utils.py

import os
import logging
import subprocess
from pathlib import Path

from math import exp

import random

import numpy as np

import torch
import torch.nn.functional as F
from torch.backends import cudnn

# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py

def gaussian(window_size, sigma) :
    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
    return gauss / gauss.sum()

def create_window(window_size, channel) :
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = torch.autograd.Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window

def _ssim_map(img1, img2, window, window_size, channel) :
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
    sigma12   = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    return ssim_map

def calc_ssim(img1, img2, window_size = 11, size_average = True) :
    (_, channel, _, _) = img1.size()
    window = create_window(window_size, channel)

    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)

    ssim_map = _ssim_map(img1, img2, window, window_size, channel)

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)

def calc_psnr(img1, img2):
    return 10. * torch.log10(1. / torch.mean((img1 - img2) ** 2))

class AverageMeter(object):
    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

def set_seed(seed) :
    random.seed(seed)
    np.random.seed(seed)
    cudnn.deterministic = True
    cudnn.benchmark = False
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

# https://github.com/ultralytics/yolov5/blob/develop/utils/torch_utils.py

try :
    import thop  # for FLOPS computation
except ImportError:
    thop = None
logger = logging.getLogger(__name__)

def git_describe() :
    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
    if Path('.git').exists() :
        return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1]
    else:
        return ''

def select_device(project_name, device = "", batch_size = None) :
    # device = 'cpu' or '0' or '0,1,2,3'1
    s = f'{project_name} {git_describe()} torch {torch.__version__} '  # string
    cpu = device.lower() == 'cpu'
    if cpu :
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device :  # non-cpu device requested
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability

    cuda = not cpu and torch.cuda.is_available()
    if cuda :
        n = torch.cuda.device_count()
        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
        space = ' ' * len(s)
        for i, d in enumerate(device.split(',') if device else range(n)):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
    else :
        s += 'CPU\n'

    logger.info(s)  # skip a line
    return torch.device('cuda:0' if cuda else 'cpu')

def set_logging(rank=-1):
    logging.basicConfig(
        format="%(message)s",
        level=logging.INFO if rank in [-1, 0] else logging.WARN)