runningnorm
CollectedStats (tuple)
¶
ObsNormLayer (Module)
¶
An observation normalizer which behaves as a PyTorch Module.
Source code in evotorch/neuroevolution/net/runningnorm.py
class ObsNormLayer(nn.Module):
"""
An observation normalizer which behaves as a PyTorch Module.
"""
def __init__(
self, mean: torch.Tensor, stdev: torch.Tensor, low: Optional[float] = None, high: Optional[float] = None
) -> None:
"""
`__init__(...)`: Initialize the ObsNormLayer.
Args:
mean: The mean according to which the observations are to be
normalized.
stdev: The standard deviation according to which the observations
are to be normalized.
low: Optionally a real number if the result of the normalization
is to be clipped. Represents the lower bound for the clipping
operation.
high: Optionally a real number if the result of the normalization
is to be clipped. Represents the upper bound for the clipping
operation.
"""
super().__init__()
self.register_buffer("_mean", mean)
self.register_buffer("_stdev", stdev)
self._lb = None if low is None else float(low)
self._ub = None if high is None else float(high)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Normalize an observation or a batch of observations.
Args:
x: The observation(s).
Returns:
The normalized counterpart of the observation(s).
"""
return _clamp((x - self._mean) / self._stdev, self._lb, self._ub)
__init__(self, mean, stdev, low=None, high=None)
special
¶
__init__(...): Initialize the ObsNormLayer.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
mean |
Tensor |
The mean according to which the observations are to be normalized. |
required |
stdev |
Tensor |
The standard deviation according to which the observations are to be normalized. |
required |
low |
Optional[float] |
Optionally a real number if the result of the normalization is to be clipped. Represents the lower bound for the clipping operation. |
None |
high |
Optional[float] |
Optionally a real number if the result of the normalization is to be clipped. Represents the upper bound for the clipping operation. |
None |
Source code in evotorch/neuroevolution/net/runningnorm.py
def __init__(
self, mean: torch.Tensor, stdev: torch.Tensor, low: Optional[float] = None, high: Optional[float] = None
) -> None:
"""
`__init__(...)`: Initialize the ObsNormLayer.
Args:
mean: The mean according to which the observations are to be
normalized.
stdev: The standard deviation according to which the observations
are to be normalized.
low: Optionally a real number if the result of the normalization
is to be clipped. Represents the lower bound for the clipping
operation.
high: Optionally a real number if the result of the normalization
is to be clipped. Represents the upper bound for the clipping
operation.
"""
super().__init__()
self.register_buffer("_mean", mean)
self.register_buffer("_stdev", stdev)
self._lb = None if low is None else float(low)
self._ub = None if high is None else float(high)
forward(self, x)
¶
Normalize an observation or a batch of observations.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Tensor |
The observation(s). |
required |
Returns:
| Type | Description |
|---|---|
Tensor |
The normalized counterpart of the observation(s). |
Source code in evotorch/neuroevolution/net/runningnorm.py
RunningNorm
¶
An online observation normalization tool
Source code in evotorch/neuroevolution/net/runningnorm.py
class RunningNorm:
"""
An online observation normalization tool
"""
def __init__(
self,
*,
shape: Union[tuple, int],
dtype: DType,
device: Optional[Device] = None,
min_variance: float = 1e-2,
clip: Optional[tuple] = None,
) -> None:
"""
`__init__(...)`: Initialize the RunningNorm
Args:
shape: Observation shape. Can be an integer or a tuple.
dtype: The dtype of the observations.
device: The device in which the observation stats are held.
If left as None, the device is assumed to be "cpu".
min_variance: A lower bound for the variance to be used in
the normalization computations.
In other words, if the computed variance according to the
collected observations ends up lower than `min_variance`,
this `min_variance` will be used instead (in an elementwise
manner) while computing the normalized observations.
As in Salimans et al. (2017), the default is 1e-2.
clip: Can be left as None (which is the default), or can be
given as a pair of real numbers.
This is used for clipping the observations after the
normalization operation.
In Salimans et al. (2017), (-5.0, +5.0) was used.
