"""Provides an API for writing protocol buffers to event files to be
consumed by TensorBoard for visualization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import numpy
import time
import logging
import atexit
from typing import Union, Optional, Dict, List
from .comet_utils import CometLogger
from .embedding import make_mat, make_sprite, make_tsv, append_pbtxt
from .event_file_writer import EventFileWriter
from .onnx_graph import load_onnx_graph
from .openvino_graph import load_openvino_graph
from .proto import event_pb2
from .proto import summary_pb2
from .proto.event_pb2 import SessionLog, Event
from .utils import figure_to_image
from .summary import (
scalar, histogram, histogram_raw, image, audio, text,
pr_curve, pr_curve_raw, video, custom_scalars, image_boxes, mesh, hparams
)
logger = logging.getLogger(__name__)
numpy_compatible = numpy.ndarray
try:
import torch
numpy_compatible = torch.Tensor
except ImportError:
pass
class DummyFileWriter(object):
"""A fake file writer that writes nothing to the disk.
"""
def __init__(self, logdir):
self._logdir = logdir
def get_logdir(self):
"""Returns the directory where event file will be written."""
return self._logdir
def add_event(self, event, step=None, walltime=None):
return
def add_summary(self, summary, global_step=None, walltime=None):
return
def add_graph(self, graph_profile, walltime=None):
return
def add_onnx_graph(self, graph, walltime=None):
return
def flush(self):
return
def close(self):
return
def reopen(self):
return
class FileWriter(object):
"""Writes protocol buffers to event files to be consumed by TensorBoard.
The `FileWriter` class provides a mechanism to create an event file in a
given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
"""
def __init__(self, logdir, max_queue=10, flush_secs=120, filename_suffix=''):
"""Creates a `FileWriter` and an event file.
On construction the writer creates a new event file in `logdir`.
The other arguments to the constructor control the asynchronous writes to
the event file.
Args:
logdir: A string. Directory where event file will be written.
max_queue: Integer. Size of the queue for pending events and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs: Number. How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix: A string. Suffix added to all event filenames
in the logdir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
"""
# Sometimes PosixPath is passed in and we need to coerce it to
# a string in all cases
# TODO: See if we can remove this in the future if we are
# actually the ones passing in a PosixPath
logdir = str(logdir)
self.event_writer = EventFileWriter(
logdir, max_queue, flush_secs, filename_suffix)
def cleanup():
self.event_writer.close()
atexit.register(cleanup)
def get_logdir(self):
"""Returns the directory where event file will be written."""
return self.event_writer.get_logdir()
def add_event(self, event, step=None, walltime=None):
"""Adds an event to the event file.
Args:
event: An `Event` protocol buffer.
step: Number. Optional global step value for training process
to record with the event.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time())
"""
event.wall_time = time.time() if walltime is None else walltime
if step is not None:
# Make sure step is converted from numpy or other formats
# since protobuf might not convert depending on version
event.step = int(step)
self.event_writer.add_event(event)
def add_summary(self, summary, global_step=None, walltime=None):
"""Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` protocol buffer
and adds it to the event file.
Args:
summary: A `Summary` protocol buffer.
global_step: Number. Optional global step value for training process
to record with the summary.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time())
"""
event = event_pb2.Event(summary=summary)
self.add_event(event, global_step, walltime)
def add_graph(self, graph_profile, walltime=None):
"""Adds a `Graph` and step stats protocol buffer to the event file.
Args:
graph_profile: A `Graph` and step stats protocol buffer.
walltime: float. Optional walltime to override the default (current)
walltime (from time.time()) seconds after epoch
"""
graph = graph_profile[0]
stepstats = graph_profile[1]
event = event_pb2.Event(graph_def=graph.SerializeToString())
self.add_event(event, None, walltime)
trm = event_pb2.TaggedRunMetadata(
tag='profiler', run_metadata=stepstats.SerializeToString())
event = event_pb2.Event(tagged_run_metadata=trm)
self.add_event(event, None, walltime)
def add_onnx_graph(self, graph, walltime=None):
"""Adds a `Graph` protocol buffer to the event file.
Args:
graph: A `Graph` protocol buffer.
walltime: float. Optional walltime to override the default (current)
_get_file_writerfrom time.time())
"""
event = event_pb2.Event(graph_def=graph.SerializeToString())
self.add_event(event, None, walltime)
def add_openvino_graph(self, graph, walltime=None):
"""Adds a `Graph` protocol buffer to the event file.
