ref: 41a4c9515d879f84e9107f14937e8bdee26d9cd7
dir: /dnn/torch/neural-pitch/models.py/
"""
Pitch Estimation Models and dataloaders
- Classification Based (Input features, output logits)
"""
import torch
import numpy as np
class large_if_ccode(torch.nn.Module):
def __init__(self,input_dim = 88,gru_dim = 64,output_dim = 192):
super(large_if_ccode,self).__init__()
self.activation = torch.nn.Tanh()
self.initial = torch.nn.Linear(input_dim,gru_dim)
self.hidden = torch.nn.Linear(gru_dim,gru_dim)
self.gru = torch.nn.GRU(input_size = gru_dim,hidden_size = gru_dim,batch_first = True)
self.upsample = torch.nn.Linear(gru_dim,output_dim)
def forward(self, x):
x = self.initial(x)
x = self.activation(x)
x = self.hidden(x)
x = self.activation(x)
x,_ = self.gru(x)
x = self.upsample(x)
x = self.activation(x)
x = x.permute(0,2,1)
return x
class large_xcorr(torch.nn.Module):
def __init__(self,input_dim = 90,gru_dim = 64,output_dim = 192):
super(large_xcorr,self).__init__()
self.activation = torch.nn.Tanh()
self.conv = torch.nn.Sequential(
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(1, 8, 3, bias = True),
self.activation,
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(8, 8, 3, bias = True),
self.activation,
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(8, 1, 3, bias = True),
self.activation,
)
# self.conv = torch.nn.Sequential(
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(64,10,3),
# self.activation,
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(10,64,3),
# self.activation,
# )
self.downsample = torch.nn.Sequential(
torch.nn.Linear(input_dim,gru_dim),
self.activation
)
self.GRU = torch.nn.GRU(input_size = gru_dim,hidden_size = gru_dim,num_layers = 1,batch_first = True)
self.upsample = torch.nn.Sequential(
torch.nn.Linear(gru_dim,output_dim),
self.activation
)
def forward(self, x):
# x = x[:,:,:257].unsqueeze(-1)
x = self.conv(x.unsqueeze(-1).permute(0,3,2,1)).squeeze(1)
# print(x.shape)
# x = self.conv(x.permute(0,3,2,1)).squeeze(1)
x,_ = self.GRU(self.downsample(x.permute(0,2,1)))
x = self.upsample(x).permute(0,2,1)
# x = self.downsample(x)
# x = self.activation(x)
# x = self.conv(x.permute(0,2,1)).permute(0,2,1)
# x,_ = self.GRU(x)
# x = self.upsample(x).permute(0,2,1)
return x
class large_joint(torch.nn.Module):
"""
Joint IF-xcorr
1D CNN on IF, merge with xcorr, 2D CNN on merged + GRU
"""
def __init__(self,input_IF_dim = 88,input_xcorr_dim = 224,gru_dim = 64,output_dim = 192):
super(large_joint,self).__init__()
self.activation = torch.nn.Tanh()
print("dim=", input_IF_dim)
self.if_upsample = torch.nn.Sequential(
torch.nn.Linear(input_IF_dim,64),
self.activation,
torch.nn.Linear(64,64),
self.activation,
)
# self.if_upsample = torch.nn.Sequential(
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(90,10,3),
# self.activation,
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(10,257,3),
# self.activation,
# )
self.conv = torch.nn.Sequential(
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(1, 8, 3, bias = True),
self.activation,
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(8, 8, 3, bias = True),
self.activation,
torch.nn.ZeroPad2d((2,0,1,1)),
torch.nn.Conv2d(8, 1, 3, bias = True),
self.activation,
)
# self.conv = torch.nn.Sequential(
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(257,10,3),
# self.activation,
# torch.nn.ConstantPad1d((2,0),0),
# torch.nn.Conv1d(10,64,3),
# self.activation,
# )
self.downsample = torch.nn.Sequential(
torch.nn.Linear(64 + input_xcorr_dim,gru_dim),
self.activation
)
self.GRU = torch.nn.GRU(input_size = gru_dim,hidden_size = gru_dim,num_layers = 1,batch_first = True)
self.upsample = torch.nn.Sequential(