"""
# Make sure that the shape is stored as a torch.Size object.
if isinstance(shape, Iterable):
self._shape = torch.Size(shape)
else:
self._shape = torch.Size([int(shape)])
# Store the number of dimensions
self._ndim = len(self._shape)
# Store the dtype and the device
self._dtype = to_torch_dtype(dtype)
self._device = "cpu" if device is None else device
# Initialize the internally stored data as empty
self._sum: Optional[torch.Tensor] = None
self._sum_of_squares: Optional[torch.Tensor] = None
self._count: int = 0
# Store the minimum variance
self._min_variance = float(min_variance)
if clip is not None:
# If a clip tuple was provided, store the specified lower and upper bounds
lb, ub = clip
self._lb = float(lb)
self._ub = float(ub)
else:
# If a clip tuple was not provided the bounds are stored as None
self._lb = None
self._ub = None
def to(self, device: Device) -> "RunningNorm":
"""
If the target device is a different device, then make a copy of this
RunningNorm instance on the target device.
If the target device is the same with this RunningNorm's device, then
return this RunningNorm itself.
Args:
device: The target device.
Returns:
The RunningNorm on the target device. This can be a copy, or the
original RunningNorm instance itself.
"""
if torch.device(device) == torch.device(self.device):
return self
else:
new_running_norm = object.__new__(type(self))
already_handled = {"_sum", "_sum_of_squares", "_device"}
new_running_norm._sum = self._sum.to(device)
new_running_norm._sum_of_squares = self._sum_of_squares.to(device)
new_running_norm._device = device
for k, v in self.__dict__.items():
if k not in already_handled:
setattr(new_running_norm, k, deepcopy(v))
return new_running_norm
@property
def device(self) -> Device:
"""
The device in which the observation stats are held
"""
return self._device
@property
def dtype(self) -> DType:
"""
The dtype of the stored observation stats
"""
return self._dtype
@property
def shape(self) -> tuple:
"""
Observation shape
"""
return self._shape
@property
def min_variance(self) -> float:
"""
Minimum variance
"""
return self._min_variance
@property
def low(self) -> Optional[float]:
"""
The lower component of the bounds given in the `clip` tuple.
If `clip` was initialized as None, this is also None.
"""
return self._lb
@property
def high(self) -> Optional[float]:
"""
The higher (upper) component of the bounds given in the `clip` tuple.
If `clip` was initialized as None, this is also None.
"""
return self._ub
def _like_its_own(self, x: Iterable) -> torch.Tensor:
return torch.as_tensor(x, dtype=self._dtype, device=self._device)
def _verify(self, x: Iterable) -> torch.Tensor:
x = self._like_its_own(x)
if x.ndim == self._ndim:
if x.shape != self._shape:
raise ValueError(
f"This RunningNorm instance was initialized with shape: {self._shape}."
f" However, the provided tensor has an incompatible shape: {x._shape}."
)
elif x.ndim == (self._ndim + 1):
if x.shape[1:] != self._shape:
raise ValueError(
f"This RunningNorm instance was initialized with shape: {self._shape}."
f" The provided tensor is shaped {x.shape}."
f" Accepting the tensor's leftmost dimension as the batch size,"
f" the remaining shape is incompatible: {x.shape[1:]}"
)
else:
raise ValueError(
f"This RunningNorm instance was initialized with shape: {self._shape}."
f" The provided tensor is shaped {x.shape}."
f" The number of dimensions of the given tensor is incompatible."
)
return x
def _has_no_data(self) -> bool:
return (self._sum is None) and (self._sum_of_squares is None) and (self._count == 0)
def _has_data(self) -> bool:
return (self._sum is not None) and (self._sum_of_squares is not None) and (self._count > 0)
def reset(self):
"""
Remove all the collected observation data.
"""
self._sum = None
self._sum_of_squares = None
self._count = 0
@torch.no_grad()
def update(self, x: Union[Iterable, "RunningNorm"], mask: Optional[Iterable] = None, *, verify: bool = True):
"""
Update the stored stats with new observation data.
Args:
x: The new observation(s), as a PyTorch tensor, or any Iterable
that can be converted to a PyTorch tensor, or another
RunningNorm instance.
If given as a tensor or as an Iterable, the shape of `x` can
be the same with observation shape, or it can be augmented
with an extra leftmost dimension.
In the case of augmented dimension, `x` is interpreted not as
a single observation, but as a batch of observations.
If `x` is another RunningNorm instance, the stats stored by
this RunningNorm instance will be updated with all the data
stored by `x`.
mask: Can be given as a 1-dimensional Iterable of booleans ONLY
if `x` represents a batch of observations.
If a `mask` is provided, the i-th observation within the
observation batch `x` will be taken into account only if
the i-th item of the `mask` is True.
verify: Whether or not to verify the shape of the given Iterable
objects. The default is True.
