lstm_genre_classifier_pytorch.py

#!/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.

    The plan, first start with a torch.nn implementation, then go for the torch.nn.LSTMCell

"""

import os
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

from GenreFeatureData import (
    GenreFeatureData,
)  # local python class with Audio feature extraction (librosa)

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))

# 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 init_hidden(self):
        return (
            torch.zeros(self.num_layers, self.batch_size, self.hidden_dim),
            torch.zeros(self.num_layers, self.batch_size, self.hidden_dim),
        )

    def forward(self, input):
        # 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)
        logits = self.linear(lstm_out[-1])
        genre_scores = F.log_softmax(logits, dim=1)
        return genre_scores

    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()


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)

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)
    model.hidden = model.init_hidden()
    for i in range(num_batches):

        # zero out gradient, so they don't accumulate btw epochs
        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 = model(X_local_minibatch)                # fwd the bass (forward pass)
        loss = loss_function(y_pred, y_local_minibatch)  # compute loss
        loss.backward()                                  # reeeeewind (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)
    )

    print("Validation ...")  # should this be done every N epochs
    if epoch % 10 == 0:
        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()

            model.hidden = model.init_hidden()
            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 = model(X_local_minibatch)
                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()

print("Testing ...")

# File issue to add pytorch data loaders, is there an open GTZAN pytorch dataloader?
# where to add them? keras or pytorch data repos?