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
import datetime

resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]

true_positive_counts=[75, 64, 21, 5, 0]
false_positive_counts=[150, 105, 18, 0, 0]
true_negative_counts=[0, 45, 132, 150, 150]
false_negative_counts=[0, 11, 54, 70, 75]
precision=[0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
recall=[1.0, 0.8533334, 0.28, 0.0666667, 0.0]


for n_iter in range(100):
    s1 = torch.rand(1) # value to keep
    s2 = torch.rand(1)
    writer.add_scalar('data/scalar_systemtime', s1[0], n_iter) # data grouping by `slash`
    writer.add_scalar('data/scalar_customtime', s1[0], n_iter, walltime=n_iter) # data grouping by `slash`
    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)
    x = torch.rand(32, 3, 64, 64) # output from network
    if n_iter%10==0:
        x = vutils.make_grid(x, normalize=True, scale_each=True)
        writer.add_image('Image', x, n_iter) # Tensor
        #writer.add_image('astronaut', skimage.data.astronaut(), n_iter) # numpy
        #writer.add_image('imread', skimage.io.imread('screenshots/audio.png'), n_iter) # numpy
        x = torch.zeros(sample_rate*2)
        for i in range(x.size(0)):
            x[i] = np.cos(freqs[n_iter//10]*np.pi*float(i)/float(sample_rate)) # sound amplitude should in [-1, 1]
        writer.add_audio('myAudio', x, n_iter)
        writer.add_text('Text', 'text logged at step:'+str(n_iter), n_iter)
        writer.add_text('markdown Text', '''a|b\n-|-\nc|d''', n_iter)
        for name, param in resnet18.named_parameters():
            if 'bn' not in name:            
                writer.add_histogram(name, param, n_iter)
        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter) #needs tensorboard 0.4RC or later
        writer.add_pr_curve_raw('prcurve with raw data', true_positive_counts,
            false_positive_counts,
            true_negative_counts,
            false_negative_counts,
            precision,
            recall, n_iter)
# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")

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))
writer.add_embedding(features, global_step=1, tag='noMetadata')
dataset = datasets.MNIST('mnist', train=True, download=True)
images_train = dataset.train_data[:100].float()
labels_train = dataset.train_labels[:100]
features_train = images_train.view(100, 784)

all_features = torch.cat((features, features_train))
all_labels = torch.cat((label, labels_train))
all_images = torch.cat((images, images_train))
dataset_label = ['test']*100 + ['train']*100
all_labels = list(zip(all_labels, dataset_label))

writer.add_embedding(all_features, metadata=all_labels, label_img=all_images.unsqueeze(1),
                     metadata_header=['digit', 'dataset'], global_step=2)

# VIDEO
vid_images = dataset.train_data[:16*48]
vid = vid_images.view(16, 1, 48, 28, 28)  # BxCxTxHxW

writer.add_video('video', vid_tensor=vid)
writer.add_video('video_1_fps', vid_tensor=vid, fps=1)

writer.close()