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from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import inspect, re
import numpy as np
import os
import collections
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8):
image_numpy = image_tensor[0].cpu().float().numpy()
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
return image_numpy.astype(imtype)
def diagnose_network(net, name='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):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)
def info(object, spacing=10, collapse=1):
"""Print methods and doc strings.
Takes module, class, list, dictionary, or string."""
methodList = [e for e in dir(object) if isinstance(getattr(object, e), collections.Callable)]
processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
print( "\n".join(["%s %s" %
(method.ljust(spacing),
processFunc(str(getattr(object, method).__doc__)))
for method in methodList]) )
def varname(p):
for line in inspect.getframeinfo(inspect.currentframe().f_back)[3]:
m = re.search(r'\bvarname\s*\(\s*([A-Za-z_][A-Za-z0-9_]*)\s*\)', line)
if m:
return m.group(1)
def print_numpy(x, val=True, shp=False):
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):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
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