import torch.nn as nn
import torch.nn.functional as F

from model import MODEL


@MODEL.register_module()
class MultiScaleDiscriminator(nn.Module):
    def __init__(self, num_scale, discriminator_cfg):
        super().__init__()

        self.discriminator_list = nn.ModuleList([
            MODEL.build_with(discriminator_cfg) for _ in range(num_scale)
        ])

    @staticmethod
    def down_sample(x):
        return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)

    def forward(self, x):
        results = []
        for discriminator in self.discriminator_list:
            results.append(discriminator(x))
            x = self.down_sample(x)
        return results