Helper code for Lab 2

[pxaris]

Issue που θα χρησιμοποιηθεί κατά τη διάρκεια του 2ου Εργαστηρίου για τη σημείωση βοηθητικού κώδικα, οδηγιών κ.α.

[pxaris]

Prep-1

https://www.kaggle.com/

• Create > New Notebook
• Add data: Click File > Add Data > Search by URL > https://www.kaggle.com/geoparslp/patreco3-multitask-affective-music
• clone labs repo: !git clone https://github.com/slp-ntua/patrec-labs.git

Prep2

add path to system and install requirements

import sys
sys.path.append('/kaggle/working/patrec-labs/lab3')

!ls
!pip install -r patrec-labs/lab3/requirements.txt

0

# step 0
import os
os.listdir('../input/patreco3-multitask-affective-music/data/')

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4

# Step 4: Data loading and analysis
from dataset import CLASS_MAPPING, SpectrogramDataset

PARENT_DATA_DIR = '../input/patreco3-multitask-affective-music/data/'

train_dataset = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, 'fma_genre_spectrograms'), class_mapping=CLASS_MAPPING, train=True
)

print(train_dataset[10])
print(f"Input: {train_dataset[10][0].shape}")
print(f"Label: {train_dataset[10][1]}")
print(f"Original length: {train_dataset[10][2]}")

def plot_class_histogram(dataset, title):
    plt.figure()
    plt.hist(dataset.labels, rwidth=0.5, align='mid')
    plt.xticks(dataset.labels, dataset.labels.astype(str))
    plt.title(title)
    plt.xlabel('Class Labels')
    plt.ylabel('Frequencies')

plot_class_histogram(train_dataset, title='Histogram of classes with merged classes')

train_dataset_all = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, 'fma_genre_spectrograms'), class_mapping=None, train=True
)
plot_class_histogram(train_dataset_all, title='Histogram of classes with all classes')

[pxaris]

5

• customize read_spectrogram

  def read_spectrogram(spectrogram_file, feat_type):
  spectrogram = np.load(spectrogram_file)
  # spectrograms contains a fused mel spectrogram and chromagram
  
  if feat_type=='mel':
      return spectrogram[:128, :].T
  elif feat_type=='chroma':
      return spectrogram[128:, :].T
  
  return spectrogram.T

• the whole updated file

  
  import copy
  import os

import numpy as np from sklearn.preprocessing import LabelEncoder from torch.utils.data import DataLoader, Dataset, SubsetRandomSampler

HINT: Use this class mapping to merge similar classes and ignore classes that do not work very well

CLASS_MAPPING = { "Rock": "Rock", "Psych-Rock": "Rock", "Indie-Rock": None, "Post-Rock": "Rock", "Psych-Folk": "Folk", "Folk": "Folk", "Metal": "Metal", "Punk": "Metal", "Post-Punk": None, "Trip-Hop": "Trip-Hop", "Pop": "Pop", "Electronic": "Electronic", "Hip-Hop": "Hip-Hop", "Classical": "Classical", "Blues": "Blues", "Chiptune": "Electronic", "Jazz": "Jazz", "Soundtrack": None, "International": None, "Old-Time": None, }

def torch_train_val_split( dataset, batch_train, batch_eval, val_size=0.2, shuffle=True, seed=420 ):

Creating data indices for training and validation splits:

dataset_size = len(dataset)
indices = list(range(dataset_size))
val_split = int(np.floor(val_size * dataset_size))
if shuffle:
    np.random.seed(seed)
    np.random.shuffle(indices)
train_indices = indices[val_split:]
val_indices = indices[:val_split]

# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)

train_loader = DataLoader(dataset, batch_size=batch_train, sampler=train_sampler)
val_loader = DataLoader(dataset, batch_size=batch_eval, sampler=val_sampler)
return train_loader, val_loader

def read_spectrogram(spectrogram_file, feat_type): spectrogram = np.load(spectrogram_file)

spectrograms contains a fused mel spectrogram and chromagram

if feat_type=='mel':
    return spectrogram[:128, :].T
elif feat_type=='chroma':
    return spectrogram[128:, :].T

return spectrogram.T

class LabelTransformer(LabelEncoder): def inverse(self, y): try: return super(LabelTransformer, self).inverse_transform(y) except: return super(LabelTransformer, self).inverse_transform([y])

def transform(self, y):
    try:
        return super(LabelTransformer, self).transform(y)
    except:
        return super(LabelTransformer, self).transform([y])

class PaddingTransform(object): def init(self, max_length, padding_value=0): self.max_length = max_length self.padding_value = padding_value

def __call__(self, s):
    if len(s) == self.max_length:
        return s

    if len(s) > self.max_length:
        return s[: self.max_length]

    if len(s) < self.max_length:
        s1 = copy.deepcopy(s)
        pad = np.zeros((self.max_length - s.shape[0], s.shape[1]), dtype=np.float32)
        s1 = np.vstack((s1, pad))
        return s1

class SpectrogramDataset(Dataset): def init( self, path, class_mapping=None, train=True, feat_type='mel', max_length=-1, regression=None ): t = "train" if train else "test" p = os.path.join(path, t) self.regression = regression

    self.full_path = p
    self.index = os.path.join(path, "{}_labels.txt".format(t))
    self.files, labels = self.get_files_labels(self.index, class_mapping)
    self.feats = [read_spectrogram(os.path.join(p, f), feat_type) for f in self.files]
    self.feat_dim = self.feats[0].shape[1]
    self.lengths = [len(i) for i in self.feats]
    self.max_length = max(self.lengths) if max_length <= 0 else max_length
    self.zero_pad_and_stack = PaddingTransform(self.max_length)
    self.label_transformer = LabelTransformer()
    if isinstance(labels, (list, tuple)):
        if not regression:
            self.labels = np.array(
                self.label_transformer.fit_transform(labels)
            ).astype("int64")
        else:
            self.labels = np.array(labels).astype("float64")

def get_files_labels(self, txt, class_mapping):
    with open(txt, "r") as fd:
        lines = [l.rstrip().split("\t") for l in fd.readlines()[1:]]
    files, labels = [], []
    for l in lines:            
        if self.regression:
            l = l[0].split(",")
            files.append(l[0] + ".fused.full.npy")
            labels.append(l[self.regression])
            continue
        label = l[1]
        if class_mapping:
            label = class_mapping[l[1]]
        if not label:
            continue
        fname = l[0]
        if fname.endswith(".gz"):
            fname = ".".join(fname.split(".")[:-1])

        if 'fma_genre_spectrograms_beat' in self.full_path.split('/'): # necessary fix 1
            fname = fname.replace('beatsync.fused', 'fused.full')            
        if 'test' in self.full_path.split('/'): # necessary fix 2
            fname = fname.replace('full.fused', 'fused.full')

        files.append(fname)
        labels.append(label)
    return files, labels

def __getitem__(self, item):
    length = min(self.lengths[item], self.max_length)
    return self.zero_pad_and_stack(self.feats[item]), self.labels[item], length

def __len__(self):
    return len(self.labels)

[pxaris]

# dataloaders for specific features type, e.g. 'mel'
BATCH_SIZE = 8
MAX_LENGTH = 150
DEVICE = 'cuda'

train_dataset = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, 'fma_genre_spectrograms'), class_mapping=CLASS_MAPPING, 
    train=True, feat_type='mel', max_length=MAX_LENGTH
)

train_loader, val_loader = torch_train_val_split(
    train_dataset, BATCH_SIZE, BATCH_SIZE
)

# get the 1st batch values of the data loader
x_b1, y_b1, lengths_b1 = next(iter(train_loader))

# print the shape of the 1st item of the 1st batch of the data loader
input_shape = x_b1[0].shape
print(input_shape)

[pxaris]

• copy paste from train.py & add implementation to the following functions

  
  import torch
  import torch.nn as nn

def overfit_with_a_couple_of_batches(model, train_loader, optimizer, device): print('Training in overfitting mode...') epochs = 400

