DeepLearningExamples/CUDA-Optimized/FastSpeech/fastspeech/utils/tensorboard.py
2020-07-31 14:59:15 +08:00

85 lines
2.9 KiB
Python

# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import matplotlib.pyplot as plt
import numpy as np
import cv2
import data as global_data
plt.switch_backend('Agg')
def image_plot(x, name='image'):
fig, ax = plt.subplots()
ax.imshow(x, cmap='magma', aspect='auto')
fig.canvas.draw()
buf = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
cv2.imshow(name, buf)
cv2.waitKey(0)
def plot_to_buf(x, align=True):
fig, ax = plt.subplots()
ax.plot(x)
if align:
ax.set_ylim([-1, 1])
fig.canvas.draw()
im = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
return np.rollaxis(im[..., :3], 2)
def imshow_to_buf(x, scale01=False):
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x) / np.sum(np.exp(x), axis=0)
if scale01:
x = (x - x.min()) / (x.max() - x.min())
if x.max() > 1.:
x = softmax(x)
if len(x.shape) == 3:
x = x[0]
fig, ax = plt.subplots()
ax.imshow(x, cmap='magma', aspect='auto')
fig.canvas.draw()
im = np.array(fig.canvas.renderer._renderer)
plt.clf()
plt.close('all')
return np.rollaxis(im[..., :3], 2)
def origin_to_chrs(target):
results = []
for t in target:
idx = t - 1 if t - 1 >= 0 else 0
if idx < len(global_data.idx2chr):
results.append(global_data.idx2chr[idx])
else:
break
return ''.join(results)