目錄
- 一，利用 tensorboardX 可視化網絡結構
- 二，利用 vistom 可視化
- 三，利用pytorchviz可視化網絡結構
一，利用 tensorboardX 可視化網絡結構
參考 https://github.com/lanpa/tensorboardX
支持scalar, image, figure, histogram, audio, text, graph, onnx_graph, embedding, pr_curve and video summaries.
例子要求tensorboardX>=1.2 and pytorch>=0.4
安裝
pip install tensorboardX 或 pip install git+https://github.com/lanpa/tensorboardX
例子
# demo.py
import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter
resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
dummy_s1 = torch.rand(1)
dummy_s2 = torch.rand(1)
# data grouping by `slash`
writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
writer.add_scalar('data/scalar2', dummy_s2[0], n_iter)
writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter),
'xcosx': n_iter * np.cos(n_iter),
'arctanx': np.arctan(n_iter)}, n_iter)
dummy_img = torch.rand(32, 3, 64, 64) # output from network
if n_iter % 10 == 0:
x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
writer.add_image('Image', x, n_iter)
dummy_audio = torch.zeros(sample_rate * 2)
for i in range(x.size(0)):
# amplitude of sound should in [-1, 1]
dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
writer.add_audio('myAudio', dummy_audio, n_iter, sample_rate=sample_rate)
writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
for name, param in resnet18.named_parameters():
writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
# needs tensorboard 0.4RC or later
writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)
dataset = datasets.MNIST('mnist', train=False, download=True)
images = dataset.test_data[:100].float()
label = dataset.test_labels[:100]
features = images.view(100, 784)
writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
運行: python demo.py 會出現runs文件夾，然后在cd到工程目錄運行tensorboard --logdir runs
結果：
二，利用 vistom 可視化
參考：https://github.com/facebookresearch/visdom
安裝和啟動
安裝： pip install visdom
啟動：python -m visdom.server示例
from visdom import Visdom
#單張
viz.image(
np.random.rand(3, 512, 256),
opts=dict(title=\\\\\'Random!\\', caption=\\\\\'How random.\\'),
)
#多張
viz.images(
np.random.randn(20, 3, 64, 64),
opts=dict(title=\\\\\'Random images\\', caption=\\\\\'How random.\\')
)
from visdom import Visdom
image = np.zeros((100,100))
vis = Visdom()
vis.text("hello world!!!")
vis.image(image)
vis.line(Y = np.column_stack((np.random.randn(10),np.random.randn(10))),
X = np.column_stack((np.arange(10),np.arange(10))),
opts = dict(title = "line", legend=["Test","Test1"]))
三，利用pytorchviz可視化網絡結構
參考：https://github.com/szagoruyko/pytorchviz
到此這篇關于Pytorch可視化的幾種實現方法的文章就介紹到這了,更多相關Pytorch可視化內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
您可能感興趣的文章:- pytorch使用tensorboardX進行loss可視化實例
- 使用pytorch實現可視化中間層的結果
- Pytorch十九種損失函數的使用詳解
- pytorch教程網絡和損失函數的可視化代碼示例
