tensorboardX 다운로드 - tensorboardX 소스 코드 다운로드

tensorboardX

파이썬

v2.5

다운로드

Tensorboardx

간단한 기능 호출로 텐서 보드 이벤트를 작성하십시오.

현재 릴리스 (v2.6.2.2)는 Anaconda3에서 테스트되며 Pytorch 1.11.0 / Torchvision 0.12 / Tensorboard 2.9.0과 함께 테스트됩니다.

scalar , image , figure , histogram , audio , text , graph , onnx_graph , embedding , pr_curve , mesh , hyper-parameters 및 video 요약을 지원합니다.
FAQ

설치하다

pip install tensorboardX

또는 소스에서 빌드 :

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

선택적으로 crc32c 설치하여 속도를 높일 수 있습니다.

pip install crc32c

Tensorboardx 2.1에서 시작하려면 add_audio() 함수 (200x Speedup) 용 soundfile 설치해야합니다.

pip install soundfile

예

https://github.com/lanpa/tensorboardx/tree/master/examples에서 파일을 복제하십시오
데모 스크립트를 실행합니다 : 예 : python examples/demo.py
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 ()