import torch
from torch import nn
from model_irse import Backbone


# Use GPU if available
if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")


class IDLoss(nn.Module):
    def __init__(self, opts):
        super(IDLoss, self).__init__()
        print('Loading ResNet ArcFace')
        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
        self.facenet.load_state_dict(torch.load("./pretrained/model_ir_se50.pth", map_location=device))
        self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
        self.facenet.eval()
        self.opts = opts

    def extract_feats(self, x):
        if x.shape[2] != 256:
            x = self.pool(x)
        x = x[:, :, 35:223, 32:220]  # Crop interesting region
        x = self.face_pool(x)
        x_feats = self.facenet(x)
        return x_feats

    def forward(self, y_hat, y):
        n_samples = y.shape[0]
        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
        y_hat_feats = self.extract_feats(y_hat)
        y_feats = y_feats.detach()
        loss = 0
        sim_improvement = 0
        count = 0
        for i in range(n_samples):
            diff_target = y_hat_feats[i].dot(y_feats[i])
            loss += 1 - diff_target
            count += 1

        return loss / count, sim_improvement / count