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import os
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import time
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import argparse
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import numpy as np
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import torch
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import torch.nn.functional as F
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def rgb2gray(img):
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r, g, b = torch.split(img, 1, dim=1)
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return torch.mul(r, 0.299) + torch.mul(g, 0.587) + torch.mul(b, 0.114)
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def reparameterize(mu, logvar):
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std = logvar.mul(0.5).exp_()
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eps = torch.cuda.FloatTensor(std.size()).normal_()
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return eps.mul(std).add_(mu)
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def kl_loss(mu, logvar, prior_mu=0):
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v_kl = mu.add(-prior_mu).pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
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v_kl = v_kl.sum(dim=-1).mul_(-0.5)
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return v_kl
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def reconstruction_loss(prediction, target, size_average=False):
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error = (prediction - target).view(prediction.size(0), -1)
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error = error ** 2
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error = torch.sum(error, dim=-1)
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if size_average:
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error = error.mean()
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else:
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error = error.sum()
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return error
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def load_model(model, pretrained):
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weights = torch.load(pretrained)
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pretrained_dict = weights['model'].state_dict()
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model_dict = model.state_dict()
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
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model_dict.update(pretrained_dict)
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model.load_state_dict(model_dict)
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def save_checkpoint(model_path, model, epoch, iteration, name):
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model_out_path = model_path + name + "model_epoch_{}_iter_{}.pth".format(epoch, iteration)
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state = {"epoch": epoch, "model": model}
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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torch.save(state, model_out_path)
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print("Checkpoint saved to {}".format(model_out_path))
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def MMD_Loss(fc_nir, fc_vis):
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mean_fc_nir = torch.mean(fc_nir, 0)
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mean_fc_vis = torch.mean(fc_vis, 0)
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loss_mmd = F.mse_loss(mean_fc_nir, mean_fc_vis)
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return loss_mmd
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def adjust_learning_rate(lr, step, optimizer, epoch):
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scale = 0.457305051927326
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lr = lr * (scale ** (epoch // step))
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print('lr: {}'.format(lr))
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if (epoch != 0) & (epoch % step == 0):
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print('Change lr')
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for param_group in optimizer.param_groups:
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param_group['lr'] = param_group['lr'] * scale
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def accuracy(output, target, topk=(1,)):
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"""Computes the precision@k for the specified values of k"""
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0)
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res.append(correct_k.mul_(100.0 / batch_size))
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return res
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