xvector.py 68.9 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
os.environ['MKL_THREADING_LAYER'] = 'GNU'
Anthony Larcher's avatar
debug    
Anthony Larcher committed
70

Anthony Larcher's avatar
Anthony Larcher committed
71
72
__license__ = "LGPL"
__author__ = "Anthony Larcher"
Anthony Larcher's avatar
v1.3.7    
Anthony Larcher committed
73
__copyright__ = "Copyright 2015-2021 Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
74
75
76
77
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
Anthony Larcher's avatar
Anthony Larcher committed
78
79


Anthony Larcher's avatar
Anthony Larcher committed
80
81
logging.basicConfig(format='%(asctime)s %(message)s')

Anthony Larcher's avatar
Anthony Larcher committed
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102

# 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
103
            self.halt(str(value))
Anthony Larcher's avatar
Anthony Larcher committed
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127

    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
128
        plt.imshow(numpy.transpose(npimg, (1, 2, 0)))
Anthony Larcher's avatar
Anthony Larcher committed
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143

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
144

Anthony Larcher's avatar
Anthony Larcher committed
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
    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
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
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
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
    #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
205
206
    return val_acc, val_loss, val_eer, test_eer

Anthony Larcher's avatar
Anthony Larcher committed
207

208
def get_lr(optimizer):
Anthony Larcher's avatar
Anthony Larcher committed
209
210
211
212
213
    """

    :param optimizer:
    :return:
    """
214
215
216
217
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
218
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
Anthony Larcher's avatar
Anthony Larcher committed
219
220
221
222
223
224
225
226
    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
227
228
229
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
Anthony Larcher's avatar
Anthony Larcher committed
230

Anthony Larcher's avatar
Anthony Larcher committed
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
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
251

Anthony Larcher's avatar
Anthony Larcher committed
252

Anthony Larcher's avatar
Anthony Larcher committed
253
254
255
256
257
258
259
260
261
262
263
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
264
        super(GruPooling, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
        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
287

Anthony Larcher's avatar
Anthony Larcher committed
288
class Xtractor(torch.nn.Module):
289
290
291
    """
    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
292

Anthony Larcher's avatar
Anthony Larcher committed
293
294
295
    def __init__(self,
                 speaker_number,
                 model_archi="xvector",
Anthony Larcher's avatar
Anthony Larcher committed
296
                 loss=None,
Anthony Larcher's avatar
Anthony Larcher committed
297
298
299
                 norm_embedding=False,
                 aam_margin=0.5,
                 aam_s=0.5):
Anthony Larcher's avatar
Anthony Larcher committed
300
301
        """
        If config is None, default architecture is created
Anthony Larcher's avatar
Anthony Larcher committed
302
        :param model_archi:
Anthony Larcher's avatar
Anthony Larcher committed
303
        """
Anthony Larcher's avatar
Anthony Larcher committed
304
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
305
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
306
        self.feature_size = None
Anthony Larcher's avatar
Anthony Larcher committed
307
        self.norm_embedding = norm_embedding
Anthony Larcher's avatar
Anthony Larcher committed
308

Anthony Larcher's avatar
Anthony Larcher committed
309
310
        print(f"Speaker number : {self.speaker_number}")

Anthony Larcher's avatar
Anthony Larcher committed
311
        if model_archi == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
312
313
314
315
316
317

            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
318
            self.feature_size = 30
Anthony Larcher's avatar
Anthony Larcher committed
319
320
321
            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
322

Anthony Larcher's avatar
xv    
Anthony Larcher committed
323
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
324
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
325
                ("activation1", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
326
                ("batch_norm1", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
327
328
                ("conv2", torch.nn.Conv1d(512, 512, 3, dilation=2)),
                ("activation2", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
329
                ("batch_norm2", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
330
331
                ("conv3", torch.nn.Conv1d(512, 512, 3, dilation=3)),
                ("activation3", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
332
                ("batch_norm3", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
333
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
334
                ("activation4", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
335
                ("batch_norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
336
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
337
                ("activation5", torch.nn.LeakyReLU(0.2)),
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
338
                ("batch_norm5", torch.nn.BatchNorm1d(1536))
Anthony Larcher's avatar
Anthony Larcher committed
339
340
            ]))

