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lstm_genre_classifier_pytorch.py
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lstm_genre_classifier_pytorch.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
PyTorch implementation of a simple 2-layer-deep LSTM for genre classification of musical audio.
Feeding the LSTM stack are spectral {centroid, contrast}, chromagram & MFCC features (33 total values)
Question: Why is there a PyTorch implementation, when we already have Keras/Tensorflow?
Answer: So that we can learn more PyTorch and experiment with modulations on basic
architectures within the space of an "easy problem". For example, SRU or SincNets.
I'm am also curious about the relative performances of both toolkits.
"""
import os
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from GenreFeatureData import (
GenreFeatureData,
) # local python class with Audio feature extraction (librosa)
# class definition
class LSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, batch_size, output_dim=8, num_layers=2):
super(LSTM, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.batch_size = batch_size
self.num_layers = num_layers
# setup LSTM layer
self.lstm = nn.LSTM(self.input_dim, self.hidden_dim, self.num_layers)
# setup output layer
self.linear = nn.Linear(self.hidden_dim, output_dim)
def forward(self, input, hidden=None):
# lstm step => then ONLY take the sequence's final timetep to pass into the linear/dense layer
# Note: lstm_out contains outputs for every step of the sequence we are looping over (for BPTT)
# but we just need the output of the last step of the sequence, aka lstm_out[-1]
lstm_out, hidden = self.lstm(input, hidden)
logits = self.linear(lstm_out[-1]) # equivalent to return_sequences=False from Keras
genre_scores = F.log_softmax(logits, dim=1)
return genre_scores, hidden
def get_accuracy(self, logits, target):
""" compute accuracy for training round """
corrects = (
torch.max(logits, 1)[1].view(target.size()).data == target.data
).sum()
accuracy = 100.0 * corrects / self.batch_size
return accuracy.item()
def main():
genre_features = GenreFeatureData()
# if all of the preprocessed files do not exist, regenerate them all for self-consistency
if (
os.path.isfile(genre_features.train_X_preprocessed_data)
and os.path.isfile(genre_features.train_Y_preprocessed_data)
and os.path.isfile(genre_features.dev_X_preprocessed_data)
and os.path.isfile(genre_features.dev_Y_preprocessed_data)
and os.path.isfile(genre_features.test_X_preprocessed_data)
and os.path.isfile(genre_features.test_Y_preprocessed_data)
):
print("Preprocessed files exist, deserializing npy files")
genre_features.load_deserialize_data()
else:
print("Preprocessing raw audio files")
genre_features.load_preprocess_data()
train_X = torch.from_numpy(genre_features.train_X).type(torch.Tensor)
dev_X = torch.from_numpy(genre_features.dev_X).type(torch.Tensor)
test_X = torch.from_numpy(genre_features.test_X).type(torch.Tensor)
# Targets is a long tensor of size (N,) which tells the true class of the sample.
train_Y = torch.from_numpy(genre_features.train_Y).type(torch.LongTensor)
dev_Y = torch.from_numpy(genre_features.dev_Y).type(torch.LongTensor)
test_Y = torch.from_numpy(genre_features.test_Y).type(torch.LongTensor)
# Convert {training, test} torch.Tensors
print("Training X shape: " + str(genre_features.train_X.shape))
print("Training Y shape: " + str(genre_features.train_Y.shape))
print("Validation X shape: " + str(genre_features.dev_X.shape))
print("Validation Y shape: " + str(genre_features.dev_Y.shape))
print("Test X shape: " + str(genre_features.test_X.shape))
print("Test Y shape: " + str(genre_features.test_Y.shape))
batch_size = 35 # num of training examples per minibatch
num_epochs = 400
# Define model
print("Build LSTM RNN model ...")
model = LSTM(
input_dim=33, hidden_dim=128, batch_size=batch_size, output_dim=8, num_layers=2
)
loss_function = nn.NLLLoss() # expects ouputs from LogSoftmax
optimizer = optim.Adam(model.parameters(), lr=0.001)
# To keep LSTM stateful between batches, you can set stateful = True, which is not suggested for training
stateful = False
train_on_gpu = torch.cuda.is_available()
if train_on_gpu:
print("\nTraining on GPU")
else:
print("\nNo GPU, training on CPU")
# all training data (epoch) / batch_size == num_batches (12)
num_batches = int(train_X.shape[0] / batch_size)
num_dev_batches = int(dev_X.shape[0] / batch_size)
val_loss_list, val_accuracy_list, epoch_list = [], [], []
print("Training ...")
