xvector.py 39.5 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
Anthony Larcher committed
25
Copyright 2014-2020 Yevhenii Prokopalo, Anthony Larcher
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 numpy
Anthony Larcher's avatar
Anthony Larcher committed
34
import pandas
Anthony Larcher's avatar
minor    
Anthony Larcher committed
35
import pickle
Anthony Larcher's avatar
Anthony Larcher committed
36
import shutil
37
import time
Anthony Larcher's avatar
Anthony Larcher committed
38
import torch
Anthony Larcher's avatar
Anthony Larcher committed
39
import torch.optim as optim
Anthony Larcher's avatar
Anthony Larcher committed
40
41
import yaml

Anthony Larcher's avatar
Anthony Larcher committed
42
from torchvision import transforms
Anthony Larcher's avatar
Anthony Larcher committed
43
from collections import OrderedDict
Anthony Larcher's avatar
Anthony Larcher committed
44
from .xsets import SideSet
Anthony Larcher's avatar
Anthony Larcher committed
45
from .xsets import IdMapSet
Anthony Larcher's avatar
Anthony Larcher committed
46
from .res_net import RawPreprocessor, ResBlockWFMS
Anthony Larcher's avatar
Anthony Larcher committed
47
48
from ..bosaris import IdMap
from ..statserver import StatServer
Anthony Larcher's avatar
Anthony Larcher committed
49
from torch.utils.data import DataLoader
Anthony Larcher's avatar
Anthony Larcher committed
50
from sklearn.model_selection import train_test_split
Anthony Larcher's avatar
Anthony Larcher committed
51
from .sincnet import SincNet
Anthony Larcher's avatar
Anthony Larcher committed
52
#from torch.utils.tensorboard import SummaryWriter
Anthony Larcher's avatar
Anthony Larcher committed
53
from .loss import ArcLinear
Anthony Larcher's avatar
Anthony Larcher committed
54

Anthony Larcher's avatar
Anthony Larcher committed
55
import tqdm
Anthony Larcher's avatar
Anthony Larcher committed
56

Anthony Larcher's avatar
Anthony Larcher committed
57
58
__license__ = "LGPL"
__author__ = "Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
59
__copyright__ = "Copyright 2015-2020 Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
60
61
62
63
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
Anthony Larcher's avatar
Anthony Larcher committed
64
65


Anthony Larcher's avatar
Anthony Larcher committed
66
67
logging.basicConfig(format='%(asctime)s %(message)s')

Anthony Larcher's avatar
Anthony Larcher committed
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136

# 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)
            halt(str(value))

    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:
        plt.imshow(np.transpose(npimg, (1, 2, 0)))

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
137

Anthony Larcher's avatar
Anthony Larcher committed
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
    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



159
def get_lr(optimizer):
Anthony Larcher's avatar
Anthony Larcher committed
160
161
162
163
164
    """

    :param optimizer:
    :return:
    """
165
166
167
168
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
169
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
Anthony Larcher's avatar
Anthony Larcher committed
170
171
172
173
174
175
176
177
    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
178
179
180
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
Anthony Larcher's avatar
Anthony Larcher committed
181

Anthony Larcher's avatar
Anthony Larcher committed
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
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
202

Anthony Larcher's avatar
Anthony Larcher committed
203

Anthony Larcher's avatar
Anthony Larcher committed
204
205
206
207
208
209
210
211
212
213
214
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
215
        super(GruPooling, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
        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
238

Anthony Larcher's avatar
Anthony Larcher committed
239
class Xtractor(torch.nn.Module):
240
241
242
    """
    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
243

Anthony Larcher's avatar
Anthony Larcher committed
244
245
246
    def __init__(self,
                 speaker_number,
                 model_archi="xvector",
Anthony Larcher's avatar
Anthony Larcher committed
247
                 loss=None,
Anthony Larcher's avatar
Anthony Larcher committed
248
249
250
                 norm_embedding=False,
                 aam_margin=0.5,
                 aam_s=0.5):
Anthony Larcher's avatar
Anthony Larcher committed
251
252
        """
        If config is None, default architecture is created
Anthony Larcher's avatar
Anthony Larcher committed
253
        :param model_archi:
Anthony Larcher's avatar
Anthony Larcher committed
254
        """
Anthony Larcher's avatar
Anthony Larcher committed
255
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
256
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
257
        self.feature_size = None
Anthony Larcher's avatar
Anthony Larcher committed
258
        self.norm_embedding = norm_embedding
Anthony Larcher's avatar
Anthony Larcher committed
259

