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utils.py
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utils.py
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import torch
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
import math
import itertools
import imageio
import natsort
from glob import glob
def get_data_loader(root, batch_size):
transform = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
dataset = datasets.ImageFolder(root=root, transform=transform)
# Data Loader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
return dataloader
def generate_images(epoch, path, fixed_noise, num_test_samples, netG, device, use_fixed=False):
z = torch.randn(num_test_samples, 100, 1, 1, device=device)
size_figure_grid = int(math.sqrt(num_test_samples))
title = None
if use_fixed:
generated_fake_images = netG(fixed_noise)
path += 'fixed_noise/'
title = 'Fixed Noise'
else:
generated_fake_images = netG(z)
path += 'variable_noise/'
title = 'Variable Noise'
fig, ax = plt.subplots(size_figure_grid, size_figure_grid, figsize=(6,6))
for i, j in itertools.product(range(size_figure_grid), range(size_figure_grid)):
ax[i,j].get_xaxis().set_visible(False)
ax[i,j].get_yaxis().set_visible(False)
for k in range(num_test_samples):
i = k//4
j = k%4
ax[i,j].cla()
ax[i,j].imshow((generated_fake_images[k].cpu().data.numpy().transpose(1, 2, 0) + 1) / 2 )
label = 'Epoch_{}'.format(epoch+1)
fig.text(0.5, 0.04, label, ha='center')
fig.suptitle(title)
fig.savefig(path+label+'.png')
def save_gif(path, fps, max_num=100, fixed_noise=False):
if fixed_noise==True:
path += 'fixed_noise/'
else:
path += 'variable_noise/'
images = glob(path + '*.png')
images = natsort.natsorted(images)
images = images[:max_num]
gif = []
for image in images:
gif.append(imageio.imread(image))
imageio.mimsave(path+'animated.gif', gif, fps=fps)