xvector.py 91.4 KB
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# coding: utf-8 -*-
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#
# This file is part of SIDEKIT.
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#
# SIDEKIT is free software: you can redistribute it and/or modify
# it under the terms of the GNU LLesser General Public License as
# published by the Free Software Foundation, either version 3 of the License,
# or (at your option) any later version.
#
# SIDEKIT is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with SIDEKIT.  If not, see <http://www.gnu.org/licenses/>.

"""
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Copyright 2014-2021 Anthony Larcher, Yevhenii Prokopalo
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"""
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import logging
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import math
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import os
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import numpy
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import pandas
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import pickle
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import shutil
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import tabulate
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import time
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import torch
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import torchaudio
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import tqdm
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import yaml

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from collections import OrderedDict
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from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split
from .preprocessor import MfccFrontEnd
from .preprocessor import MelSpecFrontEnd
from .preprocessor import RawPreprocessor
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from .xsets import SideSet
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from .xsets import IdMapSet
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from .xsets import IdMapSetPerSpeaker
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from .xsets import SideSampler
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from .res_net import ResBlockWFMS
from .res_net import ResBlock
from .res_net import PreResNet34
from .res_net import PreFastResNet34
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from ..bosaris import IdMap
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from ..bosaris import Key
from ..bosaris import Ndx
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from ..statserver import StatServer
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from ..iv_scoring import cosine_scoring
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from .sincnet import SincNet
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from ..bosaris.detplot import rocch, rocch2eer
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from .loss import SoftmaxAngularProto, ArcLinear
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from .loss import l2_norm
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from .loss import ArcMarginProduct
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from ..sidekit_io import init_logging
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os.environ['MKL_THREADING_LAYER'] = 'GNU'
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__license__ = "LGPL"
__author__ = "Anthony Larcher"
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__copyright__ = "Copyright 2015-2021 Anthony Larcher"
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__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
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#logging.basicConfig(format='%(asctime)s %(message)s')
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# Make PyTorch Deterministic
torch.manual_seed(0)
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torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
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numpy.random.seed(0)


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def eer(negatives, positives):
    """Logarithmic complexity EER computation

    Args:
        negative_scores (numpy array): impostor scores
        positive_scores (numpy array): genuine scores

    Returns:
        float: Equal Error Rate (EER)
    """

    positives = numpy.sort(positives)
    negatives = numpy.sort(negatives)[::-1]

    pos_count = positives.shape[0]
    neg_count = negatives.shape[0]

    p_score = positives[0]
    n_score = negatives[0]

    p_index = 0
    n_index = 0

    next_p_jump = pos_count//2
    next_n_jump = neg_count//2

    kdx = 0
    while True:
        kdx += 1
        if p_index < 0 or n_index < 0:
            return 0
        if p_index > pos_count or n_index > neg_count:
            return 100
        if p_score < n_score:
            p_index = p_index + next_p_jump
            n_index = n_index + next_n_jump
            if next_p_jump == 0 and next_n_jump == 0:
                break
        elif p_score >= n_score:
            p_index = p_index - next_p_jump
            n_index = n_index - next_n_jump
            if next_p_jump == 0 and next_n_jump == 0:
                break

        p_score = positives[p_index]
        n_score = negatives[n_index]
        next_p_jump = next_p_jump//2
        next_n_jump = next_n_jump//2

    eer_predicate = 100

    tfr = (abs(p_index))/pos_count
    tfa = (1+abs(n_index))/neg_count
    if (p_score == n_score and tfr == tfa):
        return tfr

    while positives[p_index] < negatives[n_index]:
        if p_index < pos_count - 1:
            p_index += 1
        elif n_index < neg_count - 1:
            n_index += 1
        else:
            break

    while positives[p_index] > negatives[n_index] and n_index >= 1:
        n_index -= 1