Anthony Larcher's avatar
Anthony Larcher committed
260
        if model_archi == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
261
262
263
264
265
266

            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
267
            self.feature_size = 30
Anthony Larcher's avatar
Anthony Larcher committed
268
269
270
            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
271

Anthony Larcher's avatar
xv    
Anthony Larcher committed
272
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
273
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
274
275
276
277
278
279
280
281
                ("activation1", torch.nn.LeakyReLU(0.2)),
                ("norm1", torch.nn.BatchNorm1d(512)),
                ("conv2", torch.nn.Conv1d(512, 512, 3, dilation=2)),
                ("activation2", torch.nn.LeakyReLU(0.2)),
                ("norm2", torch.nn.BatchNorm1d(512)),
                ("conv3", torch.nn.Conv1d(512, 512, 3, dilation=3)),
                ("activation3", torch.nn.LeakyReLU(0.2)),
                ("norm3", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
282
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
283
284
                ("activation4", torch.nn.LeakyReLU(0.2)),
                ("norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
285
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
286
287
288
289
                ("activation5", torch.nn.LeakyReLU(0.2)),
                ("norm5", torch.nn.BatchNorm1d(1536))
            ]))

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
290
291
            self.stat_pooling = MeanStdPooling()

Anthony Larcher's avatar
xv    
Anthony Larcher committed
292
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
293
                ("linear6", torch.nn.Linear(3072, 512))
Anthony Larcher's avatar
Anthony Larcher committed
294
295
            ]))

Anthony Larcher's avatar
Anthony Larcher committed
296
297
            if self.loss == "aam":
                self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
298
                  ("arclinear", ArcLinear(512, int(self.speaker_number), margin=aam_margin, s=aam_s))
Anthony Larcher's avatar
Anthony Larcher committed
299
300
301
302
303
304
305
306
307
308
309
                ]))
            elif self.loss == "cce":
                self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
                    ("activation6", torch.nn.LeakyReLU(0.2)),
                    ("norm6", torch.nn.BatchNorm1d(512)),
                    ("dropout6", torch.nn.Dropout(p=0.05)),
                    ("linear7", torch.nn.Linear(512, 512)),
                    ("activation7", torch.nn.LeakyReLU(0.2)),
                    ("norm7", torch.nn.BatchNorm1d(512)),
                    ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
                ]))
Anthony Larcher's avatar
Anthony Larcher committed
310

Anthony Larcher's avatar
Anthony Larcher committed
311
312
313
314
            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
315
        elif model_archi == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
316
317
318
319
320
321

            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
322
323
324
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

Anthony Larcher's avatar
Anthony Larcher committed
325
            self.preprocessor = RawPreprocessor(nb_samp=48000,
Anthony Larcher's avatar
Anthony Larcher committed
326
                                                in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
327
328
                                                out_channels=filts[0],
                                                kernel_size=3)
Anthony Larcher's avatar
Anthony Larcher committed
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345

            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
346
347
348
349
350
            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
351
            elif self.loss == "cce":
Anthony Larcher's avatar
Anthony Larcher committed
352
353
354
                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
355

Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
356
357
358
359
360
361
            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
362
363
        else:
            # Load Yaml configuration
Anthony Larcher's avatar
Anthony Larcher committed
364
            with open(model_archi, 'r') as fh:
Anthony Larcher's avatar
Anthony Larcher committed
365
366
                cfg = yaml.load(fh, Loader=yaml.FullLoader)

Anthony Larcher's avatar
Anthony Larcher committed
367
368
369
370
371
            self.loss = cfg["loss"]
            if self.loss == "aam":
                self.aam_margin = cfg["aam_margin"]
                self.aam_s = cfg["aam_s"]