Args:
graph: A `Graph` protocol buffer.
walltime: float. Optional walltime to override the default (current)
_get_file_writerfrom time.time())
"""
event = event_pb2.Event(graph_def=graph.SerializeToString())
self.add_event(event, None, walltime)
def flush(self):
"""Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
"""
self.event_writer.flush()
def close(self):
"""Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
"""
self.event_writer.close()
def reopen(self):
"""Reopens the EventFileWriter.
Can be called after `close()` to add more events in the same directory.
The events will go into a new events file.
Does nothing if the EventFileWriter was not closed.
"""
self.event_writer.reopen()
[docs]class SummaryWriter(object):
"""Writes entries directly to event files in the logdir to be
consumed by TensorBoard.
The `SummaryWriter` class provides a high-level API to create an event file
in a given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
"""
[docs] def __init__(
self,
logdir: Optional[str] = None,
comment: Optional[str] = "",
purge_step: Optional[int] = None,
max_queue: Optional[int] = 10,
flush_secs: Optional[int] = 120,
filename_suffix: Optional[str] = '',
write_to_disk: Optional[bool] = True,
log_dir: Optional[str] = None,
comet_config: Optional[dict] = {"disabled": True},
**kwargs):
"""Creates a `SummaryWriter` that will write out events and summaries
to the event file.
Args:
logdir: Save directory location. Default is
runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
Use hierarchical folder structure to compare
between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
for each new experiment to compare across them.
comment: Comment logdir suffix appended to the default
``logdir``. If ``logdir`` is assigned, this argument has no effect.
purge_step:
When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
any events whose global_step larger or equal to :math:`T` will be
purged and hidden from TensorBoard.
Note that crashed and resumed experiments should have the same ``logdir``.
max_queue: Size of the queue for pending events and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs: How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix: Suffix added to all event filenames in
the logdir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
write_to_disk:
If pass `False`, SummaryWriter will not write to disk.
comet_config:
A comet config dictionary. Contains parameters that need to be
passed to comet like workspace, project_name, api_key, disabled etc
Examples::
from tensorboardX import SummaryWriter
# create a summary writer with automatically generated folder name.
writer = SummaryWriter()
# folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
# create a summary writer using the specified folder name.
writer = SummaryWriter("my_experiment")
# folder location: my_experiment
# create a summary writer with comment appended.
writer = SummaryWriter(comment="LR_0.1_BATCH_16")
# folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
"""
if log_dir is not None and logdir is None:
logdir = log_dir
if not logdir:
import socket
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
logdir = os.path.join(
'runs', current_time + '_' + socket.gethostname() + comment)
self.logdir = logdir
self.purge_step = purge_step
self._max_queue = max_queue
self._flush_secs = flush_secs
self._filename_suffix = filename_suffix
self._write_to_disk = write_to_disk
self._comet_config = comet_config
self._comet_logger = None
self.kwargs = kwargs
# Initialize the file writers, but they can be cleared out on close
# and recreated later as needed.
self.file_writer = self.all_writers = None
self._get_file_writer()
# Create default bins for histograms, see generate_testdata.py in tensorflow/tensorboard
v = 1E-12
buckets = []
neg_buckets = []
while v < 1E20:
buckets.append(v)
neg_buckets.append(-v)
v *= 1.1
self.default_bins = neg_buckets[::-1] + [0] + buckets
self.scalar_dict = {}
def __append_to_scalar_dict(self, tag, scalar_value, global_step,
timestamp):
"""This adds an entry to the self.scalar_dict datastructure with format
{writer_id : [[timestamp, step, value], ...], ...}.
"""
from .x2num import make_np
if tag not in self.scalar_dict.keys():
self.scalar_dict[tag] = []
self.scalar_dict[tag].append(
[timestamp, global_step, float(make_np(scalar_value).squeeze())])
def _check_caffe2_blob(self, item):
"""
Caffe2 users have the option of passing a string representing the name of
a blob in the workspace instead of passing the actual Tensor/array containing
the numeric values. Thus, we need to check if we received a string as input
instead of an actual Tensor/array, and if so, we need to fetch the Blob
from the workspace corresponding to that name. Fetching can be done with the
following:
from caffe2.python import workspace (if not already imported)
workspace.FetchBlob(blob_name)
workspace.FetchBlobs([blob_name1, blob_name2, ...])
