Tensorboard 自述文件的图像仪表板部分说：
由于图像仪表板支持任意 png，因此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() 一起使用以将其写入 TensorBoard，但自述文件中的这一声明表明有更直接的方法。在那儿？如果是这样，是否有任何进一步的文档和/或如何有效地做到这一点的例子？
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8 回答
45
如果您将图像放在内存缓冲区中，这很容易做到。下面，我展示了一个示例，其中将 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()
这给出了以下 TensorBoard 可视化：
于 2016-07-30T17:51:10.483 回答
11
我的回答有点晚了。使用tf-matplotlib一个简单的散点图可以归结为：
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)
执行时，这会在 Tensorboard 内产生以下绘图
请注意，tf-matplotlib负责评估任何张量输入，避免pyplot线程问题并支持运行时关键绘图的 blitting。
于 2018-03-27T10:23:02.750 回答
9
下一个脚本不使用中间 RGB/PNG 编码。它还修复了执行期间额外操作构造的问题，重复使用单个摘要。
预计执行期间图形的大小保持不变
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)
于 2017-03-15T16:30:16.563 回答
3
Matplotlib 绘图可以直接使用以下add_figure函数添加到张量板上：
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
于 2021-03-08T21:14:50.127 回答
2
最后，有一些关于“记录任意图像数据”的官方文档以及 matplotlib 创建图像的示例。
他们写：
在下面的代码中，您将使用 matplotlib 的 subplot() 函数将前 25 个图像记录为一个漂亮的网格。然后，您将在 TensorBoard 中查看网格：
# Clear out prior logging data.
!rm -rf logs/plots
logdir = "logs/plots/" + datetime.now().strftime("%Y%m%d-%H%M%S")
file_writer = tf.summary.create_file_writer(logdir)
def plot_to_image(figure):
"""Converts the matplotlib plot specified by 'figure' to a PNG image and
returns it. The supplied figure is closed and inaccessible after this call."""
# Save the plot to a PNG in memory.
buf = io.BytesIO()
plt.savefig(buf, format='png')
# Closing the figure prevents it from being displayed directly inside
# the notebook.
plt.close(figure)
buf.seek(0)
# Convert PNG buffer to TF image
image = tf.image.decode_png(buf.getvalue(), channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
return image
def image_grid():
"""Return a 5x5 grid of the MNIST images as a matplotlib figure."""
# Create a figure to contain the plot.
figure = plt.figure(figsize=(10,10))
for i in range(25):
# Start next subplot.
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)
return figure
# Prepare the plot
figure = image_grid()
# Convert to image and log
with file_writer.as_default():
tf.summary.image("Training data", plot_to_image(figure), step=0)
%tensorboard --logdir logs/plots
于 2019-12-07T03:41:00.017 回答
1
这旨在完成 Andrzej Pronobis 的回答。密切关注他的好帖子，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)
其中 writer 是tf.summary.FileWriter的一个实例。这给了我以下错误： AttributeError: 'Tensor' object has no attribute 'value' 为此github 帖子有解决方案：必须在添加到 writer 之前评估摘要（转换为字符串）。所以我的工作代码如下（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，可以对他的回答发表评论，但这些很容易被忽视（我可能也在做其他不同的事情），所以就在这里，希望对您有所帮助！
于 2017-08-31T20:10:43.607 回答
0
已弃用：对于 PyTorch，请使用内置的SummaryWriter.add_figure（请参阅其他答案）！
PyTorch 解决方案：
- 使用 MatPlotLib 图
- 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)
于 2020-12-13T06:51:26.943 回答
-1
Pytorch Lightning 中的解决方案
这不是完整的类，而是您必须添加以使其在框架中工作的内容。
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,
)
于 2020-12-31T12:09:51.763 回答