    tfr = (1+p_index)/pos_count
    tfa = (1+n_index)/neg_count

    while tfa > tfr:
        p_index += 1
        while positives[p_index] > negatives[n_index] and n_index >= 1:
            n_index -= 1
        tfr = (1+p_index)/pos_count
        tfa = (1+n_index)/neg_count

    if abs(tfr - tfa) <= eer_predicate:
        eer_predicate = abs(tfr - tfa)
        eer = (tfr + tfa) / 2
    else:
        return eer

    tfr = p_index/pos_count
    tfa = (1+n_index)/neg_count
    if abs(tfr - tfa) <= eer_predicate:
        eer_predicate = abs(tfr - tfa)
        eer = (tfr + tfa) / 2
    else:
        return eer

    while True:
        while negatives[n_index + 1] <= positives[p_index - 1]:
            p_index -= 1
            tfr = p_index/pos_count
            tfa = (1+n_index)/neg_count
            if abs(tfr - tfa) <= eer_predicate:
                eer_predicate = abs(tfr - tfa)
                eer = (tfr + tfa) / 2
            else:
                return eer
        while negatives[n_index + 1] > positives[p_index - 1]:
            n_index += 1
            tfr = p_index/pos_count
            tfa = (1+n_index)/neg_count
            if abs(tfr - tfa) <= eer_predicate:
                eer_predicate = abs(tfr - tfa)
                eer = (tfr + tfa) / 2
            else:
                return eer

    return eer


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def test_metrics(model,
                 device,
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                 speaker_number,
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                 num_thread,
                 mixed_precision):
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    """Compute model metrics

    Args:
        model ([type]): [description]
        validation_loader ([type]): [description]
        device ([type]): [description]
        speaker_number ([type]): [description]
        model_archi ([type]): [description]

    Raises:
        NotImplementedError: [description]
        NotImplementedError: [description]

    Returns:
        [type]: [description]
    """
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    idmap_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_idmap.h5'
    ndx_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_ndx.h5'
    key_test_filename = '/lium/raid01_c/larcher/data/allies_dev_verif_key.h5'
    data_root_name='/lium/corpus/base/ALLIES/wav'
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    transform_pipeline = dict()
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    xv_stat = extract_embeddings(idmap_name=idmap_test_filename,
                                 model_filename=model,
                                 data_root_name=data_root_name,
                                 device=device,
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                                 loss="aam",
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                                 transform_pipeline=transform_pipeline,
                                 num_thread=num_thread,
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                                 mixed_precision=mixed_precision)
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    tar, non = cosine_scoring(xv_stat,
                              xv_stat,
                              Ndx(ndx_test_filename),
                              wccn=None,
                              check_missing=True,
                              device=device
                              ).get_tar_non(Key(key_test_filename))
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    #test_eer = eer(numpy.array(non).astype(numpy.double), numpy.array(tar).astype(numpy.double))
    pmiss, pfa = rocch(tar, non)

    return rocch2eer(pmiss, pfa)
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def new_test_metrics(model,
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                     device,
                     data_opts,
                     train_opts):
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    """Compute model metrics

    Args:
        model ([type]): [description]
        validation_loader ([type]): [description]
        device ([type]): [description]
        speaker_number ([type]): [description]
        model_archi ([type]): [description]

    Raises:
        NotImplementedError: [description]
        NotImplementedError: [description]

    Returns:
        [type]: [description]
    """
    # TODO modifier les parametres pour utiliser le dataset_description a la place de :

    #idmap_test_filename,
    #ndx_test_filename,
    #key_test_filename,
    #data_root_name,

    transform_pipeline = dict()

    xv_stat = extract_embeddings(idmap_name=data_opts["idmap_test_filename"],
                                 model_filename=model,
                                 data_root_name=data_opts["data_root_name"],
                                 device=device,
                                 loss=model.loss,
                                 transform_pipeline=transform_pipeline,
                                 num_thread=train_opts["num_thread"],
                                 mixed_precision=train_opts["mixed_precision"])

    tar, non = cosine_scoring(xv_stat,
                              xv_stat,
                              Ndx(data_opts["ndx_test_filename"]),
                              wccn=None,
                              check_missing=True,
                              device=device
                              ).get_tar_non(Key(data_opts["key_test_filename"]))