for epoch in range(num_epochs):
train_running_loss, train_acc = 0.0, 0.0
# Init hidden state - if you don't want a stateful LSTM (between epochs)
hidden_state = None
for i in range(num_batches):
# zero out gradient, so they don't accumulate btw batches
model.zero_grad()
# train_X shape: (total # of training examples, sequence_length, input_dim)
# train_Y shape: (total # of training examples, # output classes)
#
# Slice out local minibatches & labels => Note that we *permute* the local minibatch to
# match the PyTorch expected input tensor format of (sequence_length, batch size, input_dim)
X_local_minibatch, y_local_minibatch = (
train_X[i * batch_size: (i + 1) * batch_size, ],
train_Y[i * batch_size: (i + 1) * batch_size, ],
)
# Reshape input & targets to "match" what the loss_function wants
X_local_minibatch = X_local_minibatch.permute(1, 0, 2)
# NLLLoss does not expect a one-hot encoded vector as the target, but class indices
y_local_minibatch = torch.max(y_local_minibatch, 1)[1]
y_pred, hidden_state = model(X_local_minibatch, hidden_state) # forward pass
# Stateful = False for training. Do we go Stateful = True during inference/prediction time?
if not stateful:
hidden_state = None
else:
h_0, c_0 = hidden_state
h_0.detach_(), c_0.detach_()
hidden_state = (h_0, c_0)
loss = loss_function(y_pred, y_local_minibatch) # compute loss
loss.backward() # backward pass
optimizer.step() # parameter update
train_running_loss += loss.detach().item() # unpacks the tensor into a scalar value
train_acc += model.get_accuracy(y_pred, y_local_minibatch)
print(
"Epoch: %d | NLLoss: %.4f | Train Accuracy: %.2f"
% (epoch, train_running_loss / num_batches, train_acc / num_batches)
)
if epoch % 10 == 0:
print("Validation ...") # should this be done every N=10 epochs
val_running_loss, val_acc = 0.0, 0.0
# Compute validation loss, accuracy. Use torch.no_grad() & model.eval()
with torch.no_grad():
model.eval()
hidden_state = None
for i in range(num_dev_batches):
X_local_validation_minibatch, y_local_validation_minibatch = (
dev_X[i * batch_size: (i + 1) * batch_size, ],
dev_Y[i * batch_size: (i + 1) * batch_size, ],
)
X_local_minibatch = X_local_validation_minibatch.permute(1, 0, 2)
y_local_minibatch = torch.max(y_local_validation_minibatch, 1)[1]
y_pred, hidden_state = model(X_local_minibatch, hidden_state)
if not stateful:
hidden_state = None
val_loss = loss_function(y_pred, y_local_minibatch)
val_running_loss += (
val_loss.detach().item()
) # unpacks the tensor into a scalar value
val_acc += model.get_accuracy(y_pred, y_local_minibatch)
model.train() # reset to train mode after iterationg through validation data
print(
"Epoch: %d | NLLoss: %.4f | Train Accuracy: %.2f | Val Loss %.4f | Val Accuracy: %.2f"
% (
epoch,
train_running_loss / num_batches,
train_acc / num_batches,
val_running_loss / num_dev_batches,
val_acc / num_dev_batches,
)
)
epoch_list.append(epoch)
val_accuracy_list.append(val_acc / num_dev_batches)
val_loss_list.append(val_running_loss / num_dev_batches)
# visualization loss
plt.plot(epoch_list, val_loss_list)
plt.xlabel("# of epochs")
plt.ylabel("Loss")
plt.title("LSTM: Loss vs # epochs")
plt.show()
# visualization accuracy
plt.plot(epoch_list, val_accuracy_list, color="red")
plt.xlabel("# of epochs")
plt.ylabel("Accuracy")
plt.title("LSTM: Accuracy vs # epochs")
# plt.savefig('graph.png')
plt.show()
if __name__ == "__main__":
main()