xvector.py 68.5 KB
Newer Older
Anthony Larcher's avatar
Anthony Larcher committed
1
# coding: utf-8 -*-
Anthony Larcher's avatar
Anthony Larcher committed
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
#
# 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/>.

"""
Anthony Larcher's avatar
v1.3.7    
Anthony Larcher committed
25
Copyright 2014-2021 Anthony Larcher, Yevhenii Prokopalo
Anthony Larcher's avatar
Anthony Larcher committed
26
"""
Anthony Larcher's avatar
Anthony Larcher committed
27

Anthony Larcher's avatar
Anthony Larcher committed
28
29
30

import pdb
import traceback
Anthony Larcher's avatar
Anthony Larcher committed
31
import logging
Anthony Larcher's avatar
Anthony Larcher committed
32
import matplotlib.pyplot as plt
Anthony Larcher's avatar
Anthony Larcher committed
33
import multiprocessing
Anthony Larcher's avatar
Anthony Larcher committed
34
import os
Anthony Larcher's avatar
Anthony Larcher committed
35
import numpy
Anthony Larcher's avatar
Anthony Larcher committed
36
import pandas
Anthony Larcher's avatar
minor    
Anthony Larcher committed
37
import pickle
Anthony Larcher's avatar
Anthony Larcher committed
38
import shutil
Anthony Larcher's avatar
Anthony Larcher committed
39
import sys
40
import time
Anthony Larcher's avatar
Anthony Larcher committed
41
import torch
Anthony Larcher's avatar
Anthony Larcher committed
42
import tqdm
Anthony Larcher's avatar
Anthony Larcher committed
43
44
import yaml

Anthony Larcher's avatar
Anthony Larcher committed
45
from torchvision import transforms
Anthony Larcher's avatar
Anthony Larcher committed
46
from collections import OrderedDict
Anthony Larcher's avatar
Anthony Larcher committed
47
from .xsets import SideSet
Anthony Larcher's avatar
debug    
Anthony Larcher committed
48
from .xsets import FileSet
Anthony Larcher's avatar
Anthony Larcher committed
49
from .xsets import IdMapSet
Anthony Larcher's avatar
Anthony Larcher committed
50
from .xsets import IdMapSet_per_speaker
Anthony Larcher's avatar
Anthony Larcher committed
51
from .res_net import RawPreprocessor, ResBlockWFMS, ResBlock, PreResNet34
Anthony Larcher's avatar
Anthony Larcher committed
52
from ..bosaris import IdMap
Anthony Larcher's avatar
Anthony Larcher committed
53
54
55
56
from ..bosaris import Key
from ..bosaris import Ndx
from ..bosaris.detplot import rocch
from ..bosaris.detplot import rocch2eer
Anthony Larcher's avatar
Anthony Larcher committed
57
from ..statserver import StatServer
Anthony Larcher's avatar
Anthony Larcher committed
58
from ..iv_scoring import cosine_scoring
Anthony Larcher's avatar
Anthony Larcher committed
59
from torch.utils.data import DataLoader
Anthony Larcher's avatar
Anthony Larcher committed
60
from sklearn.model_selection import train_test_split
Anthony Larcher's avatar
Anthony Larcher committed
61
from .sincnet import SincNet
Anthony Larcher's avatar
arcface    
Anthony Larcher committed
62
63
64
from .loss import ArcLinear
from .loss import ArcFace
from .loss import l2_norm
Anthony Larcher's avatar
Anthony Larcher committed
65
from .loss import ArcMarginProduct
Anthony Larcher's avatar
Anthony Larcher committed
66

Anthony Larcher's avatar
ddp    
Anthony Larcher committed
67
68


Anthony Larcher's avatar
Anthony Larcher committed
69

Anthony Larcher's avatar
Anthony Larcher committed
70
os.environ['MKL_THREADING_LAYER'] = 'GNU'
Anthony Larcher's avatar
debug    
Anthony Larcher committed
71

Anthony Larcher's avatar
Anthony Larcher committed
72
73
__license__ = "LGPL"
__author__ = "Anthony Larcher"
Anthony Larcher's avatar
v1.3.7    
Anthony Larcher committed
74
__copyright__ = "Copyright 2015-2021 Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
75
76
77
78
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
Anthony Larcher's avatar
Anthony Larcher committed
79
80


Anthony Larcher's avatar
Anthony Larcher committed
81
82
logging.basicConfig(format='%(asctime)s %(message)s')

Anthony Larcher's avatar
Anthony Larcher committed
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103

