LaNAS/one-shot_LaNAS/Evaluate/utils.py [14:130]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class AvgrageMeter(object): def __init__(self): self.reset() def reset(self): self.avg = 0 self.sum = 0 self.cnt = 0 def update(self, val, n=1): self.sum += val * n self.cnt += n self.avg = self.sum / self.cnt def accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0/batch_size)) return res class Cutout(object): def __init__(self, length): self.length = length def __call__(self, img): h, w = img.size(1), img.size(2) mask = np.ones((h, w), np.float32) y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - self.length // 2, 0, h) y2 = np.clip(y + self.length // 2, 0, h) x1 = np.clip(x - self.length // 2, 0, w) x2 = np.clip(x + self.length // 2, 0, w) mask[y1: y2, x1: x2] = 0. mask = torch.from_numpy(mask) mask = mask.expand_as(img) img *= mask return img def _data_transforms_cifar10(cutout, cutout_length): CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124] CIFAR_STD = [0.24703233, 0.24348505, 0.26158768] train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD), ]) if cutout: train_transform.transforms.append(Cutout(cutout_length)) valid_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD), ]) return train_transform, valid_transform def count_parameters_in_MB(model): return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary" not in name)/1e6 def save_checkpoint(state, is_best, save): filename = os.path.join(save, 'checkpoint.pth.tar') torch.save(state, filename) if is_best: best_filename = os.path.join(save, 'model_best.pth.tar') shutil.copyfile(filename, best_filename) def save(model, model_path): torch.save(model.state_dict(), model_path) def load(model, model_path): model.load_state_dict(torch.load(model_path)) def drop_path(x, drop_prob): if drop_prob > 0.: keep_prob = 1. - drop_prob mask = torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob) x.div_(keep_prob) try: x.mul_(mask) except: mask = torch.cuda.HalfTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob) x.mul_(mask) return x def create_exp_dir(path, scripts_to_save=None): if not os.path.exists(path): os.mkdir(path) print('Experiment dir : {}'.format(path)) if scripts_to_save is not None: if not os.path.exists(os.path.join(path, 'scripts')): os.mkdir(os.path.join(path, 'scripts')) for script in scripts_to_save: dst_file = os.path.join(path, 'scripts', os.path.basename(script)) shutil.copyfile(script, dst_file) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - LaNAS/one-shot_LaNAS/supernet/utils.py [14:143]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class AvgrageMeter(object): def __init__(self): self.reset() def reset(self): self.avg = 0 self.sum = 0 self.cnt = 0 def update(self, val, n=1): self.sum += val * n self.cnt += n self.avg = self.sum / self.cnt def accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0/batch_size)) return res class Cutout(object): def __init__(self, length): self.length = length def __call__(self, img): h, w = img.size(1), img.size(2) mask = np.ones((h, w), np.float32) y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - self.length // 2, 0, h) y2 = np.clip(y + self.length // 2, 0, h) x1 = np.clip(x - self.length // 2, 0, w) x2 = np.clip(x + self.length // 2, 0, w) mask[y1: y2, x1: x2] = 0. mask = torch.from_numpy(mask) mask = mask.expand_as(img) img *= mask return img def _data_transforms_cifar10(cutout, cutout_length): CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124] CIFAR_STD = [0.24703233, 0.24348505, 0.26158768] train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD), ]) if cutout: train_transform.transforms.append(Cutout(cutout_length)) valid_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD), ]) return train_transform, valid_transform def count_parameters_in_MB(model): return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary" not in name)/1e6 def save_checkpoint(state, is_best, save): filename = os.path.join(save, 'checkpoint.pth.tar') torch.save(state, filename) if is_best: best_filename = os.path.join(save, 'model_best.pth.tar') shutil.copyfile(filename, best_filename) def save(model, model_path): torch.save(model.state_dict(), model_path) def load(model, model_path): model.load_state_dict(torch.load(model_path)) def drop_path(x, drop_prob): if drop_prob > 0.: keep_prob = 1. - drop_prob mask = torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob) x.div_(keep_prob) try: x.mul_(mask) except: mask = torch.cuda.HalfTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob) x.mul_(mask) return x # # def create_exp_dir(path, scripts_to_save=None): # if not os.path.exists(path): # os.mkdir(path) # print('Experiment dir : {}'.format(path)) # # if scripts_to_save is not None: # os.mkdir(os.path.join(path, 'scripts')) # for script in scripts_to_save: # dst_file = os.path.join(path, 'scripts', os.path.basename(script)) # shutil.copyfile(script, dst_file) def create_exp_dir(path, scripts_to_save=None): if not os.path.exists(path): os.mkdir(path) print('Experiment dir : {}'.format(path)) if scripts_to_save is not None: if not os.path.exists(os.path.join(path, 'scripts')): os.mkdir(os.path.join(path, 'scripts')) for script in scripts_to_save: dst_file = os.path.join(path, 'scripts', os.path.basename(script)) shutil.copyfile(script, dst_file) - - - - - - - - - - - - - - - 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