    #test_eer = eer(numpy.array(non).astype(numpy.double), numpy.array(tar).astype(numpy.double))
    pmiss, pfa = rocch(tar, non)

    return rocch2eer(pmiss, pfa)

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def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
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    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
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    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
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class TrainingMonitor():

    def __init__(self,
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                 output_file,
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                 log_interval=10,
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                 patience=numpy.inf,
                 best_accuracy=0.0,
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                 best_eer_epoch=1,
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                 best_eer=100,
                 compute_test_eer=False
                 ):
        # Stocker plutot des listes pour conserver l'historique complet
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        self.current_epoch = 0
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        self.log_interval = log_interval
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        self.init_patience = patience
        self.current_patience = patience
        self.best_accuracy = best_accuracy
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        self.best_eer_epoch = best_eer_epoch
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        self.best_eer = best_eer
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        self.compute_test_eer = compute_test_eer
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        self.test_eer = []
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        self.training_loss = []
        self.training_acc = []

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        self.val_loss = []
        self.val_acc = []
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        self.val_eer = []

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        self.is_best = True

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        # Initialize the logger
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        logging.basicConfig(level=logging.DEBUG,
                            format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
                            datefmt='%m-%d %H:%M',
                            filename=output_file,
                            filemode='w')

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        self.logger = logging.getLogger('monitoring')
        self.logger.setLevel(logging.INFO)
        # create file handler which logs even debug messages
        fh = logging.FileHandler(output_file)
        fh.setLevel(logging.INFO)
        self.logger.addHandler(fh)

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    def display(self):
        """

        :return:
        """
        # TODO
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        self.logger.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Training metrics - Cross validation accuracy = {val_acc} %, EER = {val_eer * 100} %")
        self.logger.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Training metrics - Test EER = {test_eer * 100} %")
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    def display_final(self):
        """

        :return:
        """
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        self.logger.critical(f"Best accuracy {self.best_accuracy * 100.} obtained at epoch {self.best_accuracy_epoch}")
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    def update(self,
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               epoch=None,
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               training_acc=None,
               training_loss=None,
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               test_eer=None,
               val_eer=None,
               val_loss=None,
               val_acc=None):

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        if epoch is not None:
            self.current_epoch = epoch
        if training_acc is not None:
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            self.training_acc.append(training_acc)
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        if training_loss is not None:
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            self.training_loss.append(training_loss)
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        if val_eer is not None:
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            self.val_eer.append(val_eer)
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        if val_loss is not None:
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            self.val_loss.append(val_loss)
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        if val_acc is not None:
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            self.val_acc.append(val_acc)
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        # remember best accuracy and save checkpoint
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        if self.compute_test_eer and test_eer is not None:
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            self.test_eer.append(test_eer)
            self.is_best = test_eer < self.best_eer
            self.best_eer = min(test_eer, self.best_eer)
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            if self.is_best:
                self.best_eer_epoch = epoch
                self.current_patience = self.init_patience
            else:
                self.current_patience -= 1
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        elif val_eer is not None:
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            self.is_best = val_eer < self.best_eer
            self.best_eer = min(val_eer, self.best_eer)
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            if self.is_best:
                self.best_eer_epoch = epoch
                self.current_patience = self.init_patience
            else:
                self.current_patience -= 1
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class MeanStdPooling(torch.nn.Module):
    """
    Mean and Standard deviation pooling
    """
    def __init__(self):
        """

        """
        super(MeanStdPooling, self).__init__()
        pass

    def forward(self, x):
        """

        :param x:
        :return:
        """
        mean = torch.mean(x, dim=2)
        std = torch.std(x, dim=2)
        return torch.cat([mean, std], dim=1)
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class GruPooling(torch.nn.Module):
    """

