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# Copyright (c) SenseTime Research. All rights reserved.
import torch
import cv2
from torchvision import transforms
import numpy as np
import math
def visual(output, out_path):
output = (output + 1)/2
output = torch.clamp(output, 0, 1)
if output.shape[1] == 1:
output = torch.cat([output, output, output], 1)
output = output[0].detach().cpu().permute(1,2,0).numpy()
output = (output*255).astype(np.uint8)
output = output[:,:,::-1]
cv2.imwrite(out_path, output)
def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
lr_ramp = min(1, (1 - t) / rampdown)
lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
lr_ramp = lr_ramp * min(1, t / rampup)
return initial_lr * lr_ramp
def latent_noise(latent, strength):
noise = torch.randn_like(latent) * strength
return latent + noise
def noise_regularize_(noises):
loss = 0
for noise in noises:
size = noise.shape[2]
while True:
loss = (
loss
+ (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2)
+ (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2)
)
if size <= 8:
break
noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2])
noise = noise.mean([3, 5])
size //= 2
return loss
def noise_normalize_(noises):
for noise in noises:
mean = noise.mean()
std = noise.std()
noise.data.add_(-mean).div_(std)
def tensor_to_numpy(x):
x = x[0].permute(1, 2, 0)
x = torch.clamp(x, -1 ,1)
x = (x+1) * 127.5
x = x.cpu().detach().numpy().astype(np.uint8)
return x
def numpy_to_tensor(x):
x = (x / 255 - 0.5) * 2
x = torch.from_numpy(x).unsqueeze(0).permute(0, 3, 1, 2)
x = x.cuda().float()
return x
def tensor_to_pil(x):
x = torch.clamp(x, -1 ,1)
x = (x+1) * 127.5
return transforms.ToPILImage()(x.squeeze_(0))
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