""" Utility File containing functions for neural networks """ import torch.nn as nn import torch.nn.functional as F import torch.nn.init as init import torch import torchaudio # 1-dimensional convolutional layer # in the order of conv -> norm -> activation class Conv1d_layer(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, \ stride=1, \ padding="SAME", dilation=1, bias=True, \ norm="batch", activation="relu", \ mode="conv"): super(Conv1d_layer, self).__init__() self.conv1d = nn.Sequential() ''' padding ''' if mode=="deconv": padding = int(dilation * (kernel_size-1) / 2) out_padding = 0 if stride==1 else 1 elif mode=="conv" or "alias_free" in mode: if padding == "SAME": pad = int((kernel_size-1) * dilation) l_pad = int(pad//2) r_pad = pad - l_pad padding_area = (l_pad, r_pad) elif padding == "VALID": padding_area = (0, 0) else: pass ''' convolutional layer ''' if mode=="deconv": self.conv1d.add_module("deconv1d", nn.ConvTranspose1d(in_channels, out_channels, kernel_size, \ stride=stride, padding=padding, output_padding=out_padding, \ dilation=dilation, \ bias=bias)) elif mode=="conv": self.conv1d.add_module(f"{mode}1d_pad", nn.ReflectionPad1d(padding_area)) self.conv1d.add_module(f"{mode}1d", nn.Conv1d(in_channels, out_channels, kernel_size, \ stride=stride, padding=0, \ dilation=dilation, \ bias=bias)) elif "alias_free" in mode: if "up" in mode: up_factor = stride * 2 down_factor = 2 elif "down" in mode: up_factor = 2 down_factor = stride * 2 else: raise ValueError("choose alias-free method : 'up' or 'down'") # procedure : conv -> upsample -> lrelu -> low-pass filter -> downsample # the torchaudio.transforms.Resample's default resampling_method is 'sinc_interpolation' which performs low-pass filter during the process # details at https://pytorch.org/audio/stable/transforms.html self.conv1d.add_module(f"{mode}1d_pad", nn.ReflectionPad1d(padding_area)) self.conv1d.add_module(f"{mode}1d", nn.Conv1d(in_channels, out_channels, kernel_size, \ stride=1, padding=0, \ dilation=dilation, \ bias=bias)) self.conv1d.add_module(f"{mode}upsample", torchaudio.transforms.Resample(orig_freq=1, new_freq=up_factor)) self.conv1d.add_module(f"{mode}lrelu", nn.LeakyReLU()) self.conv1d.add_module(f"{mode}downsample", torchaudio.transforms.Resample(orig_freq=down_factor, new_freq=1)) ''' normalization ''' if norm=="batch": self.conv1d.add_module("batch_norm", nn.BatchNorm1d(out_channels)) # self.conv1d.add_module("batch_norm", nn.SyncBatchNorm(out_channels)) ''' activation ''' if 'alias_free' not in mode: if activation=="relu": self.conv1d.add_module("relu", nn.ReLU()) elif activation=="lrelu": self.conv1d.add_module("lrelu", nn.LeakyReLU()) def forward(self, input): # input shape should be : batch x channel x height x width output = self.conv1d(input) return output # Residual Block # the input is added after the first convolutional layer, retaining its original channel size # therefore, the second convolutional layer's output channel may differ class Res_ConvBlock(nn.Module): def __init__(self, dimension, \ in_channels, out_channels, \ kernel_size, \ stride=1, padding="SAME", \ dilation=1, \ bias=True, \ norm="batch", \ activation="relu", last_activation="relu", \ mode="conv"): super(Res_ConvBlock, self).__init__() if dimension==1: self.conv1 = Conv1d_layer(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=activation) self.conv2 = Conv1d_layer(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=last_activation, mode=mode) elif dimension==2: self.conv1 = Conv2d_layer(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=activation) self.conv2 = Conv2d_layer(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=last_activation, mode=mode) def forward(self, input): c1_out = self.conv1(input) + input c2_out = self.conv2(c1_out) return c2_out # Convoluaionl Block # consists of multiple (number of layer_num) convolutional layers # only the final convoluational layer outputs the desired 'out_channels' class ConvBlock(nn.Module): def __init__(self, dimension, layer_num, \ in_channels, out_channels, \ kernel_size, \ stride=1, padding="SAME", \ dilation=1, \ bias=True, \ norm="batch", \ activation="relu", last_activation="relu", \ mode="conv"): super(ConvBlock, self).__init__() conv_block = [] if dimension==1: for i in range(layer_num-1): conv_block.append(Conv1d_layer(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=activation)) conv_block.append(Conv1d_layer(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=last_activation, mode=mode)) elif dimension==2: for i in range(layer_num-1): conv_block.append(Conv2d_layer(in_channels, in_channels, kernel_size, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=activation)) conv_block.append(Conv2d_layer(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, norm=norm, activation=last_activation, mode=mode)) self.conv_block = nn.Sequential(*conv_block) def forward(self, input): return self.conv_block(input) # Feature-wise Linear Modulation class FiLM(nn.Module): def __init__(self, condition_len=2048, feature_len=1024): super(FiLM, self).__init__() self.film_fc = nn.Linear(condition_len, feature_len*2) self.feat_len = feature_len def forward(self, feature, condition, sefa=None): # SeFA if sefa: weight = self.film_fc.weight.T weight = weight / torch.linalg.norm((weight+1e-07), dim=0, keepdims=True) eigen_values, eigen_vectors = torch.eig(torch.matmul(weight, weight.T), eigenvectors=True) ####### custom parameters ####### chosen_eig_idx = sefa[0] alpha = eigen_values[chosen_eig_idx][0] * sefa[1] ################################# An = eigen_vectors[chosen_eig_idx].repeat(condition.shape[0], 1) alpha_An = alpha * An condition += alpha_An film_factor = self.film_fc(condition).unsqueeze(-1) r, b = torch.split(film_factor, self.feat_len, dim=1) return r*feature + b