# mypy: allow-untyped-defs """Provide an API for writing protocol buffers to event files to be consumed by TensorBoard for visualization.""" import os import time from typing import List, Optional, TYPE_CHECKING, Union import torch if TYPE_CHECKING: from matplotlib.figure import Figure from tensorboard.compat import tf from tensorboard.compat.proto import event_pb2 from tensorboard.compat.proto.event_pb2 import Event, SessionLog from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig from tensorboard.summary.writer.event_file_writer import EventFileWriter from ._convert_np import make_np from ._embedding import get_embedding_info, make_mat, make_sprite, make_tsv, write_pbtxt from ._onnx_graph import load_onnx_graph from ._pytorch_graph import graph from ._utils import figure_to_image from .summary import ( audio, custom_scalars, histogram, histogram_raw, hparams, image, image_boxes, mesh, pr_curve, pr_curve_raw, scalar, tensor_proto, text, video, ) __all__ = ["FileWriter", "SummaryWriter"] class FileWriter: """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=""): """Create 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): """Return the directory where event file will be written.""" return self.event_writer.get_logdir() def add_event(self, event, step=None, walltime=None): """Add 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): """Add 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): """Add 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): """Add 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() class SummaryWriter: """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. """ def __init__( self, log_dir=None, comment="", purge_step=None, max_queue=10, flush_secs=120, filename_suffix="", ): """Create a `SummaryWriter` that will write out events and summaries to the event file. Args: log_dir (str): 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 (str): 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 (str): 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 _get_file_writer(self): """Return 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): """Return the directory where event files will be written.""" return self.log_dir def add_hparams( self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None, global_step=None, ): """Add a set of hyperparameters to be compared in TensorBoard. Args: hparam_dict (dict): Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value. The type of the value can be one of `bool`, `string`, `float`, `int`, or `None`. metric_dict (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. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold run_name (str): Name of the run, to be included as part of the logdir. If unspecified, will use current timestamp. global_step (int): Global step value to record 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_hparams") 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, hparam_domain_discrete) if not run_name: run_name = str(time.time()) logdir = os.path.join(self._get_file_writer().get_logdir(), run_name) with SummaryWriter(log_dir=logdir) as w_hp: w_hp.file_writer.add_summary(exp, global_step) w_hp.file_writer.add_summary(ssi, global_step) w_hp.file_writer.add_summary(sei, global_step) for k, v in metric_dict.items(): w_hp.add_scalar(k, v, global_step) def add_scalar( self, tag, scalar_value, global_step=None, walltime=None, new_style=False, double_precision=False, ): """Add scalar data to summary. Args: tag (str): 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 new_style (boolean): Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_scalar") summary = scalar( tag, scalar_value, new_style=new_style, double_precision=double_precision ) self._get_file_writer().add_summary(summary, global_step, walltime) def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None): """Add many scalar data to summary. Args: main_tag (str): 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_scalars") 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 assert self.all_writers is not None 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 fw.add_summary(scalar(main_tag, scalar_value), global_step, walltime) def add_tensor( self, tag, tensor, global_step=None, walltime=None, ): """Add tensor data to summary. Args: tag (str): Data identifier tensor (torch.Tensor): tensor to save global_step (int): Global step value to record Examples:: from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() x = torch.tensor([1,2,3]) writer.add_scalar('x', x) writer.close() Expected result: Summary::tensor::float_val [1,2,3] ::tensor::shape [3] ::tag 'x' """ torch._C._log_api_usage_once("tensorboard.logging.add_tensor") summary = tensor_proto(tag, tensor) self._get_file_writer().add_summary(summary, global_step, walltime) def add_histogram( self, tag, values, global_step=None, bins="tensorflow", walltime=None, max_bins=None, ): """Add histogram to summary. Args: tag (str): Data identifier values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram global_step (int): Global step value to record bins (str): 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_histogram") 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 ) def add_histogram_raw( self, tag, min, max, num, sum, sum_squares, bucket_limits, bucket_counts, global_step=None, walltime=None, ): """Add histogram with raw data. Args: tag (str): 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.ndarray): Upper value per bucket. The number of elements of it should be the same as `bucket_counts`. bucket_counts (torch.Tensor, numpy.ndarray): 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_histogram_raw") 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, ) 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 (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, 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 (str): Image data format specification of the form CHW, HWC, HW, WH, etc. 