# get only the 1st batch
x_b1, y_b1, lengths_b1 = next(iter(train_loader))    
model.train()
for epoch in range(epochs):        
    loss, logits = model(x_b1.float().to(device), y_b1.to(device), lengths_b1.to(device))
    # prepare
    optimizer.zero_grad()
    # backward
    loss.backward()
    # optimizer step
    optimizer.step()

    if epoch == 0 or (epoch+1)%20 == 0:
        print(f'Epoch {epoch+1}, Loss at training set: {loss.item()}')

def train(model, train_loader, val_loader, optimizer, epochs, device="cuda", overfit_batch=False): if overfit_batch: overfit_with_a_couple_of_batches(model, train_loader, optimizer, device) else: pass

early_stopper = EarlyStopper(patience=5)

for epoch in range(epochs):

train_loss = train_one_epoch(model, train_loader)

validation_loss = validate_one_epoch(model, val_loader)

if early_stopper.early_stop(validation_loss):

break

[pxaris]

class Classifier(nn.Module):
    def __init__(self, num_classes, backbone, load_from_checkpoint=None):
        """
        backbone (nn.Module): The nn.Module to use for spectrogram parsing
        num_classes (int): The number of classes
        load_from_checkpoint (Optional[str]): Use a pretrained checkpoint to initialize the model
        """
        super(Classifier, self).__init__()
        self.backbone = backbone  # An LSTMBackbone or CNNBackbone
        if load_from_checkpoint is not None:
            self.backbone = load_backbone_from_checkpoint(
                self.backbone, load_from_checkpoint
            )
        self.is_lstm = isinstance(self.backbone, LSTMBackbone)
        self.output_layer = nn.Linear(self.backbone.feature_size, num_classes)
        self.criterion = nn.CrossEntropyLoss()  # Loss function for classification

    def forward(self, x, targets, lengths):
        feats = self.backbone(x) if not self.is_lstm else self.backbone(x, lengths)
        logits = self.output_layer(feats)
        loss = self.criterion(logits, targets)
        return loss, logits

[pxaris]

# run Training in overfitting mode
from lstm import LSTMBackbone

DEVICE = 'cuda' 
RNN_HIDDEN_SIZE = 64
NUM_CATEGORIES = 10

LR = 1e-4
epochs = 10

backbone = LSTMBackbone(input_shape[1], rnn_size=RNN_HIDDEN_SIZE, num_layers=2, bidirectional=True)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, device=DEVICE, overfit_batch=True)

[pxaris]

# we need EarlyStopping that we'll adopt from: https://stackoverflow.com/a/73704579/19306080 and add some customizations

class EarlyStopper:
    def __init__(self, model, save_path, patience=1, min_delta=0):
        self.model = model
        self.save_path = save_path
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.min_validation_loss = np.inf

    def early_stop(self, validation_loss):
        if validation_loss < self.min_validation_loss:
            self.min_validation_loss = validation_loss
            self.counter = 0
            torch.save(self.model.state_dict(), self.save_path)
        elif validation_loss > (self.min_validation_loss + self.min_delta):
            self.counter += 1
            if self.counter >= self.patience:
                return True
        return False

[pxaris]

# implemetation of the training process

def train_one_epoch(model, train_loader, optimizer, device=DEVICE):
    model.train()
    total_loss = 0
    for x, y, lengths in train_loader:        
        loss, logits = model(x.float().to(device), y.to(device), lengths.to(device))
        # prepare
        optimizer.zero_grad()
        # backward
        loss.backward()
        # optimizer step
        optimizer.step()
        total_loss += loss.item()

    avg_loss = total_loss / len(train_loader)    
    return avg_loss

def validate_one_epoch(model, val_loader, device=DEVICE):    
    model.eval()
    total_loss = 0
    with torch.no_grad():
        for x, y, lengths in val_loader:
            loss, logits = model(x.float().to(device), y.to(device), lengths.to(device))
            total_loss += loss.item()

    avg_loss = total_loss / len(val_loader)    
    return avg_loss

def train(model, train_loader, val_loader, optimizer, epochs, save_path='checkpoint.pth', device="cuda", overfit_batch=False):
    if overfit_batch:
        overfit_with_a_couple_of_batches(model, train_loader, optimizer, device)
    else:
        print(f'Training started for model {save_path.replace(".pth", "")}...')
        early_stopper = EarlyStopper(model, save_path, patience=5)
        for epoch in range(epochs):
            train_loss = train_one_epoch(model, train_loader, optimizer)
            validation_loss = validate_one_epoch(model, val_loader)
            if epoch== 0 or (epoch+1)%5==0:
                print(f'Epoch {epoch+1}/{epochs}, Loss at training set: {train_loss}\n\tLoss at validation set: {validation_loss}')          

            if early_stopper.early_stop(validation_loss):
                print('Early Stopping was activated.')
                print(f'Epoch {epoch+1}/{epochs}, Loss at training set: {train_loss}\n\tLoss at validation set: {validation_loss}')
                print('Training has been completed.\n')
                break

