TensorBoard：TensorFlow中强大的可视化工具
一.SummaryWriter
功能：提供创建event file的高级接口
主要属性：
• log_dir：event file输出文件夹
• comment：不指定log_dir时，
文件夹后缀
• filename_suffix：event file文件名后缀
from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() for n_iter in range(100): writer.add_scalar('Loss/train', np.random.random(), n_iter) writer.add_scalar('Loss/test', np.random.random(), n_iter) writer.add_scalar('Accuracy/train', np.random.random(), n_iter) writer.add_scalar('Accuracy/test', np.random.random(), n_iter)
1.1 add_scalar()
add_scalar(tag, scalar_value, global_step=None, walltime=None, new_style=False, double_precision=False)
功能：记录标量
• tag：图像的标签名，图的唯一标识
• scalar_value：要记录的标量
• global_step：x轴
from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() x = range(100) for i in x: writer.add_scalar('y=2x', i * 2, i) writer.close()
1.2 add_scalars()
add_scalars(main_tag, tag_scalar_dict, global_step=None, walltime=None)
• main_tag：该图的标签
• tag_scalar_dict：key是变量的tag，value是变量的值
from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() r = 5 for i in range(100): writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r), 'xcosx':i*np.cos(i/r), 'tanx': np.tan(i/r)}, i) writer.close() # This call adds three values to the same scalar plot with the tag # 'run_14h' in TensorBoard's scalar section.
1.3 add_histogram()
add_histogram(tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None)
功能：统计直方图与多分位数折线图
• tag：图像的标签名，图的唯一标识
• values：要统计的参数
from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() for i in range(10): x = np.random.random(1000) writer.add_histogram('distribution centers', x + i, i) writer.close()
1.4 add_image()
add_image(tag, img_tensor, global_step=None, walltime=None, dataformats='CHW')
功能：记录图像
• tag：图像的标签名，图的唯一标识
• img_tensor：图像数据，注意尺度
• global_step：x轴 • dataformats：数据形式，CHW，HWC，HW
from torch.utils.tensorboard import SummaryWriter import numpy as np img = np.zeros((3, 100, 100)) img[0] = np.arange(0, 10000).reshape(100, 100) / 10000 img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000 img_HWC = np.zeros((100, 100, 3)) img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000 writer = SummaryWriter() writer.add_image('my_image', img, 0) # If you have non-default dimension setting, set the dataformats argument. writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC') writer.close()
1.5 add_images()
add_images(tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW')
from torch.utils.tensorboard import SummaryWriter import numpy as np img_batch = np.zeros((16, 3, 100, 100)) for i in range(16): img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i writer = SummaryWriter() writer.add_images('my_image_batch', img_batch, 0) writer.close()
1.6 torchvision.utils.make_grid
功能：制作网格图像
• tensor：图像数据, BCH*W形式
• nrow：行数（列数自动计算）
• padding：图像间距（像素单位）
• normalize：是否将像素值标准化
• range：标准化范围
• scale_each：是否单张图维度标准化
• pad_value：padding的像素值
make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)
1.7 add_graph()
add_graph(model, input_to_model=None, verbose=False, use_strict_trace=True)
功能：可视化模型计算图
• model：模型，必须是 nn.Module
• input_to_model：输出给模型的数据
• verbose：是否打印计算图结构信息
1.8 torchsummary
功能：查看模型信息，便于调试
• model：pytorch模型
• input_size：模型输入size
• batch_size：batch size
• device：“cuda” or “cpu”
summary(model, input_size, batch_size=-1, device="cuda")
1.9 add_text()
add_text(tag, text_string, global_step=None, walltime=None)
writer.add_text('lstm', 'This is an lstm', 0) writer.add_text('rnn', 'This is an rnn', 10)
