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from tensorboardX import SummaryWriter
from tensorboard.compat.tensorflow_stub.pywrap_tensorflow import PyRecordReader_New
from tensorboardX.proto import event_pb2
import numpy as np
import pytest
import unittest
import time
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
true_positive_counts = [75, 64, 21, 5, 0]
false_positive_counts = [150, 105, 18, 0, 0]
true_negative_counts = [0, 45, 132, 150, 150]
false_negative_counts = [0, 11, 54, 70, 75]
precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0]
class WriterTest(unittest.TestCase):
def test_flush(self):
N_TEST = 5
w = SummaryWriter(flush_secs=1)
f = w.file_writer.event_writer._ev_writer._file_name
for i in range(N_TEST):
w.add_scalar('a', i)
time.sleep(2)
r = PyRecordReader_New(f)
r.GetNext() # meta data, so skip
for _ in range(N_TEST): # all of the data should be flushed
r.GetNext()
def test_flush_timer_is_long_so_data_is_not_there(self):
with self.assertRaises(BaseException):
N_TEST = 5
w = SummaryWriter(flush_secs=20)
f = w.file_writer.event_writer._ev_writer._file_name
for i in range(N_TEST):
w.add_scalar('a', i)
time.sleep(2)
r = PyRecordReader_New(f)
r.GetNext() # meta data, so skip
for _ in range(N_TEST): # missing data
r.GetNext()
def test_flush_after_close(self):
N_TEST = 5
w = SummaryWriter(flush_secs=20)
f = w.file_writer.event_writer._ev_writer._file_name
for i in range(N_TEST):
w.add_scalar('a', i)
time.sleep(2)
w.close()
r = PyRecordReader_New(f)
r.GetNext() # meta data, so skip
for _ in range(N_TEST): # all of the data should be flushed
r.GetNext()
def test_flush(self):
N_TEST = 5
w = SummaryWriter(flush_secs=20)
f = w.file_writer.event_writer._ev_writer._file_name
for i in range(N_TEST):
w.add_scalar('a', i)
time.sleep(2)
w.flush()
r = PyRecordReader_New(f)
r.GetNext() # meta data, so skip
for _ in range(N_TEST): # all of the data should be flushed
r.GetNext()
def test_auto_close(self):
pass
def test_writer(self):
with SummaryWriter() as writer:
sample_rate = 44100
n_iter = 0
writer.add_scalar('data/scalar_systemtime', 0.1, n_iter)
writer.add_scalar('data/scalar_customtime', 0.2, n_iter, walltime=n_iter)
writer.add_scalars('data/scalar_group', {"xsinx": n_iter * np.sin(n_iter),
"xcosx": n_iter * np.cos(n_iter),
"arctanx": np.arctan(n_iter)}, n_iter)
x = np.zeros((32, 3, 64, 64)) # output from network
writer.add_images('Image', x, n_iter) # Tensor
writer.add_image_with_boxes('imagebox',
np.zeros((3, 64, 64)),
np.array([[10, 10, 40, 40], [40, 40, 60, 60]]),
n_iter)
x = np.zeros(sample_rate * 2)
writer.add_audio('myAudio', x, n_iter)
writer.add_video('myVideo', np.random.rand(16, 48, 1, 28, 28).astype(np.float32), n_iter)
writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
writer.add_text('markdown Text', '''a|b\n-|-\nc|d''', n_iter)
writer.add_histogram('hist', np.random.rand(100, 100), n_iter)
writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(
100), n_iter) # needs tensorboard 0.4RC or later
writer.add_pr_curve_raw('prcurve with raw data', true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall, n_iter)
# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")
imgs = []
for i in range(5):
imgs.append(np.ones((3, 100, 110)))
with SummaryWriter() as w:
w.add_images('img_list', imgs, dataformats='CHW') |