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