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#import yaml
#from munch import DefaultMunch
import torch
import torch.nn as nn
def norm(img):
low=float(img.min())
high=float(img.max())
img.sub_(low).div_(max(high - low, 1e-5))
def random_sample(batch_size, z_dim, device):
# input to the generator
# z_dim channels, 1x1 pixels
return torch.randn(batch_size,z_dim, 1, 1).to(device)
def init_weight(m):
if isinstance(m, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
# the weights are initialized according to
# https://arxiv.org/abs/1511.06434
nn.init.normal_(m.weight, 0, 0.02)
if m.bias is not None:
if m.bias.data is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
if m.bias.data is not None:
m.bias.data.zero_()
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