import torch
from torch import nn

class PointHeads(nn.Sequential):
    def __init__(self, in_channels, layers):
        d = []
        next_feature = in_channels
        for out_channels in layers:
            d.append(nn.Conv2d(next_feature, out_channels, 3, stride=1, padding=1))
            d.append(nn.ReLU(inplace=True))
            next_feature = out_channels
        super().__init__(*d)
        for m in self.children():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                nn.init.constant_(m.bias, 0)


class PointPredictor(nn.Module):
    def __init__(self, in_channels, out_channels=1 ):
        super().__init__()
        input_features = in_channels
        deconv_kernel = 4
        self.kps_score_lowres = nn.ConvTranspose2d(
            input_features,
            out_channels,
            deconv_kernel,
            stride=2,
            padding=deconv_kernel // 2 - 1,
        )
        nn.init.kaiming_normal_(self.kps_score_lowres.weight, mode="fan_out", nonlinearity="relu")
        nn.init.constant_(self.kps_score_lowres.bias, 0)
        self.up_scale = 2
        self.out_channels = out_channels

    def forward(self, x):
        # print(f'before kps_score_lowres x:{x.shape}')
        x = self.kps_score_lowres(x)
        # print(f'kps_score_lowres x:{x.shape}')
        return torch.nn.functional.interpolate(
            x, scale_factor=float(self.up_scale), mode="bilinear", align_corners=False, recompute_scale_factor=False
        )