# Make PyTorch Deterministic
torch.manual_seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
numpy.random.seed(0)


class GuruMeditation (torch.autograd.detect_anomaly):
    
    def __init__(self):
        super(GuruMeditation, self).__init__()

    def __enter__(self):
        super(GuruMeditation, self).__enter__()
        return self

    def __exit__(self, type, value, trace):
        super(GuruMeditation, self).__exit__()
        if isinstance(value, RuntimeError):
            traceback.print_tb(trace)
Anthony Larcher's avatar
Anthony Larcher committed
104
            self.halt(str(value))
Anthony Larcher's avatar
Anthony Larcher committed
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128

    def halt(msg):
        print (msg)
        pdb.set_trace()


def select_n_random(data, labels, n=100):
    '''
    Selects n random datapoints and their corresponding labels from a dataset
    '''
    assert len(data) == len(labels)

    perm = torch.randperm(len(data))
    return data[perm][:n], labels[perm][:n]


def matplotlib_imshow(img, one_channel=False):
    if one_channel:
        img = img.mean(dim=0)
    img = img / 2 + 0.5     # unnormalize
    npimg = img.cpu().numpy()
    if one_channel:
        plt.imshow(npimg, cmap="Greys")
    else:
Anthony Larcher's avatar
Anthony Larcher committed
129
        plt.imshow(numpy.transpose(npimg, (1, 2, 0)))
Anthony Larcher's avatar
Anthony Larcher committed
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144

def speech_to_probs(model, speech):
    '''
    Generates predictions and corresponding probabilities from a trained
    network and a list of images
    '''
    output = model(speech)
    # convert output probabilities to predicted class
    _, preds_tensor = torch.max(output, 1)
    preds = numpy.squeeze(preds_tensor.cpu().numpy())
    return preds, [torch.nn.functional.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]


def plot_classes_preds(model, speech, labels):
    '''
Anthony Larcher's avatar
Anthony Larcher committed
145

Anthony Larcher's avatar
Anthony Larcher committed
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
    Generates matplotlib Figure using a trained network, along with images
    and labels from a batch, that shows the network's top prediction along
    with its probability, alongside the actual label, coloring this
    information based on whether the prediction was correct or not.
    Uses the "speech_to_probs" function.
    '''
    preds, probs = speech_to_probs(model, speech)
    # plot the images in the batch, along with predicted and true labels
    fig = plt.figure(figsize=(12, 48))
    for idx in numpy.arange(4):
         ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])
         #matplotlib_imshow(speech[idx], one_channel=True)
         ax.set_title("{0}, {1:.1f}%\n(label: {2})".format(
                                                           preds[idx],
                                                           probs[idx] * 100.0,
                                                           labels[idx]),
                                                           color=("green" if preds[idx]==labels[idx].item() else "red"))
    return fig


Anthony Larcher's avatar
Anthony Larcher committed
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
def compute_metrics(model,
                    validation_loader,
                    device,
                    val_embs_shape,
                    speaker_number,
                    model_archi):
    """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]
    """
    val_acc, val_loss, val_eer = cross_validation(model, validation_loader, device, val_embs_shape)
Anthony Larcher's avatar
Anthony Larcher committed
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
    #xv_stat = extract_embeddings(idmap_name='h5f/idmap_test.h5',
    #                             speaker_number=speaker_number,
    #                             model_filename=model,
    #                             model_yaml=model_archi,
    #                             data_root_name="data/vox1/wav/" ,
    #                             device=device,
    #                             transform_pipeline="MFCC,CMVN")

    #scores = cosine_scoring(xv_stat, xv_stat,
    #                        Ndx('h5f/ndx_test.h5'),
    #                        wccn=None, check_missing=True)

    #tar, non = scores.get_tar_non(Key('h5f/key_test.h5'))
    #pmiss, pfa = rocch(numpy.array(tar).astype(numpy.double), numpy.array(non).astype(numpy.double))
    #test_eer = rocch2eer(pmiss, pfa)

    test_eer = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
206
207
    return val_acc, val_loss, val_eer, test_eer

Anthony Larcher's avatar
Anthony Larcher committed
208

209
def get_lr(optimizer):
Anthony Larcher's avatar
Anthony Larcher committed
210
211
212
213
214
    """

    :param optimizer:
    :return:
    """
215
216
217
218
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
219
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
Anthony Larcher's avatar
Anthony Larcher committed
220
221
222
223
224
225
226
227
    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
228
229
230
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
Anthony Larcher's avatar
Anthony Larcher committed
231

Anthony Larcher's avatar
Anthony Larcher committed
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
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)
Anthony Larcher's avatar
Anthony Larcher committed
252

Anthony Larcher's avatar
Anthony Larcher committed
253

Anthony Larcher's avatar
Anthony Larcher committed
254
255
256
257
258
259
260
261
262
263
264
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:
        """
Anthony Larcher's avatar
Anthony Larcher committed
265
        super(GruPooling, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
        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,
                          hidden_size = gru_node,
                          num_layers = nb_gru_layer,
                          batch_first = True)

    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)
        x = x[:,-1,:]

        return x

Anthony Larcher's avatar
Anthony Larcher committed
288

Anthony Larcher's avatar
Anthony Larcher committed
289
class Xtractor(torch.nn.Module):
290
291
292
    """
    Class that defines an x-vector extractor based on 5 convolutional layers and a mean standard deviation pooling
    """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
293

