tensorboard-pytorch

A module for visualization with tensorboard

class tensorboardX.SummaryWriter(log_dir=None, comment='')

Writes Summary directly to event files. 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__(log_dir=None, comment='')

Parameters:

• log_dir (string) – save location, default is: runs/CURRENT_DATETIME_HOSTNAME, which changes after each run. Use hierarchical folder structure to compare between runs easily. e.g. ‘runs/exp1’, ‘runs/exp2’
• comment (string) – comment that appends to the default log_dir

add_audio(tag, snd_tensor, global_step=None, sample_rate=44100)

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

Shape:

snd_tensor: \((1, L)\). The values should between [-1, 1].

add_embedding(mat, metadata=None, label_img=None, global_step=None, tag='default')

Add embedding projector data to summary.

Parameters:

• mat (torch.Tensor) – A matrix which each row is the feature vector of the data point
• metadata (list) – A list of labels, each element will be convert to string
• label_img (torch.Tensor) – Images correspond to each data point
• global_step (int) – Global step value to record
• tag (string) – Name for the embedding

Shape:

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

label_img: \((N, C, H, W)\)

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)

add_graph(model, lastVar)

Add graph data to summary.

To draw the graph, you need a model m and an input variable t that have correct size for m. Say you have runned r = m(t), then you can use writer.add_graph(m, r) to save the graph. By default, the input tensor does not require gradient, therefore it will be omitted when back tracing. To draw the input node, pass an additional parameter requires_grad=True when creating the input tensor.

Parameters:

• model (torch.nn.Module) – model to draw.
• lastVar (torch.autograd.Variable) – the root node start from.

Note

This is experimental feature. Graph drawing is based on autograd’s backward tracing. It goes along the next_functions attribute in a variable recursively, drawing each encountered nodes. In some cases, the result is strange. See https://github.com/lanpa/tensorboard-pytorch/issues/7 and https://github.com/lanpa/tensorboard-pytorch/issues/9

The implementation will be based to onnx backend as soon as onnx is stable enough.

add_histogram(tag, values, global_step=None, bins='tensorflow')

Add histogram to summary.

Parameters:

• tag (string) – Data identifier
• values (numpy.array) – 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

add_image(tag, img_tensor, global_step=None)

Add image data to summary.

Note that this requires the pillow package.

Parameters:

• tag (string) – Data identifier
• img_tensor (torch.Tensor) – Image data
• global_step (int) – Global step value to record

Shape:

img_tensor: \((3, H, W)\). Use torchvision.utils.make_grid() to prepare it is a good idea.

add_pr_curve(tag, labels, predictions, global_step=None, num_thresholds=127, weights=None)

Adds precision recall curve.

Parameters:

• tag (string) – Data identifier
• labels (torch.Tensor) – Ground thuth data. Binary label for each element.
• predictions (torch.Tensor) – 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.

add_scalar(tag, scalar_value, global_step=None)

Add scalar data to summary.

Parameters:

• tag (string) – Data identifier
• scalar_value (float) – Value to save
• global_step (int) – Global step value to record

add_scalars(main_tag, tag_scalar_dict, global_step=None)

Adds many scalar data to summary.

Parameters:

• tag (string) – Data identifier
• main_tag (float) – 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

Examples:

writer.add_scalars('run_14h',{'xsinx':i*np.sin(i/r),
                              'xcosx':i*np.cos(i/r),
                              'arctanx': numsteps*np.arctan(i/r)}, i)
#This function adds three values to the same scalar plot with the tag
#'run_14h' in TensorBoard's scalar section.

add_text(tag, text_string, global_step=None)

Add text data to summary.

Parameters:

• tag (string) – Data identifier
• text_string (string) – String to save
• global_step (int) – Global step value to record

Examples:

writer.add_text('lstm', 'This is an lstm', 0)
writer.add_text('rnn', 'This is an rnn', 10)

export_scalars_to_json(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], …], …}