"""
if isinstance(x, RunningNorm):
# If we are to update our stats according to another RunningNorm instance
if x._count > 0:
# We bother only if x is non-empty
if mask is not None:
# We were given another RunningNorm, not a batch of observations.
# So, we do not expect to receive a mask tensor.
# If a mask was provided, then this is an unexpected way of calling this function.
# We therefore raise an error.
raise ValueError(
"The `mask` argument is expected as None if the first argument is a RunningNorm."
" However, `mask` is found as something other than None."
)
if self._shape != x._shape:
# If the shapes of this RunningNorm and of the other RunningNorm
# do not match, then we cannot use `x` for updating our stats.
# It might be the case that `x` was initialized for another
# task, with differently sized observations.
# We therefore raise an error.
raise ValueError(
f"The RunningNorm to be updated has the shape {self._shape}"
f" The other RunningNorm has the shape {self._shape}"
f" These shapes are incompatible."
)
if self._has_no_data():
# If this RunningNorm has no data at all, then we clone the
# data of x.
self._sum = self._like_its_own(x._sum.clone())
self._sum_of_squares = self._like_its_own(x._sum_of_squares.clone())
self._count = x._count
elif self._has_data():
# If this RunningNorm has its own data, then we update the
# stored data with the data stored by x.
self._sum += self._like_its_own(x._sum)
self._sum_of_squares += self._like_its_own(x._sum_of_squares)
self._count += x._count
else:
assert False, "RunningNorm is in an invalid state! This might be a bug."
else:
# This is the case where the received argument x is not a
# RunningNorm object, but an Iterable.
if verify:
# If we have the `verify` flag, then we make sure that
# x is a tensor of the correct shape
x = self._verify(x)
if x.ndim == self._ndim:
# If the shape of x is exactly the same with the observation shape
# then we assume that x represents a single observation, and not a
# batch of observations.
if mask is not None:
# Since we are dealing with a single observation,
# we do not expect to receive a mask argument.
# If the mask argument was provided, then this is an unexpected
# usage of this function.
# We therefore raise an error.
raise ValueError(
"The `mask` argument is expected as None if the first argument is a single observation"
" (i.e. not a batch of observations, with an extra leftmost dimension)."
" However, `mask` is found as something other than None."
)
# Since x is a single observation,
# the sum of observations extracted from x is x itself,
# and the sum of squared observations extracted from x is
# the square of x itself.
sum_of_x = x
sum_of_x_squared = x.square()
# We extracted a single observation from x
n = 1
elif x.ndim == (self._ndim + 1):
# If the number of dimensions of x is one more than the number
# of dimensions of this RunningNorm, then we assume that x is a batch
# of observations.
if mask is not None:
# If a mask is provided, then we first make sure that it is a tensor
# of dtype bool in the correct device.
mask = torch.as_tensor(mask, dtype=torch.bool, device=self._device)
if mask.ndim != 1:
# We expect the mask to be 1-dimensional.
# If not, we raise an error.
raise ValueError(
f"The `mask` tensor was expected as a 1-dimensional tensor."
f" However, its shape is {mask.shape}."
)
if len(mask) != x.shape[0]:
# If the length of the mask is not the batch size of x,
# then there is a mismatch.
# We therefore raise an error.
raise ValueError(
f"The shape of the given tensor is {x.shape}."
f" Therefore, the batch size of observations is {x.shape[0]}."
f" However, the given `mask` tensor does not has an incompatible length: {len(mask)}."
)
# We compute how many True items we have in the mask.
# This integer gives us how many observations we extract from x.
n = int(torch.sum(torch.as_tensor(mask, dtype=torch.int64, device=self._device)))
# We now re-cast the mask as the observation dtype (so that True items turn to 1.0
# and False items turn to 0.0), and then increase its number of dimensions so that
# it can operate directly with x.
mask = self._like_its_own(mask).reshape(torch.Size([x.shape[0]] + ([1] * (x.ndim - 1))))
# Finally, we multiply x with the mask. This means that the observations with corresponding
# mask values as False are zeroed out.
x = x * mask
else:
# This is the case where we did not receive a mask.
# We can simply say that the number of observations to extract from x
# is the size of its leftmost dimension, i.e. the batch size.
n = x.shape[0]
# With or without a mask, we are now ready to extract the sum and sum of squares
# from x.
sum_of_x = torch.sum(x, dim=0)
sum_of_x_squared = torch.sum(x.square(), dim=0)
else:
