Python code for neural pitch

dnn/torch/neural-pitch/training.py

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+"""
+Training the neural pitch estimator
+
+"""
+
+import argparse
+parser = argparse.ArgumentParser()
+
+parser.add_argument('features_if', type=str, help='.f32 IF Features for training (generated by augmentation script)')
+parser.add_argument('features_xcorr', type=str, help='.f32 Xcorr Features for training (generated by augmentation script)')
+parser.add_argument('features_pitch', type=str, help='.npy Pitch file for training (generated by augmentation script)')
+parser.add_argument('output_folder', type=str, help='Output directory to store the model weights and config')
+parser.add_argument('data_format', type=str, help='Choice of Input Data',choices=['if','xcorr','both'])
+parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False)
+parser.add_argument('--confidence_threshold', type=float, help='Confidence value below which pitch will be neglected during training',default = 0.4,required = False)
+parser.add_argument('--context', type=int, help='Sequence length during training',default = 100,required = False)
+parser.add_argument('--N', type=int, help='STFT window size',default = 320,required = False)
+parser.add_argument('--H', type=int, help='STFT Hop size',default = 160,required = False)
+parser.add_argument('--xcorr_dimension', type=int, help='Dimension of Input cross-correlation',default = 257,required = False)
+parser.add_argument('--freq_keep', type=int, help='Number of Frequencies to keep',default = 30,required = False)
+parser.add_argument('--gru_dim', type=int, help='GRU Dimension',default = 64,required = False)
+parser.add_argument('--output_dim', type=int, help='Output dimension',default = 192,required = False)
+parser.add_argument('--learning_rate', type=float, help='Learning Rate',default = 1.0e-3,required = False)
+parser.add_argument('--epochs', type=int, help='Number of training epochs',default = 50,required = False)
+parser.add_argument('--choice_cel', type=str, help='Choice of Cross Entropy Loss (default or robust)',choices=['default','robust'],default = 'default',required = False)
+
+
+args = parser.parse_args()
+
+# import os
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index)
+
+# Fixing the seeds for reproducability
+import time
+np_seed = int(time.time())
+torch_seed = int(time.time())
+
+import json
+import torch
+torch.manual_seed(torch_seed)
+import numpy as np
+np.random.seed(np_seed)
+from utils import count_parameters
+import tqdm
+import sys
+from datetime import datetime
+from evaluation import rpa
+
+# print(list(range(torch.cuda.device_count())))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# device = 'cpu'
+
+from models import loader_joint as loader
+if args.data_format == 'if':
+    from models import large_if_ccode as model
+    pitch_nn = model(args.freq_keep*3,args.gru_dim,args.output_dim)
+elif args.data_format == 'xcorr':
+    from models import large_xcorr as model
+    pitch_nn = model(args.xcorr_dimension,args.gru_dim,args.output_dim)
+else:
+    from models import large_joint as model
+    pitch_nn = model(args.freq_keep*3,args.xcorr_dimension,args.gru_dim,args.output_dim)
+
+dataset_training = loader(args.features_if,args.features_pitch,args.features_xcorr,args.confidence_threshold,args.context,args.data_format)
+
+def loss_custom(logits,labels,confidence,choice = 'default',nmax = 192,q = 0.7):
+    logits_softmax = torch.nn.Softmax(dim = 1)(logits).permute(0,2,1)
+    labels_one_hot = torch.nn.functional.one_hot(labels.long(),nmax)
+
+    if choice == 'default':
+        # Categorical Cross Entropy
+        CE = -torch.sum(torch.log(logits_softmax*labels_one_hot + 1.0e-6)*labels_one_hot,dim=-1)
+        CE = torch.sum(confidence*CE)
+
+    else:
+        # Robust Cross Entropy
+        CE = (1.0/q)*(1 - torch.sum(torch.pow(logits_softmax*labels_one_hot + 1.0e-7,q),dim=-1) )
+        CE = torch.sum(confidence*CE)
+
+    return CE
+
+# features = args.features
+# pitch = args.crepe_pitch
+# dataset_training = loader(features,pitch,args.confidence_threshold,args.freq_keep,args.context)
+# dataset_training = loader(features,pitch,'../../../../testing/testing_features_10pct_xcorr.f32')
+
+train_dataset, test_dataset = torch.utils.data.random_split(dataset_training, [0.95,0.05],generator=torch.Generator().manual_seed(torch_seed))
+
+batch_size = 256
+train_dataloader = torch.utils.data.DataLoader(dataset = train_dataset,batch_size = batch_size,shuffle = True,num_workers = 0, pin_memory = False)
+test_dataloader = torch.utils.data.DataLoader(dataset = test_dataset,batch_size = batch_size,shuffle = True,num_workers = 0, pin_memory = False)
+
+# pitch_nn = model(args.freq_keep*3,args.gru_dim,args.output_dim).to(device)
+pitch_nn = pitch_nn.to(device)
+num_params = count_parameters(pitch_nn)
+learning_rate = args.learning_rate
+model_opt = torch.optim.Adam(pitch_nn.parameters(), lr = learning_rate)
+
+num_epochs = args.epochs
+
+for epoch in range(num_epochs):
+    losses = []
+    pitch_nn.train()
+    with tqdm.tqdm(train_dataloader) as train_epoch:
+        for i, (xi, yi, ci) in enumerate(train_epoch):
+            yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True)
+            pi = pitch_nn(xi.float())
+            loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim)
+
+            model_opt.zero_grad()
+            loss.backward()
+            model_opt.step()
+
+            losses.append(loss.item())
+            avg_loss = np.mean(losses)
+            train_epoch.set_postfix({"Train Epoch" : epoch, "Train Loss":avg_loss})
+
+    if epoch % 5 == 0:
+        pitch_nn.eval()
+        losses = []
+        with tqdm.tqdm(test_dataloader) as test_epoch:
+            for i, (xi, yi, ci) in enumerate(test_epoch):
+                yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True)
+                pi = pitch_nn(xi.float())
+                loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim)
+                losses.append(loss.item())
+                avg_loss = np.mean(losses)
+                test_epoch.set_postfix({"Epoch" : epoch, "Test Loss":avg_loss})
+
+pitch_nn.eval()
+rpa(pitch_nn,device,data_format = args.data_format)
+
+config = dict(
+data_format = args.data_format,
+epochs = num_epochs,
+window_size =  args.N,
+hop_factor =  args.H,
+freq_keep = args.freq_keep,
+batch_size =  batch_size,
+learning_rate = learning_rate,
+confidence_threshold = args.confidence_threshold,
+model_parameters =  num_params,
+np_seed = np_seed,
+torch_seed = torch_seed,
+xcorr_dim = args.xcorr_dimension,
+dim_input = 3*args.freq_keep,
+gru_dim = args.gru_dim,
+output_dim = args.output_dim,
+choice_cel = args.choice_cel,
+context = args.context,
+)
+
+now = datetime.now()
+dir_pth_save = args.output_folder
+dir_network = dir_pth_save + str(now) + '_net_' + args.data_format + '.pth'
+dir_dictparams = dir_pth_save + str(now) + '_config_' + args.data_format + '.json'
+# Save Weights
+torch.save(pitch_nn.state_dict(), dir_network)
+# Save Config
+with open(dir_dictparams, 'w') as fp:
+    json.dump(config, fp)