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 suitable 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_image") self._get_file_writer().add_summary( image(tag, img_tensor, dataformats=dataformats), global_step, walltime ) 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 (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, 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 (str): 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 % """ torch._C._log_api_usage_once("tensorboard.logging.add_images") 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", labels=None, ): """Add image and draw bounding boxes on the image. Args: tag (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data box_tensor (torch.Tensor, numpy.ndarray, 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()) seconds after epoch of event rescale (float): Optional scale override dataformats (str): Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. labels (list of string): The label to be shown for 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.ndarray, or string/blobname): NX4, where N is the number of boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax). """ torch._C._log_api_usage_once("tensorboard.logging.add_image_with_boxes") if labels is not None: if isinstance(labels, str): labels = [labels] if len(labels) != box_tensor.shape[0]: labels = None self._get_file_writer().add_summary( image_boxes( tag, img_tensor, box_tensor, rescale=rescale, dataformats=dataformats, labels=labels, ), global_step, walltime, ) def add_figure( self, tag: str, figure: Union["Figure", List["Figure"]], global_step: Optional[int] = None, close: bool = True, walltime: Optional[float] = None, ) -> None: """Render matplotlib figure into an image and add it to summary. Note that this requires the ``matplotlib`` package. Args: tag: Data identifier figure: Figure or a list of figures global_step: Global step value to record close: Flag to automatically close the figure walltime: Optional override default walltime (time.time()) seconds after epoch of event """ torch._C._log_api_usage_once("tensorboard.logging.add_figure") 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", ) 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 (str): 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`. """ torch._C._log_api_usage_once("tensorboard.logging.add_video") self._get_file_writer().add_summary( video(tag, vid_tensor, fps), global_step, walltime ) def add_audio( self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None ): """Add audio data to summary. Args: tag (str): 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]. """ torch._C._log_api_usage_once("tensorboard.logging.add_audio") self._get_file_writer().add_summary( audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime ) def add_text(self, tag, text_string, global_step=None, walltime=None): """Add text data to summary. Args: tag (str): Data identifier text_string (str): 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) """ torch._C._log_api_usage_once("tensorboard.logging.add_text") self._get_file_writer().add_summary( text(tag, text_string), global_step, walltime ) def add_onnx_graph(self, prototxt): torch._C._log_api_usage_once("tensorboard.logging.add_onnx_graph") self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt)) def add_graph( self, model, input_to_model=None, verbose=False, use_strict_trace=True ): """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. 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) """ torch._C._log_api_usage_once("tensorboard.logging.add_graph") # A valid PyTorch model should have a 'forward' method self._get_file_writer().add_graph( graph(model, input_to_model, verbose, 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("%", f"%{ord('%'):02x}") retval = retval.replace("/", f"%{ord('/'):02x}") retval = retval.replace("\\", "%%%02x" % (ord("\\"))) # noqa: UP031 return retval 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.ndarray): 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 (torch.Tensor): Images correspond to each data point global_step (int): Global step value to record tag (str): Name for the embedding metadata_header (list): A list of headers for multi-column metadata. If given, each metadata must be a list with values corresponding to headers. 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) .. note:: Categorical (i.e. non-numeric) metadata cannot have more than 50 unique values if they are to be used for coloring in the embedding projector. """ torch._C._log_api_usage_once("tensorboard.logging.add_embedding") 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 = f"{str(global_step).zfill(5)}/{self._encode(tag)}" save_path = os.path.join(self._get_file_writer().get_logdir(), subdir) fs = tf.io.gfile if fs.exists(save_path): if fs.isdir(save_path): print( "warning: Embedding dir exists, did you set global_step for add_embedding()?" ) else: raise NotADirectoryError( f"Path: `{save_path}` exists, but is a file. Cannot proceed." ) else: fs.makedirs(save_path) 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) # Filesystem doesn't necessarily have append semantics, so we store an # internal buffer to append to and re-write whole file after each # embedding is added if not hasattr(self, "_projector_config"): self._projector_config = ProjectorConfig() embedding_info = get_embedding_info( metadata, label_img, subdir, global_step, tag ) self._projector_config.embeddings.extend([embedding_info]) from google.protobuf import text_format config_pbtxt = text_format.MessageToString(self._projector_config) write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt) def add_pr_curve( self, tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None, ): """Add 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 (str): Data identifier labels (torch.Tensor, numpy.ndarray, or string/blobname): Ground truth data. Binary label for each element. predictions (torch.Tensor, numpy.ndarray, or string/blobname): The probability that an element be classified as true. Value should be 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() """ torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve") 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, ): """Add precision recall curve with raw data. Args: tag (str): Data identifier true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts precision (torch.Tensor, numpy.ndarray, or string/blobname): precision recall (torch.Tensor, numpy.ndarray, 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 """ torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve_raw") 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']) """ torch._C._log_api_usage_once( "tensorboard.logging.add_custom_scalars_multilinechart" ) 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']) """ torch._C._log_api_usage_once( "tensorboard.logging.add_custom_scalars_marginchart" ) assert len(tags) == 3 layout = {category: {title: ["Margin", tags]}} self._get_file_writer().add_summary(custom_scalars(layout)) def add_custom_scalars(self, layout): """Create special chart by collecting charts tags in 'scalars'. NOTE: 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) """ torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars") self._get_file_writer().add_summary(custom_scalars(layout)) 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. Args: tag (str): 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() """ torch._C._log_api_usage_once("tensorboard.logging.add_mesh") self._get_file_writer().add_summary( mesh(tag, vertices, colors, faces, config_dict), global_step, 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. """ if self.all_writers is None: return for writer in self.all_writers.values(): writer.flush() 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()