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from transformers import PreTrainedModel
# Modified version:Vajira Thambawita
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from .configurations_deepfake import DeepFakeConfig
class Transpose1dLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=11, upsample=None, output_padding=1):
super(Transpose1dLayer, self).__init__()
self.upsample = upsample
self.upsample_layer = torch.nn.Upsample(scale_factor=upsample)
reflection_pad = kernel_size // 2
self.reflection_pad = nn.ConstantPad1d(reflection_pad, value=0)
self.conv1d = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride)
self.Conv1dTrans = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding, output_padding)
def forward(self, x):
if self.upsample:
#x = torch.cat((x, in_feature), 1)
return self.conv1d(self.reflection_pad(self.upsample_layer(x)))
else:
return self.Conv1dTrans(x)
class Transpose1dLayer_multi_input(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=11, upsample=None, output_padding=1):
super(Transpose1dLayer_multi_input, self).__init__()
self.upsample = upsample
self.upsample_layer = torch.nn.Upsample(scale_factor=upsample)
reflection_pad = kernel_size // 2
self.reflection_pad = nn.ConstantPad1d(reflection_pad, value=0)
self.conv1d = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride)
self.Conv1dTrans = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding, output_padding)
def forward(self, x, in_feature):
if self.upsample:
x = torch.cat((x, in_feature), 1)
return self.conv1d(self.reflection_pad(self.upsample_layer(x)))
else:
return self.Conv1dTrans(x)
class Pulse2pulseGenerator(nn.Module):
def __init__(self, model_size=50, ngpus=1, num_channels=8,
latent_dim=100, post_proc_filt_len=512,
verbose=False, upsample=True):
super(Pulse2pulseGenerator, self).__init__()
self.ngpus = ngpus
self.model_size = model_size # d
self.num_channels = num_channels # c
self.latent_di = latent_dim
self.post_proc_filt_len = post_proc_filt_len
self.verbose = verbose
# "Dense" is the same meaning as fully connection.
self.fc1 = nn.Linear(latent_dim, 10 * model_size)
stride = 4
if upsample:
stride = 1
upsample = 5
self.deconv_1 = Transpose1dLayer(5 * model_size , 5 * model_size, 25, stride, upsample=upsample)
self.deconv_2 = Transpose1dLayer_multi_input(5 * model_size * 2, 3 * model_size, 25, stride, upsample=upsample)
self.deconv_3 = Transpose1dLayer_multi_input(3 * model_size * 2, model_size, 25, stride, upsample=upsample)
# self.deconv_4 = Transpose1dLayer( model_size, model_size, 25, stride, upsample=upsample)
self.deconv_5 = Transpose1dLayer_multi_input( model_size * 2, int(model_size / 2), 25, stride, upsample=2)
self.deconv_6 = Transpose1dLayer_multi_input( int(model_size / 2) * 2, int(model_size / 5), 25, stride, upsample=upsample)
self.deconv_7 = Transpose1dLayer( int(model_size / 5), num_channels, 25, stride, upsample=2)
#new convolutional layers
self.conv_1 = nn.Conv1d(num_channels, int(model_size / 5), 25, stride=2, padding=25 // 2)
self.conv_2 = nn.Conv1d(model_size // 5, model_size // 2, 25, stride=5, padding= 25 // 2)
self.conv_3 = nn.Conv1d(model_size // 2, model_size , 25, stride=2, padding= 25 // 2)
self.conv_4 = nn.Conv1d(model_size, model_size * 3 , 25, stride=5, padding= 25 // 2)
self.conv_5 = nn.Conv1d(model_size * 3, model_size * 5 , 25, stride=5, padding= 25 // 2)
self.conv_6 = nn.Conv1d(model_size * 5, model_size * 5 , 25, stride=5, padding= 25 // 2)
if post_proc_filt_len:
self.ppfilter1 = nn.Conv1d(num_channels, num_channels, post_proc_filt_len)
for m in self.modules():
if isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight.data)
def forward(self, x):
#print("x shape:", x.shape)
conv_1_out = F.leaky_relu(self.conv_1(x)) # x = (bs, 8, 5000)
# print("conv_1_out shape:", conv_1_out.shape)
conv_2_out = F.leaky_relu(self.conv_2(conv_1_out))
# print("conv_2_out shape:", conv_2_out.shape)
conv_3_out = F.leaky_relu(self.conv_3(conv_2_out))
# print("conv_3_out shape:", conv_3_out.shape)
conv_4_out = F.leaky_relu(self.conv_4(conv_3_out))
# print("conv_4_out shape:", conv_4_out.shape)
conv_5_out = F.leaky_relu(self.conv_5(conv_4_out))
# print("conv_5_out shape:", conv_5_out.shape)
x = F.leaky_relu(self.conv_6(conv_5_out))
#print("last x shape:", x.shape)
#x = self.fc1(x).view(-1, 5*self.model_size, 2) #x = self.fc1(x).view(-1, 16 * self.model_size, 16)
#x = F.relu(x)
#if self.verbose:
# print(x.shape)
x = F.relu(self.deconv_1(x))
if self.verbose:
print(x.shape)
x = F.relu(self.deconv_2(x, conv_5_out))
if self.verbose:
print(x.shape)
x = F.relu(self.deconv_3(x, conv_4_out))
if self.verbose:
print(x.shape)
x = F.relu(self.deconv_5(x, conv_3_out))
if self.verbose:
print(x.shape)
x = F.relu(self.deconv_6(x, conv_2_out))
if self.verbose:
print(x.shape)
output = torch.tanh(self.deconv_7(x))
if self.verbose:
print(output.shape)
return output
class PhaseShuffle(nn.Module):
"""
Performs phase shuffling, i.e. shifting feature axis of a 3D tensor
by a random integer in {-n, n} and performing reflection padding where
necessary.