Anthony Larcher's avatar
Anthony Larcher committed
372
373
374
            """
            Prepare Preprocessor
            """
Anthony Larcher's avatar
Anthony Larcher committed
375
            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
376
377
            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
Anthony Larcher's avatar
Anthony Larcher committed
378
                    self.preprocessor = SincNet(
Anthony Larcher's avatar
Anthony Larcher committed
379
380
381
382
383
384
385
386
387
388
389
390
                        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
391
                    self.feature_size = self.preprocessor.dimension
Anthony Larcher's avatar
Anthony Larcher committed
392
                elif cfg['preprocessor']["type"] == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
393
                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
Anthony Larcher's avatar
Anthony Larcher committed
394
                                                        in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
395
396
397
398
399
400
                                                        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
401
402

            """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
403
            Prepare sequence network
Anthony Larcher's avatar
Anthony Larcher committed
404
            """
Anthony Larcher's avatar
Anthony Larcher committed
405
            # Get Feature size
Anthony Larcher's avatar
Anthony Larcher committed
406
407
408
            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

Anthony Larcher's avatar
Anthony Larcher committed
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
            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
424
425
426
427
428
429
                if k.startswith("lin"):
                    segmental_layers.append((k, torch.nn.Linear(input_size,
                                                                cfg["segmental"][k]["output"])))
                    input_size = cfg["segmental"][k]["output"]

                elif k.startswith("conv"):
Anthony Larcher's avatar
Anthony Larcher committed
430
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
431
                                                                cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
432
433
                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
434
435
436
437
438
                    input_size = cfg["segmental"][k]["output_channels"]

                elif k.startswith("activation"):
                    segmental_layers.append((k, self.activation))

Anthony Larcher's avatar
Anthony Larcher committed
439
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
Anthony Larcher committed
440
441
                    segmental_layers.append((k, torch.nn.BatchNorm1d(input_size)))

Anthony Larcher's avatar
Anthony Larcher committed
442
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
443
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
444

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
445
446
447
448
449
450
451
452
453
            """
            Pooling
            """
            self.stat_pooling = MeanStdPooling()
            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
Anthony Larcher committed
454
455
            self.stat_pooling_weight_decay = cfg["stat_pooling"]["weight_decay"]

Anthony Larcher's avatar
Anthony Larcher committed
456
            """
Anthony Larcher's avatar
Anthony Larcher committed
457
            Prepare last part of the network (after pooling)
Anthony Larcher's avatar
Anthony Larcher committed
458
            """
Anthony Larcher's avatar
Anthony Larcher committed
459
460
            # Create sequential object for the second part of the network
            input_size = input_size * 2
Anthony Larcher's avatar
xv    
Anthony Larcher committed
461
462
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
463
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
464
465
                    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
466
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
467
468
                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
469
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
470
471

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
472
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
473

Anthony Larcher's avatar
Anthony Larcher committed
474
                elif k.startswith('batch_norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
475
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
476
477

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
478
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
479

Anthony Larcher's avatar
Anthony Larcher committed
480
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
481
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
482

Anthony Larcher's avatar
Anthony Larcher committed
483
            # if loss_criteria is "cce"
Anthony Larcher's avatar
xv    
Anthony Larcher committed
484
            # Create sequential object for the second part of the network
Anthony Larcher's avatar
Anthony Larcher committed
485
486
487
488
489
490
491
492
            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
493
                                                                          cfg["after_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
494
                            input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
495

Anthony Larcher's avatar
Anthony Larcher committed
496
497
498
499
500
                    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
501

Anthony Larcher's avatar
Anthony Larcher committed
502
503
                    elif k.startswith("activation"):
                        after_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
504

Anthony Larcher's avatar
Anthony Larcher committed
505
506
                    elif k.startswith('batch_norm'):
                        after_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
507

Anthony Larcher's avatar
Anthony Larcher committed
508
509
510
511
512
513
514
515
516
517
                    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":
                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
518

Anthony Larcher's avatar
Anthony Larcher committed
519
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
520

