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utils.py
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utils.py
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import matplotlib.pyplot as plt
import numpy as np
import torchvision
import torch
from model import *
from config import config
def plot_mnist_images(dataset):
fig, axs = plt.subplots(nrows=2, ncols=5, figsize=(10, 5))
axs = axs.ravel()
for i in range(10):
image = np.array(dataset[i][0]).reshape(28, 28)
axs[i].imshow(image, cmap='gray')
axs[i].axis('off')
plt.tight_layout()
plt.savefig('./figures/original_images.png')
def plot_mnist_images_noise(dataset):
fig, axs = plt.subplots(nrows=2, ncols=5, figsize=(10, 5))
axs = axs.ravel()
for i in range(10):
noise = torch.randn(dataset[0][0].shape)
timesteps = torch.LongTensor([199])
noisy_image = noise_scheduler.add_noise(dataset[0][0], noise, timesteps)
image = torchvision.transforms.ToPILImage()(noisy_image.squeeze(1)).resize((256,256))
axs[i].imshow(image, cmap='gray')
axs[i].axis('off')
plt.tight_layout()
plt.savefig('./figures/noisy_images.png')
plt.show()
def transform(dataset, config):
preprocess = torchvision.transforms.Compose(
[
torchvision.transforms.Resize(
(config.image_size, config.image_size)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Lambda(lambda x: 2*(x-0.5)),
]
)
data = [(preprocess(image), label) for image, label in dataset]
return data