Spaces:
Runtime error
Runtime error
File size: 1,543 Bytes
0164e4a |
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 46 47 48 49 50 51 52 53 54 55 56 |
import glob
import os
import torch
import torch.nn as nn
def get_padding(kernel_size, dilation=1):
return int((kernel_size*dilation - dilation)/2)
def get_padding_2d(kernel_size, dilation=(1, 1)):
return (int((kernel_size[0]*dilation[0] - dilation[0])/2), int((kernel_size[1]*dilation[1] - dilation[1])/2))
def load_checkpoint(filepath, device):
assert os.path.isfile(filepath)
print("Loading '{}'".format(filepath))
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
return checkpoint_dict
def save_checkpoint(filepath, obj):
print("Saving checkpoint to {}".format(filepath))
torch.save(obj, filepath)
print("Complete.")
def scan_checkpoint(cp_dir, prefix):
pattern = os.path.join(cp_dir, prefix + '????????')
cp_list = glob.glob(pattern)
if len(cp_list) == 0:
return None
return sorted(cp_list)[-1]
class LearnableSigmoid_1d(nn.Module):
def __init__(self, in_features, beta=1):
super().__init__()
self.beta = beta
self.slope = nn.Parameter(torch.ones(in_features))
self.slope.requiresGrad = True
def forward(self, x):
return self.beta * torch.sigmoid(self.slope * x)
class LearnableSigmoid_2d(nn.Module):
def __init__(self, in_features, beta=1):
super().__init__()
self.beta = beta
self.slope = nn.Parameter(torch.ones(in_features, 1))
self.slope.requiresGrad = True
def forward(self, x):
return self.beta * torch.sigmoid(self.slope * x)
|