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modules.py
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modules.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from INR_collection import *
#=== Decoders =================================================================
class UpsampleConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
super(UpsampleConvLayer, self).__init__()
self.upsample = upsample
if upsample:
self.upsample_layer = nn.Upsample(scale_factor=upsample)
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ConstantPad1d(reflection_padding, value = 0)
self.conv1d = nn.Conv1d(in_channels, out_channels, kernel_size, stride)
def forward(self, x):
x_in = x
if self.upsample:
x_in = self.upsample_layer(x_in)
out = self.reflection_pad(x_in)
out = self.conv1d(out)
return out
class WaveGANGenerator(nn.Module):
def __init__(self, model_size=64, ngpus=1, num_channels=1, latent_dim=256, audio_length=16000,
post_proc_filt_len=512, verbose=False, upsample=True):
super(WaveGANGenerator, self).__init__()
self.ngpus = ngpus
self.model_size = model_size # d
self.num_channels = num_channels # c
self.latent_dim = latent_dim
self.post_proc_filt_len = post_proc_filt_len
self.audio_length=16000
self.verbose = verbose
self.fc1 = nn.DataParallel(nn.Linear(latent_dim, 256 * model_size))
self.tconv1 = None
self.tconv2 = None
self.tconv3 = None
self.tconv4 = None
self.tconv5 = None
self.upSampConv1 = None
self.upSampConv2 = None
self.upSampConv3 = None
self.upSampConv4 = None
self.upSampConv5 = None
self.upsample = upsample
if self.upsample:
self.upSampConv1 = nn.DataParallel(
UpsampleConvLayer(16 * model_size, 8 * model_size, 25, stride=1, upsample=4))
self.upSampConv2 = nn.DataParallel(
UpsampleConvLayer(8 * model_size, 4 * model_size, 25, stride=1, upsample=4))
self.upSampConv3 = nn.DataParallel(
UpsampleConvLayer(4 * model_size, 2 * model_size, 25, stride=1, upsample=4))
self.upSampConv4 = nn.DataParallel(
UpsampleConvLayer(2 * model_size, model_size, 25, stride=1, upsample=4))
self.upSampConv5 = nn.DataParallel(
UpsampleConvLayer(model_size, num_channels, 25, stride=1, upsample=4))
else:
self.tconv1 = nn.DataParallel(
nn.ConvTranspose1d(16 * model_size, 8 * model_size, 25, stride=4, padding=11,
output_padding=1))
self.tconv2 = nn.DataParallel(
nn.ConvTranspose1d(8 * model_size, 4 * model_size, 25, stride=4, padding=11,
output_padding=1))
self.tconv3 = nn.DataParallel(
nn.ConvTranspose1d(4 * model_size, 2 * model_size, 25, stride=4, padding=11,
output_padding=1))
self.tconv4 = nn.DataParallel(
nn.ConvTranspose1d(2 * model_size, model_size, 25, stride=4, padding=11,
output_padding=1))
self.tconv5 = nn.DataParallel(
nn.ConvTranspose1d(model_size, num_channels, 25, stride=4, padding=11,
output_padding=1))
if post_proc_filt_len:
self.ppfilter1 = nn.DataParallel(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, coordinates, z):
assert coordinates.shape[1] == self.audio_length, \
f"received coordinate amount of {coordinates.shape[1]} in WaveGAN generator, need {self.audio_length}"
z = z[:,0,:]
x = self.fc1(z).view(-1, 16 * self.model_size, 16)
x = F.relu(x)
output = None
if self.verbose:
print(x.shape)
if self.upsample:
x = F.relu(self.upSampConv1(x))
if self.verbose:
print(x.shape)
x = F.relu(self.upSampConv2(x))
if self.verbose:
print(x.shape)
x = F.relu(self.upSampConv3(x))
if self.verbose:
print(x.shape)
x = F.relu(self.upSampConv4(x))
if self.verbose:
print(x.shape)
output = F.tanh(self.upSampConv5(x))
else:
x = F.relu(self.tconv1(x))
if self.verbose:
print(x.shape)
x = F.relu(self.tconv2(x))
if self.verbose:
print(x.shape)
x = F.relu(self.tconv3(x))
if self.verbose:
print(x.shape)
x = F.relu(self.tconv4(x))
if self.verbose:
print(x.shape)
output = F.tanh(self.tconv5(x))
if self.verbose:
print(output.shape)
if self.post_proc_filt_len:
# Pad for "same" filtering
if (self.post_proc_filt_len % 2) == 0:
pad_left = self.post_proc_filt_len // 2
pad_right = pad_left - 1
else:
pad_left = (self.post_proc_filt_len - 1) // 2
pad_right = pad_left
