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Clean up data loading and randomization; add scheduler #29

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Clean up data loading and randomization; add scheduler #29

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68 changes: 32 additions & 36 deletions lib/audio_net.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,22 @@
import csv
import os
import random
import time

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import torch.optim as optim
from numpy.random import default_rng
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import SubsetRandomSampler
import os
from lib.machinelearning import *
import numpy as np
import csv

from config.config import *
import torch.optim as optim
import time
from lib.combine_models import connect_model
from lib.key_poller import KeyPoller
import random
from lib.machinelearning import *


class TinyAudioNet(nn.Module):

Expand Down Expand Up @@ -69,18 +73,20 @@ class AudioNetTrainer:
dataset_size = 0

optimizers = []
validation_loaders = []
train_loaders = []
schedulers = []
validation_loader: torch.utils.data.DataLoader = None
train_loader: torch.utils.data.DataLoader = None
criterion = nn.NLLLoss()
batch_size = 256
validation_split = .2
max_epochs = 300
random_seeds = []
random_seed = 42
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
dataset = False
train_indices = []
input_size = 120
data_seed = 42

def __init__(self, dataset, net_count = 1, audio_settings = None):
self.net_count = net_count
Expand All @@ -94,23 +100,20 @@ def __init__(self, dataset, net_count = 1, audio_settings = None):

split = int(np.floor(self.validation_split * self.dataset_size))

# Split the dataset into validation and training data loaders
dataset_rng = torch.Generator().manual_seed(self.data_seed)
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [self.dataset_size - split, split], generator=dataset_rng)
Comment on lines +104 to +105
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@pokey pokey Dec 23, 2022 •
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Note that the dataset randomizer now has its own seed, and we do the split once, rather than doing it once per ensemble member


np.random.seed(self.random_seed)

for i in range(self.net_count):
self.nets.append(TinyAudioNet(self.input_size, len(self.dataset_labels), True))
self.optimizers.append(optim.SGD(self.nets[i].parameters(), lr=0.003, momentum=0.9, nesterov=True))
self.random_seeds.append(random.randint(0, 100000))


# Split the dataset into validation and training data loaders
indices = list(range(self.dataset_size))
np.random.seed(self.random_seeds[i])
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
self.train_indices.append( train_indices)

train_sampler = SubsetRandomSampler(self.train_indices[i])
valid_sampler = SubsetRandomSampler(val_indices)
self.train_loaders.append(torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, sampler=train_sampler))
self.validation_loaders.append(torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, sampler=valid_sampler))
optimizer = optim.SGD(self.nets[i].parameters(), lr=0.003, momentum=0.9, nesterov=True)
self.optimizers.append(optimizer)
self.schedulers.append( torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min'))
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Added a scheduler


self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
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We create a shuffling data loader, so we don't need to do shuffling during training; that happens for us automatically

self.validation_loader = torch.utils.data.DataLoader(val_dataset, batch_size=self.batch_size)

def train(self, filename):
best_accuracy = []
Expand All @@ -122,8 +125,6 @@ def train(self, filename):
starttime = int(time.time())
combined_model = TinyAudioNetEnsemble(self.nets).to(self.device)

input_size = 120

with open(REPLAYS_FOLDER + "/model_training_" + filename + str(starttime) + ".csv", 'a', newline='') as csvfile:
headers = ['epoch', 'loss', 'avg_validation_accuracy']
headers.extend(self.dataset_labels)
Expand All @@ -138,14 +139,9 @@ def train(self, filename):
running_loss.append(0.0)
self.nets[j].train(True)

# Reshuffle the indexes in the training batch to ensure the net does not memories the order of data being fed in
np.random.shuffle(self.train_indices[j])
train_sampler = SubsetRandomSampler(self.train_indices[j])
self.train_loaders[j] = torch.utils.data.DataLoader(self.dataset, batch_size=self.batch_size, sampler=train_sampler)

i = 0
with torch.set_grad_enabled(True):
for local_batch, local_labels in self.train_loaders[j]:
for local_batch, local_labels in self.train_loader:
# Transfer to GPU
local_batch, local_labels = local_batch.to(self.device), local_labels.to(self.device)

Expand Down Expand Up @@ -196,7 +192,7 @@ def train(self, filename):
accuracy_batch['correct'][dataset_label] = 0
accuracy_batch['percent'][dataset_label] = 0

for local_batch, local_labels in self.validation_loaders[j]:
for local_batch, local_labels in self.validation_loader:
# Transfer to GPU
local_batch, local_labels = local_batch.to(self.device), local_labels.to(self.device)

Expand Down Expand Up @@ -228,6 +224,7 @@ def train(self, filename):
for j in range(self.net_count):
epoch_loss.append(epoch_validation_loss[j] / ( self.dataset_size * self.validation_split ) )
accuracy.append( correct[j] / ( self.dataset_size * self.validation_split ) )
self.schedulers[j].step(epoch_loss[j])
print('[Net: %d] Validation loss: %.4f accuracy %.3f' % (j + 1, epoch_loss[j], accuracy[j]))

print('[Combined] Sum validation loss: %.4f average accuracy %.3f' % (np.sum(epoch_loss), combined_correct / ( self.dataset_size * self.validation_split )))
Expand All @@ -253,7 +250,6 @@ def train(self, filename):
'last_row': csv_row,
'loss': epoch_loss[j],
'epoch': epoch,
'random_seed': self.random_seeds[j],
}, os.path.join(CLASSIFIER_FOLDER, current_filename) + '-weights.pth.tar')

# Persist a new combined model with the best weights if new best weights are given
Expand Down