• 这个支持 https 吗?

  • 请问为什么我这执行 train_model.py 的时候,会如下错误?我的 tensorflow 版本是 2.4.0,python3.8 环境

    2020-12-18 17:54:06.073 | DEBUG    | stagesepx.classifier.base:__init__:297 - compress rate: None
    2020-12-18 17:54:06.073 | DEBUG    | stagesepx.classifier.base:__init__:298 - target size: (600, 800)
    2020-12-18 17:54:06.073 | DEBUG    | stagesepx.hook:__init__:13 - start initialing: CompressHook ...
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.hook:__init__:80 - compress rate: None
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.hook:__init__:81 - target size: (600, 800)
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.hook:__init__:13 - start initialing: GreyHook ...
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.base:add_hook:319 - add hook: CompressHook
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.base:add_hook:319 - add hook: GreyHook
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.keras:__init__:50 - score threshold: 0.0
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.keras:__init__:51 - data size: (200, 200)
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.keras:__init__:52 - nb train samples: 64
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.keras:__init__:53 - nb validation samples: 64
    2020-12-18 17:54:06.074 | DEBUG    | stagesepx.classifier.keras:__init__:54 - epochs: 10
    2020-12-18 17:54:06.075 | DEBUG    | stagesepx.classifier.keras:__init__:55 - batch size: 4
    2020-12-18 17:54:06.075 | DEBUG    | stagesepx.classifier.keras:train:151 - no model can be used. build a new one.
    2020-12-18 17:54:06.075 | INFO     | stagesepx.classifier.keras:create_model:99 - creating keras sequential model
    2020-12-18 17:54:06.080939: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
    2020-12-18 17:54:06.081188: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
    To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
    2020-12-18 17:54:06.186 | INFO     | stagesepx.classifier.keras:create_model:129 - model created
    Found 56 images belonging to 10 classes.
    Found 21 images belonging to 10 classes.
    2020-12-18 17:54:06.386301: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
    Epoch 1/10
    2020-12-18 17:54:07.361360: W tensorflow/core/framework/op_kernel.cc:1763] OP_REQUIRES failed at sparse_xent_op.cc:90 : Invalid argument: Received a label value of 9 which is outside the valid range of [0, 6).  Label values: 9 4 7 0
    Traceback (most recent call last):
      File "/Users/aaa/Downloads/work_with_stagesepx-master/dynamic/train_model.py", line 13, in <module>
        cl.train(data_home)
      File "/usr/local/lib/python3.8/site-packages/stagesepx/classifier/keras.py", line 178, in train
        self._model.fit(
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1100, in fit
        tmp_logs = self.train_function(iterator)
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
        result = self._call(*args, **kwds)
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 888, in _call
        return self._stateless_fn(*args, **kwds)
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2942, in __call__
        return graph_function._call_flat(
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1918, in _call_flat
        return self._build_call_outputs(self._inference_function.call(
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 555, in call
        outputs = execute.execute(
      File "/usr/local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
        tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
    tensorflow.python.framework.errors_impl.InvalidArgumentError:  Received a label value of 9 which is outside the valid range of [0, 6).  Label values: 9 4 7 0
       [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at usr/local/lib/python3.8/site-packages/stagesepx/classifier/keras.py:178) ]] [Op:__inference_train_function_984]
    
    Function call stack:
    train_function
    
    
  • 请问是否可以设置自定义的连接时间?在实际跑自动化过程中,经常需要强杀应用,再重新起起来,但是 perfdog 直接断掉了😂

  • 我的是 Mac,目前还没解决

  • 请问第三个 pause 是什么意思呢?

  • 我是 Mac,放弃 Jenkins 了,改用 crontab 定时器搞了,网上也没有搜到解决方案😖 😔

  • 试过了,也不行,估计是因为我的是 Mac,打算放弃 Jenkins 了,改用 crontab 定时器进行处理,也能满足我的需求😂

  • 大佬可以用 Jenkins 帮忙试试看?我的 demo 脚本也比较简单,然后我的 Execute Shell 配置的是/usr/local/Cellar/python/3.7.0/bin/python3 /Users/aaa/JenkinProjects/test.py😆 拜谢~

  • 试了,Jenkins 执行还是无效😂

  • 我的脚本用本地命令行跑是正常的,也能看到打开了浏览器,窗口也足够大。就是用 Jenkins 跑的时候,看不到有浏览器有打开,所以导致找不到元素就报错了。试用了一楼的代码依然是无效。我怀疑是 Jenkins 需要配置什么才能通过 chromedriver 调起 Chrome 浏览器

  • ~ $ python3 -m weditor
    listening on http://172.31.126.53:17310
    [I 200410 21:38:57 web:2162] 304 GET / (::1) 11.53ms
    [I 200410 21:38:57 web:2162] 304 GET /api/v1/version (::1) 0.71ms
    [W 200410 21:39:00 connectionpool:662] Retrying (Retry(total=2, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /version
    [W 200410 21:39:01 connectionpool:662] Retrying (Retry(total=1, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /version
    [W 200410 21:39:03 connectionpool:662] Retrying (Retry(total=0, connect=3, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /version
    [I 200410 21:39:03 init:129] uiautomator2 version: 2.4.3.dev8
    

    连接 weditor 无法 connect 设备。有人遇到过这样的问题吗?一直无法解决...

  • 应该是不可点,可以用两种方法:
    1、看看上一级或者上上级的父元素,看看是不是可以点得到
    2、获取该元素的中心坐标点,点坐标👈

  • 获取控件的左上角坐标点 (x,y),宽高 (width,height),然后计算三个 tab 的大致坐标点
    买入:((x+1/4*width), (y+1/2*height)
    卖出:((x+2/4*width), (y+1/2*height)
    仓位:((x+3/4*width), (y+1/2*height)
    最后用 adb shell input tap (x+1/4*width) (y+1/2*height) 去点

  • 图片为什么这么糊?请问有没有清晰一点的呢?😂

  • 测试工程师相关技能整理 at 2020年01月14日

    噗,尴尬了,怎么办?😂

  • 测试工程师相关技能整理 at 2020年01月13日

    Jmeter 用的是线程,gatling 用的是协程,所以 jmeter 的单机并发数比 gatling 要低,加上 gatling 代码控制比较方便,而 jmeter 相对比较适合不用代码操作的童鞋,我是不太喜欢操作界面😂

  • 看看生成的 reports 目录下是否有像我截图那样的 data、widgets、app.js、style.css 等文件?

  • 支持,我就用的 Chrome

  • 学习了👍

  • 并不能这样,因为你的 data 是 json 结构,不是纯 dict,是无法请求成功的😂


  • 如果是这种 form data,就用 data 就行,也可以看 content-type,再决定用哪个😂

  • window 用 fiddler,Mac 用 Charles

  • 这个 6、7K 并发说的是 rps(requests per second) 么?我发现我之前可能理解错了,我这到不了这么高。我的 Mac 用 locust 也试了下访问的百度,最高只有 350rps😂 也试了下 gatling,大概设置 2000 用户访问没问题。果然还是 Linux 做性能测试相对比较好。😂

  • 有空我再用 locust 试试看,比较下两者😀

  • 单机并发貌似也就 8k 左右,顶多到 1 万,再多应该撑不住了😅 jmeter 就比这个低多了吧