# This is the case where the number of dimensions of x is unrecognized.
# This case is actually already checked by the _verify(...) method earlier.
# This defensive fallback case is only for when verify=False and it turned out
# that the ndim is invalid.
raise ValueError(f"Invalid shape: {x.shape}")
# At this point, we handled all the valid cases regarding the Iterable x,
# and we have our sum_of_x (sum of all observations), sum_of_squares
# (sum of all squared observations), and n (number of observations extracted
# from x).
if self._has_no_data():
# If our RunningNorm is empty, the observation data we extracted from x
# become our RunningNorm's new data.
self._sum = sum_of_x
self._sum_of_squares = sum_of_x_squared
self._count = n
elif self._has_data():
# If our RunningNorm is not empty, the stored data is updated with the
# data extracted from x.
self._sum += sum_of_x
self._sum_of_squares += sum_of_x_squared
self._count += n
else:
# This is an erroneous state where the internal data looks neither
# existent nor completely empty.
# This might be the result of a bug, or maybe this instance's
# protected variables were tempered with from the outside.
assert False, "RunningNorm is in an invalid state! This might be a bug."
@property
@torch.no_grad()
def stats(self) -> CollectedStats:
"""
The collected data's mean and standard deviation (stdev) in a tuple
"""
# Using the internally stored sum, sum_of_squares, and count,
# compute E[x] and E[x^2]
E_x = self._sum / self._count
E_x2 = self._sum_of_squares / self._count
# The mean is E[x]
mean = E_x
# The variance is E[x^2] - (E[x])^2, elementwise clipped such that
# it cannot go below min_variance
variance = _clamp(E_x2 - E_x.square(), self._min_variance, None)
# Standard deviation is finally computed as the square root of the variance
stdev = torch.sqrt(variance)
# Return the stats in a named tuple
return CollectedStats(mean=mean, stdev=stdev)
@property
def mean(self) -> torch.Tensor:
"""
The collected data's mean
"""
return self._sum / self._count
@property
def stdev(self) -> torch.Tensor:
"""
The collected data's standard deviation
"""
return self.stats.stdev
@property
def sum(self) -> torch.Tensor:
"""
The collected data's sum
"""
return self._sum
@property
def sum_of_squares(self) -> torch.Tensor:
"""
Sum of squares of the collected data
"""
return self._sum_of_squares
@property
def count(self) -> int:
"""
Number of observations encountered
"""
return self._count
@torch.no_grad()
def normalize(self, x: Iterable, *, result_as_numpy: Optional[bool] = None, verify: bool = True) -> Iterable:
"""
Normalize the given observation x.
Args:
x: The observation(s), as a PyTorch tensor, or any Iterable
that is convertable to a PyTorch tensor.
`x` can be a single observation, or it can be a batch
of observations (with an extra leftmost dimension).
result_as_numpy: Whether or not to return the normalized
observation as a numpy array.
If left as None (which is the default), then the returned
type depends on x: a PyTorch tensor is returned if x is a
PyTorch tensor, and a numpy array is returned otherwise.
If True, the result is always a numpy array.
If False, the result is always a PyTorch tensor.
verify: Whether or not to check the type and dimensions of x.
This is True by default.
Note that, if `verify` is False, this function will not
properly check the type of `x` and will assume that `x`
is a PyTorch tensor.
Returns:
The normalized observation, as a PyTorch tensor or a numpy array.
"""
if self._count == 0:
# If this RunningNorm instance has no data yet,
# then we do not know how to do the normalization.
# We therefore raise an error.
raise ValueError("Cannot do normalization because no data is collected yet.")
if verify:
# Here we verify the type and shape of x.
if result_as_numpy is None:
# If there is not an explicit request about the return type,
# we infer the return type from the type of x:
# if x is a tensor, we return a tensor;
# otherwise, we assume x to be a CPU-bound iterable, and
# therefore we return a numpy array.
result_as_numpy = not isinstance(x, torch.Tensor)
else:
result_as_numpy = bool(result_as_numpy)
# We call _verify() to make sure that x is of correct shape
# and is properly converted to a PyTorch tensor.
x = self._verify(x)
# We get the mean and stdev of the collected data
mean, stdev = self.stats
# Now we compute the normalized observation, clipped according to the
# lower and upper bounds expressed by the `clip` tuple, if exists.
result = _clamp((x - mean) / stdev, self._lb, self._ub)
if result_as_numpy:
# If we are to return the result as a numpy array, we do the
# necessary conversion.
result = result.cpu().numpy()
# Finally, return the result
return result
@torch.no_grad()
def update_and_normalize(self, x: Iterable, mask: Optional[Iterable] = None) -> Iterable:
"""
Update the observation stats according to x, then normalize x.
Args:
x: The observation(s), as a PyTorch tensor, or as an Iterable
which can be converted to a PyTorch tensor.