"""
return isinstance(item, str)
def _get_file_writer(self):
"""Returns the default FileWriter instance. Recreates it if closed."""
if not self._write_to_disk:
self.file_writer = DummyFileWriter(logdir=self.logdir)
self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
return self.file_writer
if self.all_writers is None or self.file_writer is None:
self.file_writer = FileWriter(logdir=self.logdir,
max_queue=self._max_queue,
flush_secs=self._flush_secs,
filename_suffix=self._filename_suffix,
**self.kwargs)
if self.purge_step is not None:
self.file_writer.add_event(
Event(step=self.purge_step, file_version='brain.Event:2'))
self.file_writer.add_event(
Event(step=self.purge_step, session_log=SessionLog(status=SessionLog.START)))
self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
return self.file_writer
def _get_comet_logger(self):
"""Returns a comet logger instance. Recreates it if closed."""
if self._comet_logger is None:
self._comet_logger = CometLogger(self._comet_config)
return self._comet_logger
[docs] def add_hparams(
self,
hparam_dict: Dict[str, Union[bool, str, float, int]],
metric_dict: Dict[str, float],
name: Optional[str] = None,
global_step: Optional[int] = None):
"""Add a set of hyperparameters to be compared in tensorboard.
Args:
hparam_dict: Each key-value pair in the dictionary is the
name of the hyper parameter and it's corresponding value.
metric_dict: Each key-value pair in the dictionary is the
name of the metric and it's corresponding value.
Note that the key used here should be unique in the
tensorboard record. Otherwise the value you added by `add_scalar`
will be displayed in hparam plugin. In most
cases, this is unwanted.
name: Personnalised name of the hparam session
global_step: Current time step
Examples::
from tensorboardX import SummaryWriter
with SummaryWriter() as w:
for i in range(5):
w.add_hparams({'lr': 0.1*i, 'bsize': i},
{'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
Expected result:
.. image:: _static/img/tensorboard/add_hparam.png
:scale: 50 %
"""
if type(hparam_dict) is not dict or type(metric_dict) is not dict:
raise TypeError('hparam_dict and metric_dict should be dictionary.')
exp, ssi, sei = hparams(hparam_dict, metric_dict)
if not name:
name = str(time.time())
with SummaryWriter(logdir=os.path.join(self.file_writer.get_logdir(), name)) as w_hp:
w_hp.file_writer.add_summary(exp)
w_hp.file_writer.add_summary(ssi)
w_hp.file_writer.add_summary(sei)
for k, v in metric_dict.items():
w_hp.add_scalar(k, v, global_step)
self._get_comet_logger().log_parameters(hparam_dict, step=global_step)
[docs] def add_scalar(
self,
tag: str,
scalar_value: Union[float, numpy_compatible],
global_step: Optional[int] = None,
walltime: Optional[float] = None,
display_name: Optional[str] = "",
summary_description: Optional[str] = ""):
"""Add scalar data to summary.
Args:
tag: Data identifier
scalar_value: Value to save, if string is passed, it will be treated
as caffe blob name.
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event
display_name: The title of the plot. If empty string is passed,
`tag` will be used.
summary_description: The comprehensive text that will showed
by clicking the information icon on TensorBoard.
Examples::
from tensorboardX import SummaryWriter
writer = SummaryWriter()
x = range(100)
for i in x:
writer.add_scalar('y=2x', i * 2, i)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_scalar.png
:scale: 50 %
"""
if self._check_caffe2_blob(scalar_value):
if 'workspace' in globals():
scalar_value = workspace.FetchBlob(scalar_value)
else:
raise TypeError("Input value: \"{}\" is not a scalar".format(scalar_value))
self._get_file_writer().add_summary(
scalar(tag, scalar_value, display_name, summary_description), global_step, walltime)
self._get_comet_logger().log_metric(tag, display_name, scalar_value, global_step)
[docs] def add_scalars(
self,
main_tag: str,
tag_scalar_dict: Dict[str, float],
global_step: Optional[int] = None,
walltime: Optional[float] = None):
"""Adds many scalar data to summary.
Note that this function also keeps logged scalars in memory. In extreme case it explodes your RAM.
