Pytorch可视化的几种实现方法

目录

一，利用 tensorboardX 可视化网络结构
二，利用 vistom 可视化
三，利用pytorchviz可视化网络结构

一，利用 tensorboardX 可视化网络结构

参考 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

安装

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()

运行: python demo.py 会出现runs文件夹，然后在cd到工程目录运行tensorboard --logdir runs

结果：

二，利用 vistom 可视化

参考：https://github.com/facebookresearch/visdom

安装和启动
安装： pip install visdom
启动：python -m visdom.server示例

    from visdom import Visdom
    #单张
    viz.image(
        np.random.rand(3, 512, 256),
        opts=dict(title=\\\\\\\\\\\'Random!\\\\\', caption=\\\\\\\\\\\'How random.\\\\\'),
    )
    #多张
    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可视化网络结构

参考：https://github.com/szagoruyko/pytorchviz