MLBench/ReaLens

1"""This module contains simple helper functions"""2 3from __future__ import print_function4import torch5import numpy as np6from PIL import Image7from pathlib import Path8import torch.distributed as dist9import os10 11 12def tensor2im(input_image, imtype=np.uint8):13    """ "Converts a Tensor array into a numpy image array.14 15    Parameters:16        input_image (tensor) --  the input image tensor array17        imtype (type)        --  the desired type of the converted numpy array18    """19    if not isinstance(input_image, np.ndarray):20        if isinstance(input_image, torch.Tensor):  # get the data from a variable21            image_tensor = input_image.data22        else:23            return input_image24        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array25        if image_numpy.shape[0] == 1:  # grayscale to RGB26            image_numpy = np.tile(image_numpy, (3, 1, 1))27        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling28    else:  # if it is a numpy array, do nothing29        image_numpy = input_image30    return image_numpy.astype(imtype)31 32 33def diagnose_network(net, name="network"):34    """Calculate and print the mean of average absolute(gradients)35 36    Parameters:37        net (torch network) -- Torch network38        name (str) -- the name of the network39    """40    mean = 0.041    count = 042    for param in net.parameters():43        if param.grad is not None:44            mean += torch.mean(torch.abs(param.grad.data))45            count += 146    if count > 0:47        mean = mean / count48    print(name)49    print(mean)50 51 52# initialize ddp53def init_ddp():54    # Initialize DDP if LOCAL_RANK is set55    is_ddp = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 156 57    if is_ddp:58        if not dist.is_initialized():59            dist.init_process_group(backend="nccl")60        local_rank = int(os.environ["LOCAL_RANK"])61        device = torch.device(f"cuda:{local_rank}")62        torch.cuda.set_device(local_rank)63    elif torch.cuda.is_available():64        device = torch.device("cuda:0")65        torch.cuda.set_device(0)66    else:67        device = torch.device("cpu")68    print(f"Initialized with device {device}")69    return device70 71 72# cleanup ddp73def cleanup_ddp():74    if dist.is_initialized():75        dist.destroy_process_group()76 77 78def save_image(image_numpy, image_path, aspect_ratio=1.0):79    """Save a numpy image to the disk80 81    Parameters:82        image_numpy (numpy array) -- input numpy array83        image_path (str)          -- the path of the image84    """85 86    image_pil = Image.fromarray(image_numpy)87    h, w, _ = image_numpy.shape88 89    if aspect_ratio > 1.0:90        image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)91    if aspect_ratio < 1.0:92        image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)93    image_pil.save(image_path)94 95 96def print_numpy(x, val=True, shp=False):97    """Print the mean, min, max, median, std, and size of a numpy array98 99    Parameters:100        val (bool) -- if print the values of the numpy array101        shp (bool) -- if print the shape of the numpy array102    """103    x = x.astype(np.float64)104    if shp:105        print("shape,", x.shape)106    if val:107        x = x.flatten()108        print("mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f" % (np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))109 110 111def mkdirs(paths):112    """create empty directories if they don't exist113 114    Parameters:115        paths (str list) -- a list of directory paths116    """117    if isinstance(paths, list) and not isinstance(paths, str):118        for path in paths:119            mkdir(path)120    else:121        mkdir(paths)122 123 124def mkdir(path):125    """create a single empty directory if it didn't exist126 127    Parameters:128        path (str) -- a single directory path129    """130    Path(path).mkdir(parents=True, exist_ok=True)131