def pseudo_call(self, pseudo_inputs, params, state):
"""Computes shapes and types this layer would produce for the given inputs.
Args:
pseudo_inputs: A ShapeType instance (input data minus the actual values)
or a tuple of ShapeType instances, following the same conventions as
Layer.call's input arg.
params: Parameters for this layer.
state: start state.
Returns:
A ShapeType instance representing the shape and type of the output (if
this layer has one output) or a tuple of ShapeType instances (if this
layer has more than one output).
"""
try:
# Beware: using an actual RNG (as opposed to this ShapeType stub) would
# cause a large number of dropout masks to be computed and permanently
# stored in global memory.
rng = ShapeType(shape=(2, ), dtype=onp.uint32)
def call_on_input(x, params, state, rng):
return self.call(x, params=params, state=state, rng=rng)
params_shapes = nested_map(
params, lambda x: ShapeType(shape=x.shape, dtype=x.dtype))
s = backend.eval_on_shapes(call_on_input)(pseudo_inputs,
params_shapes, state,
rng)
return s
except Exception:
name, trace = self.__class__.__name__, _short_traceback(skip=3)
raise LayerError(name, 'pseudo_call', self._caller, pseudo_inputs,
trace)
def check_shape_agreement(layer_fn, input_shapes, integer_inputs=False):
"""Checks if the layer's call output agrees its pseudo_call predictions.
This function helps test layer mechanics and inter-layer connections that
aren't dependent on specific data values.
Args:
layer_fn: A Layer instance, viewed as a function from input shapes to
output shapes.
input_shapes: A tuple representing a shape (if the layer takes one input)
or a tuple of shapes (if this layer takes more than one input).
For example: (210, 160, 3) or ((210, 160, 3), (105, 80, 3)).
integer_inputs: If True, use numpy int32 as the type for the pseudo-data,
else use float32.
Returns:
A tuple representing either a single shape (if the layer has one output) or
a tuple of shape tuples (if the layer has more than one output).
"""
rng1, rng2, rng3 = backend.random.split(backend.random.get_prng(0), 3)
input_dtype = onp.int32 if integer_inputs else onp.float32
if _is_tuple_of_shapes(input_shapes):
pseudo_data = tuple(ShapeType(x, input_dtype) for x in input_shapes)
input_dtype = tuple(input_dtype for _ in input_shapes)
else:
pseudo_data = ShapeType(input_shapes, input_dtype)
params, state = layer_fn.initialize(input_shapes, input_dtype, rng1)
pseudo_output, _ = layer_fn.pseudo_call(pseudo_data, params, state)
if isinstance(pseudo_output, tuple):
output_shape = tuple(x.shape for x in pseudo_output)
else:
output_shape = pseudo_output.shape
random_input = _random_values(input_shapes, rng2, integer_inputs)
real_output, _ = layer_fn(random_input, params, state=state, rng=rng3)
result_shape = shapes(real_output)
msg = 'output shape %s != real result shape %s' % (output_shape,
result_shape)
assert output_shape == result_shape, msg
# TODO(jonni): Remove this assert? It makes test logs harder to read.
return output_shape