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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])
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