Anthony Larcher's avatar
Anthony Larcher committed
294
295
296
    def __init__(self,
                 speaker_number,
                 model_archi="xvector",
Anthony Larcher's avatar
Anthony Larcher committed
297
                 loss=None,
Anthony Larcher's avatar
Anthony Larcher committed
298
299
300
                 norm_embedding=False,
                 aam_margin=0.5,
                 aam_s=0.5):
Anthony Larcher's avatar
Anthony Larcher committed
301
302
        """
        If config is None, default architecture is created
Anthony Larcher's avatar
Anthony Larcher committed
303
        :param model_archi:
Anthony Larcher's avatar
Anthony Larcher committed
304
        """
Anthony Larcher's avatar
Anthony Larcher committed
305
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
306
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
307
        self.feature_size = None
Anthony Larcher's avatar
Anthony Larcher committed
308
        self.norm_embedding = norm_embedding
Anthony Larcher's avatar
Anthony Larcher committed
309

Anthony Larcher's avatar
Anthony Larcher committed
310
311
        print(f"Speaker number : {self.speaker_number}")

Anthony Larcher's avatar
Anthony Larcher committed
312
        if model_archi == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
313
314
315
316
317
318

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

Anthony Larcher's avatar
Anthony Larcher committed
319
            self.feature_size = 30
Anthony Larcher's avatar
Anthony Larcher committed
320
321
322
            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
323

Anthony Larcher's avatar
xv    
Anthony Larcher committed
324
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
325
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
326
                ("activation1", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
327
                ("batch_norm1", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
328
329
                ("conv2", torch.nn.Conv1d(512, 512, 3, dilation=2)),
                ("activation2", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
330
                ("batch_norm2", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
331
332
                ("conv3", torch.nn.Conv1d(512, 512, 3, dilation=3)),
                ("activation3", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
333
                ("batch_norm3", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
334
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
335
                ("activation4", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
336
                ("batch_norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
337
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
338
                ("activation5", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
339
                ("batch_norm5", torch.nn.BatchNorm1d(1536))
Anthony Larcher's avatar
Anthony Larcher committed
340
341
            ]))

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
342
            self.stat_pooling = MeanStdPooling()
Anthony Larcher's avatar
Anthony Larcher committed
343
            self.stat_pooling_weight_decay = 0
Anthony Larcher's avatar
xv    
Anthony Larcher committed
344
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
345
                ("linear6", torch.nn.Linear(3072, 512))
Anthony Larcher's avatar
Anthony Larcher committed
346
347
            ]))

348
349
            self.embedding_size = 512

Anthony Larcher's avatar
Anthony Larcher committed
350
            if self.loss == "aam":
Anthony Larcher's avatar
Anthony Larcher committed
351
352
353
354
355
                self.after_speaker_embedding = ArcMarginProduct(512,
                                                                int(self.speaker_number),
                                                                s=64,
                                                                m=0.2,
                                                                easy_margin=True)
Anthony Larcher's avatar
Anthony Larcher committed
356
357
358
            elif self.loss == "cce":
                self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
                    ("activation6", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
359
                    ("batch_norm6", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
360
361
362
                    ("dropout6", torch.nn.Dropout(p=0.05)),
                    ("linear7", torch.nn.Linear(512, 512)),
                    ("activation7", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
363
                    ("batch_norm7", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
364
365
                    ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
                ]))
Anthony Larcher's avatar
Anthony Larcher committed
366

Anthony Larcher's avatar
Anthony Larcher committed
367
368
369
            self.sequence_network_weight_decay = 0.0002
            self.before_speaker_embedding_weight_decay = 0.002
            self.after_speaker_embedding_weight_decay = 0.002
370
            self.embedding_size = 512
Anthony Larcher's avatar
Anthony Larcher committed
371

Anthony Larcher's avatar
Anthony Larcher committed
372
373
374
375
376
377
378
379
380
381
        elif model_archi == "resnet34":
            self.preprocessor = None
            self.sequence_network = PreResNet34()

            self.before_speaker_embedding = torch.nn.Linear(in_features = 5120,
                                                            out_features = 256)

            self.stat_pooling = MeanStdPooling()
            self.stat_pooling_weight_decay = 0

382
            self.embedding_size = 256
Anthony Larcher's avatar
Anthony Larcher committed
383

384
            self.loss = "aam"
Anthony Larcher's avatar
Anthony Larcher committed
385
386
387
388
389
            self.after_speaker_embedding = ArcMarginProduct(256,
                                                            int(self.speaker_number),
                                                            s = 30.0,
                                                            m = 0.50,
                                                            easy_margin = False)
Anthony Larcher's avatar
Anthony Larcher committed
390
391
392
393
394
395
396
397

            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


Anthony Larcher's avatar
Anthony Larcher committed
398
        elif model_archi == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
399
400
401
402
403
404

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

Anthony Larcher's avatar
Anthony Larcher committed
405
406
407
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

Anthony Larcher's avatar
Anthony Larcher committed
408
            self.preprocessor = RawPreprocessor(nb_samp=48000,
Anthony Larcher's avatar
Anthony Larcher committed
409
                                                in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
410
411
                                                out_channels=filts[0],
                                                kernel_size=3)
Anthony Larcher's avatar
Anthony Larcher committed
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428

            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)

