PointMLP/classification_ScanObjectNN/utils/misc.py

'''Some helper functions for PyTorch, including:
    - get_mean_and_std: calculate the mean and std value of dataset.
    - msr_init: net parameter initialization.
    - progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math
import torch
import shutil
import numpy as np
import random
import torch.nn.functional as F


import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable

__all__ = ['get_mean_and_std', 'init_params', 'mkdir_p', 'AverageMeter',
           'progress_bar','save_model',"save_args","set_seed", "IOStream", "cal_loss"]


def get_mean_and_std(dataset):
    '''Compute the mean and std value of dataset.'''
    dataloader = trainloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)

    mean = torch.zeros(3)
    std = torch.zeros(3)
    print('==> Computing mean and std..')
    for inputs, targets in dataloader:
        for i in range(3):
            mean[i] += inputs[:,i,:,:].mean()
            std[i] += inputs[:,i,:,:].std()
    mean.div_(len(dataset))
    std.div_(len(dataset))
    return mean, std

def init_params(net):
    '''Init layer parameters.'''
    for m in net.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal(m.weight, mode='fan_out')
            if m.bias:
                init.constant(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant(m.weight, 1)
            init.constant(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal(m.weight, std=1e-3)
            if m.bias:
                init.constant(m.bias, 0)

def mkdir_p(path):
    '''make dir if not exist'''
    try:
        os.makedirs(path)
    except OSError as exc:  # Python >2.5
        if exc.errno == errno.EEXIST and os.path.isdir(path):
            pass
        else:
            raise

class AverageMeter(object):
    """Computes and stores the average and current value
       Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
    """
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count



TOTAL_BAR_LENGTH = 65.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, msg=None):
    global last_time, begin_time
    if current == 0:
        begin_time = time.time()  # Reset for new bar.

    cur_len = int(TOTAL_BAR_LENGTH*current/total)
    rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1

    sys.stdout.write(' [')
    for i in range(cur_len):
        sys.stdout.write('=')
    sys.stdout.write('>')
    for i in range(rest_len):
        sys.stdout.write('.')
    sys.stdout.write(']')

    cur_time = time.time()
    step_time = cur_time - last_time
    last_time = cur_time
    tot_time = cur_time - begin_time

    L = []
    L.append('  Step: %s' % format_time(step_time))
    L.append(' | Tot: %s' % format_time(tot_time))
    if msg:
        L.append(' | ' + msg)

    msg = ''.join(L)
    sys.stdout.write(msg)
    # for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
    #     sys.stdout.write(' ')

    # Go back to the center of the bar.
    # for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
    #     sys.stdout.write('\b')
    sys.stdout.write(' %d/%d ' % (current+1, total))

    if current < total-1:
        sys.stdout.write('\r')
    else:
        sys.stdout.write('\n')
    sys.stdout.flush()


def format_time(seconds):
    days = int(seconds / 3600/24)
    seconds = seconds - days*3600*24
    hours = int(seconds / 3600)
    seconds = seconds - hours*3600
    minutes = int(seconds / 60)
    seconds = seconds - minutes*60
    secondsf = int(seconds)
    seconds = seconds - secondsf
    millis = int(seconds*1000)

    f = ''
    i = 1
    if days > 0:
        f += str(days) + 'D'
        i += 1
    if hours > 0 and i <= 2:
        f += str(hours) + 'h'
        i += 1
    if minutes > 0 and i <= 2:
        f += str(minutes) + 'm'
        i += 1
    if secondsf > 0 and i <= 2:
        f += str(secondsf) + 's'
        i += 1
    if millis > 0 and i <= 2:
        f += str(millis) + 'ms'
        i += 1
    if f == '':
        f = '0ms'
    return f


def save_model(net, epoch, path, acc, is_best, **kwargs):
    state = {
        'net': net.state_dict(),
        'epoch': epoch,
        'acc': acc
    }
    for key, value in kwargs.items():
        state[key] = value
    filepath = os.path.join(path, "last_checkpoint.pth")
    torch.save(state, filepath)
    if is_best:
        shutil.copyfile(filepath, os.path.join(path, 'best_checkpoint.pth'))



def save_args(args):
    file = open(os.path.join(args.checkpoint, 'args.txt'), "w")
    for k, v in vars(args).items():
        file.write(f"{k}:\t {v}\n")
    file.close()



def set_seed(seed=None):
    if seed is None:
        return
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = ("%s" % seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True



# create a file and write the text into it
class IOStream():
    def __init__(self, path):
        self.f = open(path, 'a')

    def cprint(self, text):
        print(text)
        self.f.write(text+'\n')
        self.f.flush()

    def close(self):
        self.f.close()


def cal_loss(pred, gold, smoothing=True):
    ''' Calculate cross entropy loss, apply label smoothing if needed. '''

    gold = gold.contiguous().view(-1)

    if smoothing:
        eps = 0.2
        n_class = pred.size(1)

        one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)
        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
        log_prb = F.log_softmax(pred, dim=1)

        loss = -(one_hot * log_prb).sum(dim=1).mean()
    else:
        loss = F.cross_entropy(pred, gold, reduction='mean')

    return loss