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| import torch.nn as nn
import torch.nn.init as init
import torch
import numpy as np
import math
from reedsolo import RSCodec
import zlib
rs = RSCodec(128)
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
class IWT(nn.Module):
def __init__(self):
super(IWT, self).__init__()
self.requires_grad = False
def forward(self, x):
r = 2
in_batch, in_channel, in_height, in_width = x.size()
out_batch, out_channel, out_height, out_width = in_batch, int(
in_channel / (r ** 2)), r * in_height, r * in_width
x1 = x[:, 0:out_channel, :, :] / 2
x2 = x[:, out_channel:out_channel * 2, :, :] / 2
x3 = x[:, out_channel * 2:out_channel * 3, :, :] / 2
x4 = x[:, out_channel * 3:out_channel * 4, :, :] / 2
h = torch.zeros([out_batch, out_channel, out_height, out_width]).float().cuda()
h[:, :, 0::2, 0::2] = x1 - x2 - x3 + x4
h[:, :, 1::2, 0::2] = x1 - x2 + x3 - x4
h[:, :, 0::2, 1::2] = x1 + x2 - x3 - x4
h[:, :, 1::2, 1::2] = x1 + x2 + x3 + x4
return h
class DWT(nn.Module):
def __init__(self):
super(DWT, self).__init__()
self.requires_grad = False
def forward(self, x):
x01 = x[:, :, 0::2, :] / 2
x02 = x[:, :, 1::2, :] / 2
x1 = x01[:, :, :, 0::2]
x2 = x02[:, :, :, 0::2]
x3 = x01[:, :, :, 1::2]
x4 = x02[:, :, :, 1::2]
x_LL = x1 + x2 + x3 + x4
x_HL = -x1 - x2 + x3 + x4
x_LH = -x1 + x2 - x3 + x4
x_HH = x1 - x2 - x3 + x4
return torch.cat((x_LL, x_HL, x_LH, x_HH), 1)
def random_data(cover,device):
return torch.zeros(cover.size(), device=device).random_(0, 2)
def auxiliary_variable(shape):
noise = torch.zeros(shape).cuda()
for i in range(noise.shape[0]):
noise[i] = torch.randn(noise[i].shape).cuda()
return noise
def computePSNR(origin,pred):
origin = np.array(origin)
origin = origin.astype(np.float32)
pred = np.array(pred)
pred = pred.astype(np.float32)
mse = np.mean((origin/1.0 - pred/1.0) ** 2 )
if mse < 1.0e-10:
return 100
return 10 * math.log10(255.0**2/mse)
def make_payload(width, height, depth, text, batch = 1):
message = text_to_bits(text) + [0] * 32
payload = message
while len(payload) < batch * width * height * depth:
payload += message
payload = payload[:batch * width * height * depth]
return torch.FloatTensor(payload).view(batch, depth, height, width)
def text_to_bits(text):
return bytearray_to_bits(text_to_bytearray(text))
def bytearray_to_bits(x):
result = []
for i in x:
bits = bin(i)[2:]
bits = '00000000'[len(bits):] + bits
result.extend([int(b) for b in bits])
return result
def text_to_bytearray(text):
assert isinstance(text, str), "expected a string"
x = zlib.compress(text.encode("utf-8"))
x = rs.encode(bytearray(x))
return x
def bits_to_bytearray(bits):
ints = []
bits = np.array(bits)
bits = 0 + bits
bits = bits = bits.tolist()
for b in range(len(bits) // 8):
byte = bits[b * 8:(b + 1) * 8]
ints.append(int(''.join([str(bit) for bit in byte]), 2))
return bytearray(ints)
def bytearray_to_text(x):
try:
text = rs.decode(x)
text = zlib.decompress(text[0])
return text.decode("utf-8")
except BaseException:
return False
|