Anthony Larcher's avatar
Anthony Larcher committed
429
430
431
432
433
            if self.loss == "aam":
                if loss == 'aam':
                    self.after_speaker_embedding = ArcLinear(1024,
                                                             int(self.speaker_number),
                                                             margin=aam_margin, s=aam_s)
Anthony Larcher's avatar
Anthony Larcher committed
434
            elif self.loss == "cce":
Anthony Larcher's avatar
Anthony Larcher committed
435
436
437
                self.after_speaker_embedding = torch.nn.Linear(in_features = 1024,
                                                               out_features = int(self.speaker_number),
                                                               bias = True)
Anthony Larcher's avatar
Anthony Larcher committed
438

Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
439
440
441
442
443
444
            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

Anthony Larcher's avatar
Anthony Larcher committed
445
        else:
Anthony Larcher's avatar
Anthony Larcher committed
446
447
            is_first_resblock = True

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
448
449
450
451
452
453
            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)
Anthony Larcher's avatar
Anthony Larcher committed
454

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
455
            self.loss = cfg["training"]["loss"]
Anthony Larcher's avatar
Anthony Larcher committed
456
            if self.loss == "aam":
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
457
458
                self.aam_margin = cfg["training"]["aam_margin"]
                self.aam_s = cfg["training"]["aam_s"]
Anthony Larcher's avatar
Anthony Larcher committed
459

Anthony Larcher's avatar
Anthony Larcher committed
460
461
462
            """
            Prepare Preprocessor
            """
Anthony Larcher's avatar
Anthony Larcher committed
463
            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
464
465
            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
Anthony Larcher's avatar
Anthony Larcher committed
466
                    self.preprocessor = SincNet(
Anthony Larcher's avatar
Anthony Larcher committed
467
468
469
470
471
472
473
474
475
476
477
478
                        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"]
                    )
Anthony Larcher's avatar
Anthony Larcher committed
479
                    self.feature_size = self.preprocessor.dimension
Anthony Larcher's avatar
Anthony Larcher committed
480
                elif cfg['preprocessor']["type"] == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
481
                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
Anthony Larcher's avatar
Anthony Larcher committed
482
                                                        in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
483
484
485
486
487
488
                                                        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"]
Anthony Larcher's avatar
Anthony Larcher committed
489
                self.preprocessor_weight_decay = 0.000
Anthony Larcher's avatar
Anthony Larcher committed
490
491

            """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
492
            Prepare sequence network
Anthony Larcher's avatar
Anthony Larcher committed
493
            """
Anthony Larcher's avatar
Anthony Larcher committed
494
            # Get Feature size
Anthony Larcher's avatar
Anthony Larcher committed
495
496
497
            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

Anthony Larcher's avatar
Anthony Larcher committed
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
            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():
Anthony Larcher's avatar
Anthony Larcher committed
513
514
515
516
517
                if k.startswith("lin"):
                    segmental_layers.append((k, torch.nn.Linear(input_size,
                                                                cfg["segmental"][k]["output"])))
                    input_size = cfg["segmental"][k]["output"]

Anthony Larcher's avatar
Anthony Larcher committed
518
519
520
521
522
                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,
Anthony Larcher's avatar
Anthony Larcher committed
523
                                                                stride=1)))
Anthony Larcher's avatar
Anthony Larcher committed
524

Anthony Larcher's avatar
Anthony Larcher committed
525
                elif k.startswith("conv"):
Anthony Larcher's avatar
Anthony Larcher committed
526
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
527
                                                                cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
528
529
                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
530
531
                    input_size = cfg["segmental"][k]["output_channels"]

Anthony Larcher's avatar
Anthony Larcher committed
532
533
                elif k.startswith("ctrans"):
                    segmental_layers.append((k, torch.nn.ConvTranspose1d(input_size,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
534
                                                                         cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
535
536
                                                                         kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                         dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
537
538
539
                elif k.startswith("activation"):
                    segmental_layers.append((k, self.activation))

Anthony Larcher's avatar
Anthony Larcher committed
540
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
Anthony Larcher committed
541
542
                    segmental_layers.append((k, torch.nn.BatchNorm1d(input_size)))

Anthony Larcher's avatar
Anthony Larcher committed
543
544
545
546
547
548
                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

Anthony Larcher's avatar
Anthony Larcher committed
549
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
550
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
551

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
552
553
554
555
            """
            Pooling
            """
            self.stat_pooling = MeanStdPooling()
Anthony Larcher's avatar
Anthony Larcher committed
556
            tmp_input_size = input_size * 2
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
557
558
559
560
            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"])
Anthony Larcher's avatar
debug    
Anthony Larcher committed
561
                tmp_input_size = cfg["stat_pooling"]["gru_node"]
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
562

Anthony Larcher's avatar
Anthony Larcher committed
563
564
            self.stat_pooling_weight_decay = cfg["stat_pooling"]["weight_decay"]

Anthony Larcher's avatar
Anthony Larcher committed
565
            """
Anthony Larcher's avatar
Anthony Larcher committed
566
            Prepare last part of the network (after pooling)
Anthony Larcher's avatar
Anthony Larcher committed
567
            """
Anthony Larcher's avatar
Anthony Larcher committed
568
            # Create sequential object for the second part of the network
Anthony Larcher's avatar
Anthony Larcher committed
569
            input_size = tmp_input_size
Anthony Larcher's avatar
xv    
Anthony Larcher committed
570
571
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
572
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
573
574
                    if cfg["before_embedding"][k]["output"] == "speaker_number":
                        before_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
Anthony Larcher's avatar
Anthony Larcher committed
575
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
576
577
                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
578
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
579
580

