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utils.py
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utils.py
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import os
import shutil
import torch
import numpy as np
from matplotlib import pyplot as plt
def set_device(gpu_ids):
has_cuda = gpu_ids[0] >= 0
if has_cuda:
device = torch.device('cuda:{}'.format(gpu_ids[0]))
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
else:
device = torch.device('cpu')
return device
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix=''):
torch.save(state, prefix + filename)
if is_best:
print("better model")
shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')
def get_weight(distances, use_weighted): # calculate weights according to the distance value
return [d/sum(distances) for d in distances] if use_weighted else np.ones(len(distances)) / len(distances)
def l2_norm(emb):
norm = torch.pow(emb, 2).sum(dim=1, keepdim=True).sqrt()
emb = torch.div(emb, norm)
return emb
def cosine_sim(emb1, emb2): # cosine similarity
return emb1.mm(emb2.t())
def plot_curve(curves, labels, save_folder, save_names): # plot and save the curves for accuracy and loss
assert len(curves) == len(labels)
assert len(curves) == len(save_names)
for i in range(len(curves)):
plt.figure(i+1)
for j in range(len(curves[i])):
if len(curves[i][j]) > 0:
plt.plot(curves[i][j], label=labels[i][j])
plt.legend(frameon=False)
plt.savefig(os.path.join(save_folder, save_names[i]))
plt.clf()
def plot_image(images, title='image', save_name=None):
num_images = len(images)
plt.figure()
plt.suptitle(title)
for i in range(num_images):
plt.subplot(1, num_images, i+1)
plt.imshow(images[i], vmin=0, vmax=4)
plt.colorbar()
plt.axis('off')
plt.savefig(save_name)
plt.clf()