Anthony Larcher's avatar
Anthony Larcher committed
521

Anthony Larcher's avatar
Anthony Larcher committed
522
    def forward(self, x, is_eval=False, target=None):
523
524
525
        """

        :param x:
Anthony Larcher's avatar
Anthony Larcher committed
526
        :param is_eval:
527
528
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
529
530
        if self.preprocessor is not None:
            x = self.preprocessor(x)
Anthony Larcher's avatar
Anthony Larcher committed
531
            print("go through preprocessor")
Anthony Larcher's avatar
Anthony Larcher committed
532

Anthony Larcher's avatar
Anthony Larcher committed
533
        x = self.sequence_network(x)
534

Anthony Larcher's avatar
Anthony Larcher committed
535
        # Mean and Standard deviation pooling
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
536
        x = self.stat_pooling(x)
Anthony Larcher's avatar
Anthony Larcher committed
537
538

        x = self.before_speaker_embedding(x)
539

Anthony Larcher's avatar
Anthony Larcher committed
540
541
542
543
        if self.norm_embedding:
            x_norm = x.norm(p=2,dim=1, keepdim=True) / 10.
            x = torch.div(x, x_norm)

Anthony Larcher's avatar
Anthony Larcher committed
544
545
546
        if is_eval:
            return x

Anthony Larcher's avatar
Anthony Larcher committed
547
548
        if self.loss == "cce":
            x = self.after_speaker_embedding(x)
Anthony Larcher's avatar
Anthony Larcher committed
549

Anthony Larcher's avatar
Anthony Larcher committed
550
551
552
553
554
555
        elif self.loss == "aam":
            if not is_eval:
                x = self.after_speaker_embedding(x,target=target)
            else:
                x = self.after_speaker_embedding(x, target=None)

Anthony Larcher's avatar
Anthony Larcher committed
556
        return x
Anthony Larcher's avatar
Anthony Larcher committed
557

558
559
    def context_size(self):
        context = 1
Anthony Larcher's avatar
Anthony Larcher committed
560
561
562
563
564
565
566
567
568
        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
569

Anthony Larcher's avatar
Anthony Larcher committed
570
def xtrain(speaker_number,
Anthony Larcher's avatar
Anthony Larcher committed
571
           dataset_yaml,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
572
           epochs=100,
Anthony Larcher's avatar
Anthony Larcher committed
573
           lr=0.01,
Anthony Larcher's avatar
Anthony Larcher committed
574
           model_yaml=None,
Anthony Larcher's avatar
Anthony Larcher committed
575
           model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
576
577
578
579
           loss="cce",
           aam_margin=0.5,
           aam_s=30,
           patience=10,
Anthony Larcher's avatar
Anthony Larcher committed
580
           tmp_model_name=None,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
581
           best_model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
582
           multi_gpu=True,
583
           clipping=False,
Anthony Larcher's avatar
Anthony Larcher committed
584
           opt='sgd',
Anthony Larcher's avatar
Anthony Larcher committed
585
586
           reset_parts=[],
           freeze_parts=[],
Anthony Larcher's avatar
Anthony Larcher committed
587
           num_thread=1):
588
589
    """

Anthony Larcher's avatar
Anthony Larcher committed
590
591
592
593
594
595
    :param speaker_number:
    :param dataset_yaml:
    :param epochs:
    :param lr:
    :param model_yaml:
    :param model_name:
Anthony Larcher's avatar
Anthony Larcher committed
596
597
598
599
    :param loss:
    :param aam_margin:
    :param aam_s:
    :param patience:
Anthony Larcher's avatar
Anthony Larcher committed
600
601
602
603
    :param tmp_model_name:
    :param best_model_name:
    :param multi_gpu:
    :param clipping:
Anthony Larcher's avatar
Anthony Larcher committed
604
605
606
    :param opt:
    :param reset_parts:
    :param freeze_parts:
Anthony Larcher's avatar
Anthony Larcher committed
607
    :param num_thread:
608
609
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
610
611
612
613
    # 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
614

Anthony Larcher's avatar
Anthony Larcher committed
615
    logging.critical(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
616