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
581
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
582

Anthony Larcher's avatar
Anthony Larcher committed
583
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
584
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
585
586

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
587
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
588

Anthony Larcher's avatar
Anthony Larcher committed
589
            self.embedding_size = input_size
Anthony Larcher's avatar
Anthony Larcher committed
590
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
591
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
592

Anthony Larcher's avatar
Anthony Larcher committed
593
            # if loss_criteria is "cce"
Anthony Larcher's avatar
xv    
Anthony Larcher committed
594
            # Create sequential object for the second part of the network
Anthony Larcher's avatar
Anthony Larcher committed
595
596
597
598
599
600
601
602
            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,
Anthony Larcher's avatar
Anthony Larcher committed
603
                                                                          cfg["after_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
604
                            input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
605

Anthony Larcher's avatar
Anthony Larcher committed
606
607
608
609
610
                    elif k.startswith('arc'):
                        after_embedding_layers.append((k, ArcLinear(input_size,
                                                                    self.speaker_number,
                                                                    margin=self.aam_margin,
                                                                    s=self.aam_s)))
Anthony Larcher's avatar
Anthony Larcher committed
611

Anthony Larcher's avatar
Anthony Larcher committed
612
613
                    elif k.startswith("activation"):
                        after_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
614

Anthony Larcher's avatar
Anthony Larcher committed
615
616
                    elif k.startswith('batch_norm'):
                        after_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
617

Anthony Larcher's avatar
Anthony Larcher committed
618
619
620
621
622
623
                    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":
Anthony Larcher's avatar
Anthony Larcher committed
624
625
626
627
628
629
630
                self.norm_embedding = True
                self.after_speaker_embedding = ArcMarginProduct(input_size,
                                                                int(self.speaker_number),
                                                                s=64,
                                                                m=0.2,
                                                                easy_margin=True)

Anthony Larcher's avatar
arcface    
Anthony Larcher committed
631
632
633
634
                #self.after_speaker_embedding = ArcLinear(input_size,
                #                                         self.speaker_number,
                #                                         margin=self.aam_margin,
                #                                         s=self.aam_s)
Anthony Larcher's avatar
Anthony Larcher committed
635
636
637
638
                #self.after_speaker_embedding = ArcFace(embedding_size=input_size,
                #                                       classnum=self.speaker_number,
                #                                       s=64.,
                #                                       m=0.5)
Anthony Larcher's avatar
Anthony Larcher committed
639

Anthony Larcher's avatar
Anthony Larcher committed
640
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
641

Anthony Larcher's avatar
Anthony Larcher committed
642

643
    def forward(self, x, is_eval=False, target=None, extract_after_pooling=False):
644
645
646
        """

        :param x:
647
        :param is_eval: False for training
648
649
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
650
651
652
        if self.preprocessor is not None:
            x = self.preprocessor(x)

Anthony Larcher's avatar
Anthony Larcher committed
653
        x = self.sequence_network(x)
654

Anthony Larcher's avatar
Anthony Larcher committed
655
        # Mean and Standard deviation pooling
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
656
        x = self.stat_pooling(x)
Anthony Larcher's avatar
Anthony Larcher committed
657

658
659
660
        if extract_after_pooling:
            return x

Anthony Larcher's avatar
Anthony Larcher committed
661
        x = self.before_speaker_embedding(x)
662

Anthony Larcher's avatar
Anthony Larcher committed
663
        if self.norm_embedding:
Anthony Larcher's avatar
arcface    
Anthony Larcher committed
664
            #x_norm = x.norm(p=2,dim=1, keepdim=True) / 10. # Why  10. ?
Anthony Larcher's avatar
arcface    
Anthony Larcher committed
665
666
667
            #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)
Anthony Larcher's avatar
Anthony Larcher committed
668

Anthony Larcher's avatar
Anthony Larcher committed
669
        if self.loss == "cce":
Anthony Larcher's avatar
Anthony Larcher committed
670
671
672
673
            if is_eval:
                return self.after_speaker_embedding(x), x
            else:
                return self.after_speaker_embedding(x)
Anthony Larcher's avatar
Anthony Larcher committed
674

Anthony Larcher's avatar
Anthony Larcher committed
675
676
        elif self.loss == "aam":
            if not is_eval:
Anthony Larcher's avatar
Anthony Larcher committed
677
                x = self.after_speaker_embedding(l2_norm(x), target=target), l2_norm(x)
Anthony Larcher's avatar
Anthony Larcher committed
678
            else:
Anthony Larcher's avatar
Anthony Larcher committed
679
                x = self.after_speaker_embedding(l2_norm(x), target=None), l2_norm(x)
Anthony Larcher's avatar
Anthony Larcher committed
680

Anthony Larcher's avatar
Anthony Larcher committed
681
        return x
Anthony Larcher's avatar
Anthony Larcher committed
682

683
684
    def context_size(self):
        context = 1
Anthony Larcher's avatar
Anthony Larcher committed
685
686
687
688
689
690
691
692
693
        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
Anthony Larcher's avatar
Anthony Larcher committed
694

