utils/tools.py

import collections
import os
import sys

import numpy as np
import torch
import matplotlib.pyplot as plt
import pandas as pd
from torch.utils.data._utils.collate import np_str_obj_array_pattern, default_collate_err_msg_format

plt.switch_backend('agg')


def adjust_learning_rate(optimizer, epoch, args):
    # lr = args.learning_rate * (0.2 ** (epoch // 2))
    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.param_groups:
            param_group['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 ...')
        torch.save(model.state_dict(), path + '/' + 'checkpoint.pth')
        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, mean, std):
        self.mean = mean
        self.std = std

    def transform(self, data):
        return (data - self.mean) / self.std

    def inverse_transform(self, data):
        return (data * self.std) + self.mean


def visual(true, preds=None, name='./pic/test.pdf'):
    """
    Results visualization
    """
    plt.figure()
    plt.plot(true, label='GroundTruth', linewidth=2)
    if preds is not None:
        plt.plot(preds, label='Prediction', linewidth=2)
    plt.legend()
    plt.savefig(name, bbox_inches='tight')


def adjustment(gt, pred):
    anomaly_state = False
    for i in range(len(gt)):
        if gt[i] == 1 and pred[i] == 1 and not anomaly_state:
            anomaly_state = True
            for j in range(i, 0, -1):
                if gt[j] == 0:
                    break
                else:
                    if pred[j] == 0:
                        pred[j] = 1
            for j in range(i, len(gt)):
                if gt[j] == 0:
                    break
                else:
                    if pred[j] == 0:
                        pred[j] = 1
        elif gt[i] == 0:
            anomaly_state = False
        if anomaly_state:
            pred[i] = 1
    return gt, pred


def cal_accuracy(y_pred, y_true):
    return np.mean(y_pred == y_true)


def custom_collate(batch):
    r"""source: pytorch 1.9.0, only one modification to original code """

    elem = batch[0]
    elem_type = type(elem)
    if isinstance(elem, torch.Tensor):
        out = None
        if torch.utils.data.get_worker_info() is not None:
            # If we're in a background process, concatenate directly into a
            # shared memory tensor to avoid an extra copy
            numel = sum([x.numel() for x in batch])
            storage = elem.storage()._new_shared(numel)
            out = elem.new(storage)
        return torch.stack(batch, 0, out=out)
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
            # array of string classes and object
            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
                raise TypeError(default_collate_err_msg_format.format(elem.dtype))

            return custom_collate([torch.as_tensor(b) for b in batch])
        elif elem.shape == ():  # scalars
            return torch.as_tensor(batch)
    elif isinstance(elem, float):
        return torch.tensor(batch, dtype=torch.float64)
    elif isinstance(elem, int):
        return torch.tensor(batch)
    elif isinstance(elem, str):
        return batch
    elif isinstance(elem, collections.abc.Mapping):
        return {key: custom_collate([d[key] for d in batch]) for key in elem}
    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
        return elem_type(*(custom_collate(samples) for samples in zip(*batch)))
    elif isinstance(elem, collections.abc.Sequence):
        # check to make sure that the elements in batch have consistent size
        it = iter(batch)
        elem_size = len(next(it))
        if not all(len(elem) == elem_size for elem in it):
            raise RuntimeError('each element in list of batch should be of equal size')
        transposed = zip(*batch)
        return [custom_collate(samples) for samples in transposed]

    raise TypeError(default_collate_err_msg_format.format(elem_type))

class HiddenPrints:
    def __init__(self, rank):
        # 如果rank是none，那么就是单机单卡，不需要隐藏打印，将rank设置为0
        if rank is None:
            rank = 0
        self.rank = rank
    def __enter__(self):
        if self.rank == 0:
            return
        self._original_stdout = sys.stdout
        sys.stdout = open(os.devnull, 'w')

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.rank == 0:
            return
        sys.stdout.close()
        sys.stdout = self._original_stdout