The shape of x can be the same with the observaiton shape,
or it can be augmented with an extra leftmost dimension
to express a batch of observations.
mask: Can be given as a 1-dimensional Iterable of booleans ONLY
if `x` represents a batch of observations.
If a `mask` is provided, the i-th observation within the
observation batch `x` will be taken into account only if
the the i-th item of the `mask` is True.
Returns:
The normalized counterpart of the observation(s) expressed by x.
"""
result_as_numpy = not isinstance(x, torch.Tensor)
x = self._verify(x)
self.update(x, mask, verify=False)
result = self.normalize(x, verify=False)
if result_as_numpy:
result = result.cpu().numpy()
return result
def to_layer(self) -> "ObsNormLayer":
"""
Make a PyTorch module which normalizes the its inputs.
Returns:
An ObsNormLayer instance.
"""
mean, stdev = self.stats
low = self.low
high = self.high
return ObsNormLayer(mean=mean, stdev=stdev, low=low, high=high)
def __repr__(self) -> str:
return f"<{self.__class__.__name__}, count: {self.count}>"
def __copy__(self) -> "RunningNorm":
return deepcopy(self)
count: int
property
readonly
¶
Number of observations encountered
device: Union[str, torch.device]
property
readonly
¶
The device in which the observation stats are held
dtype: Union[str, torch.dtype, numpy.dtype, Type]
property
readonly
¶
The dtype of the stored observation stats
high: Optional[float]
property
readonly
¶
The higher (upper) component of the bounds given in the clip tuple.
If clip was initialized as None, this is also None.
low: Optional[float]
property
readonly
¶
The lower component of the bounds given in the clip tuple.
If clip was initialized as None, this is also None.
mean: Tensor
property
readonly
¶
The collected data's mean
min_variance: float
property
readonly
¶
Minimum variance
shape: tuple
property
readonly
¶
Observation shape
stats: CollectedStats
property
readonly
¶
The collected data's mean and standard deviation (stdev) in a tuple
stdev: Tensor
property
readonly
¶
The collected data's standard deviation
sum: Tensor
property
readonly
¶
The collected data's sum
sum_of_squares: Tensor
property
readonly
¶
Sum of squares of the collected data
__init__(self, *, shape, dtype, device=None, min_variance=0.01, clip=None)
special
¶
__init__(...): Initialize the RunningNorm
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
shape |
Union[tuple, int] |
Observation shape. Can be an integer or a tuple. |
required |
dtype |
Union[str, torch.dtype, numpy.dtype, Type] |
The dtype of the observations. |
required |
device |
Union[str, torch.device] |
The device in which the observation stats are held. If left as None, the device is assumed to be "cpu". |
None |
min_variance |
float |
A lower bound for the variance to be used in
the normalization computations.
In other words, if the computed variance according to the
collected observations ends up lower than |
0.01 |
clip |
Optional[tuple] |
Can be left as None (which is the default), or can be given as a pair of real numbers. This is used for clipping the observations after the normalization operation. In Salimans et al. (2017), (-5.0, +5.0) was used. |
None |
Source code in evotorch/neuroevolution/net/runningnorm.py
def __init__(
self,
*,
shape: Union[tuple, int],
dtype: DType,
device: Optional[Device] = None,
min_variance: float = 1e-2,
clip: Optional[tuple] = None,
) -> None:
"""
`__init__(...)`: Initialize the RunningNorm
Args:
shape: Observation shape. Can be an integer or a tuple.
dtype: The dtype of the observations.
device: The device in which the observation stats are held.
If left as None, the device is assumed to be "cpu".
min_variance: A lower bound for the variance to be used in
the normalization computations.
In other words, if the computed variance according to the
collected observations ends up lower than `min_variance`,
this `min_variance` will be used instead (in an elementwise
manner) while computing the normalized observations.
As in Salimans et al. (2017), the default is 1e-2.
clip: Can be left as None (which is the default), or can be
given as a pair of real numbers.
This is used for clipping the observations after the
normalization operation.
In Salimans et al. (2017), (-5.0, +5.0) was used.
