File size: 6,683 Bytes
b84b595
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
from datetime import datetime
from time import perf_counter as timer
import matplotlib.pyplot as plt
import numpy as np
# import webbrowser
import visdom
import umap

colormap = np.array([
    [76, 255, 0],
    [0, 127, 70],
    [255, 0, 0],
    [255, 217, 38],
    [0, 135, 255],
    [165, 0, 165],
    [255, 167, 255],
    [0, 255, 255],
    [255, 96, 38],
    [142, 76, 0],
    [33, 0, 127],
    [0, 0, 0],
    [183, 183, 183],
], dtype=np.float) / 255 


class Visualizations:
    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
        # Tracking data
        self.last_update_timestamp = timer()
        self.update_every = update_every
        self.step_times = []
        self.losses = []
        self.eers = []
        print("Updating the visualizations every %d steps." % update_every)
        
        # If visdom is disabled TODO: use a better paradigm for that
        self.disabled = disabled    
        if self.disabled:
            return 
        
        # Set the environment name
        now = str(datetime.now().strftime("%d-%m %Hh%M"))
        if env_name is None:
            self.env_name = now
        else:
            self.env_name = "%s (%s)" % (env_name, now)
        
        # Connect to visdom and open the corresponding window in the browser
        try:
            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
        except ConnectionError:
            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
                            "start it.")
        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
        
        # Create the windows
        self.loss_win = None
        self.eer_win = None
        # self.lr_win = None
        self.implementation_win = None
        self.projection_win = None
        self.implementation_string = ""
        
    def log_params(self):
        if self.disabled:
            return 
        from speaker_encoder import params_data
        from speaker_encoder import params_model
        param_string = "<b>Model parameters</b>:<br>"
        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
            value = getattr(params_model, param_name)
            param_string += "\t%s: %s<br>" % (param_name, value)
        param_string += "<b>Data parameters</b>:<br>"
        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
            value = getattr(params_data, param_name)
            param_string += "\t%s: %s<br>" % (param_name, value)
        self.vis.text(param_string, opts={"title": "Parameters"})
        
    def log_dataset(self, dataset: SpeakerVerificationDataset):
        if self.disabled:
            return 
        dataset_string = ""
        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
        dataset_string += "\n" + dataset.get_logs()
        dataset_string = dataset_string.replace("\n", "<br>")
        self.vis.text(dataset_string, opts={"title": "Dataset"})
        
    def log_implementation(self, params):
        if self.disabled:
            return 
        implementation_string = ""
        for param, value in params.items():
            implementation_string += "<b>%s</b>: %s\n" % (param, value)
            implementation_string = implementation_string.replace("\n", "<br>")
        self.implementation_string = implementation_string
        self.implementation_win = self.vis.text(
            implementation_string, 
            opts={"title": "Training implementation"}
        )

    def update(self, loss, eer, step):
        # Update the tracking data
        now = timer()
        self.step_times.append(1000 * (now - self.last_update_timestamp))
        self.last_update_timestamp = now
        self.losses.append(loss)
        self.eers.append(eer)
        print(".", end="")
        
        # Update the plots every <update_every> steps
        if step % self.update_every != 0:
            return
        time_string = "Step time:  mean: %5dms  std: %5dms" % \
                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
              (step, np.mean(self.losses), np.mean(self.eers), time_string))
        if not self.disabled:
            self.loss_win = self.vis.line(
                [np.mean(self.losses)],
                [step],
                win=self.loss_win,
                update="append" if self.loss_win else None,
                opts=dict(
                    legend=["Avg. loss"],
                    xlabel="Step",
                    ylabel="Loss",
                    title="Loss",
                )
            )
            self.eer_win = self.vis.line(
                [np.mean(self.eers)],
                [step],
                win=self.eer_win,
                update="append" if self.eer_win else None,
                opts=dict(
                    legend=["Avg. EER"],
                    xlabel="Step",
                    ylabel="EER",
                    title="Equal error rate"
                )
            )
            if self.implementation_win is not None:
                self.vis.text(
                    self.implementation_string + ("<b>%s</b>" % time_string), 
                    win=self.implementation_win,
                    opts={"title": "Training implementation"},
                )

        # Reset the tracking
        self.losses.clear()
        self.eers.clear()
        self.step_times.clear()
        
    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
                         max_speakers=10):
        max_speakers = min(max_speakers, len(colormap))
        embeds = embeds[:max_speakers * utterances_per_speaker]
        
        n_speakers = len(embeds) // utterances_per_speaker
        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
        colors = [colormap[i] for i in ground_truth]
        
        reducer = umap.UMAP()
        projected = reducer.fit_transform(embeds)
        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
        plt.gca().set_aspect("equal", "datalim")
        plt.title("UMAP projection (step %d)" % step)
        if not self.disabled:
            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
        if out_fpath is not None:
            plt.savefig(out_fpath)
        plt.clf()
        
    def save(self):
        if not self.disabled:
            self.vis.save([self.env_name])