[pxaris]

# run Training
DEVICE = 'cuda'
RNN_HIDDEN_SIZE = 128
NUM_CATEGORIES = 10

LR = 1e-4
epochs = 40
save_path='lstm_genre_mel.pth'

backbone = LSTMBackbone(input_shape[1], rnn_size=RNN_HIDDEN_SIZE, num_layers=2, bidirectional=True)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, save_path=save_path, device=DEVICE, overfit_batch=False)

[pxaris]

7

# 2D CNN

# copy paste from convolution.py and add the implementation
import torch
import torch.nn as nn

class CNNBackbone(nn.Module):
    def __init__(self, input_dims, in_channels, filters, feature_size):
        super(CNNBackbone, self).__init__()
        self.input_dims = input_dims
        self.in_channels = in_channels
        self.filters = filters
        self.feature_size = feature_size

        self.conv1 = nn.Sequential(
            nn.Conv2d(self.in_channels, filters[0], kernel_size=(5,5), stride=1, padding=2),
            nn.BatchNorm2d((self.in_channels**1) * filters[0]),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))

        self.conv2 = nn.Sequential(
                nn.Conv2d(filters[0], filters[1], kernel_size=(5,5), stride=1, padding=2),
                nn.BatchNorm2d((self.in_channels**2) * filters[1]),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=2, stride=2))

        self.conv3 = nn.Sequential(
                nn.Conv2d(filters[1], filters[2], kernel_size=(3,3), stride=1, padding=1),
                nn.BatchNorm2d((self.in_channels**3) * filters[2]),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=2, stride=2))

        self.conv4 = nn.Sequential(
                nn.Conv2d(filters[2], filters[3], kernel_size=(3,3), stride=1, padding=1),
                nn.BatchNorm2d((self.in_channels**4) * filters[3]),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=2, stride=2))

        shape_after_convs = [input_dims[0]//2**(len(filters)), input_dims[1]//2**(len(filters))]
        self.fc1 = nn.Linear(filters[3] * shape_after_convs[0] * shape_after_convs[1], self.feature_size)

    def forward(self, x):
        x = x.view(x.shape[0], self.in_channels, x.shape[1], x.shape[2])
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc1(out)
        return out

[pxaris]

# run Training in overfitting mode for the CNN 
DEVICE = 'cuda'
NUM_CATEGORIES = 10
cnn_in_channels = 1
cnn_filters = [32, 64, 128, 256]
cnn_out_feature_size = 1000

LR = 1e-4
epochs = 10

# get the input shape
x_b1, y_b1, lengths_b1 = next(iter(train_loader))
input_shape = x_b1[0].shape

backbone = CNNBackbone(input_shape, cnn_in_channels, cnn_filters, cnn_out_feature_size)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, device=DEVICE, overfit_batch=True)

[pxaris]

8

# add implementation of Regressor

class Regressor(nn.Module):
    def __init__(self, backbone, load_from_checkpoint=None):
        """
        backbone (nn.Module): The nn.Module to use for spectrogram parsing
        load_from_checkpoint (Optional[str]): Use a pretrained checkpoint to initialize the model
        """
        super(Regressor, self).__init__()
        self.backbone = backbone  # An LSTMBackbone or CNNBackbone
        if load_from_checkpoint is not None:
            self.backbone = load_backbone_from_checkpoint(
                self.backbone, load_from_checkpoint
            )
        self.is_lstm = isinstance(self.backbone, LSTMBackbone)
        self.output_layer = nn.Linear(self.backbone.feature_size, 1)
        self.criterion = nn.MSELoss()  # Loss function for regression

    def forward(self, x, targets, lengths):
        feats = self.backbone(x) if not self.is_lstm else self.backbone(x, lengths)
        out = self.output_layer(feats)
        loss = self.criterion(out.float(), targets.float())
        return loss, out

[pxaris]

Re-opening the issue to be used during tomorrow's lab.