    """
    def __init__(self, input_size, gru_node, nb_gru_layer):
        """

        :param input_size:
        :param gru_node:
        :param nb_gru_layer:
        """
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        super(GruPooling, self).__init__()
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        self.lrelu_keras = torch.nn.LeakyReLU(negative_slope = 0.3)
        self.bn_before_gru = torch.nn.BatchNorm1d(num_features = input_size)
        self.gru = torch.nn.GRU(input_size = input_size,
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                                hidden_size = gru_node,
                                num_layers = nb_gru_layer,
                                batch_first = True)
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    def forward(self, x):
        """

        :param x:
        :return:
        """
        x = self.bn_before_gru(x)
        x = self.lrelu_keras(x)
        x = x.permute(0, 2, 1)  #(batch, filt, time) >> (batch, time, filt)
        self.gru.flatten_parameters()
        x, _ = self.gru(x)
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        x = x[:, -1, :]
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        return x

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class Xtractor(torch.nn.Module):
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    """
    Class that defines an x-vector extractor based on 5 convolutional layers and a mean standard deviation pooling
    """
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    def __init__(self,
                 speaker_number,
                 model_archi="xvector",
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                 loss=None,
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                 norm_embedding=False,
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                 aam_margin=0.2,
                 aam_s=30):
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        """
        If config is None, default architecture is created
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        :param model_archi:
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        """
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        super(Xtractor, self).__init__()
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        self.speaker_number = speaker_number
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        self.feature_size = None
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        self.norm_embedding = norm_embedding
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        print(f"Speaker number : {self.speaker_number}")

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        if model_archi == "xvector":
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            self.input_nbdim = 2

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            if loss not in ["cce", 'aam']:
                raise NotImplementedError(f"The valid loss are for now cce and aam ")
            else:
                self.loss = loss

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            self.activation = torch.nn.LeakyReLU(0.2)

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            self.preprocessor = MfccFrontEnd()
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            self.feature_size = self.preprocessor.n_mfcc
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            self.sequence_network = torch.nn.Sequential(OrderedDict([
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                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
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                ("activation1", torch.nn.LeakyReLU(0.2)),
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                ("batch_norm1", torch.nn.BatchNorm1d(512)),
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                ("conv2", torch.nn.Conv1d(512, 512, 3, dilation=2)),
                ("activation2", torch.nn.LeakyReLU(0.2)),
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                ("batch_norm2", torch.nn.BatchNorm1d(512)),
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                ("conv3", torch.nn.Conv1d(512, 512, 3, dilation=3)),
                ("activation3", torch.nn.LeakyReLU(0.2)),
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                ("batch_norm3", torch.nn.BatchNorm1d(512)),
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                ("conv4", torch.nn.Conv1d(512, 512, 1)),
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                ("activation4", torch.nn.LeakyReLU(0.2)),
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                ("batch_norm4", torch.nn.BatchNorm1d(512)),
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                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
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                ("activation5", torch.nn.LeakyReLU(0.2)),
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                ("batch_norm5", torch.nn.BatchNorm1d(1536))
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            ]))

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pooling    
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            self.stat_pooling = MeanStdPooling()
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            self.stat_pooling_weight_decay = 0
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            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
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                ("linear6", torch.nn.Linear(3072, 512))
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            ]))

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            self.embedding_size = 512

