nn.py 文件源码

def loss(self, x, samples):
        _, proposal_output = self.forward(x, samples)
        batch_size = len(samples)
        locations = proposal_output[:, 0]
        scales = proposal_output[:, 1] + util.epsilon
        log_two_scales = torch.log(2 * scales)
        l = 0
        for b in range(batch_size):
            value = samples[b].value[0]
            location = locations[b]
            scale = scales[b]
            l += log_two_scales[b] + torch.abs(value - location) / scale
        return l