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• WARN : Tried to pass invalid video frame, marking as broken : Your frame has data type int64, but we require uint8

  5 septembre 2019, par Tavo Diaz

  I am doing some Udemy AI courses and came across with one that "teaches" a bidimensional cheetah how to walk. I was doing the exercises on my computer, but it takes too much time. I decided to use Google Cloud to run the code and see the results some hours after. Nevertheless, when I run the code I get the following error " WARN : Tried to pass
  invalid video frame, marking as broken : Your frame has data type int64, but we require uint8 (i.e. RGB values from 0-255)".

  After the code is executed, I see into the folder and I don’t see any videos (just the meta info).

  Some more info (if it helps) :
  I have a 1 CPU (4g), SSD Ubuntu 16.04 LTS

  I have not tried anything yet to solve it because I don´t know what to try. Im looking for solutions on the web, but nothing I could try.

  This is the code

  import os 
  
  import numpy as np
  
  import gym
  
  from gym import wrappers
  
  import pybullet_envs
  
  
  
  
  
  class Hp():
  
      def __init__(self):
  
          self.nb_steps = 1000
  
          self.episode_lenght =   1000
  
          self.learning_rate = 0.02
  
          self.nb_directions = 32
  
          self.nb_best_directions = 32
  
          assert self.nb_best_directions <= self.nb_directions
  
          self.noise = 0.03
  
          self.seed = 1
  
          self.env_name = 'HalfCheetahBulletEnv-v0'
  
  
  
  
  
  class Normalizer():
  
      def __init__(self, nb_inputs):
  
          self.n = np.zeros(nb_inputs)
  
          self.mean = np.zeros(nb_inputs)
  
          self.mean_diff = np.zeros(nb_inputs)
  
          self.var = np.zeros(nb_inputs)
  
  
  
      def observe(self, x):
  
          self.n += 1.
  
          last_mean = self.mean.copy()
  
          self.mean += (x - self.mean) / self.n
  
          #abajo es el online numerator update
  
          self.mean_diff += (x - last_mean) * (x - self.mean)
  
          #abajo online computation de la varianza
  
          self.var = (self.mean_diff / self.n).clip(min = 1e-2)  
  
  
  
      def normalize(self, inputs):
  
          obs_mean = self.mean
  
          obs_std = np.sqrt(self.var)
  
          return (inputs - obs_mean) / obs_std
  
  
  
  class Policy():
  
      def __init__(self, input_size, output_size):
  
          self.theta = np.zeros((output_size, input_size))
  
  
  
      def evaluate(self, input, delta = None, direction = None):
  
          if direction is None:
  
              return self.theta.dot(input)
  
          elif direction == 'positive':
  
              return (self.theta + hp.noise * delta).dot(input)
  
          else:
  
              return (self.theta - hp.noise * delta).dot(input)
  
  
  
      def sample_deltas(self):
  
          return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
  
  
  
      def update (self, rollouts, sigma_r):
  
          step = np.zeros(self.theta.shape)
  
          for r_pos, r_neg, d in rollouts:
  
              step += (r_pos - r_neg) * d
  
          self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
  
  
  
  
  
  def explore(env, normalizer, policy, direction = None, delta = None):
  
      state = env.reset()
  
      done = False
  
      num_plays = 0.
  
      #abajo puede ser promedio de las rewards
  
      sum_rewards = 0
  
      while not done and num_plays < hp.episode_lenght:
  
          normalizer.observe(state)
  
          state = normalizer.normalize(state)
  
          action = policy.evaluate(state, delta, direction)
  
          state, reward, done, _ = env.step(action)
  
          reward = max(min(reward, 1), -1)
  
          #abajo sería poner un promedio
  
          sum_rewards += reward
  
          num_plays += 1
  
      return sum_rewards
  
  
  
  def train (env, policy, normalizer, hp):
  
      for step in range(hp.nb_steps):
  
          #iniciar las perturbaciones deltas y los rewards positivos/negativos
  
          deltas = policy.sample_deltas()
  
          positive_rewards = [0] * hp.nb_directions
  
          negative_rewards = [0] * hp.nb_directions
  
          #sacar las rewards en la dirección positiva
  
          for k in range(hp.nb_directions):
  
              positive_rewards[k] = explore(env, normalizer, policy, direction = 'positive', delta = deltas[k])
  
          #sacar las rewards en dirección negativo
  
          for k in range(hp.nb_directions):
  
              negative_rewards[k] = explore(env, normalizer, policy, direction = 'negative', delta = deltas[k])
  
          #sacar todas las rewards para sacar la desvest
  
          all_rewards = np.array(positive_rewards + negative_rewards)
  
          sigma_r = all_rewards.std()
  
          #acomodar los rollauts por el max (r_pos, r_neg) y seleccionar la mejor dirección
  
          scores = {k:max(r_pos, r_neg) for k, (r_pos, r_neg) in enumerate(zip(positive_rewards, negative_rewards))}
  
          order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions]
  
          rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
  
          #actualizar policy
  
          policy.update (rollouts, sigma_r)
  
