update

utils/tools.py

@@ -1,77 +0,0 @@
-import numpy as np
-import mindspore.numpy as mnp
-import mindspore
-from mindspore import Tensor, Parameter
-
-def adjust_learning_rate(optimizer, epoch, args):
-    if args.lradj == 'type1':
-        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch-1) // 1))}
-    elif args.lradj == 'type2':
-        lr_adjust = {
-            2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
-            10: 5e-7, 15: 1e-7, 20: 5e-8
-        }
-    if epoch in lr_adjust.keys():
-        lr = lr_adjust[epoch]
-        for param_group in optimizer.parameters():
-            param_group.set_lr(lr)
-        print('Updating learning rate to {}'.format(lr))
-
-class EarlyStopping:
-    def __init__(self, patience=7, verbose=False, delta=0):
-        self.patience = patience
-        self.verbose = verbose
-        self.counter = 0
-        self.best_score = None
-        self.early_stop = False
-        self.val_loss_min = np.Inf
-        self.delta = delta
-
-    def __call__(self, val_loss, model, path):
-        score = -val_loss
-        if self.best_score is None:
-            self.best_score = score
-            self.save_checkpoint(val_loss, model, path)
-        elif score < self.best_score + self.delta:
-            self.counter += 1
-            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
-            if self.counter >= self.patience:
-                self.early_stop = True
-        else:
-            self.best_score = score
-            self.save_checkpoint(val_loss, model, path)
-            self.counter = 0
-
-    def save_checkpoint(self, val_loss, model, path):
-        if self.verbose:
-            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
-        model.save_checkpoint(path + '/' + 'checkpoint.ckpt')
-        self.val_loss_min = val_loss
-
-class dotdict(dict):
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = dict.get
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-class StandardScaler():
-    def __init__(self):
-        self.mean = 0.
-        self.std = 1.
-
-    def fit(self, data):
-        self.mean = mnp.mean(data, 0)
-        self.std = mnp.std(data, 0)
-
-    def transform(self, data):
-        mean = Tensor(self.mean, mindspore.float32)
-        std = Tensor(self.std, mindspore.float32)
-        return (data - mean) / std
-
-    def inverse_transform(self, data):
-        mean = Tensor(self.mean, mindspore.float32)
-        std = Tensor(self.std, mindspore.float32)
-        if data.shape[-1] != mean.shape[-1]:
-            mean = mean[-1:]
-            std = std[-1:]
-        return (data * std) + mean