Tensorflow：如何在Tensorboard中显示自定义图像（例如Matplotlib图）

Question

Tensorboard自述文件的Image Dashboard部分说：

  

由于图像仪表板支持任意png，您可以使用它将自定义可视化（例如matplotlib散点图）嵌入到TensorBoard中。

我看到如何将pyplot图像写入文件，作为张量读回，然后与tf.image_summary（）一起使用将其写入TensorBoard，但是自述文件中的这一陈述表明存在更直接的方式。在那儿？如果是这样，是否有任何进一步的文档和/或示例如何有效地执行此操作？

Answer 1

如果将图像放在内存缓冲区中，这很容易。下面，我展示了一个示例，其中将一个pyplot保存到缓冲区，然后转换为TF图像表示，然后将其发送到图像摘要。

import io
import matplotlib.pyplot as plt
import tensorflow as tf


def gen_plot():
    """Create a pyplot plot and save to buffer."""
    plt.figure()
    plt.plot([1, 2])
    plt.title("test")
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    return buf


# Prepare the plot
plot_buf = gen_plot()

# Convert PNG buffer to TF image
image = tf.image.decode_png(plot_buf.getvalue(), channels=4)

# Add the batch dimension
image = tf.expand_dims(image, 0)

# Add image summary
summary_op = tf.summary.image("plot", image)

# Session
with tf.Session() as sess:
    # Run
    summary = sess.run(summary_op)
    # Write summary
    writer = tf.train.SummaryWriter('./logs')
    writer.add_summary(summary)
    writer.close()

这提供了以下TensorBoard可视化：

Answer 2

我的回答有点晚了。使用tf-matplotlib，简单的散点图可归结为：

content := :F||counter;

执行时，会在Tensorboard中生成以下图表

请注意， tf-matplotlib 负责评估任何张量输入，避免import tensorflow as tf import numpy as np import tfmpl @tfmpl.figure_tensor def draw_scatter(scaled, colors): '''Draw scatter plots. One for each color.''' figs = tfmpl.create_figures(len(colors), figsize=(4,4)) for idx, f in enumerate(figs): ax = f.add_subplot(111) ax.axis('off') ax.scatter(scaled[:, 0], scaled[:, 1], c=colors[idx]) f.tight_layout() return figs with tf.Session(graph=tf.Graph()) as sess: # A point cloud that can be scaled by the user points = tf.constant( np.random.normal(loc=0.0, scale=1.0, size=(100, 2)).astype(np.float32) ) scale = tf.placeholder(tf.float32) scaled = points*scale # Note, `scaled` above is a tensor. Its being passed `draw_scatter` below. # However, when `draw_scatter` is invoked, the tensor will be evaluated and a # numpy array representing its content is provided. image_tensor = draw_scatter(scaled, ['r', 'g']) image_summary = tf.summary.image('scatter', image_tensor) all_summaries = tf.summary.merge_all() writer = tf.summary.FileWriter('log', sess.graph) summary = sess.run(all_summaries, feed_dict={scale: 2.}) writer.add_summary(summary, global_step=0) 线程问题并支持运行时关键绘图的blitting。

Answer 3

下一个脚本不使用中间RGB / PNG编码。它还解决了执行期间额外操作构造的问题，重复使用单个摘要。

在执行期间，预计数字的大小将保持不变

有效的解决方案：

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np

def get_figure():
  fig = plt.figure(num=0, figsize=(6, 4), dpi=300)
  fig.clf()
  return fig


def fig2rgb_array(fig, expand=True):
  fig.canvas.draw()
  buf = fig.canvas.tostring_rgb()
  ncols, nrows = fig.canvas.get_width_height()
  shape = (nrows, ncols, 3) if not expand else (1, nrows, ncols, 3)
  return np.fromstring(buf, dtype=np.uint8).reshape(shape)


def figure_to_summary(fig):
  image = fig2rgb_array(fig)
  summary_writer.add_summary(
    vis_summary.eval(feed_dict={vis_placeholder: image}))


if __name__ == '__main__':
      # construct graph
      x = tf.Variable(initial_value=tf.random_uniform((2, 10)))
      inc = x.assign(x + 1)

      # construct summary
      fig = get_figure()
      vis_placeholder = tf.placeholder(tf.uint8, fig2rgb_array(fig).shape)
      vis_summary = tf.summary.image('custom', vis_placeholder)

      with tf.Session() as sess:
        tf.global_variables_initializer().run()
        summary_writer = tf.summary.FileWriter('./tmp', sess.graph)

        for i in range(100):
          # execute step
          _, values = sess.run([inc, x])
          # draw on the plot
          fig = get_figure()
          plt.subplot('111').scatter(values[0], values[1])
          # save the summary
          figure_to_summary(fig)