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            if self.loss == "aam":
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                self.after_speaker_embedding = ArcMarginProduct(512,
                                                                int(self.speaker_number),
                                                                s=64,
                                                                m=0.2,
                                                                easy_margin=True)
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            elif self.loss == "cce":
                self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
                    ("activation6", torch.nn.LeakyReLU(0.2)),
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                    ("batch_norm6", torch.nn.BatchNorm1d(512)),
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                    ("dropout6", torch.nn.Dropout(p=0.05)),
                    ("linear7", torch.nn.Linear(512, 512)),
                    ("activation7", torch.nn.LeakyReLU(0.2)),
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                    ("batch_norm7", torch.nn.BatchNorm1d(512)),
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                    ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
                ]))
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            self.preprocessor_weight_decay = 0.0002
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            self.sequence_network_weight_decay = 0.0002
            self.before_speaker_embedding_weight_decay = 0.002
            self.after_speaker_embedding_weight_decay = 0.002
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            self.embedding_size = 512
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        elif model_archi == "resnet34":
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            self.preprocessor = MelSpecFrontEnd(n_mels=80)
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            self.sequence_network = PreResNet34()
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            self.embedding_size = 256
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            self.before_speaker_embedding = torch.nn.Linear(in_features=5120,
                                                            out_features=self.embedding_size)
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            self.stat_pooling = MeanStdPooling()
            self.stat_pooling_weight_decay = 0

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            self.loss = "aam"
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            self.after_speaker_embedding = ArcMarginProduct(self.embedding_size,
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                                                            int(self.speaker_number),
                                                            s = 30.0,
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                                                            m = 0.20,
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                                                            easy_margin = False)
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            self.preprocessor_weight_decay = 0.000
            self.sequence_network_weight_decay = 0.000
            self.stat_pooling_weight_decay = 0.000
            self.before_speaker_embedding_weight_decay = 0.00
            self.after_speaker_embedding_weight_decay = 0.00

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        elif model_archi == "fastresnet34":
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            self.preprocessor = MelSpecFrontEnd()
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            self.sequence_network = PreFastResNet34()
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            self.embedding_size = 256
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            self.before_speaker_embedding = torch.nn.Linear(in_features = 2560,
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                                                            out_features = self.embedding_size)
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            self.stat_pooling = MeanStdPooling()
            self.stat_pooling_weight_decay = 0

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            self.loss = loss
            if self.loss == "aam":
                self.after_speaker_embedding = ArcMarginProduct(self.embedding_size,
                                                                int(self.speaker_number),
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                                                                s = 30,
                                                                m = 0.2,
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                                                                easy_margin = False)

            elif self.loss == 'aps':
                self.after_speaker_embedding = SoftmaxAngularProto(int(self.speaker_number))
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            self.preprocessor_weight_decay = 0.000
            self.sequence_network_weight_decay = 0.000
            self.stat_pooling_weight_decay = 0.000
            self.before_speaker_embedding_weight_decay = 0.00
            self.after_speaker_embedding_weight_decay = 0.00
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        elif model_archi == "rawnet2":
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            if loss not in ["cce", 'aam']:
                raise NotImplementedError(f"The valid loss are for now cce and aam ")
            else:
                self.loss = loss

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            self.input_nbdim = 2

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            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

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            self.preprocessor = RawPreprocessor(nb_samp=48000,
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                                                in_channels=1,
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                                                out_channels=filts[0],
                                                kernel_size=3)
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            self.sequence_network = torch.nn.Sequential(OrderedDict([
                ("block0", ResBlockWFMS(nb_filts=filts[1], first=True)),
                ("block1", ResBlockWFMS(nb_filts=filts[1])),
                ("block2", ResBlockWFMS(nb_filts=filts[2])),
                ("block3", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]])),
                ("block4", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]])),
                ("block5", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]]))
            ]))

            self.stat_pooling = GruPooling(input_size=filts[2][-1],
                                           gru_node=1024,
                                           nb_gru_layer=1)

            self.before_speaker_embedding = torch.nn.Linear(in_features = 1024,
                                                            out_features = 1024)

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            if self.loss == "aam":
                if loss == 'aam':
                    self.after_speaker_embedding = ArcLinear(1024,
                                                             int(self.speaker_number),
                                                             margin=aam_margin, s=aam_s)
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            elif self.loss == "cce":
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                self.after_speaker_embedding = torch.nn.Linear(in_features = 1024,
                                                               out_features = int(self.speaker_number),
                                                               bias = True)
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            self.preprocessor_weight_decay = 0.000
            self.sequence_network_weight_decay = 0.000
            self.stat_pooling_weight_decay = 0.000
            self.before_speaker_embedding_weight_decay = 0.00
            self.after_speaker_embedding_weight_decay = 0.00

