def test_histogram_doane(self):
self.assertTrue(
compare_proto(
summary.histogram('dummy',
np.random.rand(1024),
bins='doane',
max_bins=5), self))
def test_histogram_doane(self):
self.assertTrue(
compare_proto(
summary.histogram('dummy',
tensor_N(shape=(1024, )),
bins='doane',
max_bins=5), self))
def add_histogram(self, tag, values, global_step=None, bins='tensorflow', walltime=None):
"""Add histogram to summary.
Args:
tag (string): Data identifier
values (torch.Tensor, numpy.array, or string/blobname): Values to build histogram
global_step (int): Global step value to record
bins (string): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
for i in range(10):
x = np.random.random(1000)
writer.add_histogram('distribution centers', x + i, i)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_histogram.png
:scale: 50 %
"""
if isinstance(bins, six.string_types) and bins == 'tensorflow':
bins = self.default_bins
self._get_file_writer().add_summary(
histogram(tag, values, bins, max_bins=100), global_step, walltime)
def add_histogram(self,
tag,
values,
global_step=None,
bins='tensorflow',
walltime=None,
max_bins=None):
torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
if self._check_caffe2_blob(values):
values = workspace.FetchBlob(values)
if isinstance(bins, six.string_types) and bins == 'tensorflow':
bins = self.default_bins
self._get_file_writer().add_summary(
histogram(tag, values, bins, max_bins=max_bins), global_step,
walltime)
def test_empty_input(self):
with self.assertRaises(Exception) as e_info:
summary.histogram('dummy', np.ndarray(0), 'tensorflow')
def test_list_input(self):
with self.assertRaises(Exception) as e_info:
summary.histogram('dummy', [1, 3, 4, 5, 6], 'tensorflow')
def test_empty_input(self):
print('expect error here:')
with self.assertRaises(Exception) as e_info:
summary.histogram('dummy', np.ndarray(0), 'tensorflow')
