def interceptor(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
_value = ed.interceptable(f)(*args, **kwargs).value
conditional_value = tf.cond(var_condition, lambda: var_value,
lambda: _value)
return ed.interceptable(f)(*args,
value=tf.broadcast_to(
conditional_value, _value.shape),
**kwargs)
def interceptor(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
# need to create the variable to obtain its value, and use it in the fn_false condition
_value = ed.interceptable(f)(*args, **kwargs).value
conditional_value = tf.cond(var_condition, lambda: var_value,
lambda: _value)
# need to broadcast to fix the shape of the tensor (which always be _value.shape)
return ed.interceptable(f)(*args,
value=tf.broadcast_to(
conditional_value, _value.shape),
**kwargs)
def interceptor(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
name = kwargs.get("name")
if name in model_kwargs:
kwargs["value"] = model_kwargs[name]
return ed.interceptable(f)(*args, **kwargs)
def interceptor(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
name = kwargs.get("name")
if name in model_kwargs:
kwargs["value"] = model_kwargs[name]
else:
print(f"set_values not interested in {name}.")
return ed.interceptable(f)(*args, **kwargs)
def interceptor(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
name = kwargs.get("name")
# if name in model_kwargs, include the value as a new argument using model_kwargs[name]
if name in model_kwargs:
interception_value = model_kwargs[name]
if ALLOW_CONDITIONS and CURRENT_ENABLE_INTERCEPTOR is not None:
# local variable points to the real condition variable (created in the inference method object)
# this way, even if CURRENT_ENABLE_INTERCEPTOR is set to None, this local variable points to the real one
enable_globals, enable_locals = CURRENT_ENABLE_INTERCEPTOR
# if any of them are None, set to constant False to work with the following tf.logical_and's
if enable_globals is None:
enable_globals = tf.constant(False)
if enable_locals is None:
enable_locals = tf.constant(False)
# need to create the variable to obtain its value, and use it in the fn_false condition
_value = ed.interceptable(f)(*args, **kwargs).value
# Need to know if this variable is global hidden or local hidden. Can do it using the contextmanager
is_local_hidden = data_model.is_active()
conditional_value = tf.cond(
tf.logical_or(
tf.logical_and(enable_globals, tf.constant(not is_local_hidden)),
tf.logical_and(enable_locals, tf.constant(is_local_hidden))
),
lambda: interception_value,
lambda: _value
)
# need to broadcast to fix the shape of the tensor (which always be _value.shape)
kwargs['value'] = tf.broadcast_to(conditional_value, _value.shape)
else:
kwargs['value'] = interception_value
return ed.interceptable(f)(*args, **kwargs)
def set_values(f, *args, **kwargs):
name = kwargs.get("name")
if name in model_kwargs:
kwargs["value"] = model_kwargs[name]
return ed.interceptable(f)(*args, **kwargs)
def set_xy(f, *args, **kwargs):
if kwargs.get("name") == "x":
kwargs["value"] = 1.
if kwargs.get("name") == "y":
kwargs["value"] = 0.42
return ed.interceptable(f)(*args, **kwargs)
def double(f, *args, **kwargs): return 2. * ed.interceptable(f)(*args, **kwargs)
def trivial_interceptor(fn, *args, **kwargs): # An interceptor that does nothing. return ed.interceptable(fn)(*args, **kwargs)
def set_values(f, *args, **kwargs):
"""Sets random variable values to its aligned value."""
name = kwargs.get("name")
if name in model_kwargs:
kwargs["value"] = model_kwargs[name]
return ed.interceptable(f)(*args, **kwargs)
def set_xy(f, *args, **kwargs):
if kwargs.get("name") == "x":
kwargs["value"] = 1.
if kwargs.get("name") == "y":
kwargs["value"] = 0.42
return ed.interceptable(f)(*args, **kwargs)
def double(f, *args, **kwargs): return 2. * ed.interceptable(f)(*args, **kwargs)
def trivial_interceptor(fn, *args, **kwargs): # An interceptor that does nothing. return ed.interceptable(fn)(*args, **kwargs)