Anthony Larcher's avatar
Anthony Larcher committed
617
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Anthony Larcher's avatar
Anthony Larcher committed
618

Anthony Larcher's avatar
Anthony Larcher committed
619
620
    # Start from scratch
    if model_name is None:
Anthony Larcher's avatar
Anthony Larcher committed
621
        # Initialize a first model
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
622
        if model_yaml == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
623
            model = Xtractor(speaker_number, "xvector")
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
624
        elif model_yaml == "rawnet2":
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
625
            model = Xtractor(speaker_number, "rawnet2")
Anthony Larcher's avatar
Anthony Larcher committed
626
        else:
Anthony Larcher's avatar
Anthony Larcher committed
627
            model = Xtractor(speaker_number, model_yaml)
Anthony Larcher's avatar
Anthony Larcher committed
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
    # If we start from an existing model
    else:
        # Load the model
        logging.critical(f"*** Load model from = {model_name}")
        checkpoint = torch.load(model_name)
        model = Xtractor(speaker_number, model_yaml)

        """
        Here we remove all layers that we don't want to reload
        
        """
        pretrained_dict = checkpoint["model_state_dict"]
        for part in reset_parts:
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}

Anthony Larcher's avatar
Anthony Larcher committed
643
        new_model_dict = model.state_dict()
Anthony Larcher's avatar
Anthony Larcher committed
644
645
646
647
648
649
650
        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
651

Anthony Larcher's avatar
Anthony Larcher committed
652

Anthony Larcher's avatar
Anthony Larcher committed
653
    if torch.cuda.device_count() > 1 and multi_gpu:
Anthony Larcher's avatar
Anthony Larcher committed
654
655
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
Anthony Larcher's avatar
Anthony Larcher committed
656
657
    else:
        print("Train on a single GPU")
Anthony Larcher's avatar
Anthony Larcher committed
658
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
659
660

    """
Anthony Larcher's avatar
Anthony Larcher committed
661
662
663
664
    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
665
    """
Anthony Larcher's avatar
Anthony Larcher committed
666
667
668
669
    with open(dataset_yaml, "r") as fh:
        dataset_params = yaml.load(fh, Loader=yaml.FullLoader)
        df = pandas.read_csv(dataset_params["dataset_description"])
    training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"])
670

Anthony Larcher's avatar
Anthony Larcher committed
671
    torch.manual_seed(dataset_params['seed'])
672
673
674
    training_set = SideSet(dataset_yaml, 
                           set_type="train", 
                           dataset_df=training_df, 
Anthony Larcher's avatar
Anthony Larcher committed
675
676
                           chunk_per_segment=dataset_params['train']['chunk_per_segment'], 
                           overlap=dataset_params['train']['overlap'])
Anthony Larcher's avatar
Anthony Larcher committed
677
678
679
    training_loader = DataLoader(training_set,
                                 batch_size=dataset_params["batch_size"],
                                 shuffle=True,
Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
680
                                 drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
681
                                 pin_memory=True,
Anthony Larcher's avatar
Anthony Larcher committed
682
                                 num_workers=num_thread)
683

Anthony Larcher's avatar
Anthony Larcher committed
684
685
686
    validation_set = SideSet(dataset_yaml, set_type="validation", dataset_df=validation_df)
    validation_loader = DataLoader(validation_set,
                                   batch_size=dataset_params["batch_size"],
Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
687
                                   drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
688
                                   pin_memory=True,
Anthony Larcher's avatar
Anthony Larcher committed
689
                                   num_workers=num_thread)
Anthony Larcher's avatar
Anthony Larcher committed
690

Anthony Larcher's avatar
Anthony Larcher committed
691
692
693
694
695
696
    # Add for TensorBoard
    #dataiter = iter(training_loader)
    #data, labels = dataiter.next()
    #writer.add_graph(model, data)