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
341
            self.stat_pooling = MeanStdPooling()
Anthony Larcher's avatar
Anthony Larcher committed
342
            self.stat_pooling_weight_decay = 0
Anthony Larcher's avatar
xv    
Anthony Larcher committed
343
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
344
                ("linear6", torch.nn.Linear(3072, 512))
Anthony Larcher's avatar
Anthony Larcher committed
345
346
            ]))

Anthony Larcher's avatar
Anthony Larcher committed
347
            if self.loss == "aam":
Anthony Larcher's avatar
Anthony Larcher committed
348
349
350
351
352
                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
353
354
355
            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
356
                    ("batch_norm6", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
357
358
359
                    ("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
360
                    ("batch_norm7", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
361
362
                    ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
                ]))
Anthony Larcher's avatar
Anthony Larcher committed
363

Anthony Larcher's avatar
Anthony Larcher committed
364
365
366
367
            self.sequence_network_weight_decay = 0.0002
            self.before_speaker_embedding_weight_decay = 0.002
            self.after_speaker_embedding_weight_decay = 0.002

Anthony Larcher's avatar
Anthony Larcher committed
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
        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

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

            if self.loss == "aam":
                if loss == 'aam':
                    self.after_speaker_embedding = ArcLinear(256,
                                                             int(self.speaker_number),
                                                             margin=aam_margin, s=aam_s)
            elif self.loss == "cce":
                self.after_speaker_embedding = torch.nn.Linear(in_features = 256,
                                                               out_features = int(self.speaker_number),
                                                               bias = True)

            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
400
        elif model_archi == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
401
402
403
404
405
406

            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
407
408
409
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

Anthony Larcher's avatar
Anthony Larcher committed
410
            self.preprocessor = RawPreprocessor(nb_samp=48000,
Anthony Larcher's avatar
Anthony Larcher committed
411
                                                in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
412
413
                                                out_channels=filts[0],
                                                kernel_size=3)
Anthony Larcher's avatar
Anthony Larcher committed
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430

            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
431
432
433
434
435
            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
436
            elif self.loss == "cce":
Anthony Larcher's avatar
Anthony Larcher committed
437
438
439
                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
440

Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
441
442
443
444
445
446
            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
447
        else:
Anthony Larcher's avatar
Anthony Larcher committed
448
449
            is_first_resblock = True

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
450
451
452
453
454
455
            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
456

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
457
            self.loss = cfg["training"]["loss"]
Anthony Larcher's avatar
Anthony Larcher committed
458
            if self.loss == "aam":
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
459
460
                self.aam_margin = cfg["training"]["aam_margin"]
                self.aam_s = cfg["training"]["aam_s"]
Anthony Larcher's avatar
Anthony Larcher committed
461

Anthony Larcher's avatar
Anthony Larcher committed
462
463
464
            """
            Prepare Preprocessor
            """
Anthony Larcher's avatar
Anthony Larcher committed
465
            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
466
467
            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
Anthony Larcher's avatar
Anthony Larcher committed
468
                    self.preprocessor = SincNet(
Anthony Larcher's avatar
Anthony Larcher committed
469
470
471
472
473
474
475
476
477
478
479
480
                        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
481
                    self.feature_size = self.preprocessor.dimension
Anthony Larcher's avatar
Anthony Larcher committed
482
                elif cfg['preprocessor']["type"] == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
483
                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
Anthony Larcher's avatar
Anthony Larcher committed
484
                                                        in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
485
486
487
488
489
490
                                                        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
491
                self.preprocessor_weight_decay = 0.000
Anthony Larcher's avatar
Anthony Larcher committed
492
493

            """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
494
            Prepare sequence network
Anthony Larcher's avatar
Anthony Larcher committed
495
            """
Anthony Larcher's avatar
Anthony Larcher committed
496
            # Get Feature size
Anthony Larcher's avatar
Anthony Larcher committed
497
498
499
            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

Anthony Larcher's avatar
Anthony Larcher committed
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
            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
515
516
517
518
519
                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
520
521
522
523
524
                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
525
                                                                stride=1)))
Anthony Larcher's avatar
Anthony Larcher committed
526

Anthony Larcher's avatar
Anthony Larcher committed
527
                elif k.startswith("conv"):
Anthony Larcher's avatar
Anthony Larcher committed
528
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
529
                                                                cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
530
531
                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
532
533
                    input_size = cfg["segmental"][k]["output_channels"]