"""
# Make sure that the shape is stored as a torch.Size object.
if isinstance(shape, Iterable):
self._shape = torch.Size(shape)
else:
self._shape = torch.Size([int(shape)])
# Store the number of dimensions
self._ndim = len(self._shape)
# Store the dtype and the device
self._dtype = to_torch_dtype(dtype)
self._device = "cpu" if device is None else device
# Initialize the internally stored data as empty
self._sum: Optional[torch.Tensor] = None
self._sum_of_squares: Optional[torch.Tensor] = None
self._count: int = 0
# Store the minimum variance
self._min_variance = float(min_variance)
if clip is not None:
# If a clip tuple was provided, store the specified lower and upper bounds
lb, ub = clip
self._lb = float(lb)
self._ub = float(ub)
else:
# If a clip tuple was not provided the bounds are stored as None
self._lb = None
self._ub = None
normalize(self, x, *, result_as_numpy=None, verify=True)
¶
Normalize the given observation x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Iterable |
The observation(s), as a PyTorch tensor, or any Iterable
that is convertable to a PyTorch tensor.
|
required |
result_as_numpy |
Optional[bool] |
Whether or not to return the normalized observation as a numpy array. If left as None (which is the default), then the returned type depends on x: a PyTorch tensor is returned if x is a PyTorch tensor, and a numpy array is returned otherwise. If True, the result is always a numpy array. If False, the result is always a PyTorch tensor. |
None |
verify |
bool |
Whether or not to check the type and dimensions of x.
This is True by default.
Note that, if |
True |
Returns:
| Type | Description |
|---|---|
Iterable |
The normalized observation, as a PyTorch tensor or a numpy array. |
Source code in evotorch/neuroevolution/net/runningnorm.py
@torch.no_grad()
def normalize(self, x: Iterable, *, result_as_numpy: Optional[bool] = None, verify: bool = True) -> Iterable:
"""
Normalize the given observation x.
Args:
x: The observation(s), as a PyTorch tensor, or any Iterable
that is convertable to a PyTorch tensor.
`x` can be a single observation, or it can be a batch
of observations (with an extra leftmost dimension).
result_as_numpy: Whether or not to return the normalized
observation as a numpy array.
If left as None (which is the default), then the returned
type depends on x: a PyTorch tensor is returned if x is a
PyTorch tensor, and a numpy array is returned otherwise.
If True, the result is always a numpy array.
If False, the result is always a PyTorch tensor.
verify: Whether or not to check the type and dimensions of x.
This is True by default.
Note that, if `verify` is False, this function will not
properly check the type of `x` and will assume that `x`
is a PyTorch tensor.
Returns:
The normalized observation, as a PyTorch tensor or a numpy array.
"""
if self._count == 0:
# If this RunningNorm instance has no data yet,
# then we do not know how to do the normalization.
# We therefore raise an error.
raise ValueError("Cannot do normalization because no data is collected yet.")
if verify:
# Here we verify the type and shape of x.
if result_as_numpy is None:
# If there is not an explicit request about the return type,
# we infer the return type from the type of x:
# if x is a tensor, we return a tensor;
# otherwise, we assume x to be a CPU-bound iterable, and
# therefore we return a numpy array.
result_as_numpy = not isinstance(x, torch.Tensor)
else:
result_as_numpy = bool(result_as_numpy)
# We call _verify() to make sure that x is of correct shape
# and is properly converted to a PyTorch tensor.
x = self._verify(x)
# We get the mean and stdev of the collected data
mean, stdev = self.stats
# Now we compute the normalized observation, clipped according to the
# lower and upper bounds expressed by the `clip` tuple, if exists.
result = _clamp((x - mean) / stdev, self._lb, self._ub)
if result_as_numpy:
# If we are to return the result as a numpy array, we do the
# necessary conversion.
result = result.cpu().numpy()
# Finally, return the result
return result
reset(self)
¶
to(self, device)
¶
If the target device is a different device, then make a copy of this RunningNorm instance on the target device. If the target device is the same with this RunningNorm's device, then return this RunningNorm itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
device |
Union[str, torch.device] |
The target device. |
required |
Returns:
| Type | Description |
|---|---|
RunningNorm |
The RunningNorm on the target device. This can be a copy, or the original RunningNorm instance itself. |
Source code in evotorch/neuroevolution/net/runningnorm.py
def to(self, device: Device) -> "RunningNorm":
"""
If the target device is a different device, then make a copy of this
RunningNorm instance on the target device.
If the target device is the same with this RunningNorm's device, then
return this RunningNorm itself.
Args:
device: The target device.
Returns:
The RunningNorm on the target device. This can be a copy, or the
original RunningNorm instance itself.
"""
if torch.device(device) == torch.device(self.device):
return self
else:
new_running_norm = object.__new__(type(self))
already_handled = {"_sum", "_sum_of_squares", "_device"}
new_running_norm._sum = self._sum.to(device)
new_running_norm._sum_of_squares = self._sum_of_squares.to(device)
new_running_norm._device = device
for k, v in self.__dict__.items():
if k not in already_handled:
setattr(new_running_norm, k, deepcopy(v))
return new_running_norm
to_layer(self)
¶
Make a PyTorch module which normalizes the its inputs.