Args:
main_tag: The parent name for the tags
tag_scalar_dict: Key-value pair storing the tag and corresponding values
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event
Examples::
from tensorboardX import SummaryWriter
writer = SummaryWriter()
r = 5
for i in range(100):
writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
'xcosx':i*np.cos(i/r),
'tanx': np.tan(i/r)}, i)
writer.close()
# This call adds three values to the same scalar plot with the tag
# 'run_14h' in TensorBoard's scalar section.
Expected result:
.. image:: _static/img/tensorboard/add_scalars.png
:scale: 50 %
"""
walltime = time.time() if walltime is None else walltime
fw_logdir = self._get_file_writer().get_logdir()
for tag, scalar_value in tag_scalar_dict.items():
fw_tag = fw_logdir + "/" + main_tag + "/" + tag
if fw_tag in self.all_writers.keys():
fw = self.all_writers[fw_tag]
else:
fw = FileWriter(logdir=fw_tag)
self.all_writers[fw_tag] = fw
if self._check_caffe2_blob(scalar_value):
scalar_value = workspace.FetchBlob(scalar_value)
fw.add_summary(scalar(main_tag, scalar_value),
global_step, walltime)
self.__append_to_scalar_dict(
fw_tag, scalar_value, global_step, walltime)
self._get_comet_logger().log_metrics(tag_scalar_dict, main_tag, step=global_step)
[docs] def export_scalars_to_json(self, path):
"""Exports to the given path an ASCII file containing all the scalars written
so far by this instance, with the following format:
{writer_id : [[timestamp, step, value], ...], ...}
The scalars saved by ``add_scalars()`` will be flushed after export.
"""
with open(path, "w") as f:
json.dump(self.scalar_dict, f)
self.scalar_dict = {}
[docs] def add_histogram(
self,
tag: str,
values: numpy_compatible,
global_step: Optional[int] = None,
bins: Optional[str] = 'tensorflow',
walltime: Optional[float] = None,
max_bins=None):
"""Add histogram to summary.
Args:
tag: Data identifier
values: Values to build histogram
global_step: Global step value to record
bins: One of {'tensorflow','auto', 'fd', ...}. This determines how the
bins are made. You can find other options in the `numpy reference
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html>`_.
walltime: Optional override default walltime (time.time()) of event
Examples::
from tensorboardX 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 self._check_caffe2_blob(values):
values = workspace.FetchBlob(values)
if isinstance(bins, str) and bins == 'tensorflow':
bins = self.default_bins
self._get_file_writer().add_summary(
histogram(tag, values, bins, max_bins=max_bins), global_step, walltime)
self._get_comet_logger().log_histogram(values, tag, global_step)
[docs] def add_histogram_raw(
self,
tag: str,
min,
max,
num,
sum,
sum_squares,
bucket_limits,
bucket_counts,
global_step: Optional[int] = None,
walltime: Optional[float] = None):
"""Adds histogram with raw data.
Args:
tag: Data identifier
min (float or int): Min value
max (float or int): Max value
num (int): Number of values
sum (float or int): Sum of all values
sum_squares (float or int): Sum of squares for all values
bucket_limits (torch.Tensor, numpy.array): Upper value per
bucket, note that the bucket_limits returned from `np.histogram`
has one more element. See the comment in the following example.
bucket_counts (torch.Tensor, numpy.array): Number of values per bucket
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event
Examples::
import numpy as np
dummy_data = []
for idx, value in enumerate(range(30)):
dummy_data += [idx + 0.001] * value
values = np.array(dummy_data).astype(float).reshape(-1)
counts, limits = np.histogram(values)
sum_sq = values.dot(values)
with SummaryWriter() as summary_writer:
summary_writer.add_histogram_raw(
tag='hist_dummy_data',
min=values.min(),
max=values.max(),
num=len(values),
sum=values.sum(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(), # <- note here.
bucket_counts=counts.tolist(),
global_step=0)
"""
if len(bucket_limits) != len(bucket_counts):
raise ValueError('len(bucket_limits) != len(bucket_counts), see the document.')
summary = histogram_raw(tag,
min,
max,
num,
sum,
sum_squares,
bucket_limits,
bucket_counts)
self._get_file_writer().add_summary(
summary,
global_step,
walltime)
self._get_comet_logger().log_histogram_raw(tag, summary, step=global_step)
[docs] def add_image(
self,
tag: str,
img_tensor: numpy_compatible,
global_step: Optional[int] = None,
walltime: Optional[float] = None,
dataformats: Optional[str] = 'CHW'):
"""Add image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag: Data identifier
img_tensor: An `uint8` or `float` Tensor of shape `
[channel, height, width]` where `channel` is 1, 3, or 4.
The elements in img_tensor can either have values
in [0, 1] (float32) or [0, 255] (uint8).
Users are responsible to scale the data in the correct range/type.
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event.
dataformats: This parameter specifies the meaning of each dimension of the input tensor.
Shape:
img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
convert a batch of tensor into 3xHxW format or use ``add_images()`` and let us do the job.
Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitible as long as
corresponding ``dataformats`` argument is passed. e.g. CHW, HWC, HW.
Examples::
from tensorboardX import SummaryWriter
import numpy as np
img = np.zeros((3, 100, 100))
img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC = np.zeros((100, 100, 3))
img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
writer = SummaryWriter()
writer.add_image('my_image', img, 0)
# If you have non-default dimension setting, set the dataformats argument.
writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_image.png
:scale: 50 %
"""
if self._check_caffe2_blob(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
summary = image(tag, img_tensor, dataformats=dataformats)
encoded_image_string = summary.value[0].image.encoded_image_string
self._get_file_writer().add_summary(
summary, global_step, walltime)
self._get_comet_logger().log_image_encoded(encoded_image_string, tag, step=global_step)
[docs] def add_images(
self,
tag: str,
img_tensor: numpy_compatible,
global_step: Optional[int] = None,
walltime: Optional[float] = None,
dataformats: Optional[str] = 'NCHW'):
"""Add batched (4D) image data to summary.
Besides passing 4D (NCHW) tensor, you can also pass a list of tensors of the same size.
In this case, the ``dataformats`` should be `CHW` or `HWC`.
Note that this requires the ``pillow`` package.
Args:
tag: Data identifier
img_tensor: Image data
The elements in img_tensor can either have values in [0, 1] (float32) or [0, 255] (uint8).
Users are responsible to scale the data in the correct range/type.
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event
Shape:
img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
accepted. e.g. NCHW or NHWC.
Examples::
from tensorboardX import SummaryWriter
import numpy as np
img_batch = np.zeros((16, 3, 100, 100))
for i in range(16):
img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
writer = SummaryWriter()
writer.add_images('my_image_batch', img_batch, 0)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_images.png
:scale: 30 %
"""
if self._check_caffe2_blob(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
if isinstance(img_tensor, list): # a list of tensors in CHW or HWC
if dataformats.upper() != 'CHW' and dataformats.upper() != 'HWC':
print('A list of image is passed, but the dataformat is neither CHW nor HWC.')
print('Nothing is written.')
return
import torch
try:
img_tensor = torch.stack(img_tensor, 0)
except TypeError as e:
import numpy as np
img_tensor = np.stack(img_tensor, 0)
dataformats = 'N' + dataformats
summary = image(tag, img_tensor, dataformats=dataformats)
encoded_image_string = summary.value[0].image.encoded_image_string
self._get_file_writer().add_summary(
summary, global_step, walltime)
self._get_comet_logger().log_image_encoded(encoded_image_string, tag, step=global_step)
[docs] def add_image_with_boxes(
self,
tag: str,
img_tensor: numpy_compatible,
box_tensor: numpy_compatible,
global_step: Optional[int] = None,
walltime: Optional[float] = None,
dataformats: Optional[str] = 'CHW',
labels: Optional[List[str]] = None,
**kwargs):
"""Add image and draw bounding boxes on the image.
Args:
tag: Data identifier
img_tensor: Image data
box_tensor: Box data (for detected objects)
box should be represented as [x1, y1, x2, y2].
global_step: Global step value to record
walltime: override default walltime (time.time()) of event
labels: The strings to be show on each bounding box.
Shape:
img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.
e.g. CHW or HWC
box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4, where N is the number of
boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax).
"""
if self._check_caffe2_blob(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
if self._check_caffe2_blob(box_tensor):
box_tensor = workspace.FetchBlob(box_tensor)
if labels is not None:
if isinstance(labels, str):
labels = [labels]
if len(labels) != box_tensor.shape[0]:
logger.warning('Number of labels do not equal to number of box, skip the labels.')
labels = None
summary = image_boxes(
tag, img_tensor, box_tensor, dataformats=dataformats, labels=labels, **kwargs)
encoded_image_string = summary.value[0].image.encoded_image_string
self._get_file_writer().add_summary(
summary, global_step, walltime)
self._get_comet_logger().log_image_encoded(encoded_image_string, tag, step=global_step)
[docs] def add_video(
self,
tag: str,
vid_tensor: numpy_compatible,
global_step: Optional[int] = None,
fps: Optional[Union[int, float]] = 4,
walltime: Optional[float] = None,
dataformats: Optional[str] = 'NTCHW'):
"""Add video data to summary.