Anthony Larcher's avatar
Anthony Larcher committed
534
535
                elif k.startswith("ctrans"):
                    segmental_layers.append((k, torch.nn.ConvTranspose1d(input_size,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
536
                                                                         cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
537
538
                                                                         kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                         dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
539
540
541
                elif k.startswith("activation"):
                    segmental_layers.append((k, self.activation))

Anthony Larcher's avatar
Anthony Larcher committed
542
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
Anthony Larcher committed
543
544
                    segmental_layers.append((k, torch.nn.BatchNorm1d(input_size)))

Anthony Larcher's avatar
Anthony Larcher committed
545
546
547
548
549
550
                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
551
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
552
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
553

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
554
555
556
557
            """
            Pooling
            """
            self.stat_pooling = MeanStdPooling()
Anthony Larcher's avatar
Anthony Larcher committed
558
            tmp_input_size = input_size * 2
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
559
560
561
562
            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
563
                tmp_input_size = cfg["stat_pooling"]["gru_node"]
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
564

Anthony Larcher's avatar
Anthony Larcher committed
565
566
            self.stat_pooling_weight_decay = cfg["stat_pooling"]["weight_decay"]

Anthony Larcher's avatar
Anthony Larcher committed
567
            """
Anthony Larcher's avatar
Anthony Larcher committed
568
            Prepare last part of the network (after pooling)
Anthony Larcher's avatar
Anthony Larcher committed
569
            """
Anthony Larcher's avatar
Anthony Larcher committed
570
            # Create sequential object for the second part of the network
Anthony Larcher's avatar
Anthony Larcher committed
571
            input_size = tmp_input_size
Anthony Larcher's avatar
xv    
Anthony Larcher committed
572
573
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
574
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
575
576
                    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
577
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
578
579
                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
580
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
581
582

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
583
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
584

Anthony Larcher's avatar
Anthony Larcher committed
585
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
586
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
587
588

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
589
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
590

Anthony Larcher's avatar
Anthony Larcher committed
591
            self.embedding_size = input_size
Anthony Larcher's avatar
Anthony Larcher committed
592
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
593
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
594

Anthony Larcher's avatar
Anthony Larcher committed
595
            # if loss_criteria is "cce"
Anthony Larcher's avatar
xv    
Anthony Larcher committed
596
            # Create sequential object for the second part of the network
Anthony Larcher's avatar
Anthony Larcher committed
597
598
599
600
601
602
603
604
            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
605
                                                                          cfg["after_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
606
                            input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
607

Anthony Larcher's avatar
Anthony Larcher committed
608
609
610
611
612
                    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
613

Anthony Larcher's avatar
Anthony Larcher committed
614
615
                    elif k.startswith("activation"):
                        after_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
616

Anthony Larcher's avatar
Anthony Larcher committed
617
618
                    elif k.startswith('batch_norm'):
                        after_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
619

Anthony Larcher's avatar
Anthony Larcher committed
620
621
622
623
624
625
                    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
626
627
628
629
630
631
632
                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
633
634
635
636
                #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
637
638
639
640
                #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
641

Anthony Larcher's avatar
Anthony Larcher committed
642
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
643

Anthony Larcher's avatar
Anthony Larcher committed
644

Anthony Larcher's avatar
Anthony Larcher committed
645
    def forward(self, x, is_eval=False, target=None):
646
647
648
        """

        :param x:
Anthony Larcher's avatar
Anthony Larcher committed
649
        :param is_eval:
650
651
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
652
653
654
        if self.preprocessor is not None:
            x = self.preprocessor(x)

Anthony Larcher's avatar
Anthony Larcher committed
655
        x = self.sequence_network(x)
656

Anthony Larcher's avatar
Anthony Larcher committed
657
        # Mean and Standard deviation pooling
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
658
        x = self.stat_pooling(x)
Anthony Larcher's avatar
Anthony Larcher committed
659
660

        x = self.before_speaker_embedding(x)
661

Anthony Larcher's avatar
Anthony Larcher committed
662
        if self.norm_embedding:
Anthony Larcher's avatar
arcface    
Anthony Larcher committed
663
            #x_norm = x.norm(p=2,dim=1, keepdim=True) / 10. # Why  10. ?
Anthony Larcher's avatar
arcface    
Anthony Larcher committed
664
665
666
            #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
667