Returns:
| Type | Description |
|---|---|
ObsNormLayer |
An ObsNormLayer instance. |
Source code in evotorch/neuroevolution/net/runningnorm.py
update(self, x, mask=None, *, verify=True)
¶
Update the stored stats with new observation data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Union[Iterable, RunningNorm] |
The new observation(s), as a PyTorch tensor, or any Iterable
that can be converted to a PyTorch tensor, or another
RunningNorm instance.
If given as a tensor or as an Iterable, the shape of |
required |
mask |
Optional[Iterable] |
Can be given as a 1-dimensional Iterable of booleans ONLY
if |
None |
verify |
bool |
Whether or not to verify the shape of the given Iterable objects. The default is True. |
True |
Source code in evotorch/neuroevolution/net/runningnorm.py
@torch.no_grad()
def update(self, x: Union[Iterable, "RunningNorm"], mask: Optional[Iterable] = None, *, verify: bool = True):
"""
Update the stored stats with new observation data.
Args:
x: The new observation(s), as a PyTorch tensor, or any Iterable
that can be converted to a PyTorch tensor, or another
RunningNorm instance.
If given as a tensor or as an Iterable, the shape of `x` can
be the same with observation shape, or it can be augmented
with an extra leftmost dimension.
In the case of augmented dimension, `x` is interpreted not as
a single observation, but as a batch of observations.
If `x` is another RunningNorm instance, the stats stored by
this RunningNorm instance will be updated with all the data
stored by `x`.
mask: Can be given as a 1-dimensional Iterable of booleans ONLY
if `x` represents a batch of observations.
If a `mask` is provided, the i-th observation within the
observation batch `x` will be taken into account only if
the i-th item of the `mask` is True.
verify: Whether or not to verify the shape of the given Iterable
objects. The default is True.
"""
if isinstance(x, RunningNorm):
# If we are to update our stats according to another RunningNorm instance
if x._count > 0:
# We bother only if x is non-empty
if mask is not None:
# We were given another RunningNorm, not a batch of observations.
# So, we do not expect to receive a mask tensor.
# If a mask was provided, then this is an unexpected way of calling this function.
# We therefore raise an error.
raise ValueError(
"The `mask` argument is expected as None if the first argument is a RunningNorm."
" However, `mask` is found as something other than None."
)
if self._shape != x._shape:
# If the shapes of this RunningNorm and of the other RunningNorm
# do not match, then we cannot use `x` for updating our stats.
# It might be the case that `x` was initialized for another
# task, with differently sized observations.
# We therefore raise an error.
raise ValueError(
f"The RunningNorm to be updated has the shape {self._shape}"
f" The other RunningNorm has the shape {self._shape}"
f" These shapes are incompatible."
)
if self._has_no_data():
# If this RunningNorm has no data at all, then we clone the
# data of x.
self._sum = self._like_its_own(x._sum.clone())
self._sum_of_squares = self._like_its_own(x._sum_of_squares.clone())
self._count = x._count
elif self._has_data():
# If this RunningNorm has its own data, then we update the
# stored data with the data stored by x.
self._sum += self._like_its_own(x._sum)
self._sum_of_squares += self._like_its_own(x._sum_of_squares)
self._count += x._count
else:
assert False, "RunningNorm is in an invalid state! This might be a bug."
else:
# This is the case where the received argument x is not a
# RunningNorm object, but an Iterable.
if verify:
# If we have the `verify` flag, then we make sure that
# x is a tensor of the correct shape
x = self._verify(x)
if x.ndim == self._ndim:
# If the shape of x is exactly the same with the observation shape
# then we assume that x represents a single observation, and not a
# batch of observations.
if mask is not None:
# Since we are dealing with a single observation,
# we do not expect to receive a mask argument.
# If the mask argument was provided, then this is an unexpected
# usage of this function.
# We therefore raise an error.
raise ValueError(
"The `mask` argument is expected as None if the first argument is a single observation"
" (i.e. not a batch of observations, with an extra leftmost dimension)."
" However, `mask` is found as something other than None."
