tensorboardX

A module for visualization with tensorboard

class tensorboardX.SummaryWriter(logdir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='', write_to_disk=True, log_dir=None, **kwargs)[source]

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.

__init__(logdir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='', write_to_disk=True, log_dir=None, **kwargs)[source]

Creates a SummaryWriter that will write out events and summaries to the event file.

Parameters:
  • 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 \(T+X\) and restarts at step \(T\), any events whose global_step larger or equal to \(T\) 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/
add_audio(tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None)[source]

Add audio data to summary.

Parameters:
  • 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: \((1, L)\). The values should lie between [-1, 1].
add_custom_scalars(layout)[source]

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.

Parameters: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)
add_custom_scalars_marginchart(tags, category='default', title='untitled')[source]

Shorthand for creating marginchart. Similar to add_custom_scalars(), but the only necessary argument is tags, which should have exactly 3 elements.

Parameters:tags (list) – list of tags that have been used in add_scalar()

Examples:

writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
add_custom_scalars_multilinechart(tags, category='default', title='untitled')[source]

Shorthand for creating multilinechart. Similar to add_custom_scalars(), but the only necessary argument is tags.

Parameters:tags (list) – list of tags that have been used in add_scalar()

Examples:

writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
add_embedding(mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None)[source]

Add embedding projector data to summary.

Parameters:
  • 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 or numpy.array) – Images correspond to each data point. Each image should be square. The amount and size of the images are limited by the Tensorboard frontend, see limits below.
  • global_step (int) – Global step value to record
  • tag (string) – Name for the embedding
Shape:

mat: \((N, D)\), where N is number of data and D is feature dimension

label_img: \((N, C, H, W)\), where Height should be equal to Width. Also, \(\sqrt{N}*W\) must be less than or equal to 8192, so that the generated sprite image can be loaded by the Tensorboard frontend (see tensorboardX#516 for more).

Examples:

import keyword
import torch
meta = []
while len(meta)<100:
    meta = meta+keyword.kwlist # get some strings
meta = meta[:100]

for i, v in enumerate(meta):
    meta[i] = v+str(i)

label_img = torch.rand(100, 3, 32, 32)
for i in range(100):
    label_img[i]*=i/100.0

writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
writer.add_embedding(torch.randn(100, 5), label_img=label_img)
writer.add_embedding(torch.randn(100, 5), metadata=meta)
add_figure(tag, figure, global_step=None, close=True, walltime=None)[source]

Render matplotlib figure into an image and add it to summary.

Note that this requires the matplotlib package.

Parameters:
  • tag (string) – Data identifier
  • figure (matplotlib.pyplot.figure) – 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
add_graph(model, input_to_model=None, verbose=False, profile_with_cuda=False, **kwargs)[source]

Add graph data to summary.

Parameters:
  • 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.
add_histogram(tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None)[source]

Add histogram to summary.

Parameters:

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:

_images/add_histogram.png
add_histogram_raw(tag, min, max, num, sum, sum_squares, bucket_limits, bucket_counts, global_step=None, walltime=None)[source]

Adds histogram with raw data.

Parameters:
  • 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)
add_hparams(hparam_dict=None, metric_dict=None)[source]

Add a set of hyperparameters to be compared in tensorboard.

Parameters:
  • 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:

_images/add_hparam.png
add_image(tag, img_tensor, global_step=None, walltime=None, dataformats='CHW')[source]

Add image data to summary.

Note that this requires the pillow package.

Parameters:
  • 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 \((3, H, W)\). 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 \((1, H, W)\), \((H, W)\), \((H, W, 3)\) 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:

_images/add_image.png
add_image_with_boxes(tag, img_tensor, box_tensor, global_step=None, walltime=None, dataformats='CHW', labels=None, **kwargs)[source]

Add image and draw bounding boxes on the image.

Parameters:
  • 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 \((3, H, W)\). It can be specified with dataformats argument. e.g. CHW or HWC

box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4, where N is the number of boxes and each 4 elememts in a row represents (xmin, ymin, xmax, ymax).

add_images(tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW')[source]

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.

Parameters:
  • 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 \((N, 3, H, W)\). 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:

_images/add_images.png
add_mesh(tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None)[source]

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.

Parameters:
  • 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: \((B, N, 3)\). (batch, number_of_vertices, channels). If you see nothing on
tensorboard, try normalizing the values to [-1, 1].

colors: \((B, N, 3)\). The values should lie in [0, 255].

faces: \((B, N, 3)\). 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()
add_onnx_graph(onnx_model_file)[source]

Add onnx graph to TensorBoard.

Parameters:onnx_model_file (string) – The path to the onnx model.
add_openvino_graph(xmlname)[source]

Add openvino graph to TensorBoard.

Parameters:xmlname (string) – The path to the openvino model. (the xml file)
add_pr_curve(tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None)[source]

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.

Parameters:
  • 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()
add_pr_curve_raw(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)[source]

Adds precision recall curve with raw data.

Parameters:
add_scalar(tag, scalar_value, global_step=None, walltime=None)[source]

Add scalar data to summary.

Parameters:
  • 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:

_images/add_scalar.png
add_scalars(main_tag, tag_scalar_dict, global_step=None, walltime=None)[source]

Adds many scalar data to summary.

Note that this function also keeps logged scalars in memory. In extreme case it explodes your RAM.

Parameters:
  • 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:

_images/add_scalars.png
add_text(tag, text_string, global_step=None, walltime=None)[source]

Add text data to summary.

Parameters:
  • 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)
add_video(tag, vid_tensor, global_step=None, fps=4, walltime=None)[source]

Add video data to summary.

Note that this requires the moviepy package.

Parameters:
  • 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: \((N, T, C, H, W)\). The values should lie in [0, 255] for type
uint8 or [0, 1] for type float.
close()[source]

Close the current SummaryWriter. This call flushes the unfinished write operation. Use context manager (with statement) whenever it’s possible.

export_scalars_to_json(path)[source]

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.

flush()[source]

Force the data in memory to be flushed to disk. Use this call if tensorboard does not update reqularly. Another way is to set the flush_secs when creating the SummaryWriter.

class tensorboardX.TorchVis(*args, **init_kwargs)[source]
__init__(*args, **init_kwargs)[source]
Parameters: