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import torch
import torch.nn as nn
from typing import Iterable
from src.simulation.effect import Effect
################################################################################
# Wrap effects units to apply in sequence
################################################################################
class Simulation(nn.Module):
"""
Wrapper for sequential application of effects units. Allows for straight-
through gradient estimation and random effect parameter sampling.
"""
def __init__(self, *args):
super().__init__()
effects = []
if len(args) == 1 and isinstance(args[0], Iterable):
for effect in args[0]:
assert isinstance(effect, Effect), \
"Arguments must be Effect objects"
effects.append(effect)
else:
for effect in args:
assert isinstance(effect, Effect), \
"Arguments must be Effect objects"
effects.append(effect)
self.effects = nn.ModuleList(effects)
def forward(self, x: torch.Tensor):
for effect in self.effects:
if effect.compute_grad:
x = effect(x)
else:
# allow straight-through gradient estimation on backward pass
output = effect(x)
x = x + (output-x).detach()
return x
def sample_params(self):
for effect in self.effects:
effect.sample_params()
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