"""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 os
import six
import time
import torch
from tensorboard.compat.proto.event_pb2 import SessionLog
from tensorboard.compat.proto.event_pb2 import Event
from tensorboard.compat.proto import event_pb2
from tensorboard.summary.writer.event_file_writer import EventFileWriter
from ._convert_np import make_np
from ._embedding import make_mat, make_sprite, make_tsv, append_pbtxt
from ._onnx_graph import load_onnx_graph
from ._pytorch_graph import graph
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
)
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, log_dir, 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 `log_dir`.
The other arguments to the constructor control the asynchronous writes to
the event file.
Args:
log_dir: 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 log_dir 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
log_dir = str(log_dir)
self.event_writer = EventFileWriter(
log_dir, max_queue, flush_secs, filename_suffix)
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()) seconds after epoch
"""
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()) seconds after epoch
"""
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='step1', 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 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 log_dir 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, log_dir=None, comment='', purge_step=None, max_queue=10,
flush_secs=120, filename_suffix=''):
"""Creates a `SummaryWriter` that will write out events and summaries
to the event file.
Args:
log_dir (string): 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 (string): Comment log_dir suffix appended to the default
``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
purge_step (int):
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 ``log_dir``.
max_queue (int): 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 (int): How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix (string): Suffix added to all event filenames in
the log_dir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
Examples::
from torch.utils.tensorboard 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/
"""
torch._C._log_api_usage_once("tensorboard.create.summarywriter")
if not log_dir:
import socket
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join(
'runs', current_time + '_' + socket.gethostname() + comment)
self.log_dir = log_dir
self.purge_step = purge_step
self.max_queue = max_queue
self.flush_secs = flush_secs
self.filename_suffix = filename_suffix
# 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
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, six.string_types)
def _get_file_writer(self):
"""Returns the default FileWriter instance. Recreates it if closed."""
if self.all_writers is None or self.file_writer is None:
self.file_writer = FileWriter(self.log_dir, self.max_queue,
self.flush_secs, self.filename_suffix)
self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
if self.purge_step is not None:
most_recent_step = self.purge_step
self.file_writer.add_event(
Event(step=most_recent_step, file_version='brain.Event:2'))
self.file_writer.add_event(
Event(step=most_recent_step, session_log=SessionLog(status=SessionLog.START)))
self.purge_step = None
return self.file_writer
def get_logdir(self):
"""Returns the directory where event files will be written."""
return self.log_dir
[docs] def add_hparams(self, hparam_dict=None, metric_dict=None):
"""Add a set of hyperparameters to be compared in TensorBoard.
Args:
hparam_dict (dictionary): Each key-value pair in the dictionary is the
name of the hyper parameter and it's corresponding value.
metric_dict (dictionary): 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.
p.s. The value in the dictionary can be `int`, `float`, `bool`, `str`, or
0-dim tensor
Examples::
from torch.utils.tensorboard 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)
with SummaryWriter(log_dir=os.path.join(self.file_writer.get_logdir(), str(time.time()))) 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)
[docs] def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
"""Add scalar data to summary.
Args:
tag (string): Data identifier
scalar_value (float or string/blobname): Value to save
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
with seconds after epoch of event
Examples::
from torch.utils.tensorboard 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):
scalar_value = workspace.FetchBlob(scalar_value)
self._get_file_writer().add_summary(
scalar(tag, scalar_value), global_step, walltime)
[docs] def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=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 (string): The parent name for the tags
tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard 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.replace("/", "_") + "_" + tag
if fw_tag in self.all_writers.keys():
fw = self.all_writers[fw_tag]
else:
fw = FileWriter(fw_tag, self.max_queue, self.flush_secs,
self.filename_suffix)
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)
[docs] def add_histogram(self, tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=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 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 add_histogram_raw(self, tag, min, max, num, sum, sum_squares,
bucket_limits, bucket_counts, global_step=None,
walltime=None):
"""Adds histogram with raw data.
Args:
tag (string): 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.
The number of elements of it should be the same as `bucket_counts`.
bucket_counts (torch.Tensor, numpy.array): Number of values per bucket
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md
Examples::
from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
dummy_data = []
for idx, value in enumerate(range(50)):
dummy_data += [idx + 0.001] * value
bins = list(range(50+2))
bins = np.array(bins)
values = np.array(dummy_data).astype(float).reshape(-1)
counts, limits = np.histogram(values, bins=bins)
sum_sq = values.dot(values)
writer.add_histogram_raw(
tag='histogram_with_raw_data',
min=values.min(),
max=values.max(),
num=len(values),
sum=values.sum(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(),
bucket_counts=counts.tolist(),
global_step=0)
writer.close()
Expected result:
.. image:: _static/img/tensorboard/add_histogram_raw.png
:scale: 50 %
"""
if len(bucket_limits) != len(bucket_counts):
raise ValueError('len(bucket_limits) != len(bucket_counts), see the document.')
self._get_file_writer().add_summary(
histogram_raw(tag,
min,
max,
num,
sum,
sum_squares,
bucket_limits,
bucket_counts),
global_step,
walltime)
[docs] def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats='CHW'):
"""Add image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
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 call ``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 torch.utils.tensorboard 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)
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
[docs] def add_images(self, tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW'):
"""Add batched image data to summary.
Note that this requires the ``pillow`` package.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
dataformats (string): Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.