torch.nn.Linear(gru_dim,output_dim),
self.activation
)
def forward(self, x):
xcorr_feat = x[:,:,:224]
if_feat = x[:,:,224:]
# x = torch.cat([xcorr_feat.unsqueeze(-1),self.if_upsample(if_feat).unsqueeze(-1)],axis = -1)
xcorr_feat = self.conv(xcorr_feat.unsqueeze(-1).permute(0,3,2,1)).squeeze(1).permute(0,2,1)
if_feat = self.if_upsample(if_feat)
x = torch.cat([xcorr_feat,if_feat],axis = - 1)
# x = self.conv(x.permute(0,3,2,1)).squeeze(1)
x,_ = self.GRU(self.downsample(x))
x = self.upsample(x).permute(0,2,1)
return x
# Dataloaders
class loader(torch.utils.data.Dataset):
def __init__(self, features_if, file_pitch,confidence_threshold = 0.4,dimension_if = 30,context = 100):
self.if_feat = np.memmap(features_if, dtype=np.float32).reshape(-1,3*dimension_if)
# Resolution of 20 cents
self.cents = np.rint(np.load(file_pitch)[0,:]/20)
self.cents = np.clip(self.cents,0,179)
self.confidence = np.load(file_pitch)[1,:]
# Filter confidence for CREPE
self.confidence[self.confidence < confidence_threshold] = 0
self.context = context
# Clip both to same size
size_common = min(self.if_feat.shape[0],self.cents.shape[0])
self.if_feat = self.if_feat[:size_common,:]
self.cents = self.cents[:size_common]
self.confidence = self.confidence[:size_common]
frame_max = self.if_feat.shape[0]//context
self.if_feat = np.reshape(self.if_feat[:frame_max*context,:],(frame_max,context,3*dimension_if))
self.cents = np.reshape(self.cents[:frame_max*context],(frame_max,context))
self.confidence = np.reshape(self.confidence[:frame_max*context],(frame_max,context))
def __len__(self):
return self.if_feat.shape[0]
def __getitem__(self, index):
return torch.from_numpy(self.if_feat[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index])
class loader_joint(torch.utils.data.Dataset):
def __init__(self, features, file_pitch, confidence_threshold = 0.4,context = 100, choice_data = 'both'):
self.feat = np.memmap(features, mode='r', dtype=np.int8).reshape(-1,312)
#Skip first first two frames for dump_data to sync with CREPE
self.feat = self.feat[2:,:]
self.xcorr = self.feat[:,:224]
self.if_feat = self.feat[:,224:]
ground_truth = np.memmap(file_pitch, mode='r', dtype=np.float32).reshape(-1,2)
self.cents = np.rint(60*np.log2(ground_truth[:,0]/62.5))
mask = (self.cents>=0).astype('float32') * (self.cents<=180).astype('float32')
self.cents = np.clip(self.cents,0,179)
self.confidence = ground_truth[:,1] * mask
# Filter confidence for CREPE
self.confidence[self.confidence < confidence_threshold] = 0
self.context = context
print(np.mean(self.confidence), np.mean(self.cents))
self.choice_data = choice_data
frame_max = self.if_feat.shape[0]//context
self.if_feat = np.reshape(self.if_feat[:frame_max*context,:],(frame_max,context,88))
self.cents = np.reshape(self.cents[:frame_max*context],(frame_max,context))
self.xcorr = np.reshape(self.xcorr[:frame_max*context,:],(frame_max,context,224))
# self.cents = np.rint(60*np.log2(256/(self.periods + 1.0e-8))).astype('int')
# self.cents = np.clip(self.cents,0,239)
self.confidence = np.reshape(self.confidence[:frame_max*context],(frame_max,context))
# print(self.if_feat.shape)
def __len__(self):
return self.if_feat.shape[0]
def __getitem__(self, index):
if self.choice_data == 'both':
return torch.cat([torch.from_numpy((1./127)*self.xcorr[index,:,:]),torch.from_numpy((1./127)*self.if_feat[index,:,:])],dim=-1),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index])
elif self.choice_data == 'if':
return torch.from_numpy((1./127)*self.if_feat[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index])
else:
return torch.from_numpy((1./127)*self.xcorr[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index])