File size: 1,311 Bytes
4aed92d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import os
import requests
import torch
import torch.nn as nn
Gl_z = torch.ones(64,10)
def download_file(url, local_filename, chunk_size=1024):
if os.path.exists(local_filename):
return local_filename
r = requests.get(url, stream=True)
with open(local_filename, 'wb') as f:
for chunk in r.iter_content(chunk_size=chunk_size):
if chunk:
f.write(chunk)
return local_filename
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class EDMLoss(nn.Module):
def __init__(self):
super(EDMLoss, self).__init__()
def forward(self, p_target, p_estimate):
assert p_target.shape == p_estimate.shape
cdf_target = torch.cumsum(p_target, dim=1)
cdf_estimate = torch.cumsum(p_estimate, dim=1)
cdf_diff = cdf_estimate - cdf_target
# samplewise_emd = torch.sqrt(torch.mean(torch.pow(torch.abs(cdf_diff), 2))) # train
samplewise_emd = torch.mean(torch.pow(torch.abs(cdf_diff), 1)) # test
return samplewise_emd.mean() |