[pxaris]

8 (dataloaders for Regression)

example code:

tasks = ['valence', 'energy', 'danceability']
task = 'valence'
label_index = tasks.index(task) + 1

train_dataset = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, data_dir), 
    train=True, feat_type=feat_type, max_length=MAX_LENGTH, regression=label_index
)

train_loader, val_loader = torch_train_val_split(
    train_dataset, BATCH_SIZE, BATCH_SIZE
)

[pxaris]

6 (example function for evaluation on test set)

def get_labels_predictions(model, data_loader, device):
    model.eval()
    y = []
    y_ = []
    with torch.no_grad():
        for x, labels, lengths in data_loader:
            loss, logits = model(x.float().to(device), labels.to(device), lengths.to(device))
            y.append(labels)
            y_.append(logits.argmax(dim=-1).detach().cpu().numpy())

    return y, y_

[pxaris]

9 (Transfer learning example code)

source_model_path = 'cnn_genre_fused_beat.pth'

# target model
backbone = CNNBackbone(input_shape, CNN_IN_CHANNELS, CNN_FILTERS, CNN_OUT_FEATURE_SIZE)
model = Regressor(backbone)
model.to(DEVICE)

# load source model
source_model = torch.load(source_model_path)

# remove weights of final layer
del source_model['output_layer.weight']
del source_model['output_layer.bias']

# initialize target model with the state of source model
model.load_state_dict(source_model, strict=False)

# train the model, initialized with genre's model weights, for valence (regression)
save_path = 'cnn_valence_transfer_fused_beat.pth'
model.to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, save_path=save_path, device=DEVICE, overfit_batch=False)

[pxaris]

Εδώ θα γράψουμε βοηθητικό κώδικα για το σημερινό εργαστήριο.

[pxaris]

from dataset import CLASS_MAPPING, torch_train_val_split
# dataloaders for specific features type, e.g. 'mel'
BATCH_SIZE = 8
MAX_LENGTH = 150
DEVICE = 'cuda'

train_dataset = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, 'fma_genre_spectrograms'), class_mapping=CLASS_MAPPING, 
    train=True, feat_type='mel', max_length=MAX_LENGTH
)

train_loader, val_loader = torch_train_val_split(
    train_dataset, BATCH_SIZE, BATCH_SIZE
)

# get the 1st batch values of the data loader
x_b1, y_b1, lengths_b1 = next(iter(train_loader))

# print the shape of the 1st item of the 1st batch of the data loader
input_shape = x_b1[0].shape
print(input_shape)

[pxaris]

https://github.com/slp-ntua/patrec-labs/issues/180#issuecomment-1375355402

[pxaris]

https://github.com/slp-ntua/patrec-labs/issues/180#issuecomment-1375370544

[pxaris]

# run Training in overfitting mode
from lstm import LSTMBackbone

DEVICE = 'cuda' 
RNN_HIDDEN_SIZE = 64
NUM_CATEGORIES = 10

LR = 1e-4
epochs = 10

backbone = LSTMBackbone(input_shape[1], rnn_size=RNN_HIDDEN_SIZE, num_layers=2, bidirectional=True)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, device=DEVICE, overfit_batch=True)

[pxaris]

class EarlyStopper:
    def __init__(self, model, save_path, patience=1, min_delta=0):
        self.model = model
        self.save_path = save_path
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.min_validation_loss = np.inf

    def early_stop(self, validation_loss):
        if validation_loss < self.min_validation_loss:
            self.min_validation_loss = validation_loss
            self.counter = 0
            torch.save(self.model.state_dict(), self.save_path)
        elif validation_loss > (self.min_validation_loss + self.min_delta):
            self.counter += 1
            if self.counter >= self.patience:
                return True
        return False

[pxaris]