Note that this requires the ``moviepy`` package.
Args:
tag: Data identifier
vid_tensor: Video data
global_step: Global step value to record
fps: Frames per second
walltime: Optional override default walltime (time.time()) of event
dataformats: Specify different permutation of the video tensor
Shape:
vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255]
for type `uint8` or [0, 1] for type `float`.
"""
summary = video(tag, vid_tensor, fps, dataformats=dataformats)
encoded_image_string = summary.value[0].image.encoded_image_string
self._get_file_writer().add_summary(
summary, global_step, walltime)
self._get_comet_logger().log_image_encoded(encoded_image_string, tag, step=global_step)
[docs] def add_audio(
self,
tag: str,
snd_tensor: numpy_compatible,
global_step: Optional[int],
sample_rate: Optional[int] = 44100,
walltime: Optional[float] = None):
"""Add audio data to summary.
Args:
tag: Data identifier
snd_tensor: Sound data
global_step: Global step value to record
sample_rate: sample rate in Hz
walltime: Optional override default walltime (time.time()) of event
Shape:
snd_tensor: :math:`(L, C)`. The values should lie between [-1, 1].
Where `L` is the number of audio frames and `C` is the channel. Set
channel equals to 2 for stereo.
"""
if self._check_caffe2_blob(snd_tensor):
snd_tensor = workspace.FetchBlob(snd_tensor)
self._get_file_writer().add_summary(
audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime)
self._get_comet_logger().log_audio(snd_tensor, sample_rate, tag, step=global_step)
[docs] def add_text(
self,
tag: str,
text_string: str,
global_step: Optional[int] = None,
walltime: Optional[float] = None):
"""Add text data to summary.
Args:
tag: Data identifier
text_string: String to save
global_step: Global step value to record
walltime: Optional override default walltime (time.time()) of event
Examples::
writer.add_text('lstm', 'This is an lstm', 0)
writer.add_text('rnn', 'This is an rnn', 10)
"""
self._get_file_writer().add_summary(
text(tag, text_string), global_step, walltime)
self._get_comet_logger().log_text(text_string, global_step)
[docs] def add_onnx_graph(
self,
onnx_model_file):
"""Add onnx graph to TensorBoard.
Args:
onnx_model_file (string): The path to the onnx model.
"""
self._get_file_writer().add_onnx_graph(load_onnx_graph(onnx_model_file))
self._get_comet_logger().log_asset(onnx_model_file)
[docs] def add_openvino_graph(
self,
xmlname):
"""Add openvino graph to TensorBoard.
Args:
xmlname (string): The path to the openvino model. (the xml file)
"""
self._get_file_writer().add_openvino_graph(load_openvino_graph(xmlname))
self._get_comet_logger().log_asset(xmlname)
[docs] def add_graph(
self,
model,
input_to_model=None,
verbose=False,
use_strict_trace=True):
"""Add graph data to summary. The graph is actually processed by `torch.utils.tensorboard.add_graph()`
Args:
model (torch.nn.Module): Model to draw.
input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of
variables to be fed.
verbose (bool): Whether to print graph structure in console.
use_strict_trace (bool): Whether to pass keyword argument `strict` to
`torch.jit.trace`. Pass False when you want the tracer to
record your mutable container types (list, dict)
"""
from torch.utils.tensorboard._pytorch_graph import graph
self._get_file_writer().add_graph(graph(model, input_to_model, verbose, use_strict_trace=use_strict_trace))
@staticmethod
def _encode(rawstr):
# I'd use urllib but, I'm unsure about the differences from python3 to python2, etc.
retval = rawstr
retval = retval.replace("%", "%%%02x" % (ord("%")))
retval = retval.replace("/", "%%%02x" % (ord("/")))
retval = retval.replace("\\", "%%%02x" % (ord("\\")))
return retval
[docs] def add_embedding(
self,
mat: numpy_compatible,
metadata=None,
label_img: numpy_compatible = None,
global_step: Optional[int] = None,
tag='default',
metadata_header=None):
r"""Add embedding projector data to summary.