"""
# Copied from https://github.com/jtcramer/wavegan/blob/master/wavegan.py#L8
def __init__(self, shift_factor):
super(PhaseShuffle, self).__init__()
self.shift_factor = shift_factor
def forward(self, x):
if self.shift_factor == 0:
return x
# uniform in (L, R)
k_list = torch.Tensor(x.shape[0]).random_(0, 2 * self.shift_factor + 1) - self.shift_factor
k_list = k_list.numpy().astype(int)
# Combine sample indices into lists so that less shuffle operations
# need to be performed
k_map = {}
for idx, k in enumerate(k_list):
k = int(k)
if k not in k_map:
k_map[k] = []
k_map[k].append(idx)
# Make a copy of x for our output
x_shuffle = x.clone()
# Apply shuffle to each sample
for k, idxs in k_map.items():
if k > 0:
x_shuffle[idxs] = F.pad(x[idxs][..., :-k], (k, 0), mode='reflect')
else:
x_shuffle[idxs] = F.pad(x[idxs][..., -k:], (0, -k), mode='reflect')
assert x_shuffle.shape == x.shape, "{}, {}".format(x_shuffle.shape,
x.shape)
return x_shuffle
class PhaseRemove(nn.Module):
def __init__(self):
super(PhaseRemove, self).__init__()
def forward(self, x):
pass
class Pulse2pulseDiscriminator(nn.Module):
def __init__(self, model_size=64, ngpus=1, num_channels=8, shift_factor=2,
alpha=0.2, verbose=False):
super(Pulse2pulseDiscriminator, self).__init__()
self.model_size = model_size # d
self.ngpus = ngpus
self.num_channels = num_channels # c
self.shift_factor = shift_factor # n
self.alpha = alpha
self.verbose = verbose
self.conv1 = nn.Conv1d(num_channels, model_size, 25, stride=2, padding=11)
self.conv2 = nn.Conv1d(model_size, 2 * model_size, 25, stride=2, padding=11)
self.conv3 = nn.Conv1d(2 * model_size, 5 * model_size, 25, stride=2, padding=11)
self.conv4 = nn.Conv1d(5 * model_size, 10 * model_size, 25, stride=2, padding=11)
self.conv5 = nn.Conv1d(10 * model_size, 20 * model_size, 25, stride=4, padding=11)
self.conv6 = nn.Conv1d(20 * model_size, 25 * model_size, 25, stride=4, padding=11)
self.conv7 = nn.Conv1d(25 * model_size, 100 * model_size, 25, stride=4, padding=11)
self.ps1 = PhaseShuffle(shift_factor)
self.ps2 = PhaseShuffle(shift_factor)
self.ps3 = PhaseShuffle(shift_factor)
self.ps4 = PhaseShuffle(shift_factor)
self.ps5 = PhaseShuffle(shift_factor)
self.ps6 = PhaseShuffle(shift_factor)
self.fc1 = nn.Linear(25000, 1)
for m in self.modules():
if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight.data)
def forward(self, x):
x = F.leaky_relu(self.conv1(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps1(x)
x = F.leaky_relu(self.conv2(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps2(x)
x = F.leaky_relu(self.conv3(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps3(x)
x = F.leaky_relu(self.conv4(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps4(x)
x = F.leaky_relu(self.conv5(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps5(x)
x = F.leaky_relu(self.conv6(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
x = self.ps6(x)
x = F.leaky_relu(self.conv7(x), negative_slope=self.alpha)
if self.verbose:
print(x.shape)
#print("x shape:", x.shape)
x = x.view(-1, x.shape[1] * x.shape[2])
if self.verbose:
print(x.shape)
return self.fc1(x)
"""
from torch.autograd import Variable
x = Variable(torch.randn(10, 100))
G = WaveGANGenerator(verbose=True, upsample=False)
out = G(x)
print(out.shape)
D = WaveGANDiscriminator(verbose=True)
out2 = D(out)
print(out2.shape)
"""
class DeepFakeECGFromPulse2Pulse(PreTrainedModel):
config_class = DeepFakeConfig
def __init__(self, config):
super().__init__(config)
# block_layer = BLOCK_MAPPING[config.block_type]
self.model = Pulse2pulseGenerator(model_size=50, ngpus=1, num_channels=8,
latent_dim=100, post_proc_filt_len=512,
verbose=False, upsample=True)
def forward(self, num_samples, labels=None):
outputs = []
for i in range(num_samples):
noise = torch.Tensor(1, 8, 5000).uniform_(-1, 1)
x = self.model(noise)
x = x*6000
x = x.int()
x = torch.t(x.squeeze())
outputs.append(x)
return outputs
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