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        else:
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            is_first_resblock = True

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            if isinstance(model_archi, dict):
                cfg = model_archi
            else:
                # Load Yaml configuration
                with open(model_archi, 'r') as fh:
                    cfg = yaml.load(fh, Loader=yaml.FullLoader)
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            self.loss = cfg["loss"]["type"]
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            if self.loss == "aam":
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                self.aam_margin = cfg["loss"]["aam_margin"]
                self.aam_s = cfg["loss"]["aam_s"]
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            """
            Prepare Preprocessor
            """
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            self.preprocessor = None
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            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
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                    self.preprocessor = SincNet(
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                        waveform_normalize=cfg['preprocessor']["waveform_normalize"],
                        sample_rate=cfg['preprocessor']["sample_rate"],
                        min_low_hz=cfg['preprocessor']["min_low_hz"],
                        min_band_hz=cfg['preprocessor']["min_band_hz"],
                        out_channels=cfg['preprocessor']["out_channels"],
                        kernel_size=cfg['preprocessor']["kernel_size"],
                        stride=cfg['preprocessor']["stride"],
                        max_pool=cfg['preprocessor']["max_pool"],
                        instance_normalize=cfg['preprocessor']["instance_normalize"],
                        activation=cfg['preprocessor']["activation"],
                        dropout=cfg['preprocessor']["dropout"]
                    )
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                    self.feature_size = self.preprocessor.dimension
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                elif cfg['preprocessor']["type"] == "rawnet2":
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                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
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                                                        in_channels=1,
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                                                        out_channels=cfg["feature_size"],
                                                        kernel_size=cfg['preprocessor']["kernel_size"],
                                                        stride=cfg['preprocessor']["stride"],
                                                        padding=cfg['preprocessor']["padding"],
                                                        dilation=cfg['preprocessor']["dilation"])
                    self.feature_size = cfg["feature_size"]
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                self.preprocessor_weight_decay = 0.000
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            """
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            Prepare sequence network
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            """
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            # Get Feature size
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            if self.feature_size is None:
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                self.feature_size = cfg["preprocessor"]["feature_size"]
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            input_size = self.feature_size

            # Get activation function
            if cfg["activation"] == 'LeakyReLU':
                self.activation = torch.nn.LeakyReLU(0.2)
            elif cfg["activation"] == 'PReLU':
                self.activation = torch.nn.PReLU()
            elif cfg["activation"] == 'ReLU6':
                self.activation = torch.nn.ReLU6()
            else:
                self.activation = torch.nn.ReLU()

            # Create sequential object for the first part of the network
            segmental_layers = []
            for k in cfg["segmental"].keys():
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                if k.startswith("lin"):
                    segmental_layers.append((k, torch.nn.Linear(input_size,
                                                                cfg["segmental"][k]["output"])))
                    input_size = cfg["segmental"][k]["output"]

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                elif k.startswith("conv2D"):
                    segmental_layers.append((k, torch.nn.Conv2d(in_channels=1,
                                                                out_channels=entry_conv_out_channels,
                                                                kernel_size=entry_conv_kernel_size,
                                                                padding=3,
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                                                                stride=1)))
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                elif k.startswith("conv"):
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                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
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                                                                cfg["segmental"][k]["output_channels"],
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                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
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                    input_size = cfg["segmental"][k]["output_channels"]

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                elif k.startswith("ctrans"):
                    segmental_layers.append((k, torch.nn.ConvTranspose1d(input_size,
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                                                                         cfg["segmental"][k]["output_channels"],
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                                                                         kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                         dilation=cfg["segmental"][k]["dilation"])))
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                elif k.startswith("activation"):
                    segmental_layers.append((k, self.activation))