Anthony Larcher's avatar
Anthony Larcher committed
697
698
699
    """
    Set the training options
    """
Anthony Larcher's avatar
Anthony Larcher committed
700
    if opt == 'adam':
Anthony Larcher's avatar
Anthony Larcher committed
701
        _optimizer = torch.optim.Adam
Anthony Larcher's avatar
Anthony Larcher committed
702
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
703
704
    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
Anthony Larcher's avatar
Anthony Larcher committed
705
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
706
707
708
    else: # opt == 'sgd'
        _optimizer = torch.optim.SGD
        _options = {'lr': lr, 'momentum': 0.9}
Anthony Larcher's avatar
Anthony Larcher committed
709

Anthony Larcher's avatar
Anthony Larcher committed
710
711
712
713
714
715
716
717
718
719
720
721
722
723
    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
724
    param_list = []
Anthony Larcher's avatar
Anthony Larcher committed
725
    if type(model) is Xtractor:
Anthony Larcher's avatar
Anthony Larcher committed
726
727
728
729
730
731
732
        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
733
    else:
Anthony Larcher's avatar
Anthony Larcher committed
734
735
736
737
738
739
740
741
742
        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
743
744
745
746
747
748

    #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
749

Anthony Larcher's avatar
Anthony Larcher committed
750
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
751

Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
752
    best_accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
753
    best_accuracy_epoch = 1
Anthony Larcher's avatar
Anthony Larcher committed
754
    curr_patience = patience
Anthony Larcher's avatar
Anthony Larcher committed
755
    for epoch in range(1, epochs + 1):
756
        # Process one epoch and return the current model
Anthony Larcher's avatar
Anthony Larcher committed
757
758
759
        if curr_patience == 0:
            print(f"Stopping at epoch {epoch} for cause of patience")
            break
Anthony Larcher's avatar
Anthony Larcher committed
760
761
762
763
764
765
        model = train_epoch(model,
                            epoch,
                            training_loader,
                            optimizer,
                            dataset_params["log_interval"],
                            device=device,
Anthony Larcher's avatar
Anthony Larcher committed
766
767
                            clipping=clipping,
                            tb_writer=writer)
768
769

        # Add the cross validation here
Anthony Larcher's avatar
Anthony Larcher committed
770
        accuracy, val_loss = cross_validation(model, validation_loader, device=device)
Anthony Larcher's avatar
Anthony Larcher committed
771
        logging.critical(f"***{time.strftime('%H:%M:%S', time.localtime())} Cross validation accuracy = {accuracy} %")
772
773
774
775

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

Anthony Larcher's avatar
Anthony Larcher committed
776
777
778
779
        # remember best accuracy and save checkpoint
        is_best = accuracy > best_accuracy
        best_accuracy = max(accuracy, best_accuracy)

Anthony Larcher's avatar
Anthony Larcher committed
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
        if type(model) is Xtractor:
            save_checkpoint({
                'epoch': epoch,
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
                'accuracy': best_accuracy,
                'scheduler': scheduler
            }, 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(),
                'optimizer_state_dict': optimizer.state_dict(),
                'accuracy': best_accuracy,
                'scheduler': scheduler
            }, is_best, filename=tmp_model_name+".pt", best_filename=best_model_name+'.pt')
Anthony Larcher's avatar
Anthony Larcher committed
796
797
798

        if is_best:
            best_accuracy_epoch = epoch
Anthony Larcher's avatar
Anthony Larcher committed
799
800
801
            curr_patience = patience
        else:
            curr_patience -= 1
Anthony Larcher's avatar
Anthony Larcher committed
802
    #writer.close()
803

804
805
806
    for ii in range(torch.cuda.device_count()):
        print(torch.cuda.memory_summary(ii))

Anthony Larcher's avatar
Anthony Larcher committed
807
    logging.critical(f"Best accuracy {best_accuracy * 100.} obtained at epoch {best_accuracy_epoch}")
808

Anthony Larcher's avatar
Anthony Larcher committed
809
def train_epoch(model, epoch, training_loader, optimizer, log_interval, device, clipping=False, tb_writer=None):
810
811
812
813
    """