Anthony Larcher's avatar
Anthony Larcher committed
695
def xtrain(speaker_number,
Anthony Larcher's avatar
Anthony Larcher committed
696
           dataset_yaml,
Anthony Larcher's avatar
Anthony Larcher committed
697
698
           epochs=None,
           lr=None,
Anthony Larcher's avatar
Anthony Larcher committed
699
           model_yaml=None,
Anthony Larcher's avatar
Anthony Larcher committed
700
           model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
701
702
703
704
           loss=None,
           aam_margin=None,
           aam_s=None,
           patience=None,
Anthony Larcher's avatar
Anthony Larcher committed
705
           tmp_model_name=None,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
706
           best_model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
707
           multi_gpu=True,
708
           clipping=False,
Anthony Larcher's avatar
Anthony Larcher committed
709
           opt=None,
Anthony Larcher's avatar
Anthony Larcher committed
710
711
           reset_parts=[],
           freeze_parts=[],
Anthony Larcher's avatar
Anthony Larcher committed
712
713
714
715
           num_thread=None,
           write_batches_to_disk=False,
           load_batches_from_disk=False,
           tmp_batch_dir=None):
716
717
    """

Anthony Larcher's avatar
Anthony Larcher committed
718
719
720
721
722
723
    :param speaker_number:
    :param dataset_yaml:
    :param epochs:
    :param lr:
    :param model_yaml:
    :param model_name:
Anthony Larcher's avatar
Anthony Larcher committed
724
725
726
727
    :param loss:
    :param aam_margin:
    :param aam_s:
    :param patience:
Anthony Larcher's avatar
Anthony Larcher committed
728
729
730
731
    :param tmp_model_name:
    :param best_model_name:
    :param multi_gpu:
    :param clipping:
Anthony Larcher's avatar
Anthony Larcher committed
732
733
734
    :param opt:
    :param reset_parts:
    :param freeze_parts:
Anthony Larcher's avatar
Anthony Larcher committed
735
    :param num_thread:
736
737
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
738
739
740
741
    # Add for tensorboard
    # Writer will output to ./runs/ directory by default
    #writer = SummaryWriter("runs/xvectors_experiments_2")
    writer = None
Anthony Larcher's avatar
Anthony Larcher committed
742

Anthony Larcher's avatar
Anthony Larcher committed
743
744
745
    if write_batches_to_disk:
        load_batches_from_disk = True

Anthony Larcher's avatar
Anthony Larcher committed
746
747
748
    if num_thread is None:
        num_thread = multiprocessing.cpu_count()

Anthony Larcher's avatar
Anthony Larcher committed
749
    logging.critical(f"Use {num_thread} cpus")
Anthony Larcher's avatar
Anthony Larcher committed
750
    logging.critical(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
751

Anthony Larcher's avatar
Anthony Larcher committed
752
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Anthony Larcher's avatar
Anthony Larcher committed
753
    # Start from scratch
754
    if model_name is None and model_yaml in ["xvector", "rawnet2", "resnet34"]:
Anthony Larcher's avatar
Anthony Larcher committed
755
        # Initialize a first model
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
756
        if model_yaml == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
757
            model = Xtractor(speaker_number, "xvector", loss=loss)
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
758
        elif model_yaml == "rawnet2":
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
759
            model = Xtractor(speaker_number, "rawnet2")
760
761
        elif model_yaml == "resnet34":
            model = Xtractor(speaker_number, "resnet34")
Anthony Larcher's avatar
Anthony Larcher committed
762
        model_archi = model_yaml
Anthony Larcher's avatar
Anthony Larcher committed
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
    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"]

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
789
        if model_name is None:
Anthony Larcher's avatar
Anthony Larcher committed
790
791
792
            model = Xtractor(speaker_number, model_yaml)

         # If we start from an existing model
Anthony Larcher's avatar
Anthony Larcher committed
793
        else:
Anthony Larcher's avatar
Anthony Larcher committed
794
795
796
            # Load the model
            logging.critical(f"*** Load model from = {model_name}")
            checkpoint = torch.load(model_name)
Anthony Larcher's avatar
Anthony Larcher committed
797
            model = Xtractor(speaker_number, model_yaml)
Anthony Larcher's avatar
Anthony Larcher committed
798

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
799
800
            """
            Here we remove all layers that we don't want to reload
Anthony Larcher's avatar
Anthony Larcher committed
801
        
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
802
803
804
805
806
807
808
809
810
811
812
813
814
            """
            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
Anthony Larcher's avatar
Anthony Larcher committed
815

Anthony Larcher's avatar
Anthony Larcher committed
816
    print(model)
Anthony Larcher's avatar
Anthony Larcher committed
817
    embedding_size = model.embedding_size
Anthony Larcher's avatar
Anthony Larcher committed
818

Anthony Larcher's avatar
Anthony Larcher committed
819
    if torch.cuda.device_count() > 1 and multi_gpu:
Anthony Larcher's avatar
Anthony Larcher committed
820
821
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
Anthony Larcher's avatar
Anthony Larcher committed
822
        #model = DDP(model)
Anthony Larcher's avatar
Anthony Larcher committed
823
824
    else:
        print("Train on a single GPU")
Anthony Larcher's avatar
Anthony Larcher committed
825
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
826