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                elif k.startswith('batch_norm'):
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                    segmental_layers.append((k, torch.nn.BatchNorm1d(input_size)))

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                elif k.startswith('resblock'):
                    segmental_layers.append((ResBlock(cfg["segmental"][k]["input_channel"],
                                                      cfg["segmental"][k]["output_channel"],
                                                      is_first_resblock)))
                    is_first_resblock = False

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            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
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            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
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pooling    
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            """
            Pooling
            """
            self.stat_pooling = MeanStdPooling()
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            tmp_input_size = input_size * 2
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            if cfg["stat_pooling"]["type"] == "GRU":
                self.stat_pooling = GruPooling(input_size=cfg["stat_pooling"]["input_size"],
                                               gru_node=cfg["stat_pooling"]["gru_node"],
                                               nb_gru_layer=cfg["stat_pooling"]["nb_gru_layer"])
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                tmp_input_size = cfg["stat_pooling"]["gru_node"]
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            self.stat_pooling_weight_decay = cfg["stat_pooling"]["weight_decay"]

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            """
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            Prepare last part of the network (after pooling)
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            """
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            # Create sequential object for the second part of the network
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            input_size = tmp_input_size
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            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
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                if k.startswith("lin"):
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                    if cfg["before_embedding"][k]["output"] == "speaker_number":
                        before_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
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                    else:
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                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
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                        input_size = cfg["before_embedding"][k]["output"]
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                elif k.startswith("activation"):
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                    before_embedding_layers.append((k, self.activation))
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                elif k.startswith('batch_norm'):
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                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
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                elif k.startswith('dropout'):
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                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
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            self.embedding_size = input_size
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            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
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            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
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            # if loss_criteria is "cce"
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            # Create sequential object for the second part of the network
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            if self.loss == "cce":
                after_embedding_layers = []
                for k in cfg["after_embedding"].keys():
                    if k.startswith("lin"):
                        if cfg["after_embedding"][k]["output"] == "speaker_number":
                            after_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
                        else:
                            after_embedding_layers.append((k, torch.nn.Linear(input_size,
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                                                                          cfg["after_embedding"][k]["output"])))
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                            input_size = cfg["after_embedding"][k]["output"]
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                    elif k.startswith('arc'):
                        after_embedding_layers.append((k, ArcLinear(input_size,
                                                                    self.speaker_number,
                                                                    margin=self.aam_margin,
                                                                    s=self.aam_s)))
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                    elif k.startswith("activation"):
                        after_embedding_layers.append((k, self.activation))
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                    elif k.startswith('batch_norm'):
                        after_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
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                    elif k.startswith('dropout'):
                        after_embedding_layers.append((k, torch.nn.Dropout(p=cfg["after_embedding"][k])))

                self.after_speaker_embedding = torch.nn.Sequential(OrderedDict(after_embedding_layers))

            elif self.loss == "aam":
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                self.norm_embedding = True
                self.after_speaker_embedding = ArcMarginProduct(input_size,
                                                                int(self.speaker_number),
                                                                s=64,
                                                                m=0.2,
                                                                easy_margin=True)

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                #self.after_speaker_embedding = ArcLinear(input_size,
                #                                         self.speaker_number,
                #                                         margin=self.aam_margin,
                #                                         s=self.aam_s)
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                #self.after_speaker_embedding = ArcFace(embedding_size=input_size,
                #                                       classnum=self.speaker_number,
                #                                       s=64.,
                #                                       m=0.5)
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            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
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    def forward(self, x, is_eval=False, target=None, extract_after_pooling=False):
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        """

        :param x:
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        :param is_eval: False for training
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        :return:
        """
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        if self.preprocessor is not None:
            x = self.preprocessor(x)

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        x = self.sequence_network(x)
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        # Mean and Standard deviation pooling
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        x = self.stat_pooling(x)
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        if extract_after_pooling:
            return x