)
# Since x is a single observation,
# the sum of observations extracted from x is x itself,
# and the sum of squared observations extracted from x is
# the square of x itself.
sum_of_x = x
sum_of_x_squared = x.square()
# We extracted a single observation from x
n = 1
elif x.ndim == (self._ndim + 1):
# If the number of dimensions of x is one more than the number
# of dimensions of this RunningNorm, then we assume that x is a batch
# of observations.
if mask is not None:
# If a mask is provided, then we first make sure that it is a tensor
# of dtype bool in the correct device.
mask = torch.as_tensor(mask, dtype=torch.bool, device=self._device)
if mask.ndim != 1:
# We expect the mask to be 1-dimensional.
# If not, we raise an error.
raise ValueError(
f"The `mask` tensor was expected as a 1-dimensional tensor."
f" However, its shape is {mask.shape}."
)
if len(mask) != x.shape[0]:
# If the length of the mask is not the batch size of x,
# then there is a mismatch.
# We therefore raise an error.
raise ValueError(
f"The shape of the given tensor is {x.shape}."
f" Therefore, the batch size of observations is {x.shape[0]}."
f" However, the given `mask` tensor does not has an incompatible length: {len(mask)}."
)
# We compute how many True items we have in the mask.
# This integer gives us how many observations we extract from x.
n = int(torch.sum(torch.as_tensor(mask, dtype=torch.int64, device=self._device)))
# We now re-cast the mask as the observation dtype (so that True items turn to 1.0
# and False items turn to 0.0), and then increase its number of dimensions so that
# it can operate directly with x.
mask = self._like_its_own(mask).reshape(torch.Size([x.shape[0]] + ([1] * (x.ndim - 1))))
# Finally, we multiply x with the mask. This means that the observations with corresponding
# mask values as False are zeroed out.
x = x * mask
else:
# This is the case where we did not receive a mask.
# We can simply say that the number of observations to extract from x
# is the size of its leftmost dimension, i.e. the batch size.
n = x.shape[0]
# With or without a mask, we are now ready to extract the sum and sum of squares
# from x.
sum_of_x = torch.sum(x, dim=0)
sum_of_x_squared = torch.sum(x.square(), dim=0)
else:
# This is the case where the number of dimensions of x is unrecognized.
# This case is actually already checked by the _verify(...) method earlier.
# This defensive fallback case is only for when verify=False and it turned out
# that the ndim is invalid.
raise ValueError(f"Invalid shape: {x.shape}")
# At this point, we handled all the valid cases regarding the Iterable x,
# and we have our sum_of_x (sum of all observations), sum_of_squares
# (sum of all squared observations), and n (number of observations extracted
# from x).
if self._has_no_data():
# If our RunningNorm is empty, the observation data we extracted from x
# become our RunningNorm's new data.
self._sum = sum_of_x
self._sum_of_squares = sum_of_x_squared
self._count = n
elif self._has_data():
# If our RunningNorm is not empty, the stored data is updated with the
# data extracted from x.
self._sum += sum_of_x
self._sum_of_squares += sum_of_x_squared
self._count += n
else:
# This is an erroneous state where the internal data looks neither
# existent nor completely empty.
# This might be the result of a bug, or maybe this instance's
# protected variables were tempered with from the outside.
assert False, "RunningNorm is in an invalid state! This might be a bug."
update_and_normalize(self, x, mask=None)
¶
Update the observation stats according to x, then normalize x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Iterable |
The observation(s), as a PyTorch tensor, or as an Iterable which can be converted to a PyTorch tensor. The shape of x can be the same with the observaiton shape, or it can be augmented with an extra leftmost dimension to express a batch of observations. |
required |
mask |
Optional[Iterable] |
Can be given as a 1-dimensional Iterable of booleans ONLY
if |
None |
Returns:
| Type | Description |
|---|---|
Iterable |
The normalized counterpart of the observation(s) expressed by x. |
Source code in evotorch/neuroevolution/net/runningnorm.py
@torch.no_grad()
def update_and_normalize(self, x: Iterable, mask: Optional[Iterable] = None) -> Iterable:
"""
Update the observation stats according to x, then normalize x.
Args:
x: The observation(s), as a PyTorch tensor, or as an Iterable
which can be converted to a PyTorch tensor.
The shape of x can be the same with the observaiton shape,
or it can be augmented with an extra leftmost dimension
to express a batch of observations.
mask: Can be given as a 1-dimensional Iterable of booleans ONLY
if `x` represents a batch of observations.
If a `mask` is provided, the i-th observation within the
observation batch `x` will be taken into account only if
the the i-th item of the `mask` is True.
Returns:
The normalized counterpart of the observation(s) expressed by x.
"""
result_as_numpy = not isinstance(x, torch.Tensor)
x = self._verify(x)
self.update(x, mask, verify=False)
result = self.normalize(x, verify=False)
if result_as_numpy:
result = result.cpu().numpy()
return result