# implemetation of the training process

def train_one_epoch(model, train_loader, optimizer, device=DEVICE):
    model.train()
    total_loss = 0
    for x, y, lengths in train_loader:        
        loss, logits = model(x.float().to(device), y.to(device), lengths.to(device))
        # prepare
        optimizer.zero_grad()
        # backward
        loss.backward()
        # optimizer step
        optimizer.step()
        total_loss += loss.item()

    avg_loss = total_loss / len(train_loader)    
    return avg_loss

def validate_one_epoch(model, val_loader, device=DEVICE):    
    model.eval()
    total_loss = 0
    with torch.no_grad():
        for x, y, lengths in val_loader:
            loss, logits = model(x.float().to(device), y.to(device), lengths.to(device))
            total_loss += loss.item()

    avg_loss = total_loss / len(val_loader)    
    return avg_loss

def train(model, train_loader, val_loader, optimizer, epochs, save_path='checkpoint.pth', device="cuda", overfit_batch=False):
    if overfit_batch:
        overfit_with_a_couple_of_batches(model, train_loader, optimizer, device)
    else:
        print(f'Training started for model {save_path.replace(".pth", "")}...')
        early_stopper = EarlyStopper(model, save_path, patience=5)
        for epoch in range(epochs):
            train_loss = train_one_epoch(model, train_loader, optimizer)
            validation_loss = validate_one_epoch(model, val_loader)
            if epoch== 0 or (epoch+1)%5==0:
                print(f'Epoch {epoch+1}/{epochs}, Loss at training set: {train_loss}\n\tLoss at validation set: {validation_loss}')          

            if early_stopper.early_stop(validation_loss):
                print('Early Stopping was activated.')
                print(f'Epoch {epoch+1}/{epochs}, Loss at training set: {train_loss}\n\tLoss at validation set: {validation_loss}')
                print('Training has been completed.\n')
                break

[pxaris]

# run Training
DEVICE = 'cuda'
RNN_HIDDEN_SIZE = 128
NUM_CATEGORIES = 10

LR = 1e-4
epochs = 40
save_path='lstm_genre_mel.pth'

backbone = LSTMBackbone(input_shape[1], rnn_size=RNN_HIDDEN_SIZE, num_layers=2, bidirectional=True)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)

optimizer = torch.optim.Adam(model.parameters(), lr=LR)

train(model, train_loader, val_loader, optimizer, epochs, save_path=save_path, device=DEVICE, overfit_batch=False)

[pxaris]

6

from sklearn.metrics import classification_report

def get_labels_predictions(model, data_loader, device):
    model.eval()
    y = []
    y_ = []
    with torch.no_grad():
        for x, labels, lengths in data_loader:
            loss, logits = model(x.float().to(device), labels.to(device), lengths.to(device))
            y.append(labels)
            y_.append(logits.argmax(dim=-1).detach().cpu().numpy())

    return y, y_ 

data_dir = 'fma_genre_spectrograms'
saved_model_path = 'lstm_genre_mel.pth'

print(f'Evaluation of model {saved_model_path.replace(".pth", "")} on the test set...')

test_dataset = SpectrogramDataset(
    os.path.join(PARENT_DATA_DIR, data_dir), class_mapping=CLASS_MAPPING, 
    train=False, feat_type='mel', max_length=MAX_LENGTH
)

test_loader, _ = torch_train_val_split(
    test_dataset, BATCH_SIZE, BATCH_SIZE, val_size=0.0
)

# get the input shape
x_b1, y_b1, lengths_b1 = next(iter(test_loader))
input_shape = x_b1[0].shape

backbone = LSTMBackbone(input_shape[1], rnn_size=RNN_HIDDEN_SIZE, num_layers=2, bidirectional=True)
model = Classifier(NUM_CATEGORIES, backbone)
model.to(DEVICE)
model.load_state_dict(torch.load(saved_model_path))

y, y_ = get_labels_predictions(model, test_loader, DEVICE)
print(classification_report(np.hstack(y), np.hstack(y_)))

[pxaris]

CNN: https://github.com/slp-ntua/patrec-labs/issues/180#issuecomment-1375406902

[pxaris]

cnn_in_channels = 1
cnn_filters = [32, 64, 128, 256]
cnn_out_feature_size = 1000

LR = 1e-4
epochs = 10

backbone = CNNBackbone(input_shape, cnn_in_channels, cnn_filters, cnn_out_feature_size)

[pxaris]

Regression: https://github.com/slp-ntua/patrec-labs/issues/180#issuecomment-1375411017

[pxaris]

Transfer Learning: https://github.com/slp-ntua/patrec-labs/issues/180#issuecomment-1824005869