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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)

• Revision 36980 : La bonne méta dans la base pour éviter le message "Echec" en 2.1 Une ...

  5 avril 2010, par kent1@… — Log

  La bonne méta dans la base pour éviter le message "Echec" en 2.1
  Une icone pour les boutons de récupération d’infos
  Certains codecs (libx264) nécessitent de pouvoir diviser la taille de la video par 2 ... on fait en sorte que cela passe

• [Aug-Sept 2013] Piwik 2.0 Development Update !

  3 octobre 2013, par Fabian Becker — Development

  This Development Update is the first in a new series of posts we’ll be writing to keep you, our loyal users, informed of our efforts. We hope these updates keep you excited about Piwik’s future, and if you’re a developer, we hope they inspire and challenge you to accomplish more yourself !

  Despite this being our first update, it will probably be one of our biggest. We’ve gotten a lot done as we race towards the Piwik 2.0 release ! Just see for yourself :

  What we’ve accomplished

  Theming

  Piwik now supports theming, a feature that was requested often in the past. Because of our switch to the Twig template engine and other major code changes it is now possible to change the way Piwik looks. Additionally, developers can use the dynamic stylesheet language LESS, instead of CSS. Piwik will automatically transform the LESS code into CSS.

  Piwik 2.0 will ship with a new dark theme called PleineLune (french for Full Moon) that makes use of the new theming feature. Another theme with a left-aligned menu was created during the Piwik Meetup in Paris. Both of these themes were created by Thomas Zilliox, a very talented designer and CSS expert.

  

  PHP 5.3 Namespaces

  For Piwik 2.0 we decided to make use of namespaces, a feature introduced in PHP 5.3. The usage of namespaces makes our code more readable and allows us to better modularize the platform. This is in part why we are raising the required minimum PHP version to 5.3 for Piwik 2.0. (Remember to update your server !)

  Translations in JSON

  All translations are now stored in JSON files which makes storing translations in Piwik a lot cleaner that the giant PHP array we previously used.

  Side note : if you’d like to make Piwik available to more languages, please sign up at translations.piwik.org. We’d love to have your help !

  UI Tests

  We now use UI tests to make sure that changes to the code don’t break the UI. UI tests use PhantomJS and CutyCapt and are automatically executed on Travis CI. Whenever an integration test fails the script produces a screenshot diff that shows the difference. Learn more.

  

  AnonymizeIP supports IPv6

  The AnonymizeIP plugin now masks IPv6 addresses. The concept of the config option ‘ip_address_mask_length’ has now changed to reflect the level of masking that should be applied to the IP. With a masking level of 1 Piwik will mask the last octet of an IPv4 address and the last 80 bits of an IPv6 address.

  All Websites Dashboard usable with 20,000+ Websites

  The All Websites Dashboard is now usable even if you track many thousands of websites in your Piwik instance. We rewrote parts of the archiving process in order to make this possible. Making Piwik fast and memory efficient is a constant concern for core developers.

  Plugins can now add new Visualizations

  Piwik Plugins and Themes can now create new visualizations for your report data. They can also specify their own ViewDataTable footer icons or modify existing ones. This will allow plugin developers to create new ways for you to view your data, customize existing reports so they look great in new visualizations and provide extra analytics functionality accessible in each of your reports.

  The new TreemapVisualization plugin makes use of this feature to let you view your reports as treemaps. It serves as an example of this new functionality.

  Piwik Marketplace

  The Piwik Marketplace is a new platform developers can use to publish their plugins and themes so all Piwik users can easily access them. The marketplace is hosted at plugins.piwik.org and is currently in an early development state, but we’re already able to host plugins !

  Developers can easily publish their plugins by adding a commit hook to their Github repositories. Every time you push a new tag, the marketplace will make a new version of your plugin available. The marketplace will provide a centralized platform to search for plugins and also provide statistics on plugin usage.

  Install Plugins and Themes in one click from within Piwik

  Piwik has offered since the beginning the much-loved “one click update” feature. We are bringing the same functionnality to the Marketplace : you will be able to install Plugins and Themes in one click directly within the Piwik interface ! Similarly to WordPress or Firefox, Piwik will let you extend the functionnality of your analytics platform.

  Conclusion

  In Piwik 2.0 you will be able to install plugins and themes from the marketplace. And, if you’re so inclined, you will be able to create and host your own plugins/themes on the marketplace so everyone can use them. This is by far the accomplishment we are most excited by… the possibilities it opens up for Piwik’s future are truly unlimited. We hope you share our excitement !

  Au revoir, until next time !

  PS : our mission is to liberate web analytics ; thank you for sharing the word about Piwik 2.0 !