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 torch.utils.tensorboard 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)
self._get_file_writer().add_summary(
image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
def add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None,
walltime=None, rescale=1, dataformats='CHW'):
"""Add image and draw bounding boxes on the image.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
rescale (float): Optional scale override
dataformats (string): Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.
Shape:
img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformat`` agrument.
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 elememts 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)
self._get_file_writer().add_summary(image_boxes(
tag, img_tensor, box_tensor, rescale=rescale, dataformats=dataformats), global_step, walltime)
[docs] def add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None):
"""Add video data to summary.
Note that this requires the ``moviepy`` package.
Args:
tag (string): Data identifier
vid_tensor (torch.Tensor): Video data
global_step (int): Global step value to record
fps (float or int): Frames per second
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
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`.
"""
self._get_file_writer().add_summary(
video(tag, vid_tensor, fps), global_step, walltime)
[docs] def add_audio(self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None):
"""Add audio data to summary.
Args:
tag (string): Data identifier
snd_tensor (torch.Tensor): Sound data
global_step (int): Global step value to record
sample_rate (int): sample rate in Hz
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].
"""
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)
[docs] def add_text(self, tag, text_string, global_step=None, walltime=None):
"""Add text data to summary.
Args:
tag (string): Data identifier
text_string (string): String to save
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch 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)
def add_onnx_graph(self, prototxt):
self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt))
[docs] def add_graph(self, model, input_to_model=None, verbose=False):
# prohibit second call?
# no, let tensorboard handle it and show its warning message.
"""Add graph data to summary.
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.
"""
if hasattr(model, 'forward'):
# A valid PyTorch model should have a 'forward' method
self._get_file_writer().add_graph(graph(model, input_to_model, verbose))
else:
# Caffe2 models do not have the 'forward' method
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from ._caffe2_graph import (
model_to_graph_def, nets_to_graph_def, protos_to_graph_def
)
if isinstance(model, list):
if isinstance(model[0], core.Net):
current_graph = nets_to_graph_def(model)
elif isinstance(model[0], caffe2_pb2.NetDef):
current_graph = protos_to_graph_def(model)
else:
# Handles cnn.CNNModelHelper, model_helper.ModelHelper
current_graph = model_to_graph_def(model)
event = event_pb2.Event(
graph_def=current_graph.SerializeToString())
self._get_file_writer().add_event(event)
@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, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None):
"""Add embedding projector data to summary.
Args:
mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point
metadata (list): A list of labels, each element will be convert to string
label_img (torch.Tensor): Images correspond to each data point
global_step (int): Global step value to record
tag (string): 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)`
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, 10, 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)
"""
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'
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)
[docs] def add_pr_curve(self, tag, labels, predictions, global_step=None,
num_thresholds=127, weights=None, walltime=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 (string): Data identifier
labels (torch.Tensor, numpy.array, or string/blobname):
Ground truth data. Binary label for each element.
predictions (torch.Tensor, numpy.array, or string/blobname):
The probability that an element be classified as true.
Value should in [0, 1]
global_step (int): Global step value to record
num_thresholds (int): Number of thresholds used to draw the curve.
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Examples::
from torch.utils.tensorboard 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()
"""
labels, predictions = make_np(labels), make_np(predictions)
self._get_file_writer().add_summary(
pr_curve(tag, labels, predictions, num_thresholds, weights),
global_step, walltime)
def add_pr_curve_raw(self, tag, true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
global_step=None,
num_thresholds=127,
weights=None,
walltime=None):
"""Adds precision recall curve with raw data.
Args:
tag (string): Data identifier
true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts
false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts
true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts
false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts
precision (torch.Tensor, numpy.array, or string/blobname): precision
recall (torch.Tensor, numpy.array, or string/blobname): recall
global_step (int): Global step value to record
num_thresholds (int): Number of thresholds used to draw the curve.
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
see: 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)
def add_custom_scalars_multilinechart(self, tags, category='default', title='untitled'):
"""Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument
is *tags*.
Args:
tags (list): 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))
def add_custom_scalars_marginchart(self, tags, category='default', title='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): 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):
"""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.
Args:
layout (dict): {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, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=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. Note that currently this depends on tb-nightly to show.
Args:
tag (string): Data identifier
vertices (torch.Tensor): List of the 3D coordinates of vertices.
colors (torch.Tensor): Colors for each vertex
faces (torch.Tensor): Indices of vertices within each triangle. (Optional)
config_dict: Dictionary with ThreeJS classes names and configuration.
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time())
seconds after epoch of event
Shape:
vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)
colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
Examples::
from torch.utils.tensorboard import SummaryWriter
vertices_tensor = torch.as_tensor([
[1, 1, 1],
[-1, -1, 1],
[1, -1, -1],
[-1, 1, -1],
], dtype=torch.float).unsqueeze(0)
colors_tensor = torch.as_tensor([
[255, 0, 0],
[0, 255, 0],
[0, 0, 255],
[255, 0, 255],
], dtype=torch.int).unsqueeze(0)
faces_tensor = torch.as_tensor([
[0, 2, 3],
[0, 3, 1],
[0, 1, 2],
[1, 3, 2],
], dtype=torch.int).unsqueeze(0)
writer = SummaryWriter()
writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
writer.close()
"""
self._get_file_writer().add_summary(mesh(tag, vertices, colors, faces, config_dict), global_step, walltime)
[docs] def flush(self):
"""Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
"""
if self.all_writers is None:
return
for writer in self.all_writers.values():
writer.flush()
[docs] def close(self):
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
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
