Unduh tensorboardX - Unduh Kode Sumber tensorboardX

tensorboardX

ular piton

v2.5

Unduh

Tensorboardx

Tulis acara Tensorboard dengan panggilan fungsi sederhana.

Rilis saat ini (v2.6.2.2) diuji pada Anaconda3, dengan Pytorch 1.11.0 / TorchVision 0.12 / Tensorboard 2.9.0.

Dukung scalar , image , figure , histogram , audio , text , graph , onnx_graph , embedding , pr_curve , mesh , hyper-parameters dan ringkasan video .
FAQ

Memasang

pip install tensorboardX

atau membangun dari sumber:

pip install 'git+https://github.com/lanpa/tensorboardX'

Anda secara opsional dapat menginstal crc32c untuk mempercepat.

pip install crc32c

Mulai dari Tensorboardx 2.1, Anda perlu menginstal soundfile untuk fungsi add_audio() (200x Speedup).

pip install soundfile

Contoh

Klon file di https://github.com/lanpa/tensorboardx/tree/master/examples
Jalankan skrip demo: misalnya python examples/demo.py
Mulai Tensorboard dengan tensorboard --logdir runs

 # 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 ()

Tangkapan layar

Menggunakan Tensorboardx dengan Comet

Tensorboardx sekarang mendukung logging langsung ke Comet. Comet adalah solusi berbasis cloud gratis yang memungkinkan Anda untuk secara otomatis melacak, membandingkan, dan menjelaskan eksperimen Anda. Ini menambahkan banyak fungsi di atas papan tensor seperti manajemen dataset, eksperimen yang berbeda, melihat kode yang menghasilkan hasil dan banyak lagi.

Ini bekerja di luar kotak dan hanya membutuhkan baris kode tambahan. Lihat contoh kode lengkap dalam buku catatan Colab ini