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utils.py
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utils.py
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import os
import time
import random
import shutil
import torch
import numpy as np
from torch.optim import SGD, Adam
from tensorboardX import SummaryWriter
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed) # sets the seed for cpu
torch.cuda.manual_seed(seed) # Sets the seed for the current GPU.
torch.cuda.manual_seed_all(seed) # Sets the seed for the all GPU.
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark=False
torch.set_deterministic(True)
def numpy_random_init(worker_id):
process_seed = torch.initial_seed()
base_seed = process_seed - worker_id
ss = np.random.SeedSequence([worker_id, base_seed])
np.random.seed(ss.generate_state(4))
def numpy_fix_init(worker_id):
np.random.seed(2<<16 + worker_id)
numpy_init_dict = {
"train": numpy_random_init,
"val" : numpy_fix_init,
"test" : numpy_fix_init
}
class Averager():
def __init__(self):
self.n = 0.0
self.v = 0.0
def add(self, v, n=1.0):
self.v = (self.v * self.n + v * n) / (self.n + n)
self.n += n
def item(self):
return self.v
class Timer():
def __init__(self):
self.v = time.time()
def s(self):
self.v = time.time()
def t(self):
return time.time() - self.v
def time_text(t):
if t >= 3600:
return '{:.1f}h'.format(t / 3600)
elif t >= 60:
return '{:.1f}m'.format(t / 60)
else:
return '{:.1f}s'.format(t)
_log_path = None
def set_log_path(path):
global _log_path
_log_path = path
def log(obj, filename='log.txt'):
print(obj)
if _log_path is not None:
with open(os.path.join(_log_path, filename), 'a') as f:
print(obj, file=f)
def ensure_path(path, remove=True):
basename = os.path.basename(path.rstrip('/'))
if os.path.exists(path):
if remove and (basename.startswith('_')
or input('{} exists, remove? (y/[n]): '.format(path)) == 'y'):
shutil.rmtree(path)
os.makedirs(path)
else:
os.makedirs(path)
def set_save_path(save_path, remove=True):
ensure_path(save_path, remove=remove)
set_log_path(save_path)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
return log, writer
def compute_num_params(model, text=False):
tot = int(sum([np.prod(p.shape) for p in model.parameters()]))
if text:
if tot >= 1e6:
return '{:.1f}M'.format(tot / 1e6)
else:
return '{:.1f}K'.format(tot / 1e3)
else:
return tot
def make_optimizer(param_list, optimizer_spec, load_sd=False):
Optimizer = {
'sgd': SGD,
'adam': Adam,
}[optimizer_spec['name']]
optimizer = Optimizer(param_list, **optimizer_spec['args'])
if load_sd:
optimizer.load_state_dict(optimizer_spec['sd'])
return optimizer
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
coord_x = -1+(2*i+1)/W
coord_y = -1+(2*i+1)/H
normalize to (-1, 1)
"""
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
def to_pixel_samples(img, flatten=True):
""" Convert the image to coord-RGB pairs.
img: Tensor, (3, H, W)
"""
coord = make_coord(img.shape[-2:], flatten=flatten)
if flatten:
rgb = img.view(3, -1).permute(1, 0)
else:
rgb = img
return coord, rgb
def calc_psnr(sr, hr, dataset=None, scale=1, rgb_range=1):
diff = (sr - hr) / rgb_range
if dataset is not None:
if dataset in ['sci1k', 'scid', 'siqad']:
gray_coeffs = [65.738, 129.057, 25.064]
convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
diff = diff.mul(convert).sum(dim=1)
shave = 1
elif dataset in ['set5', 'set14', 'urban100', 'b100']:
gray_coeffs = [65.738, 129.057, 25.064]
convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
diff = diff.mul(convert).sum(dim=1)
shave = scale
elif dataset == 'div2k':
shave = scale + 6
else:
raise NotImplementedError
valid = diff[..., shave:-shave, shave:-shave]
else:
valid = diff
mse = valid.pow(2).mean()
return -10 * torch.log10(mse)