Anthony Larcher's avatar
Anthony Larcher committed
668
        if self.loss == "cce":
Anthony Larcher's avatar
Anthony Larcher committed
669
670
671
672
            if is_eval:
                return self.after_speaker_embedding(x), x
            else:
                return self.after_speaker_embedding(x)
Anthony Larcher's avatar
Anthony Larcher committed
673

Anthony Larcher's avatar
Anthony Larcher committed
674
675
        elif self.loss == "aam":
            if not is_eval:
Anthony Larcher's avatar
Anthony Larcher committed
676
                x = self.after_speaker_embedding(l2_norm(x), target=target), l2_norm(x)
Anthony Larcher's avatar
Anthony Larcher committed
677
            else:
Anthony Larcher's avatar
Anthony Larcher committed
678
                x = self.after_speaker_embedding(l2_norm(x), target=None), l2_norm(x)
Anthony Larcher's avatar
Anthony Larcher committed
679

Anthony Larcher's avatar
Anthony Larcher committed
680
        return x
Anthony Larcher's avatar
Anthony Larcher committed
681

682
683
    def context_size(self):
        context = 1
Anthony Larcher's avatar
Anthony Larcher committed
684
685
686
687
688
689
690
691
692
        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
693

Anthony Larcher's avatar
Anthony Larcher committed
694
def xtrain(speaker_number,
Anthony Larcher's avatar
Anthony Larcher committed
695
           dataset_yaml,
Anthony Larcher's avatar
Anthony Larcher committed
696
697
           epochs=None,
           lr=None,
Anthony Larcher's avatar
Anthony Larcher committed
698
           model_yaml=None,
Anthony Larcher's avatar
Anthony Larcher committed
699
           model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
700
701
702
703
           loss=None,
           aam_margin=None,
           aam_s=None,
           patience=None,
Anthony Larcher's avatar
Anthony Larcher committed
704
           tmp_model_name=None,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
705
           best_model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
706
           multi_gpu=True,
707
           clipping=False,
Anthony Larcher's avatar
Anthony Larcher committed
708
           opt=None,
Anthony Larcher's avatar
Anthony Larcher committed
709
710
           reset_parts=[],
           freeze_parts=[],
Anthony Larcher's avatar
Anthony Larcher committed
711
712
713
714
           num_thread=None,
           write_batches_to_disk=False,
           load_batches_from_disk=False,
           tmp_batch_dir=None):
715
716
    """

Anthony Larcher's avatar
Anthony Larcher committed
717
718
719
720
721
722
    :param speaker_number:
    :param dataset_yaml:
    :param epochs:
    :param lr:
    :param model_yaml:
    :param model_name:
Anthony Larcher's avatar
Anthony Larcher committed
723
724
725
726
    :param loss:
    :param aam_margin:
    :param aam_s:
    :param patience:
Anthony Larcher's avatar
Anthony Larcher committed
727
728
729
730
    :param tmp_model_name:
    :param best_model_name:
    :param multi_gpu:
    :param clipping:
Anthony Larcher's avatar
Anthony Larcher committed
731
732
733
    :param opt:
    :param reset_parts:
    :param freeze_parts:
Anthony Larcher's avatar
Anthony Larcher committed
734
    :param num_thread:
735
736
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
737
738
739
740
    # 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
741

Anthony Larcher's avatar
Anthony Larcher committed
742
743
744
    if write_batches_to_disk:
        load_batches_from_disk = True

Anthony Larcher's avatar
Anthony Larcher committed
745
746
747
    if num_thread is None:
        num_thread = multiprocessing.cpu_count()

Anthony Larcher's avatar
Anthony Larcher committed
748
    logging.critical(f"Use {num_thread} cpus")
Anthony Larcher's avatar
Anthony Larcher committed
749
    logging.critical(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
750

Anthony Larcher's avatar
Anthony Larcher committed
751
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Anthony Larcher's avatar
Anthony Larcher committed
752
    # Start from scratch
Anthony Larcher's avatar
Anthony Larcher committed
753
    if model_name is None and model_yaml in ["xvector", "rawnet2"]:
Anthony Larcher's avatar
Anthony Larcher committed
754
        # Initialize a first model
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
755
        if model_yaml == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
756
            model = Xtractor(speaker_number, "xvector", loss=loss)
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
757
        elif model_yaml == "rawnet2":
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
758
            model = Xtractor(speaker_number, "rawnet2")
Anthony Larcher's avatar
Anthony Larcher committed
759
        model_archi = model_yaml
Anthony Larcher's avatar
Anthony Larcher committed
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
    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
786
        if model_name is None:
Anthony Larcher's avatar
Anthony Larcher committed
787
788
789
            model = Xtractor(speaker_number, model_yaml)