          #poner el final reward del policy luego del update
  
          reward_evaluation = explore (env, normalizer, policy)
  
          print('Paso: ', step, 'Lejania: ', reward_evaluation)
  
  
  
  def mkdir(base, name):
  
      path = os.path.join(base, name)
  
      if not os.path.exists(path):
  
          os.makedirs(path)
  
      return path
  
  work_dir = mkdir('exp', 'brs')
  
  monitor_dir = mkdir(work_dir, 'monitor')
  
  
  
  hp = Hp()
  
  np.random.seed(hp.seed)
  
  env = gym.make(hp.env_name)
  
  env = wrappers.Monitor(env, monitor_dir, force = True)
  
  nb_inputs = env.observation_space.shape[0]
  
  nb_outputs = env.action_space.shape[0]
  
  policy = Policy(nb_inputs, nb_outputs)
  
  normalizer = Normalizer(nb_inputs)
  
  train(env, policy, normalizer, hp)

• Cannot play audio from a link using a Discord Bot

  2 mars 2020, par Guilhermeffable

  I’m trying to code a bot so me and my friends can hear the local radio on our Discord Server but I’m having this error.

  This is part of my code, it’s the play.js file that handles the playback stuff.

  module.exports = (client,message) => {
  
  
  
  const voiceChannel = message.member.voiceChannel;
  
  const idChannel = voiceChannel.id;
  
  
  
  console.log(idChannel)
  
      //vê se o user está numa sala de voz
  
  if(!voiceChannel) {
  
      return message.channel.send("Precisas de estar num voice channel para usar este comando.")
  
  }
  
  const permissions = voiceChannel.permissionsFor(message.client.user);
  
  
  
   //vê se tem permissões para entrar na sala
  
  if(!permissions.has('CONNECT') || !permissions.has('SPEAK')) {
  
      return message.channel.send("Não tenho permissões para entrar nessa sala.")
  
  }
  
  
  
  voiceChannel.join()
  
      .then(connection => {
  
          console.log("Successfully connected.");
  
          connection.playStream('http://centova.radios.pt:8401/stream.mp3/1')
  
  }).catch(e =>{
  
      console.error(e);
  
  
  
  });

  }

  And this is the error I’m getting :

  TypeError [ERR_INVALID_ARG_TYPE]: The "file" argument must be of type string. Received an instance of         
  
  Object
  
  at validateString (internal/validators.js:117:11)
  
  at normalizeSpawnArguments (child_process.js:406:3)
  
  at Object.spawn (child_process.js:542:16)
  
  at new FfmpegProcess (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\prism-media\src\transcoders\ffmpeg\FfmpegProcess.js:14:33)
  
  at FfmpegTranscoder.transcode (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\prism-media\src\transcoders\ffmpeg\Ffmpeg.js:34:18)
  
  at MediaTranscoder.transcode (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\prism-media\src\transcoders\MediaTranscoder.js:27:31)
  
  at Prism.transcode (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\prism-media\src\Prism.js:13:28)
  
  at AudioPlayer.playUnknownStream (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\discord.js\src\client\voice\player\AudioPlayer.js:97:35)
  
  at VoiceConnection.playStream (C:\Users\guilh\desktop\BOT\orbitalbot\node_modules\discord.js\src\client\voice\VoiceConnection.js:546:24)
  
  at C:\Users\guilh\desktop\BOT\orbitalbot\commands\play.js:24:24 {
  
    code: 'ERR_INVALID_ARG_TYPE'

• Evolution #4496 (En cours) : [Cohérence d’interface] Harmonisation de la gestion des doc/logos en 3.3

  25 mai 2020

  Bonjour,

  Contexte¶

  Je viens de tester la migration de 2 sites en mutualisation facile de 3.2.7 SVN à 3.3 SVN.
  Nickel, ça marche.
  J’ai pu vérifier que les logos étaient bien déplacés dans sites/nomdedomaine/IMG/logo/

  Mais, j’avais cru comprendre que les logos devenaient des documents comme les autres, ce qui permettrait de les réutiliser via la bibliothèque.

  Constat¶

  Et là, c’est le drame.
  Les logos ne sont pas listés dans la bibliothèque.

  Réciproquement, l’upload des logos est identique aux versions précédentes de SPIP.
  Donc, ne permet :

  • ni d’aller chercher un logo dans la bibliothèque
  • ni de modifier un logo (il faut comme avant le supprimer, ce qui pose problème quand on veut modifier le logo normal et qu’il y a un logo de survol).

  Bref, c’est peut-être un chantier pour 3.4, mais en l’état, cette conversion des logos en documents ne me semble rien apporter pour l’utilisateur final.

  Comportements attendus¶

  • téléversement des logos utilisant les mêmes mécanismes que ceux des documents/images
  • boite d’édition d’un logo semblable à celle des documents, permettant entre autre de changer le fichiers du logo
  • logos listés dans la médiathèque
  • logos utilisable comme images dans les articles