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        x = self.before_speaker_embedding(x)
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        if self.norm_embedding:
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            #x_norm = x.norm(p=2,dim=1, keepdim=True) / 10. # Why  10. ?
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            #x_norm = torch.linalg.norm(x, ord=2, dim=1, keepdim=True, out=None, dtype=None)
            #x = torch.div(x, x_norm)
            x = l2_norm(x)
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        if self.loss == "cce":
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            if is_eval:
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                return x
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            else:
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                return self.after_speaker_embedding(x), x
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        elif self.loss in ['aam', 'aps']:
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            if is_eval:
                x = torch.nn.functional.normalize(x, dim=1)
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            else:
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                x = self.after_speaker_embedding(x, target=target), torch.nn.functional.normalize(x, dim=1)
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        return x
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    def context_size(self):
        context = 1
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        if isinstance(self, Xtractor):
            for name, module in self.sequence_network.named_modules():
                if name.startswith("conv"):
                    context += module.dilation[0] * (module.kernel_size[0] - 1)
        else:
            for name, module in self.module.sequence_network.named_modules():
                if name.startswith("conv"):
                    context += module.dilation[0] * (module.kernel_size[0] - 1)
        return context
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def fill_dict(target_dict, source_dict, prefix = ""):
    """
    Recursively Fill a dictionary target_dict by taking values from source_dict

    :param target_dict: output dictionary that is initialized with default values
    :param source_dict: input dictionary
    :return:
    """
    for k1, v1 in target_dict.items():

        if isinstance(v1, dict):
            if k1 in source_dict and isinstance(source_dict[k1], dict):
                #print(f"\n{prefix}{k1}")
                fill_dict(v1, source_dict[k1], prefix + "\t")
                #print("\n")
            else:
                #print(f"{prefix}{k1} set to default value: {target_dict[k1]}")
                pass
        else:
            if k1 in source_dict and source_dict[k1] is not None:
                target_dict[k1] = source_dict[k1]
                #print(f"{prefix}{k1} set to: {source_dict[k1]}")
            else:
                #print(f"{prefix}{k1} set to default value: {target_dict[k1]}")
                pass


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def update_training_dictionary(dataset_description,
                               model_description,
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                               training_description,
                               kwargs=None):
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    """

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    :param dataset_description:
    :param model_description:
    :param training_description:
    :param kwargs:
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    :return:
    """
    dataset_opts=dict()
    model_opts=dict()
    training_opts=dict()

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    if isinstance(dataset_description, str) and os.path.isfile(dataset_description):
        with open(dataset_description, 'r') as fh:
            tmp_data_dict = yaml.load(fh, Loader=yaml.FullLoader)
    else:
        tmp_data_dict = dataset_description
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    if isinstance(model_description, str) and os.path.isfile(model_description):
        with open(model_description, 'r') as fh:
            tmp_model_dict = yaml.load(fh, Loader=yaml.FullLoader)
    else:
        tmp_model_dict = model_description
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    if isinstance(training_description, str) and os.path.isfile(training_description):
        with open(training_description, 'r') as fh:
            tmp_train_dict = yaml.load(fh, Loader=yaml.FullLoader)
    else:
        tmp_train_dict = training_description

    # Initialize default dictionaries
    dataset_opts["data_path"] = None
    dataset_opts["dataset_csv"] = None
    dataset_opts["data_file_extension"] = ".wav"
    dataset_opts["sample_rate"] = 16000

    dataset_opts["validation_ratio"] = 0.1
    dataset_opts["batch_size"] = 64

    dataset_opts["train"] = dict()
    dataset_opts["train"]["duration"] = 2.
    dataset_opts["train"]["chunk_per_segment"] = -1
    dataset_opts["train"]["overlap"] = 1.9
    dataset_opts["train"]["transformation"] = dict()
    dataset_opts["train"]["transformation"]["pipeline"] = ""
    dataset_opts["train"]["transformation"]["add_noise"] = dict()
    dataset_opts["train"]["transformation"]["add_noise"]["noise_db_csv"] = ""
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