Pytorch 1.9.0 + Tensorboard 2.5.0可視化工具使用記錄

這裡主要記錄Tensorboard所能夠記錄的日志，主要參考官網文檔：

https://pytorch.org/docs/stable/tensorboard.html

建立寫入器，寫入圖檔和計算圖的方法

SummaryWriter：建立一個将把事件和摘要寫出到事件檔案的 SummaryWriter

import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms

# Writer will output to ./runs/ directory by default
writer = SummaryWriter()

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
trainset = datasets.MNIST('mnist_train', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
model = torchvision.models.resnet50(False)
# Have ResNet model take in grayscale rather than RGB
model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
images, labels = next(iter(trainloader))

grid = torchvision.utils.make_grid(images)
writer.add_image('images', grid, 0)
writer.add_graph(model, images)
writer.close()
           

曲線可視化

這裡，在可視化的時候，如果需要分欄顯示，可以用 / 分割名字

import numpy as np
for n_iter in range(100):
    writer.add_scalar('Loss/train', np.random.random(), n_iter)
    writer.add_scalar('Loss/test', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/test', np.random.random(), n_iter)
           

多條曲線在同一個圖中可視化

from torch.utils.tensorboard 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.
           

多個圖檔同時可視化

from torch.utils.tensorboard 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()
           

還有一些可用的可視化方式：

将matplotlib的圖像渲染成一個圖檔并可視化

視訊資料

音頻資料

文本資料

高維空間資料的低次元可視化

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)
           

可視化PR曲線

from torch.utils.tensorboard 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()
           

自定義圖表

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)
           

添加3D點雲圖

from torch.utils.tensorboard import SummaryWriter
vertices_tensor = torch.as_tensor([
    [1, 1, 1],
    [-1, -1, 1],
    [1, -1, -1],
    [-1, 1, -1],
], dtype=torch.float).unsqueeze(0)
colors_tensor = torch.as_tensor([
    [255, 0, 0],
    [0, 255, 0],
    [0, 0, 255],
    [255, 0, 255],
], dtype=torch.int).unsqueeze(0)
faces_tensor = torch.as_tensor([
    [0, 2, 3],
    [0, 3, 1],
    [0, 1, 2],
    [1, 3, 2],
], dtype=torch.int).unsqueeze(0)

writer = SummaryWriter()
writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)

writer.close()
           

添加超參數記錄

from torch.utils.tensorboard 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})