    :param model:
    :param epoch:
Anthony Larcher's avatar
Anthony Larcher committed
814
    :param training_loader:
815
    :param optimizer:
Anthony Larcher's avatar
Anthony Larcher committed
816
817
818
    :param log_interval:
    :param device:
    :param clipping:
819
820
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
821
    model.train()
Anthony Larcher's avatar
Anthony Larcher committed
822
    criterion = torch.nn.CrossEntropyLoss(reduction='mean')
823

Anthony Larcher's avatar
Anthony Larcher committed
824
825
826
827
828
    if isinstance(model, Xtractor):
        loss_criteria = model.loss
    else:
        loss_criteria = model.module.loss

829
    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
830
    running_loss = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
831
    for batch_idx, (data, target) in enumerate(training_loader):
832
        target = target.squeeze()
Anthony Larcher's avatar
Anthony Larcher committed
833
        target = target.to(device)
834
        optimizer.zero_grad()
Anthony Larcher's avatar
Anthony Larcher committed
835
836
837
838
839
840

        if loss_criteria == 'aam':
            output = model(data.to(device), target=target)
        else:
            output = model(data.to(device), target=None)

Anthony Larcher's avatar
Anthony Larcher committed
841
        #with GuruMeditation():
Anthony Larcher's avatar
Anthony Larcher committed
842
        loss = criterion(output, target)
Anthony Larcher's avatar
Anthony Larcher committed
843
844
845
846
847
848
849
850
        if not torch.isnan(loss):
            loss.backward()
            if clipping:
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
            running_loss += loss.item()
            optimizer.step()

            running_loss += loss.item()
Anthony Larcher's avatar
Anthony Larcher committed
851
            accuracy += (torch.argmax(output.data, 1) == target).sum()
Anthony Larcher's avatar
Anthony Larcher committed
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
            if batch_idx % log_interval == 0:
                batch_size = target.shape[0]
                logging.critical('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
                    epoch, batch_idx + 1, training_loader.__len__(),
                    100. * batch_idx / training_loader.__len__(), loss.item(),
                    100.0 * accuracy.item() / ((batch_idx + 1) * batch_size)))
                #tb_writer.add_scalar('training loss',
                #                     running_loss / log_interval,
                #                     epoch * len(training_loader) + batch_idx)
                #tb_writer.add_scalar('training_accuracy',
                #                      100.0 * accuracy.item() / ((batch_idx + 1) * batch_size),
                #                      epoch * len(training_loader) + batch_idx)

                # ...log a Matplotlib Figure showing the model's predictions on a
                # random mini-batch
                #tb_writer.add_figure('predictions vs. actuals',
                #                     plot_classes_preds(model, data.to(device), target.to(device)),
                #                     global_step=epoch * len(training_loader) + batch_idx)

871
872
873
874
875
876
877
878
879
880
881
882
883
        else:
            save_checkpoint({
                             'epoch': epoch,
                             'model_state_dict': model.state_dict(),
                             'optimizer_state_dict': optimizer.state_dict(),
                             'accuracy': 0.0,
                             'scheduler': 0.0
                             }, False, filename="model_loss_NAN.pt", best_filename='toto.pt')
            with open("batch_loss_NAN.pkl", "wb") as fh:
                pickle.dump(data.cpu(), fh)
            import sys
            sys.exit()
        running_loss = 0.0
884
885
886
    return model


Anthony Larcher's avatar
Anthony Larcher committed
887
def cross_validation(model, validation_loader, device):
888
889
890
    """

    :param model:
Anthony Larcher's avatar
Anthony Larcher committed
891
892
    :param validation_loader:
    :param device:
893
894
895
896
    :return:
    """
    model.eval()

Anthony Larcher's avatar
Anthony Larcher committed
897
898
899
900
901
    if isinstance(model, Xtractor):
        loss_criteria = model.loss
    else:
        loss_criteria = model.module.loss

902
    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
903
    loss = 0.0
904
    criterion = torch.nn.CrossEntropyLoss()
Anthony Larcher's avatar
Anthony Larcher committed
905
906
907
908
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(validation_loader):
            batch_size = target.shape[0]
            target = target.squeeze()
Anthony Larcher's avatar
Anthony Larcher committed
909
910
911
912
913
914

            if loss_criteria == "aam":
                output = model(data.to(device), target=target)
            else:
                output = model(data.to(device), target=None)

