How to use the visualdl.LogWriter function in visualdl

def test_modes(self):
        store = LogWriter(self.dir, sync_cycle=1)

        scalars = []

        for i in range(10):
            with store.mode("mode-%d" % i) as writer:
                scalar = writer.scalar("add/scalar0")
                scalars.append(scalar)

        for scalar in scalars[:-1]:
            for i in range(10):
                scalar.add_record(i, float(i))

def setUp(self):
        self.dir = "./tmp/storage_test"
        self.writer = LogWriter(self.dir, sync_cycle=1).as_mode("train")

def _finetune_cls_task(task, data_reader, feed_list, config=None,
                       do_eval=False):
    main_program = task.main_program()
    startup_program = task.startup_program()
    loss = task.variable("loss")
    accuracy = task.variable("accuracy")

    num_epoch = config.num_epoch
    batch_size = config.batch_size
    log_writer = LogWriter(
        os.path.join(config.checkpoint_dir, "vdllog"), sync_cycle=1)

    place, dev_count = hub.common.get_running_device_info(config)
    with fluid.program_guard(main_program, startup_program):
        exe = fluid.Executor(place=place)
        data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place)

        # select strategy
        if isinstance(config.strategy, hub.AdamWeightDecayStrategy):
            scheduled_lr = config.strategy.execute(loss, main_program,
                                                   data_reader, config)
        elif isinstance(config.strategy, hub.DefaultStrategy):
            config.strategy.execute(loss)
        #TODO: add more finetune strategy

        _do_memory_optimization(task, config)

#!/user/bin/env python
import os
import random

import numpy as np
from PIL import Image
from visualdl import ROOT, LogWriter
from visualdl.server.log import logger as log

logdir = './scratch_log'

logw = LogWriter(logdir, sync_cycle=30)

# create scalars in mode train and test.
with logw.mode('train') as logger:
    scalar0 = logger.scalar("scratch/scalar")

with logw.mode('test') as logger:
    scalar1 = logger.scalar("scratch/scalar")

# add scalar records.
last_record0 = 0.
last_record1 = 0.
for step in range(1, 100):
    last_record0 += 0.1 * (random.random() - 0.3)
    last_record1 += 0.1 * (random.random() - 0.7)
    scalar0.add_record(step, last_record0)
    scalar1.add_record(step, last_record1)

# =======================================================================

from __future__ import print_function

import numpy as np
from visualdl import LogWriter

import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.initializer import NormalInitializer
from paddle.v2.fluid.param_attr import ParamAttr

# create VisualDL logger and directory
logdir = "./tmp"
logwriter = LogWriter(logdir, sync_cycle=10)

# create 'train' run
with logwriter.mode("train") as writer:
    # create 'loss' scalar tag to keep track of loss function
    loss_scalar = writer.scalar("loss")

with logwriter.mode("train") as writer:
    acc_scalar = writer.scalar("acc")

num_samples = 4
with logwriter.mode("train") as writer:
    conv_image = writer.image("conv_image", num_samples,
                              1)  # show 4 samples for every 1 step
    input_image = writer.image("input_image", num_samples, 1)

with logwriter.mode("train") as writer:

import mobilenet_v2
import paddle as paddle
import paddle.dataset.cifar as cifar
import paddle.fluid as fluid
from visualdl import LogWriter

# 创建记录器
log_writer = LogWriter(dir='log/', sync_cycle=10)

# 创建训练和测试记录数据工具
with log_writer.mode('train') as writer:
    train_cost_writer = writer.scalar('cost')
    train_acc_writer = writer.scalar('accuracy')
    histogram = writer.histogram('histogram', num_buckets=50)

with log_writer.mode('test') as writer:
    test_cost_writer = writer.scalar('cost')
    test_acc_writer = writer.scalar('accuracy')

# 定义输入层
image = fluid.layers.data(name='image', shape=[3, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')

# 获取分类器

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(
    loss=keras.losses.categorical_crossentropy,
    optimizer=keras.optimizers.Adadelta(),
    metrics=['accuracy'])

# create VisualDL logger
logdir = "/workspace"
logger = LogWriter(logdir, sync_cycle=100)

# mark the components with 'train' label.
with logger.mode("train"):
    # create a scalar component called 'scalars/'
    scalar_keras_train_loss = logger.scalar(
        "scalars/scalar_keras_mnist_train_loss")
    image_input = logger.image("images/input", 1)
    image0 = logger.image("images/image0", 1)
    image1 = logger.image("images/image1", 1)
    histogram0 = logger.histogram("histogram/histogram0", num_buckets=50)
    histogram1 = logger.histogram("histogram/histogram1", num_buckets=50)

train_step = 0


class LossHistory(keras.callbacks.Callback):

'ship', 'truck')


# functions to show an image
def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    fig, ax = plt.subplots()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    # we can either show the image or save it locally
    # plt.show()
    fig.savefig('out' + str(np.random.randint(0, 10000)) + '.pdf')


logdir = "./workspace"
logger = LogWriter(logdir, sync_cycle=100)

# mark the components with 'train' label.
with logger.mode("train"):
    # create a scalar component called 'scalars/'
    scalar_pytorch_train_loss = logger.scalar(
        "scalars/scalar_pytorch_train_loss")
    image1 = logger.image("images/image1", 1)
    image2 = logger.image("images/image2", 1)
    histogram0 = logger.histogram("histogram/histogram0", num_buckets=100)

# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))

data_reader,
                             feed_list,
                             config=None,
                             do_eval=False):
    """
    Finetune sequence labeling task, evaluate metric is F1, precision and recall

    """
    main_program = task.main_program()
    startup_program = task.startup_program()
    loss = task.variable("loss")
    seq_len = task.variable("seq_len")

    num_epoch = config.num_epoch
    batch_size = config.batch_size
    log_writer = LogWriter(
        os.path.join(config.checkpoint_dir, "vdllog"), sync_cycle=1)

    place, dev_count = hub.common.get_running_device_info(config)
    with fluid.program_guard(main_program, startup_program):
        exe = fluid.Executor(place=place)
        data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place)

        # Select strategy
        if isinstance(config.strategy, hub.AdamWeightDecayStrategy):
            scheduled_lr = config.strategy.execute(loss, main_program,
                                                   data_reader, config)
        elif isinstance(config.strategy, hub.DefaultStrategy):
            config.strategy.execute(loss)
        #TODO: add more finetune strategy

        _do_memory_optimization(task, config)