Args:
mat: A matrix which each row is the feature vector of the data point
metadata (list): A list of labels, each element will be converted to
string.
label_img: Images correspond to each
data point. Each image should be square sized. The amount and
the size of the images are limited by the Tensorboard frontend,
see limits below.
global_step: Global step value to record
tag: Name for the embedding
Shape:
mat: :math:`(N, D)`, where N is number of data and D is feature dimension
label_img: :math:`(N, C, H, W)`, where `Height` should be equal to `Width`.
Also, :math:`\sqrt{N}*W` must be less than or equal to 8192, so that the generated sprite
image can be loaded by the Tensorboard frontend
(see `tensorboardX#516 <https://github.com/lanpa/tensorboardX/issues/516>`_ for more).
Examples::
import keyword
import torch
meta = []
while len(meta)<100:
meta = meta+keyword.kwlist # get some strings
meta = meta[:100]
for i, v in enumerate(meta):
meta[i] = v+str(i)
label_img = torch.rand(100, 3, 32, 32)
for i in range(100):
label_img[i]*=i/100.0
writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
writer.add_embedding(torch.randn(100, 5), label_img=label_img)
writer.add_embedding(torch.randn(100, 5), metadata=meta)
"""
# programmer's note: This function has nothing to do with event files.
# The hard-coded projector_config.pbtxt is the only source for TensorBoard's
# current implementation. (as of Dec. 2019)
from .x2num import make_np
mat = make_np(mat)
if global_step is None:
global_step = 0
# clear pbtxt?
# Maybe we should encode the tag so slashes don't trip us up?
# I don't think this will mess us up, but better safe than sorry.
subdir = "%s/%s" % (str(global_step).zfill(5), self._encode(tag))
save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
try:
os.makedirs(save_path)
except OSError:
print(
'warning: Embedding dir exists, did you set global_step for add_embedding()?')
if metadata is not None:
assert mat.shape[0] == len(
metadata), '#labels should equal with #data points'
make_tsv(metadata, save_path, metadata_header=metadata_header)
if label_img is not None:
assert mat.shape[0] == label_img.shape[0], '#images should equal with #data points'
assert label_img.shape[2] == label_img.shape[3], 'Image should be square, see tensorflow/tensorboard#670'
make_sprite(label_img, save_path)
assert mat.ndim == 2, 'mat should be 2D, where mat.size(0) is the number of data points'
make_mat(mat, save_path)
# new funcion to append to the config file a new embedding
append_pbtxt(metadata, label_img,
self._get_file_writer().get_logdir(), subdir, global_step, tag)
if tag is not None:
template_filename = "%s.json" % tag
else:
template_filename = None
self._get_comet_logger().log_embedding(mat, metadata, label_img, template_filename=template_filename)
[docs] def add_pr_curve(
self,
tag: str,
labels: numpy_compatible,
predictions: numpy_compatible,
global_step: Optional[int] = None,
num_thresholds: Optional[int] = 127,
weights=None,
walltime: Optional[float] = None):
"""Adds precision recall curve.
Plotting a precision-recall curve lets you understand your model's
performance under different threshold settings. With this function,
you provide the ground truth labeling (T/F) and prediction confidence
(usually the output of your model) for each target. The TensorBoard UI
will let you choose the threshold interactively.
Args:
tag: Data identifier
labels: Ground truth data. Binary label for each element.
predictions:
The probability that an element be classified as true.
Value should in [0, 1]
global_step: Global step value to record
num_thresholds: Number of thresholds used to draw the curve.
walltime: Override default walltime (time.time()) of event
Examples::
from tensorboardX import SummaryWriter
import numpy as np
labels = np.random.randint(2, size=100) # binary label
predictions = np.random.rand(100)
writer = SummaryWriter()
writer.add_pr_curve('pr_curve', labels, predictions, 0)
writer.close()
"""
from .x2num import make_np
labels, predictions = make_np(labels), make_np(predictions)
summary = pr_curve(tag, labels, predictions, num_thresholds, weights)
self._get_file_writer().add_summary(
summary,
global_step, walltime)
self._get_comet_logger().log_pr_data(tag, summary, num_thresholds, step=global_step)
[docs] def add_pr_curve_raw(
self,
tag: str,
true_positive_counts: numpy_compatible,
false_positive_counts: numpy_compatible,
true_negative_counts: numpy_compatible,
false_negative_counts: numpy_compatible,
precision: numpy_compatible,
recall: numpy_compatible,
global_step: Optional[int] = None,
num_thresholds: Optional[int] = 127,
weights=None,
walltime: Optional[float] = None):
"""Adds precision recall curve with raw data.
