_variable(dy)
scale = as_tensor_variable(scale)
x_mean = as_tensor_variable(x_mean)
x_invstd = as_tensor_variable(x_invstd)
epsilon = as_tensor_variable(epsilon)
# Upcast to common dtype on the non-scalar
# Keep as is dtype of scalar (epsilon)
x, dy, scale, x_mean, x_invstd = as_common_dtype(
x, dy, scale, x_mean, x_invstd)
assert x.ndim == dy.ndim == scale.ndim == x_mean.ndim == x_invstd.ndim
return Apply(self, [x, dy, scale, x_mean, x_invstd, epsilon],
[x.type(), scale.type(), scale.type()])