         # If we start from an existing model
Anthony Larcher's avatar
Anthony Larcher committed
790
        else:
Anthony Larcher's avatar
Anthony Larcher committed
791
792
793
            # Load the model
            logging.critical(f"*** Load model from = {model_name}")
            checkpoint = torch.load(model_name)
Anthony Larcher's avatar
Anthony Larcher committed
794
            model = Xtractor(speaker_number, model_yaml)
Anthony Larcher's avatar
Anthony Larcher committed
795

Anthony Larcher's avatar
fix API    
Anthony Larcher committed
796
797
            """
            Here we remove all layers that we don't want to reload
Anthony Larcher's avatar
Anthony Larcher committed
798
        
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
799
800
801
802
803
804
805
806
807
808
809
810
811
            """
            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
812

Anthony Larcher's avatar
Anthony Larcher committed
813
    print(model)
Anthony Larcher's avatar
Anthony Larcher committed
814
    embedding_size = model.embedding_size
Anthony Larcher's avatar
Anthony Larcher committed
815

Anthony Larcher's avatar
Anthony Larcher committed
816
    if torch.cuda.device_count() > 1 and multi_gpu:
Anthony Larcher's avatar
Anthony Larcher committed
817
818
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
Anthony Larcher's avatar
Anthony Larcher committed
819
        #model = DDP(model)
Anthony Larcher's avatar
Anthony Larcher committed
820
821
    else:
        print("Train on a single GPU")
Anthony Larcher's avatar
Anthony Larcher committed
822
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
823

Anthony Larcher's avatar
debug    
Anthony Larcher committed
824
825
826
827
    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
828
829
830
    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"
831

Anthony Larcher's avatar
Anthony Larcher committed
832
833
834
835
836
837
838
    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
839

Anthony Larcher's avatar
minor    
Anthony Larcher committed
840
        if write_batches_to_disk or dataset_params["batch_size"] > 1:
Anthony Larcher's avatar
Anthony Larcher committed
841
842
843
844
            output_format = "numpy"
        else:
            output_format = "pytorch"

Anthony Larcher's avatar
Anthony Larcher committed
845
846
847
848
849
850
        training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"])
        torch.manual_seed(dataset_params['seed'])
        training_set = SideSet(dataset_yaml,
                               set_type="train",
                               dataset_df=training_df,
                               chunk_per_segment=dataset_params['train']['chunk_per_segment'],
Anthony Larcher's avatar
Anthony Larcher committed
851
852
                               overlap=dataset_params['train']['overlap'],
                               output_format=output_format)
Anthony Larcher's avatar
Anthony Larcher committed
853

Anthony Larcher's avatar
Anthony Larcher committed
854
855
856
857
        validation_set = SideSet(dataset_yaml,
                                 set_type="validation",
                                 dataset_df=validation_df,
                                 output_format=output_format)
Anthony Larcher's avatar
Anthony Larcher committed
858
859


Anthony Larcher's avatar
Anthony Larcher committed
860
        if write_batches_to_disk:
Anthony Larcher's avatar
debug    
Anthony Larcher committed
861
            logging.critical("Start writing batches on disk")
Anthony Larcher's avatar
Anthony Larcher committed
862
863
            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
864
            logging.critical("---> Done")
Anthony Larcher's avatar
Anthony Larcher committed
865
866

    if load_batches_from_disk:
Anthony Larcher's avatar
debug    
Anthony Larcher committed
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
        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,
                                 num_workers=num_thread)

    validation_loader = DataLoader(validation_set,
                                   batch_size=batch_size,
                                   drop_last=True,
                                   pin_memory=True,
                                   num_workers=num_thread)

Anthony Larcher's avatar
Anthony Larcher committed
888

Anthony Larcher's avatar
Anthony Larcher committed
889
890
891
    """
    Set the training options
    """
Anthony Larcher's avatar
Anthony Larcher committed
892
    if opt == 'adam':
Anthony Larcher's avatar
Anthony Larcher committed
893
        _optimizer = torch.optim.Adam
Anthony Larcher's avatar
Anthony Larcher committed
894
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
895
896
    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
Anthony Larcher's avatar
Anthony Larcher committed
897
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
898
899
900
    else: # opt == 'sgd'
        _optimizer = torch.optim.SGD
        _options = {'lr': lr, 'momentum': 0.9}
Anthony Larcher's avatar
Anthony Larcher committed
901