Args:
tag: Data identifier
global_step: Global step value to record
num_thresholds (int): Number of thresholds used to draw the curve.
walltime: Optional override default walltime (time.time()) of event
see: `Tensorboard refenence
<https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md>`_
"""
self._get_file_writer().add_summary(
pr_curve_raw(tag,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds,
weights),
global_step,
walltime)
self._get_comet_logger().log_pr_raw_data(tag, step=global_step,
true_positive_counts=true_positive_counts,
false_positive_counts=false_positive_counts,
true_negative_counts=true_negative_counts,
false_negative_counts=false_negative_counts,
precision=precision,
recall=recall,
num_thresholds=num_thresholds,
weights=weights)
[docs] def add_custom_scalars_multilinechart(
self,
tags: List[str],
category: str = 'default',
title: str = 'untitled'):
"""Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument
is *tags*.
Args:
tags: list of tags that have been used in ``add_scalar()``
Examples::
writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
"""
layout = {category: {title: ['Multiline', tags]}}
self._get_file_writer().add_summary(custom_scalars(layout))
[docs] def add_custom_scalars_marginchart(
self,
tags: List[str],
category: str = 'default',
title: str = 'untitled'):
"""Shorthand for creating marginchart. Similar to ``add_custom_scalars()``, but the only necessary argument
is *tags*, which should have exactly 3 elements.
Args:
tags: list of tags that have been used in ``add_scalar()``
Examples::
writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
"""
assert len(tags) == 3
layout = {category: {title: ['Margin', tags]}}
self._get_file_writer().add_summary(custom_scalars(layout))
[docs] def add_custom_scalars(
self,
layout: Dict[str, Dict[str, List]]):
"""Create special chart by collecting charts tags in 'scalars'. Note that this function can only be called once
for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called
before or after the training loop. See ``examples/demo_custom_scalars.py`` for more.
Args:
layout: {categoryName: *charts*}, where *charts* is also a dictionary
{chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type
(one of **Multiline** or **Margin**) and the second element should be a list containing the tags
you have used in add_scalar function, which will be collected into the new chart.
Examples::
layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
'USA':{ 'dow':['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
'nasdaq':['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
writer.add_custom_scalars(layout)
"""
self._get_file_writer().add_summary(custom_scalars(layout))
[docs] def add_mesh(
self,
tag: str,
vertices: numpy_compatible,
colors: numpy_compatible = None,
faces: numpy_compatible = None,
config_dict=None,
global_step: Optional[int] = None,
walltime: Optional[float] = None):
"""Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js,
so it allows users to interact with the rendered object. Besides the basic definitions
such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for
advanced usage.
Args:
tag: Data identifier
vertices: List of the 3D coordinates of vertices.
colors: Colors for each vertex
faces: Indices of vertices within each triangle. (Optional)
config_dict: Dictionary with ThreeJS classes names and configuration.
global_step: Global step value to record
walltime: Optional override default walltime (time.time())
seconds after epoch of event
Shape:
vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels). If
Nothing show on tensorboard, try normalizing the values to [-1, 1].
colors: :math:`(B, N, 3)`. The values should lie in [0, 255].
faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
Expected result after running ``examples/demo_mesh.py``:
.. image:: _static/img/tensorboard/add_mesh.png
:scale: 30 %
"""
self._get_file_writer().add_summary(mesh(tag, vertices, colors, faces, config_dict), global_step, walltime)
self._get_comet_logger().log_mesh(tag, vertices, colors, faces,
config_dict, global_step, walltime)
[docs] def close(self):
"""Close the current SummaryWriter. This call flushes the unfinished write operation.
Use context manager (with statement) whenever it's possible.
"""
if self.all_writers is None:
return # ignore double close
for writer in self.all_writers.values():
writer.flush()
writer.close()
self.file_writer = self.all_writers = None
self._get_comet_logger().end()
self._comet_logger = None
[docs] def flush(self):
"""Force the data in memory to be flushed to disk. Use this call if tensorboard does not update reqularly.
Another way is to set the `flush_secs` when creating the SummaryWriter.
"""
if self.all_writers is None:
return # ignore double close
for writer in self.all_writers.values():
writer.flush()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()