sd
/
stable-diffusion-webui
/extensions
/sd-webui-controlnet
/annotator
/leres
/pix2pix
/util
/util.py
"""This module contains simple helper functions """ | |
from __future__ import print_function | |
import torch | |
import numpy as np | |
from PIL import Image | |
import os | |
def tensor2im(input_image, imtype=np.uint16): | |
""""Converts a Tensor array into a numpy image array. | |
Parameters: | |
input_image (tensor) -- the input image tensor array | |
imtype (type) -- the desired type of the converted numpy array | |
""" | |
if not isinstance(input_image, np.ndarray): | |
if isinstance(input_image, torch.Tensor): # get the data from a variable | |
image_tensor = input_image.data | |
else: | |
return input_image | |
image_numpy = torch.squeeze(image_tensor).cpu().numpy() # convert it into a numpy array | |
image_numpy = (image_numpy + 1) / 2.0 * (2**16-1) # | |
else: # if it is a numpy array, do nothing | |
image_numpy = input_image | |
return image_numpy.astype(imtype) | |
def diagnose_network(net, name='network'): | |
"""Calculate and print the mean of average absolute(gradients) | |
Parameters: | |
net (torch network) -- Torch network | |
name (str) -- the name of the network | |
""" | |
mean = 0.0 | |
count = 0 | |
for param in net.parameters(): | |
if param.grad is not None: | |
mean += torch.mean(torch.abs(param.grad.data)) | |
count += 1 | |
if count > 0: | |
mean = mean / count | |
print(name) | |
print(mean) | |
def save_image(image_numpy, image_path, aspect_ratio=1.0): | |
"""Save a numpy image to the disk | |
Parameters: | |
image_numpy (numpy array) -- input numpy array | |
image_path (str) -- the path of the image | |
""" | |
image_pil = Image.fromarray(image_numpy) | |
image_pil = image_pil.convert('I;16') | |
# image_pil = Image.fromarray(image_numpy) | |
# h, w, _ = image_numpy.shape | |
# | |
# if aspect_ratio > 1.0: | |
# image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC) | |
# if aspect_ratio < 1.0: | |
# image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC) | |
image_pil.save(image_path) | |
def print_numpy(x, val=True, shp=False): | |
"""Print the mean, min, max, median, std, and size of a numpy array | |
Parameters: | |
val (bool) -- if print the values of the numpy array | |
shp (bool) -- if print the shape of the numpy array | |
""" | |
x = x.astype(np.float64) | |
if shp: | |
print('shape,', x.shape) | |
if val: | |
x = x.flatten() | |
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))) | |
def mkdirs(paths): | |
"""create empty directories if they don't exist | |
Parameters: | |
paths (str list) -- a list of directory paths | |
""" | |
if isinstance(paths, list) and not isinstance(paths, str): | |
for path in paths: | |
mkdir(path) | |
else: | |
mkdir(paths) | |
def mkdir(path): | |
"""create a single empty directory if it didn't exist | |
Parameters: | |
path (str) -- a single directory path | |
""" | |
if not os.path.exists(path): | |
os.makedirs(path) | |