Anthony Larcher's avatar
debug    
Anthony Larcher committed
827
828
829
830
    with open(dataset_yaml, "r") as fh:
        dataset_params = yaml.load(fh, Loader=yaml.FullLoader)
        df = pandas.read_csv(dataset_params["dataset_description"])

Anthony Larcher's avatar
Anthony Larcher committed
831
832
833
    if load_batches_from_disk:
        train_batch_fn_format = tmp_batch_dir + "/train/train_{}_batch.h5"
        val_batch_fn_format = tmp_batch_dir + "/val/val_{}_batch.h5"
834

Anthony Larcher's avatar
Anthony Larcher committed
835
836
837
838
839
840
841
    if not load_batches_from_disk or write_batches_to_disk:
        """
        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 
        """
Anthony Larcher's avatar
Anthony Larcher committed
842

Anthony Larcher's avatar
minor    
Anthony Larcher committed
843
        if write_batches_to_disk or dataset_params["batch_size"] > 1:
Anthony Larcher's avatar
Anthony Larcher committed
844
845
846
847
            output_format = "numpy"
        else:
            output_format = "pytorch"

Anthony Larcher's avatar
Anthony Larcher committed
848
849
        training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"])
        torch.manual_seed(dataset_params['seed'])
Anthony Larcher's avatar
spkset    
Anthony Larcher committed
850
851
852
853
854
855
856
        training_set = SpkSet(dataset_yaml,
                              set_type="train",
                              dataset_df=training_df,
                              chunk_per_segment=dataset_params['train']['chunk_per_segment'],
                              overlap=dataset_params['train']['overlap'],
                              output_format="pytorch",
                              windowed=True)
Anthony Larcher's avatar
Anthony Larcher committed
857

Anthony Larcher's avatar
Anthony Larcher committed
858
859
860
861
        validation_set = SideSet(dataset_yaml,
                                 set_type="validation",
                                 dataset_df=validation_df,
                                 output_format=output_format)
Anthony Larcher's avatar
Anthony Larcher committed
862
863


Anthony Larcher's avatar
Anthony Larcher committed
864
        if write_batches_to_disk:
Anthony Larcher's avatar
debug    
Anthony Larcher committed
865
            logging.critical("Start writing batches on disk")
Anthony Larcher's avatar
Anthony Larcher committed
866
867
            training_set.write_to_disk(dataset_params["batch_size"], train_batch_fn_format, num_thread)
            validation_set.write_to_disk(dataset_params["batch_size"], val_batch_fn_format, num_thread)
Anthony Larcher's avatar
debug    
Anthony Larcher committed
868
            logging.critical("---> Done")
Anthony Larcher's avatar
Anthony Larcher committed
869
870

    if load_batches_from_disk:
Anthony Larcher's avatar
debug    
Anthony Larcher committed
871
872
873
874
875
876
877
878
879
880
881
882
883
        training_set = FileSet(train_batch_fn_format)
        validation_set = FileSet(train_batch_fn_format)
        batch_size = 1
    else:
        batch_size = dataset_params["batch_size"]


    print(f"Size of batches = {batch_size}")
    training_loader = DataLoader(training_set,
                                 batch_size=batch_size,
                                 shuffle=True,
                                 drop_last=True,
                                 pin_memory=True,
Anthony Larcher's avatar
spkset    
Anthony Larcher committed
884
885
                                 num_workers=num_thread,
                                 persistent_workers=True)
Anthony Larcher's avatar
debug    
Anthony Larcher committed
886
887
888
889
890

    validation_loader = DataLoader(validation_set,
                                   batch_size=batch_size,
                                   drop_last=True,
                                   pin_memory=True,
Anthony Larcher's avatar
spkset    
Anthony Larcher committed
891
892
                                   num_workers=num_thread,
                                   persistent_workers=True)
Anthony Larcher's avatar
debug    
Anthony Larcher committed
893

Anthony Larcher's avatar
Anthony Larcher committed
894
895
896
    """
    Set the training options
    """
Anthony Larcher's avatar
Anthony Larcher committed
897
    if opt == 'adam':
Anthony Larcher's avatar
Anthony Larcher committed
898
        _optimizer = torch.optim.Adam
Anthony Larcher's avatar
Anthony Larcher committed
899
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
900
901
    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
Anthony Larcher's avatar
Anthony Larcher committed
902
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
903
904
905
    else: # opt == 'sgd'
        _optimizer = torch.optim.SGD
        _options = {'lr': lr, 'momentum': 0.9}
Anthony Larcher's avatar
Anthony Larcher committed
906

Anthony Larcher's avatar
Anthony Larcher committed
907
    param_list = []
Anthony Larcher's avatar
Anthony Larcher committed
908
    if type(model) is Xtractor:
Anthony Larcher's avatar
Anthony Larcher committed
909
910
911
912
913
914
915
        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})