Anthony Larcher's avatar
debug    
Anthony Larcher committed
902
903
904
905
906
907
908
909
910
911
912
913
914
    #params = [
    #    {
    #        'params': [
    #            param for name, param in model.named_parameters() if 'bn' not in name
    #        ]
    #    },
    #    {
    #        'params': [
    #            param for name, param in model.named_parameters() if 'bn' in name
    #        ],
    #        'weight_decay': 0
    #    },
    #]
Anthony Larcher's avatar
Anthony Larcher committed
915

Anthony Larcher's avatar
Anthony Larcher committed
916
    param_list = []
Anthony Larcher's avatar
Anthony Larcher committed
917
    if type(model) is Xtractor:
Anthony Larcher's avatar
Anthony Larcher committed
918
919
920
921
922
923
924
        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
925
    else:
Anthony Larcher's avatar
Anthony Larcher committed
926
927
928
929
930
931
932
933
934
        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
935
936
937
938
939
940

    #optimizer = torch.optim.SGD(params,
    #                            lr=lr,
    #                            momentum=0.9,
    #                            weight_decay=0.0005)
    #print(f"Learning rate = {lr}")
Anthony Larcher's avatar
Anthony Larcher committed
941

Anthony Larcher's avatar
Anthony Larcher committed
942
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
943

Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
944
    best_accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
945
    best_accuracy_epoch = 1
Anthony Larcher's avatar
Anthony Larcher committed
946
    curr_patience = patience
Anthony Larcher's avatar
eer    
Anthony Larcher committed
947
948
    
    logging.critical("Compute EER before starting")
Anthony Larcher's avatar
Anthony Larcher committed
949
950
951
    val_acc, val_loss, val_eer, test_eer = compute_metrics(model,
                                                           validation_loader,
                                                           device,
Anthony Larcher's avatar
Anthony Larcher committed
952
                                                           [validation_set.__len__(), embedding_size],
Anthony Larcher's avatar
Anthony Larcher committed
953
954
955
956
957
958
959
                                                           speaker_number,
                                                           model_archi)

    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
960
    for epoch in range(1, epochs + 1):
961
        # Process one epoch and return the current model
Anthony Larcher's avatar
Anthony Larcher committed
962
963
964
        if curr_patience == 0:
            print(f"Stopping at epoch {epoch} for cause of patience")
            break
Anthony Larcher's avatar
Anthony Larcher committed
965
966
967
968
969
970
        model = train_epoch(model,
                            epoch,
                            training_loader,
                            optimizer,
                            dataset_params["log_interval"],
                            device=device,
Anthony Larcher's avatar
Anthony Larcher committed
971
972
                            clipping=clipping,
                            tb_writer=writer)
973
974

        # Add the cross validation here
Anthony Larcher's avatar
Anthony Larcher committed
975
976
977
        val_acc, val_loss, val_eer, test_eer = compute_metrics(model,
                                                               validation_loader,
                                                               device,
Anthony Larcher's avatar
Anthony Larcher committed
978
                                                               [validation_set.__len__(), embedding_size],
Anthony Larcher's avatar
Anthony Larcher committed
979
980
981
982
                                                               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} %")
983
984
985
986

        # Decrease learning rate according to the scheduler policy
        scheduler.step(val_loss)

Anthony Larcher's avatar
Anthony Larcher committed
987
        # remember best accuracy and save checkpoint
Anthony Larcher's avatar
Anthony Larcher committed
988
989
        is_best = val_acc > best_accuracy
        best_accuracy = max(val_acc, best_accuracy)
Anthony Larcher's avatar
Anthony Larcher committed
990

Anthony Larcher's avatar
Anthony Larcher committed
991
992
993
994
995
996
        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
997
998
                'scheduler': scheduler,
                'speaker_number' : speaker_number,
Anthony Larcher's avatar
fix API    
Anthony Larcher committed
999
                'model_archi': model_archi
Anthony Larcher's avatar
Anthony Larcher committed
1000
            }, is_best, filename=tmp_model_name+".pt", best_filename=best_model_name+'.pt')