TMM

Sleeping

File size: 11,374 Bytes

7ca9b42

from PIL import Image
import os
import json
import logging
import shutil
import csv
# from lib.network.munit import Vgg16
from torch.autograd import Variable
from torch.optim import lr_scheduler
from easydict import EasyDict as edict

import torch
import torch.nn as nn
import os
import math
import torchvision.utils as vutils
import yaml
import numpy as np
import torch.nn.init as init
import time


def get_config(config_path):
    with open(config_path, 'r') as stream:
        config = yaml.load(stream, Loader=yaml.SafeLoader)
    config = edict(config)
    _, config_filename = os.path.split(config_path)
    config_name, _ = os.path.splitext(config_filename)
    config.name = config_name
    return config

class TextLogger:

    def __init__(self, log_path):
        self.log_path = log_path
        with open(self.log_path, "w") as f:
            f.write("")
    def log(self, log):
        with open(self.log_path, "a+") as f:
            f.write(log + "\n")

def eformat(f, prec):
    s = "%.*e"%(prec, f)
    mantissa, exp = s.split('e')
    # add 1 to digits as 1 is taken by sign +/-
    return "%se%d"%(mantissa, int(exp))


def __write_images(image_outputs, display_image_num, file_name):
    image_outputs = [images.expand(-1, 3, -1, -1) for images in image_outputs] # expand gray-scale images to 3 channels
    image_tensor = torch.cat([images[:display_image_num] for images in image_outputs], 0)
    image_grid = vutils.make_grid(image_tensor.data, nrow=display_image_num, padding=0, normalize=True)
    vutils.save_image(image_grid, file_name, nrow=1)


def write_2images(image_outputs, display_image_num, image_directory, postfix):
    n = len(image_outputs)
    __write_images(image_outputs[0:n//2], display_image_num, '%s/gen_a2b_%s.jpg' % (image_directory, postfix))
    __write_images(image_outputs[n//2:n], display_image_num, '%s/gen_b2a_%s.jpg' % (image_directory, postfix))


def write_one_row_html(html_file, iterations, img_filename, all_size):
    html_file.write("<h3>iteration [%d] (%s)</h3>" % (iterations,img_filename.split('/')[-1]))
    html_file.write("""
        <p><a href="%s">
          <img src="%s" style="width:%dpx">
        </a><br>
        <p>
        """ % (img_filename, img_filename, all_size))
    return


def write_html(filename, iterations, image_save_iterations, image_directory, all_size=1536):
    html_file = open(filename, "w")
    html_file.write('''
    <!DOCTYPE html>
    <html>
    <head>
      <title>Experiment name = %s</title>
      <meta http-equiv="refresh" content="30">
    </head>
    <body>
    ''' % os.path.basename(filename))
    html_file.write("<h3>current</h3>")
    write_one_row_html(html_file, iterations, '%s/gen_a2b_train_current.jpg' % (image_directory), all_size)
    write_one_row_html(html_file, iterations, '%s/gen_b2a_train_current.jpg' % (image_directory), all_size)
    for j in range(iterations, image_save_iterations-1, -1):
        if j % image_save_iterations == 0:
            write_one_row_html(html_file, j, '%s/gen_a2b_test_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_b2a_test_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_a2b_train_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_b2a_train_%08d.jpg' % (image_directory, j), all_size)
    html_file.write("</body></html>")
    html_file.close()


def write_loss(iterations, trainer, train_writer):
    members = [attr for attr in dir(trainer) \
               if not callable(getattr(trainer, attr)) and not attr.startswith("__") and ('loss' in attr or 'grad' in attr or 'nwd' in attr)]
    for m in members:
        train_writer.add_scalar(m, getattr(trainer, m), iterations + 1)


def slerp(val, low, high):
    """
    original: Animating Rotation with Quaternion Curves, Ken Shoemake
    https://arxiv.org/abs/1609.04468
    Code: https://github.com/soumith/dcgan.torch/issues/14, Tom White
    """
    omega = np.arccos(np.dot(low / np.linalg.norm(low), high / np.linalg.norm(high)))
    so = np.sin(omega)
    return np.sin((1.0 - val) * omega) / so * low + np.sin(val * omega) / so * high


def get_slerp_interp(nb_latents, nb_interp, z_dim):
    """
    modified from: PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot
    https://github.com/ptrblck/prog_gans_pytorch_inference
    """

    latent_interps = np.empty(shape=(0, z_dim), dtype=np.float32)
    for _ in range(nb_latents):
        low = np.random.randn(z_dim)
        high = np.random.randn(z_dim)  # low + np.random.randn(512) * 0.7
        interp_vals = np.linspace(0, 1, num=nb_interp)
        latent_interp = np.array([slerp(v, low, high) for v in interp_vals],
                                 dtype=np.float32)
        latent_interps = np.vstack((latent_interps, latent_interp))

    return latent_interps[:, :, np.newaxis, np.newaxis]


