Python src.graph_utils.rng_next_goal_rejection_sampling() Examples

The following are 9 code examples of src.graph_utils.rng_next_goal_rejection_sampling(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module src.graph_utils , or try the search function .
Example #1
Source File: nav_env.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #2
Source File: nav_env.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #3
Source File: nav_env.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #4
Source File: nav_env.py    From hands-detection with MIT License 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #5
Source File: nav_env.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #6
Source File: nav_env.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #7
Source File: nav_env.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #8
Source File: nav_env.py    From models with Apache License 2.0 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
Example #9
Source File: nav_env.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def _debug_save_hardness(self, seed):
    out_path = os.path.join(self.logdir, '{:s}_{:d}_hardness.png'.format(self.building_name, seed))
    batch_size = 4000
    rng = np.random.RandomState(0)
    start_node_ids, end_node_ids, dists, pred_maps, paths, hardnesss, gt_dists = \
      rng_next_goal_rejection_sampling(
          None, batch_size, self.task.gtG, rng, self.task_params.max_dist,
          self.task_params.min_dist, self.task_params.max_dist,
          self.task.sampling_distribution, self.task.target_distribution,
          self.task.nodes, self.task_params.n_ori, self.task_params.step_size,
          self.task.distribution_bins, self.task.rejection_sampling_M)
    bins = self.task.distribution_bins 
    n_bins = self.task.n_bins
    with plt.style.context('ggplot'):
      fig, axes = utils.subplot(plt, (1,2), (10,10))
      ax = axes[0]
      _ = ax.hist(hardnesss, bins=bins, weights=np.ones_like(hardnesss)/len(hardnesss))
      ax.plot(bins[:-1]+0.5/n_bins, self.task.target_distribution, 'g')
      ax.plot(bins[:-1]+0.5/n_bins, self.task.sampling_distribution, 'b')
      ax.grid('on')
      
      ax = axes[1]
      _ = ax.hist(gt_dists, bins=np.arange(self.task_params.max_dist+1))
      ax.grid('on')
      ax.set_title('Mean: {:0.2f}, Median: {:0.2f}'.format(np.mean(gt_dists),
                                                           np.median(gt_dists)))
      with fu.fopen(out_path, 'w') as f:
        fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)