xvector.py 64.3 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 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 .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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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)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
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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                 idmap_test_filename,
                 ndx_test_filename,
                 key_test_filename,
                 data_root_name,
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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 = 'h5f/idmap_test.h5'
    #ndx_test_filename = 'h5f/ndx_test.h5'
    #key_test_filename = 'h5f/key_test.h5'
    #data_root_name='/lium/scratch/larcher/voxceleb1/test/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=model.loss,
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                                 transform_pipeline=transform_pipeline,
                                 num_thread=num_thread,
                                 mixed_precision=mixed_precision)
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    scores = cosine_scoring(xv_stat,
                            xv_stat,
                            Ndx(ndx_test_filename),
                            wccn=None,
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                            check_missing=True,
                            device=device)
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    k = Key(key_test_filename)
    tar, non = scores.get_tar_non(k)
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    test_eer = eer(numpy.array(non).astype(numpy.double), numpy.array(tar).astype(numpy.double))
    return test_eer
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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 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(n_mels=80)
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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 = 20,
                                                                m = 0.3,
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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["training"]["loss"]
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            if self.loss == "aam":
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                self.aam_margin = cfg["training"]["aam_margin"]
                self.aam_s = cfg["training"]["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:
                self.feature_size = cfg["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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arcface    
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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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merge    
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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(torch.nn.functional.normalize(x, dim=1), 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 xtrain(speaker_number,
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           dataset_yaml,
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           epochs=None,
           lr=None,
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           model_yaml=None,
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           model_name=None,
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           loss=None,
           aam_margin=None,
           aam_s=None,
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           scheduler_type="ReduceLROnPlateau",
           scheduler_params={},
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           patience=None,
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           tmp_model_name=None,
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           best_model_name=None,
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           multi_gpu=True,
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           mixed_precision=False,
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           clipping=False,
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           opt=None,
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           reset_parts=[],
           freeze_parts=[],
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           num_thread=None,
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           compute_test_eer=True):
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    """

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    :param speaker_number:
    :param dataset_yaml:
    :param epochs:
    :param lr:
    :param model_yaml:
    :param model_name:
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    :param loss:
    :param aam_margin:
    :param aam_s:
    :param patience:
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    :param tmp_model_name:
    :param best_model_name:
    :param multi_gpu:
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    :param mixed_precision:
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    :param clipping:
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    :param opt:
    :param reset_parts:
    :param freeze_parts:
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    :param num_thread:
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    :param compute_test_eer:
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    :return:
    """
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    # Test to optimize
    torch.autograd.profiler.emit_nvtx(enabled=False)

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    if num_thread is None:
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        import multiprocessing
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        num_thread = multiprocessing.cpu_count()

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    logging.critical(f"Use {num_thread} cpus")
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    logging.critical(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
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    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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    # Use a predefined architecture
    if model_yaml in ["xvector", "rawnet2", "resnet34", "fastresnet34"]:

        if model_name is None:
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            model = Xtractor(speaker_number, model_yaml, loss=loss)
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        else:
            logging.critical(f"*** Load model from = {model_name}")
            checkpoint = torch.load(model_name)
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            model = Xtractor(speaker_number, model_yaml, loss=loss)
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            """
            Here we remove all layers that we don't want to reload

            """
            pretrained_dict = checkpoint["model_state_dict"]
            for part in reset_parts:
                pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}

            new_model_dict = model.state_dict()
            new_model_dict.update(pretrained_dict)
            model.load_state_dict(new_model_dict)

        # Freeze required layers
        for name, param in model.named_parameters():
            if name.split(".")[0] in freeze_parts:
                param.requires_grad = False

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        model_archi = model_yaml
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    # Here use a config file to build the architecture
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    else:
        with open(model_yaml, 'r') as fh:
            model_archi = yaml.load(fh, Loader=yaml.FullLoader)
            if epochs is None:
                epochs = model_archi["training"]["epochs"]
            if patience is None:
                patience = model_archi["training"]["patience"]
            if opt is None:
                opt = model_archi["training"]["opt"]
            if lr is None:
                lr = model_archi["training"]["lr"]
            if loss is None:
                loss = model_archi["training"]["loss"]
            if aam_margin is None and model_archi["training"]["loss"] == "aam":
                aam_margin = model_archi["training"]["aam_margin"]
            if aam_s is None and model_archi["training"]["loss"] == "aam":
                aam_s = model_archi["training"]["aam_s"]
            if tmp_model_name is None:
                tmp_model_name = model_archi["training"]["tmp_model_name"]
            if best_model_name is None:
                best_model_name = model_archi["training"]["best_model_name"]
            if multi_gpu is None:
                multi_gpu = model_archi["training"]["multi_gpu"]
            if clipping is None:
                clipping = model_archi["training"]["clipping"]

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        if model_name is None:
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            model = Xtractor(speaker_number, model_yaml)

         # If we start from an existing model
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        else:
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            # Load the model
            logging.critical(f"*** Load model from = {model_name}")
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            checkpoint = torch.load(model_name, map_location=device)
            model = Xtractor(speaker_number, model_yaml, loss=loss)
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            """
            Here we remove all layers that we don't want to reload
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            """
            pretrained_dict = checkpoint["model_state_dict"]
            for part in reset_parts:
                pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}

            new_model_dict = model.state_dict()
            new_model_dict.update(pretrained_dict)
            model.load_state_dict(new_model_dict)

        # Freeze required layers
        for name, param in model.named_parameters():
            if name.split(".")[0] in freeze_parts:
                param.requires_grad = False
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    logging.critical(model)

    logging.critical("model_parameters_count: {:d}".format(
        sum(p.numel()
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            for p in model.sequence_network.parameters()
            if p.requires_grad) + \
        sum(p.numel()
            for p in model.before_speaker_embedding.parameters()
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            if p.requires_grad) + \
        sum(p.numel()
            for p in model.stat_pooling.parameters()
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            if p.requires_grad)))

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    embedding_size = model.embedding_size
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    if torch.cuda.device_count() > 1 and multi_gpu:
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        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
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    else:
        print("Train on a single GPU")
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    model.to(device)
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    with open(dataset_yaml, "r") as fh:
        dataset_params = yaml.load(fh, Loader=yaml.FullLoader)
        df = pandas.read_csv(dataset_params["dataset_description"])

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    """
    Set the dataloaders according to the dataset_yaml
    
    First we load the dataframe from CSV file in order to split it for training and validation purpose
    Then we provide those two 
    """
    training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"], stratify=df["speaker_idx"])
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    torch.manual_seed(dataset_params['seed'])
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    training_set = SideSet(dataset_yaml,
                           set_type="train",
                           chunk_per_segment=-1,
                           overlap=dataset_params['train']['overlap'],
                           dataset_df=training_df,
                           output_format="pytorch",
                           )

    validation_set = SideSet(dataset_yaml,
                             set_type="validation",
                             dataset_df=validation_df,
                             output_format="pytorch")
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    side_sampler = SideSampler(training_set.sessions['speaker_idx'],
                               speaker_number,
                               1,
                               100,
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                               dataset_params["batch_size"])
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    training_loader = DataLoader(training_set,
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                                 batch_size=dataset_params["batch_size"],
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                                 shuffle=False,
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                                 drop_last=True,
                                 pin_memory=True,
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                                 sampler=side_sampler,
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                                 num_workers=num_thread,
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                                 persistent_workers=True)
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    validation_loader = DataLoader(validation_set,
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                                   batch_size=dataset_params["batch_size"],
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                                   drop_last=False,
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                                   pin_memory=True,
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                                   num_workers=num_thread,
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                                   persistent_workers=False)
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    """
    Set the training options
    """
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    if opt == 'adam':
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        _optimizer = torch.optim.Adam
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        _options = {'lr': lr}
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    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
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        _options = {'lr': lr}
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    else:  # opt == 'sgd'
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        _optimizer = torch.optim.SGD
        _options = {'lr': lr, 'momentum': 0.9}
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    param_list = []
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    if type(model) is Xtractor:
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        if model.preprocessor is not None:
            param_list.append({'params': model.preprocessor.parameters(), 'weight_decay': model.preprocessor_weight_decay})
        param_list.append({'params': model.sequence_network.parameters(), 'weight_decay': model.sequence_network_weight_decay})
        param_list.append({'params': model.stat_pooling.parameters(), 'weight_decay': model.stat_pooling_weight_decay})
        param_list.append({'params': model.before_speaker_embedding.parameters(), 'weight_decay': model.before_speaker_embedding_weight_decay})
        param_list.append({'params': model.after_speaker_embedding.parameters(), 'weight_decay': model.after_speaker_embedding_weight_decay})
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