标签：idx tensorboard img 示例 imshow preds PyTorch TensorBoard images
原文： https://www.emperinter.info/2020/07/30/tensorboard-in-pytorch/
缘由
自己上次安装好PyTorch以及训练了一下官方的数据，今天看到了这个TensorBoard来可视化的用法，感觉不错就尝试玩了一下！自己只是尝试了一下追踪模型训练的过程，其他自己去看官网教程吧！
用法
具体详细说明请参考https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html
简单说就是：
设置TensorBoard。
写入TensorBoard。
运行
打开http://localhost:6006/
例子
我把训练的图片分类的loss用tensorboard给可视化出来了：
步骤
- 设置TensorBoard。
简单说是设置基本tensorboard运行需要的东西，我这代码中的imshow(img)和matplotlib_imshow(img, one_channel=False)都是显示图片的函数，可以统一替换，我自己测试就没改了！
# helper function to show an image# (used in the `plot_classes_preds` function below)def matplotlib_imshow(img, one_channel=False):
if one_channel:
img = img.mean(dim=0)
img = img / 2 + 0.5 # unnormalize
npimg = img.cpu().numpy()
if one_channel:
plt.imshow(npimg, cmap="Greys")
else:
plt.imshow(np.transpose(npimg, (1, 2, 0))) # 设置tensorBoard# default `log_dir` is "runs" - we'll be more specific herewriter = SummaryWriter('runs/image_classify_tensorboard')# get some random training imagesdataiter = iter(trainloader)images, labels = dataiter.next()# create grid of imagesimg_grid = torchvision.utils.make_grid(images)# show images# matplotlib_imshow(img_grid, one_channel=True)imshow(img_grid)# write to tensorboardwriter.add_image('imag_classify', img_grid)# Tracking model training with TensorBoard# helper functionsdef images_to_probs(net, images):
'''
Generates predictions and corresponding probabilities from a trained
network and a list of images
'''
output = net(images)
# convert output probabilities to predicted class
_, preds_tensor = torch.max(output, 1)
# preds = np.squeeze(preds_tensor.numpy())
preds = np.squeeze(preds_tensor.cpu().numpy())
return preds, [F.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]def plot_classes_preds(net, images, labels):
'''
Generates matplotlib Figure using a trained network, along with images
and labels from a batch, that shows the network's top prediction along
with its probability, alongside the actual label, coloring this
information based on whether the prediction was correct or not.
Uses the "images_to_probs" function.
'''
preds, probs = images_to_probs(net, images)
# plot the images in the batch, along with predicted and true labels
fig = plt.figure(figsize=(12, 48))
for idx in np.arange(4):
ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])
matplotlib_imshow(images[idx], one_channel=True)
ax.set_title("{0}, {1:.1f}%\n(label: {2})".format(
classes[preds[idx]],
probs[idx] * 100.0,
classes[labels[idx]]),
color=("green" if preds[idx]==labels[idx].item() else "red"))
return fig- 写入TensorBoard。
这个在训练的每一阶段写入tensorboard
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
# 把数据写入tensorflow
# ...log the running loss
writer.add_scalar('image training loss',
running_loss / 2000,
epoch * len(trainloader) + i)
# ...log a Matplotlib Figure showing the model's predictions on a
# random mini-batch
writer.add_figure('predictions vs. actuals',
plot_classes_preds(net, inputs, labels),
global_step=epoch * len(trainloader) + i)- 运行
tensorboard --logdir=runs
- 打开http://localhost:6006/ 即可查看
完整版代码参考
如需了解完整代码请访问：https://www.emperinter.info/2020/07/30/tensorboard-in-pytorch/
import torchimport torchvisionimport torchvision.transforms as transformsimport matplotlib.pyplot as pltimport numpy as npimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom datetime import datetimefrom torch.utils.tensorboard import SummaryWriter..........................................
标签：idx,tensorboard,img,示例,imshow,preds,PyTorch,TensorBoard,images 来源： https://blog.51cto.com/u_15193557/2760339
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