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import glob
import os
import sys
import time
import math
from datetime import datetime
import random
import logging
from collections import OrderedDict
import natsort
import numpy as np
import cv2
import torch
from torchvision.utils import make_grid
from shutil import get_terminal_size
import yaml
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
def OrderedYaml():
'''yaml orderedDict support'''
_mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
def dict_representer(dumper, data):
return dumper.represent_dict(data.items())
def dict_constructor(loader, node):
return OrderedDict(loader.construct_pairs(node))
Dumper.add_representer(OrderedDict, dict_representer)
Loader.add_constructor(_mapping_tag, dict_constructor)
return Loader, Dumper
####################
# miscellaneous
####################
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
logger = logging.getLogger('base')
logger.info('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path)
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False):
'''set up logger'''
lg = logging.getLogger(logger_name)
formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s',
datefmt='%y-%m-%d %H:%M:%S')
lg.setLevel(level)
if tofile:
log_file = os.path.join(root, phase + '_{}.log'.format(get_timestamp()))
fh = logging.FileHandler(log_file, mode='w')
fh.setFormatter(formatter)
lg.addHandler(fh)
if screen:
sh = logging.StreamHandler()
sh.setFormatter(formatter)
lg.addHandler(sh)
####################
# image convert
####################
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
if hasattr(tensor, 'detach'):
tensor = tensor.detach()
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
def save_img(img, img_path, mode='RGB'):
cv2.imwrite(img_path, img)
####################
# metric
####################
def calculate_psnr(img1, img2):
# img1 and img2 have range [0, 255]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
def get_resume_paths(opt):
resume_state_path = None
resume_model_path = None
ts = opt_get(opt, ['path', 'training_state'])
if opt.get('path', {}).get('resume_state', None) == "auto" and ts is not None:
wildcard = os.path.join(ts, "*")
paths = natsort.natsorted(glob.glob(wildcard))
if len(paths) > 0:
resume_state_path = paths[-1]
resume_model_path = resume_state_path.replace('training_state', 'models').replace('.state', '_G.pth')
else:
resume_state_path = opt.get('path', {}).get('resume_state')
return resume_state_path, resume_model_path
def opt_get(opt, keys, default=None):
if opt is None:
return default
ret = opt
for k in keys:
ret = ret.get(k, None)
if ret is None:
return default
return ret
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