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"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import torchvision
import sys
import shutil
import datetime
def check_path_is_static_data(path):
last_extension = path.split(".")[-1]
acceptable_extensions = ['png', 'jpg', 'jpeg', 'npy', 'npz']
if last_extension.lower() in acceptable_extensions:
return True
return False
def check_path(path):
try:
if not os.path.exists(path):
os.makedirs(path)
except OSError as e:
print(e)
def tensor2file(input_image, file_path, ext_name):
"""Convert a tensor into a file.
Parameters:
input_image -- the input image tensor
file_path -- the file path without extension name
"""
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 = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
if image_numpy.shape[2] <= 3:
image_numpy = image_numpy.astype(np.uint8)
# save as image
image_pil = Image.fromarray(image_numpy)
image_pil.save(file_path + '.' + ext_name)
else:
# save as numpy
np.save(file_path + '.npy', image_numpy)
def tensor2im(input_image, imtype=np.uint8):
""""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 = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
if image_numpy.shape[2] > 3: # clip to 3 channel
print('Warning: the channel count of output image exceeds 3.')
image_numpy = image_numpy[:,:,:3]
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)
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)
def print_losses(epoch, iters, len_dataset, current_losses, average_losses, mode='training'):
"""print current losses on console"""
if mode=='training':
message = mode + ': (epoch: %d, iters: %d / %d) ' % (epoch, iters, len_dataset)
else:
message = mode + ': (epoch: %d) ' % epoch
for k, v in current_losses.items():
message += '%s: %.3f ' % (k, v)
for k, v in average_losses.items():
message += 'average %s: %.3f ' % (k, v.avg())
message += datetime.datetime.now().strftime("%Y_%m_%d %H:%M:%S")
print(message) # print the message
def make_grid(model):
"""
create an image grid to be visualized by tensorboard.
"""
visuals = model.get_current_visuals()
names, grids = [], []
for name, img in visuals.items():
names.append(name)
grid = torchvision.utils.make_grid(img[:,:3,:,:], nrow=img.size()[0], normalize=True)
grids.append(grid)
return grids, names
class AverageMeter:
def __init__(self):
self.sum = 0
self.num_item = 0
def update(self, value):
self.sum += value
self.num_item += 1
def avg(self):
return self.sum / self.num_item
def clear(self):
self.sum = 0
self.num_item = 0
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