Permalink
Find file Copy path
1e8f6ad Jan 1, 2019
2 contributors

Users who have contributed to this file

@lanpa @orionr
87 lines (76 sloc) 3.68 KB
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)
# data grouping by `slash`
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)
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_with_boxes('imagebox', x, torch.Tensor([[10, 10, 40, 40], [40, 40, 60, 60]]), n_iter)
x = torch.zeros(sample_rate * 2)
for i in range(x.size(0)):
# sound amplitude should in [-1, 1]
x[i] = np.cos(freqs[n_iter // 10] * np.pi *
float(i) / float(sample_rate))
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, 48, 1, 28, 28) # BxTxCxHxW
writer.add_video('video', vid_tensor=vid)
writer.add_video('video_1_fps', vid_tensor=vid, fps=1)
writer.close()