Anthony Larcher's avatar
Anthony Larcher committed
916
    else:
Anthony Larcher's avatar
Anthony Larcher committed
917
918
919
920
921
922
923
924
        if model.module.preprocessor is not None:
            param_list.append({'params': model.module.preprocessor.parameters(), 'weight_decay': model.module.preprocessor_weight_decay})
        param_list.append({'params': model.module.sequence_network.parameters(), 'weight_decay': model.module.sequence_network_weight_decay})
        param_list.append({'params': model.module.stat_pooling.parameters(), 'weight_decay': model.module.stat_pooling_weight_decay})
        param_list.append({'params': model.module.before_speaker_embedding.parameters(), 'weight_decay': model.module.before_speaker_embedding_weight_decay})
        param_list.append({'params': model.module.after_speaker_embedding.parameters(), 'weight_decay': model.module.after_speaker_embedding_weight_decay})

    optimizer = _optimizer(param_list, **_options)
Anthony Larcher's avatar
Anthony Larcher committed
925
926
927
928
929
930
931
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
                                                     milestones=[50000, 60000, 70000, 80000, 90000,
                                                                 100000, 110000, 120000, 130000,
                                                                 140000, 150000],
                                                     gamma=0.1,
                                                     last_epoch=-1,
                                                     verbose=True)
932

Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
933
    best_accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
934
    best_accuracy_epoch = 1
Anthony Larcher's avatar
spkset    
Anthony Larcher committed
935
    best_eer = 100
Anthony Larcher's avatar
Anthony Larcher committed
936
    curr_patience = patience
Anthony Larcher's avatar
eer    
Anthony Larcher committed
937
938
    
    logging.critical("Compute EER before starting")
Anthony Larcher's avatar
Anthony Larcher committed
939
940
941
942
943
944
    #val_acc, val_loss, val_eer, test_eer = compute_metrics(model,
    #                                                       validation_loader,
    #                                                       device,
    #                                                       [validation_set.__len__(), embedding_size],
    #                                                       speaker_number,
    #                                                       model_archi)
Anthony Larcher's avatar
Anthony Larcher committed
945

Anthony Larcher's avatar
Anthony Larcher committed
946
947
    #logging.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Initial metrics - Cross validation accuracy = {val_acc} %, EER = {val_eer * 100} %")
    #logging.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Initial metrics - Test EER = {test_eer * 100} %")
Anthony Larcher's avatar
Anthony Larcher committed
948
949


Anthony Larcher's avatar
Anthony Larcher committed
950
    for epoch in range(1, epochs + 1):
951
        # Process one epoch and return the current model
Anthony Larcher's avatar
Anthony Larcher committed
952
953
954
        if curr_patience == 0:
            print(f"Stopping at epoch {epoch} for cause of patience")
            break
Anthony Larcher's avatar
Anthony Larcher committed
955
956
957
958
        model = train_epoch(model,
                            epoch,
                            training_loader,
                            optimizer,
Anthony Larcher's avatar
Anthony Larcher committed
959
                            scheduler,
Anthony Larcher's avatar
Anthony Larcher committed
960
961
                            dataset_params["log_interval"],
                            device=device,
Anthony Larcher's avatar
Anthony Larcher committed
962
963
                            clipping=clipping,
                            tb_writer=writer)
964
965

        # Add the cross validation here
Anthony Larcher's avatar
Anthony Larcher committed
966
967
968
        val_acc, val_loss, val_eer, test_eer = compute_metrics(model,
                                                               validation_loader,
                                                               device,
Anthony Larcher's avatar
Anthony Larcher committed
969
                                                               [validation_set.__len__(), embedding_size],
Anthony Larcher's avatar
Anthony Larcher committed
970
971
972
973
                                                               speaker_number,
                                                               model_archi)

        logging.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Training metrics - Cross validation accuracy = {val_acc} %, EER = {val_eer * 100} %")
974
975

        # Decrease learning rate according to the scheduler policy
Anthony Larcher's avatar
Anthony Larcher committed
976
        #scheduler.step(val_loss)
977

Anthony Larcher's avatar
Anthony Larcher committed
978
        # remember best accuracy and save checkpoint
Anthony Larcher's avatar
Anthony Larcher committed
979
980
        is_best = val_acc > best_accuracy
        best_accuracy = max(val_acc, best_accuracy)
Anthony Larcher's avatar
Anthony Larcher committed
981

982
983
984
985
986
        if tmp_model_name is None:
            tmp_model_name = "tmp_model"
        if best_model_name is None:
            best_model_name = "best_model"

Anthony Larcher's avatar
Anthony Larcher committed
987
988
989
990
991
992
        if type(model) is Xtractor:
            save_checkpoint({
                'epoch': epoch,
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
                'accuracy': best_accuracy,
Anthony Larcher's avatar
Anthony Larcher committed
993
994
                'scheduler': scheduler,
                'speaker_number' : speaker_number,
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
995
                'model_archi': model_archi
Anthony Larcher's avatar
Anthony Larcher committed
996
997
998
999
1000
            }, is_best, filename=tmp_model_name+".pt", best_filename=best_model_name+'.pt')
        else:
            save_checkpoint({
                'epoch': epoch,
                'model_state_dict': model.module.state_dict(),
For faster browsing, not all history is shown. View entire blame