# Get model list for resume
def get_model_list(dirname, key):
    if os.path.exists(dirname) is False:
        return None
    gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
                  os.path.isfile(os.path.join(dirname, f)) and key in f and ".pt" in f]
    if gen_models is None:
        return None
    gen_models.sort()
    last_model_name = gen_models[-1]
    return last_model_name


def get_scheduler(optimizer, hyperparameters, iterations=-1):
    if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant':
        scheduler = None # constant scheduler
    elif hyperparameters['lr_policy'] == 'step':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'],
                                        gamma=hyperparameters['gamma'], last_epoch=iterations)
    else:
        return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy'])
    return scheduler


def weights_init(init_type='gaussian'):
    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):
            # print m.__class__.__name__
            if init_type == 'gaussian':
                init.normal_(m.weight.data, 0.0, 0.02)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, "Unsupported initialization: {}".format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)

    return init_fun


class Timer:
    def __init__(self, msg):
        self.msg = msg
        self.start_time = None

    def __enter__(self):
        self.start_time = time.time()

    def __exit__(self, exc_type, exc_value, exc_tb):
        print(self.msg % (time.time() - self.start_time))


class TrainClock(object):
    def __init__(self):
        self.epoch = 1
        self.minibatch = 0
        self.step = 0

    def tick(self):
        self.minibatch += 1
        self.step += 1

    def tock(self):
        self.epoch += 1
        self.minibatch = 0

    def make_checkpoint(self):
        return {
            'epoch': self.epoch,
            'minibatch': self.minibatch,
            'step': self.step
        }

    def restore_checkpoint(self, clock_dict):
        self.epoch = clock_dict['epoch']
        self.minibatch = clock_dict['minibatch']
        self.step = clock_dict['step']


class Table(object):
    def __init__(self, filename):
        '''
        create a table to record experiment results that can be opened by excel
        :param filename: using '.csv' as postfix
        '''
        assert '.csv' in filename
        self.filename = filename

    @staticmethod
    def merge_headers(header1, header2):
        #return list(set(header1 + header2))
        if len(header1) > len(header2):
            return header1
        else:
            return header2

    def write(self, ordered_dict):
        '''
        write an entry
        :param ordered_dict: something like {'name':'exp1', 'acc':90.5, 'epoch':50}
        :return:
        '''
        if os.path.exists(self.filename) == False:
            headers = list(ordered_dict.keys())
            prev_rec = None
        else:
            with open(self.filename) as f:
                reader = csv.DictReader(f)
                headers = reader.fieldnames
                prev_rec = [row for row in reader]
            headers = self.merge_headers(headers, list(ordered_dict.keys()))

        with open(self.filename, 'w', newline='') as f:
            writer = csv.DictWriter(f, headers)
            writer.writeheader()
            if not prev_rec == None:
                writer.writerows(prev_rec)
            writer.writerow(ordered_dict)


class WorklogLogger:
    def __init__(self, log_file):
        logging.basicConfig(filename=log_file,
                            level=logging.DEBUG,
                            format='%(asctime)s - %(threadName)s -  %(levelname)s - %(message)s')

        self.logger = logging.getLogger()

    def put_line(self, line):
        self.logger.info(line)


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name):
        self.name = name
        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 save_args(args, save_dir):
    param_path = os.path.join(save_dir, 'params.json')

    with open(param_path, 'w') as fp:
        json.dump(args.__dict__, fp, indent=4, sort_keys=True)


def ensure_dir(path):
    """
    create path by first checking its existence,
    :param paths: path
    :return:
    """
    if not os.path.exists(path):
        os.makedirs(path)


def ensure_dirs(paths):
    """
    create paths by first checking their existence
    :param paths: list of path
    :return:
    """
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            ensure_dir(path)
    else:
        ensure_dir(paths)


def remkdir(path):
    """
    if dir exists, remove it and create a new one
    :param path:
    :return:
    """
    if os.path.exists(path):
        shutil.rmtree(path)
    os.makedirs(path)


def cycle(iterable):
    while True:
        for x in iterable:
            yield x


def save_image(image_numpy, image_path):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)


def pad_to_16x(x):
    if x % 16 > 0:
        return x - x % 16 + 16
    return x


def pad_to_height(tar_height, img_height, img_width):
    scale = tar_height / img_height
    h = pad_to_16x(tar_height)
    w = pad_to_16x(int(img_width * scale))
    return h, w, scale


def to_gpu(data):
    for key, item in data.items():
        if torch.is_tensor(item):
            data[key] = item.cuda()
    return data