Question 1: In the forward(self, x) method of the SpecAugment class, there s a property named self._forward that seems to behave like a function. However, it also receives an x variable as if it were a function. How does this interaction work?

Question 2: After passing the x value to the self._forward property, the code selects a policy from the policies dictionary based on the policy parameter. Assuming I understand the answer to the first question correctly, it seems that the x value provided to self._forward is then used to choose one of self.policy1 , self.policy2 , or self.policy3 . How does this selection process work, and how do these policies run without explicitly taking any values?

下面是原始参考法:

class SpecAugment(nn.Module):
    def __init__(self,rate,policy=3,freq_mask=2,time_mask=4):
        super(SpecAugment, self).__init__()
        self.rate = rate
        self.specaug1 = nn.Sequential(
            torchaudio.transforms.FrequencyMasking(freq_mask_param=freq_mask),
            torchaudio.transforms.TimeMasking(time_mask_param=time_mask)
            
        )
        self.specaug2 = nn.Sequential(
            torchaudio.transforms.FrequencyMasking(freq_mask_param=freq_mask),
            torchaudio.transforms.TimeMasking(time_mask_param=time_mask),
            torchaudio.transforms.FrequencyMasking(freq_mask_param=freq_mask),
            torchaudio.transforms.TimeMasking(time_mask_param=time_mask)
        )

        policies = {1:self.policy1, 2:self.policy2, 3:self.policy3}
        self._forward = policies[policy]

    def forward(self,x):
        return self._forward(x)

    #this makes specaug1
    def policy1(self,x):
        probability = torch.rand(1,1).item()
        if self.rate > probability:
            return self.specaug1(x)
        return x

    #this makes specaug2
    def policy2(self,x):
        probability = torch.rand(1,1).item()
        if self.rate > probability:
            return self.specaug2(x)
        return x

    #this makes random choice because we did torch.rand
    def policy3(self,x):
        probability = torch.rand(1,1).item()
        if probability > 0.5:
            return self.policy1(x)
        return self.policy2(x)