Answer 4

这打算完成Andrzej Pronobis＆＃39;回答。紧接着他的好帖子，我设置了这个最小的工作示例：

    plt.figure()
    plt.plot([1, 2])
    plt.title("test")
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    image = tf.expand_dims(image, 0)
    summary = tf.summary.image("test", image, max_outputs=1)
    writer.add_summary(summary, step)

编写者是tf.summary.FileWriter的实例。 这给了我以下错误： 属性错误：＆＃39; Tensor＆＃39;对象没有属性＆＃39;值＆＃39; this github post有解决方案：在添加到编写器之前，必须评估摘要（转换为字符串）。所以我的工作代码保持如下（只需在最后一行添加.eval（）调用）：

    plt.figure()
    plt.plot([1, 2])
    plt.title("test")
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    image = tf.expand_dims(image, 0)
    summary = tf.summary.image("test", image, max_outputs=1)
    writer.add_summary(summary.eval(), step)

这可能足够短，可以评论他的答案，但这些很容易被忽视（我可能会做其他不同的事情），所以在这里，希望它有所帮助！

干杯，
安德烈斯

Answer 5

Matplotlib 图可以直接使用 add_figure 函数添加到张量板：

import numpy as np, matplotlib.pyplot as plt
from torch.utils.tensorboard import SummaryWriter

# Example plot
x = np.linspace(0,10)
plt.plot(x, np.sin(x))

# Adding plot to tensorboard
with SummaryWriter('runs/SO_test') as writer:
  writer.add_figure('Fig1', plt.gcf())

# Loading tensorboard
%tensorboard --logdir=runs

Answer 6

最后有一些official documentation关于“记录任意图像数据”的示例，并以matplotlib创建的图像为例。

Answer 7

添加在PyTorch中有效的选项。我们将使用MatPlotLib图形，将其绘制到画布上，然后转换为numpy：

# make the canvas
figure = plt.figure(figsize=(10,10))
canvas = matplotlib.backends.backend_agg.FigureCanvas(figure)

# insert plotting code here; you can use imshow or subplot, etc.
for i in range(25):
    plt.subplot(5, 5, i + 1, title=class_names[train_labels[i]])
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)

# convert canvas to figure
canvas.draw()
image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape((1000,1000,3)).transpose((2, 0, 1))

结果可以直接添加到Tensorboard：

tensorboard.add_image('name', image, global_step)

Answer 8

Pytorch Lightning 中的解决方案

这不是完整的类，而是您必须添加的内容才能使其在框架中工作。

import pytorch_lightning as pl
import seaborn as sn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

def __init__(self, config, trained_vae, latent_dim):
    self.val_confusion = pl.metrics.classification.ConfusionMatrix(num_classes=self._config.n_clusters)
    self.logger: Optional[TensorBoardLogger] = None

def forward(self, x):
    ...
    return log_probs

def validation_step(self, batch, batch_index):
    if self._config.dataset == "mnist":
        orig_batch, label_batch = batch
        orig_batch = orig_batch.reshape(-1, 28 * 28)

    log_probs = self.forward(orig_batch)
    loss = self._criterion(log_probs, label_batch)

    self.val_confusion.update(log_probs, label_batch)
    return {"loss": loss, "labels": label_batch}

def validation_step_end(self, outputs):
    return outputs

def validation_epoch_end(self, outs):
    tb = self.logger.experiment

    # confusion matrix
    conf_mat = self.val_confusion.compute().detach().cpu().numpy().astype(np.int)
    df_cm = pd.DataFrame(
        conf_mat,
        index=np.arange(self._config.n_clusters),
        columns=np.arange(self._config.n_clusters))
    plt.figure()
    sn.set(font_scale=1.2)
    sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}, fmt='d')
    buf = io.BytesIO()
    
    plt.savefig(buf, format='jpeg')
    buf.seek(0)
    im = Image.open(buf)
    im = torchvision.transforms.ToTensor()(im)
    tb.add_image("val_confusion_matrix", im, global_step=self.current_epoch)

和电话

logger = TensorBoardLogger(save_dir=tb_logs_folder, name='Classifier')
trainer = Trainer(
    default_root_dir=classifier_checkpoints_path,
    logger=logger,
)