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Zero
Running
on
Zero
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from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import pickle
def tensor2im(input_image, imtype=np.uint8):
""""Convert 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
else: # if it is a numpy array, do nothing
image_numpy = input_image
return image_numpy.astype(imtype)
def tensor2vec(vector_tensor):
numpy_vec = vector_tensor.data.cpu().numpy()
if numpy_vec.ndim == 4:
return numpy_vec[:, :, 0, 0]
else:
return numpy_vec
def pickle_load(file_name):
data = None
with open(file_name, 'rb') as f:
data = pickle.load(f)
return data
def pickle_save(file_name, data):
with open(file_name, 'wb') as f:
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
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 interp_z(z0, z1, num_frames, interp_mode='linear'):
zs = []
if interp_mode == 'linear':
for n in range(num_frames):
ratio = n / float(num_frames - 1)
z_t = (1 - ratio) * z0 + ratio * z1
zs.append(z_t[np.newaxis, :])
zs = np.concatenate(zs, axis=0).astype(np.float32)
if interp_mode == 'slerp':
z0_n = z0 / (np.linalg.norm(z0) + 1e-10)
z1_n = z1 / (np.linalg.norm(z1) + 1e-10)
omega = np.arccos(np.dot(z0_n, z1_n))
sin_omega = np.sin(omega)
if sin_omega < 1e-10 and sin_omega > -1e-10:
zs = interp_z(z0, z1, num_frames, interp_mode='linear')
else:
for n in range(num_frames):
ratio = n / float(num_frames - 1)
z_t = np.sin((1 - ratio) * omega) / sin_omega * z0 + np.sin(ratio * omega) / sin_omega * z1
zs.append(z_t[np.newaxis, :])
zs = np.concatenate(zs, axis=0).astype(np.float32)
return zs
def save_image(image_numpy, image_path):
"""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.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, exist_ok=True)
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