output = self.ppfilter1(F.pad(output, (pad_left, pad_right)))
if self.verbose:
print(output.shape)
output = output[:, :, :self.audio_length].transpose(1,2)
return output
#=== Encoders =================================================================
class ConvEncoder(nn.Module):
def __init__(self, latent_dim, first_filter_size=320):
super(ConvEncoder, self).__init__()
self.conv1 = nn.Conv1d(1, 128, first_filter_size, 4)
self.pool1 = nn.MaxPool1d(4)
self.conv2 = nn.Conv1d(128, 128, 3)
self.pool2 = nn.MaxPool1d(4)
self.conv3 = nn.Conv1d(128, 256, 3)
self.pool3 = nn.MaxPool1d(4)
self.conv4 = nn.Conv1d(256, 512, 3)
self.pool4 = nn.MaxPool1d(4)
self.avgPool = nn.AvgPool1d(14)
self.fc_mu = nn.Linear(512, latent_dim)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pool2(x)
x = self.conv3(x)
x = F.relu(x)
x = self.pool3(x)
x = self.conv4(x)
x = F.relu(x)
x = self.pool4(x)
x = self.avgPool(x)
x = x.permute(0, 2, 1)
x_mu = self.fc_mu(x)
return x_mu
#=== Latent inference =================================================================
class Autoencoder(nn.Module):
def __init__(self, encoder, decoder, audio_length=16000, device='cuda'):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.audio_length = audio_length
self.device = device
def forward(self, x, coordinates):
z = self.encoder(x)
x_recon = self.decoder(coordinates, z)
return x_recon, z
def get_decoder(config):
if config.architecture == "wavegan":
return WaveGANGenerator(model_size=8, ngpus=4, num_channels=1,
latent_dim=config.num_latent, audio_length=16000,
post_proc_filt_len=512)
if config.architecture == "im-net":
return IMNET(config.input_dim, config.output_dim, bias=True, coord_multi=config.coord_multi,
num_layers=5, initial_hidden=1024, z_size=config.num_latent,
init_method={"weights": 'basic', "bias": "zero"})
if config.architecture.startswith("pi-gan"):
arch = config.architecture
if arch == "pi-gan":
return piGAN(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0)
if arch == "pi-gan_prog":
return piGAN_prog(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
total_epochs=config.num_epochs, num_groups=config.num_groups)
if arch == "pi-gan_sine_first":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "relu", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_sine_last":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["relu", "relu", "sine"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_relu":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["relu", "relu", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_concat_middle":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "concat", # "concat", "film", "both"
conditioning_location = "middle", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_concat_all":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "concat", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_min_mapping":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=0, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_five_mapping":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=5, num_INR_layers=8, num_hidden_mapping=256,
num_hidden_INR=256, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_shrinking":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=4, num_hidden_mapping=256,
num_hidden_INR=1024, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "shrinking", # "consistent", "shrinking"
)
if arch == "pi-gan_deep":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=12, num_hidden_mapping=256,
num_hidden_INR=210, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)
if arch == "pi-gan_wide":
return piGAN_custom(config.input_dim, config.output_dim, bias=True,
num_mapping_layers=3, num_INR_layers=4, num_hidden_mapping=256,
num_hidden_INR=365, z_size=config.num_latent,
first_omega_0=config.first_omega_0, hidden_omega_0=config.hidden_omega_0,
activations= ["sine", "sine", "none"], # "sine", "relu", "none"
conditioning_method = "film", # "concat", "film", "both"
conditioning_location = "all", # "all, middle"
network_shape= "consistent", # "consistent", "shrinking"
)