utils.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 os
import sys
import time
import math
from sklearn.metrics import f1_score

import torch.nn as nn
import torch.nn.init as init

import os
import random
import numpy as np
import torch
# from torch import nn

def seed_everything(seed=1):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    # torch.cuda.manual_seed(seed)
    ## torch.backends.cudnn.deterministic = True

def get_mean_and_std(dataset):
    '''Compute the mean and std value of dataset.'''
    dataloader = 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)


_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)

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_txt(dirpath, fname, content):
    if not os.path.exists(dirpath):
        os.makedirs(dirpath)
        
    with open(os.path.join(dirpath,fname), 'w+') as f:
        f.write(content)

def calculate_score(y_true, y_pred, average="macro"):
    
    return f1_score(y_true, y_pred, average=average)

def get_results(data_name, num_devices, num_repeats, main_dir):
    results = {}
    for seed_idx in range(num_repeats):
        results[seed_idx] = { }
        for device_idx in range(num_devices):
            
            target_path = os.path.join(main_dir, f"{data_name}_{num_devices}devices_seed{seed_idx}", f"{device_idx}.pth")
            
            f = torch.load(target_path, map_location=torch.device('cpu'))

            results[seed_idx][device_idx] = { k:v for k, v in f.items() if k in ["y_test_true", "y_test_pred_beliefs", "y_val_true", "y_val_pred_beliefs"] }
    
    return results

def get_y_test_beliefs(results, num_repeats):
    res = {}
    for seed_idx in range(num_repeats):
        res[seed_idx] = ({ 
                device_idx: seed_dict["y_test_pred_beliefs"] for device_idx, seed_dict in results[seed_idx].items()
            },
            results[seed_idx][0]["y_test_true"]
        )

    return res

def get_y_val_test_beliefs(results, num_repeats):
    res = {}
    for seed_idx in range(num_repeats):
        res[seed_idx] = ({ 
                device_idx: seed_dict["y_val_pred_beliefs"] for device_idx, seed_dict in results[seed_idx].items()
            },
            results[seed_idx][0]["y_val_true"],
            { 
                device_idx: seed_dict["y_test_pred_beliefs"] for device_idx, seed_dict in results[seed_idx].items()
            },
            results[seed_idx][0]["y_test_true"]
        )

    return res