Anthony Larcher's avatar
Anthony Larcher committed
915
            accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
916

Anthony Larcher's avatar
Anthony Larcher committed
917
            loss += criterion(output, target.to(device))
Anthony Larcher's avatar
Anthony Larcher committed
918

Anthony Larcher's avatar
Anthony Larcher committed
919
920
921
922
    return 100. * accuracy.cpu().numpy() / ((batch_idx + 1) * batch_size), \
           loss.cpu().numpy() / ((batch_idx + 1) * batch_size)


Anthony Larcher's avatar
Anthony Larcher committed
923
924
925
926
927
928
929
def extract_embeddings(idmap_name,
                       speaker_number,
                       model_filename,
                       model_yaml,
                       data_root_name ,
                       device,
                       file_extension="wav",
930
                       transform_pipeline=None,
931
932
                       frame_shift=0.01,
                       frame_duration=0.025,
933
                       num_thread=1):
934
935
936
937
938
939
940
    # Load the model
    if isinstance(model_filename, str):
        checkpoint = torch.load(model_filename)
        model = Xtractor(speaker_number, model_archi=model_yaml)
        model.load_state_dict(checkpoint["model_state_dict"])
    else:
        model = model_filename
Anthony Larcher's avatar
Anthony Larcher committed
941

Anthony Larcher's avatar
Anthony Larcher committed
942
    if isinstance(idmap_name, IdMap):
943
944
945
946
        idmap = idmap_name
    else:
        idmap = IdMap(idmap_name)

947
948
    min_duration = (model.context_size() - 1) * frame_shift + frame_duration

Anthony Larcher's avatar
Anthony Larcher committed
949
    # Create dataset to load the data
Anthony Larcher's avatar
Anthony Larcher committed
950
951
952
    dataset = IdMapSet(idmap_name=idmap_name,
                       data_root_path=data_root_name,
                       file_extension=file_extension,
953
                       transform_pipeline=transform_pipeline,
Anthony Larcher's avatar
Anthony Larcher committed
954
                       frame_rate=int(1 / frame_shift),
955
956
                       min_duration=model.context_size()
                       )
Anthony Larcher's avatar
Anthony Larcher committed
957

958

959
960
961
962
963
964
    dataloader = DataLoader(dataset,
                            batch_size=1,
                            shuffle=False,
                            drop_last=False,
                            pin_memory=True,
                            num_workers=num_thread)
965

Anthony Larcher's avatar
Anthony Larcher committed
966
    with torch.no_grad():
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996

        model.eval()
        model.to(device)

        # Get the size of embeddings to extract
        name = list(model.before_speaker_embedding.state_dict().keys())[-1].split('.')[0] + '.weight'
        emb_size = model.before_speaker_embedding.state_dict()[name].shape[0]
    
        # Create the StatServer
        embeddings = StatServer()
        embeddings.modelset = idmap.leftids
        embeddings.segset = idmap.rightids
        embeddings.start = idmap.start
        embeddings.stop = idmap.stop
        embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
        embeddings.stat1 = numpy.ones((embeddings.modelset.shape[0], emb_size))

        # Process the data
        with torch.no_grad():
            #for idx in tqdm.tqdm(range(len(dataset))):
            for idx, (data, mod, seg, start, stop) in tqdm.tqdm(enumerate(dataloader)):
                #data, mod, seg, start, stop = dataset[idx]
                vec = model(data.to(device), is_eval=True)
                #vec = model(data[None, :, :].to(device), is_eval=True)
                #current_idx = numpy.argwhere(numpy.logical_and(idmap.leftids == mod, idmap.rightids == seg))[0][0]
                #embeddings.start[idx] = start
                #embeddings.stop[idx] = stop
                #embeddings.modelset[idx] = mod
                #embeddings.segset[idx] = seg
                embeddings.stat1[idx, :] = vec.detach().cpu()
Anthony Larcher's avatar
Anthony Larcher committed
997
998
999
1000

    return embeddings