Source code for tensorboardX.writer

"""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 six
import time
import logging

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 .pytorch_graph import 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
)


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 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='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 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=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='', write_to_disk=True, log_dir=None, **kwargs): """Creates a `SummaryWriter` that will write out events and summaries to the event file. Args: logdir (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 logdir suffix appended to the default ``logdir``. If ``logdir`` 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 ``logdir``. 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 logdir directory. More details on filename construction in tensorboard.summary.writer.event_file_writer.EventFileWriter. write_to_disk (boolean): If pass `False`, SummaryWriter will not write to disk. 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.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))]) 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 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: if 'purge_step' in self.kwargs.keys(): most_recent_step = self.kwargs.pop('purge_step') self.file_writer = FileWriter(logdir=self.logdir, max_queue=self._max_queue, flush_secs=self._flush_secs, filename_suffix=self._filename_suffix, **self.kwargs) 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))) else: self.file_writer = FileWriter(logdir=self.logdir, max_queue=self._max_queue, flush_secs=self._flush_secs, filename_suffix=self._filename_suffix, **self.kwargs) self.all_writers = {self.file_writer.get_logdir(): self.file_writer} return self.file_writer
[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. 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) with SummaryWriter(logdir=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()) of event 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): 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()) 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)
[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, 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()) 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, 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)
[docs] 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, 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 (int): Global step value to record walltime (float): 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.') 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): 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 (int): Global step value to record walltime (float): Optional override default walltime (time.time()) of event. dataformats (string): 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) 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 (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 (string): Data identifier img_tensor (torch.Tensor, numpy.array, or string/blobname): 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 (int): Global step value to record walltime (float): 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 self._get_file_writer().add_summary( image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
[docs] def add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None, walltime=None, dataformats='CHW', labels=None, **kwargs): """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) box should be represented as [x1, y1, x2, y2]. global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) of event labels (list of string): The strings to be show on each bounding box. 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) if labels is not None: if isinstance(labels, str): labels = [labels] if len(labels) != box_tensor.shape[0]: logging.warning('Number of labels do not equal to number of box, skip the labels.') labels = None self._get_file_writer().add_summary(image_boxes( tag, img_tensor, box_tensor, dataformats=dataformats, labels=labels, **kwargs), global_step, walltime)
[docs] def add_figure(self, tag, figure, global_step=None, close=True, walltime=None): """Render matplotlib figure into an image and add it to summary. Note that this requires the ``matplotlib`` package. Args: tag (string): Data identifier figure (matplotlib.pyplot.figure) or list of figures: Figure or a list of figures global_step (int): Global step value to record close (bool): Flag to automatically close the figure walltime (float): Optional override default walltime (time.time()) of event """ if isinstance(figure, list): self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='NCHW') else: self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='CHW')
[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()) 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()) 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()) 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, **kwargs): # 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. omit_useless_nodes (bool): Default to ``true``, which eliminates unused nodes. operator_export_type (string): One of: ``"ONNX"``, ``"RAW"``. This determines the optimization level of the graph. If error happens during exporting the graph, using ``"RAW"`` might help. """ if hasattr(model, 'forward'): # A valid PyTorch model should have a 'forward' method import torch from distutils.version import LooseVersion if LooseVersion(torch.__version__) >= LooseVersion("0.3.1"): pass else: if LooseVersion(torch.__version__) >= LooseVersion("0.3.0"): print('You are using PyTorch==0.3.0, use add_onnx_graph()') return if not hasattr(torch.autograd.Variable, 'grad_fn'): print('add_graph() only supports PyTorch v0.2.') return self._get_file_writer().add_graph(graph(model, input_to_model, verbose, **kwargs)) 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, **kwargs) elif isinstance(model[0], caffe2_pb2.NetDef): current_graph = protos_to_graph_def( model, **kwargs) else: # Handles cnn.CNNModelHelper, model_helper.ModelHelper current_graph = model_to_graph_def( model, **kwargs) 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. Each image should be square. 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)`, where `Height` should be equal to `Width`. 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) """ 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)
[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()) 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) self._get_file_writer().add_summary( pr_curve(tag, labels, predictions, num_thresholds, weights), global_step, walltime)
[docs] 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()) 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)
[docs] 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))
[docs] 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. See ``examples/demo_custom_scalars.py`` for more. 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). If you see nothing 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`. Examples:: from tensorboardX import SummaryWriter vertices_tensor = np.array([[ [1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1], ]], dtype=float) colors_tensor = np.array([[ [255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255], ]], dtype=int) faces_tensor = np.array([[ [0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2], ]], dtype=int) 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)
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 flush(self): 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()