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# Copyright (c) 2021 ContinualAI. #
# Copyrights licensed under the MIT License. #
# See the accompanying LICENSE file for terms. #
# #
# Date: 19-01-2021 #
# Author(s): Vincenzo Lomonaco, Lorenzo Pellegrini #
# E-mail: contact@continualai.org #
# Website: www.continualai.org #
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from torch.nn import Module
from typing import List, Optional
from torch import Tensor
from avalanche.evaluation import Metric, PluginMetric, GenericPluginMetric
[docs]class MAC(Metric[int]):
"""
Standalone Multiply-and-accumulate metric. Provides a lower bound of the
computational cost of a model in a hardware-independent way by
computing the number of multiplications. Currently supports only
Linear or Conv2d modules. Other operations are ignored.
"""
[docs] def __init__(self):
"""
Creates an instance of the MAC metric.
"""
self.hooks = []
self._compute_cost: Optional[int] = 0
def update(self, model: Module, dummy_input: Tensor):
"""
Computes the MAC metric.
:param model: current model.
:param dummy_input: A tensor of the correct size to feed as input
to model. It includes batch size
:return: MAC metric.
"""
for mod in model.modules():
if MAC.is_recognized_module(mod):
def foo(a, b, c):
return self.update_compute_cost(a, b, c)
handle = mod.register_forward_hook(foo)
self.hooks.append(handle)
self._compute_cost = 0
model(dummy_input) # trigger forward hooks
for handle in self.hooks:
handle.remove()
self.hooks = []
def result(self) -> Optional[int]:
"""
Return the number of MAC operations as computed in the previous call
to the `update` method.
:return: The number of MAC operations or None if `update` has not been
called yet.
"""
return self._compute_cost
def reset(self):
pass
def update_compute_cost(self, module, dummy_input, output):
modname = module.__class__.__name__
if modname == "Linear":
self._compute_cost += dummy_input[0].shape[1] * output.shape[1]
elif modname == "Conv2d":
n, cout, hout, wout = output.shape # Batch, Channels, Height, Width
ksize = module.kernel_size[0] * module.kernel_size[1]
self._compute_cost += cout * hout * wout * ksize
@staticmethod
def is_recognized_module(mod):
modname = mod.__class__.__name__
return modname == "Linear" or modname == "Conv2d"
class MACPluginMetric(GenericPluginMetric):
def __init__(self, reset_at, emit_at, mode):
self._mac = MAC()
super(MACPluginMetric, self).__init__(
self._mac, reset_at=reset_at, emit_at=emit_at, mode=mode
)
def update(self, strategy):
self._mac.update(strategy.model, strategy.mb_x[0].unsqueeze(0))
[docs]class MinibatchMAC(MACPluginMetric):
"""
The minibatch MAC metric.
This plugin metric only works at training time.
This metric computes the MAC over 1 pattern
from a single minibatch.
It reports the result after each iteration.
If a more coarse-grained logging is needed, consider using
:class:`EpochMAC` instead.
"""
[docs] def __init__(self):
"""
Creates an instance of the MinibatchMAC metric.
"""
super(MinibatchMAC, self).__init__(
reset_at="iteration", emit_at="iteration", mode="train"
)
def __str__(self):
return "MAC_MB"
[docs]class EpochMAC(MACPluginMetric):
"""
The MAC at the end of each epoch computed on a
single pattern.
This plugin metric only works at training time.
The MAC will be logged after each training epoch.
"""
[docs] def __init__(self):
"""
Creates an instance of the EpochMAC metric.
"""
super(EpochMAC, self).__init__(
reset_at="epoch", emit_at="epoch", mode="train"
)
def __str__(self):
return "MAC_Epoch"
[docs]class ExperienceMAC(MACPluginMetric):
"""
At the end of each experience, this metric reports the
MAC computed on a single pattern.
This plugin metric only works at eval time.
"""
[docs] def __init__(self):
"""
Creates an instance of ExperienceMAC metric
"""
super(ExperienceMAC, self).__init__(
reset_at="experience", emit_at="experience", mode="eval"
)
def __str__(self):
return "MAC_Exp"
[docs]def MAC_metrics(
*, minibatch=False, epoch=False, experience=False
) -> List[PluginMetric]:
"""
Helper method that can be used to obtain the desired set of
plugin metrics.
:param minibatch: If True, will return a metric able to log
the MAC after each iteration at training time.
:param epoch: If True, will return a metric able to log
the MAC after each epoch at training time.
:param experience: If True, will return a metric able to log
the MAC after each eval experience.
:return: A list of plugin metrics.
"""
metrics = []
if minibatch:
metrics.append(MinibatchMAC())
if epoch:
metrics.append(EpochMAC())
if experience:
metrics.append(ExperienceMAC())
return metrics
__all__ = ["MAC", "MinibatchMAC", "EpochMAC", "ExperienceMAC", "MAC_metrics"]