Fix Python 3 Syntax Errors (en masse)

research/cognitive_mapping_and_planning/scripts/script_env_vis.py

@@ -30,8 +30,8 @@
 import datasets.nav_env_config as nec
 import datasets.nav_env as nav_env
 import cv2
-from datasets import factory 
-import render.swiftshader_renderer as renderer 
+from datasets import factory
+import render.swiftshader_renderer as renderer
 
 SwiftshaderRenderer = renderer.SwiftshaderRenderer
 VisualNavigationEnv = nav_env.VisualNavigationEnv
@@ -53,10 +53,10 @@ def get_args():
   navtask.camera_param.width = sz
   navtask.task_params.img_height = sz
   navtask.task_params.img_width = sz
-  
+
   # navtask.task_params.semantic_task.class_map_names = ['chair', 'door', 'table']
   # navtask.task_params.type = 'to_nearest_obj_acc'
-  
+
   logging.info('navtask: %s', navtask)
   return navtask
 
@@ -90,12 +90,12 @@ def walk_through(b):
 
   root = tk.Tk()
   image = b.render_nodes(b.task.nodes[[current_node],:])[0]
-  print image.shape
+  print(image.shape)
   image = image.astype(np.uint8)
   im = Image.fromarray(image)
   im = ImageTk.PhotoImage(im)
   panel = tk.Label(root, image=im)
- 
+
   map_size = b.traversible.shape
   sc = np.max(map_size)/256.
   loc = np.array([[map_size[1]/2., map_size[0]/2.]])
@@ -128,15 +128,15 @@ def up_key(event):
     global current_node
     current_node = b.take_action([current_node], [3], 1)[0][0]
     refresh()
-  
+
   def right_key(event):
     global current_node
     current_node = b.take_action([current_node], [1], 1)[0][0]
     refresh()
 
   def quit(event):
-    root.destroy() 
-  
+    root.destroy()
+
   panel_overhead.grid(row=4, column=5, rowspan=1, columnspan=1,
                       sticky=tk.W+tk.E+tk.N+tk.S)
   panel.bind('<Left>', left_key)
@@ -150,19 +150,19 @@ def quit(event):
 
 def simple_window():
   root = tk.Tk()
-  
+
   image = np.zeros((128, 128, 3), dtype=np.uint8)
   image[32:96, 32:96, 0] = 255
   im = Image.fromarray(image)
   im = ImageTk.PhotoImage(im)
-  
+
   image = np.zeros((128, 128, 3), dtype=np.uint8)
   image[32:96, 32:96, 1] = 255
   im2 = Image.fromarray(image)
   im2 = ImageTk.PhotoImage(im2)
-  
+
   panel = tk.Label(root, image=im)
-  
+
   def left_key(event):
     panel.configure(image=im2)
     panel.image = im2
@@ -176,7 +176,7 @@ def quit(event):
   panel.bind('q', quit)
   panel.focus_set()
   panel.pack(side = "bottom", fill = "both", expand = "yes")
-  root.mainloop() 
+  root.mainloop()
 
 def main(_):
   b = load_building(FLAGS.dataset_name, FLAGS.building_name)

research/cognitive_mapping_and_planning/scripts/script_plot_trajectory.py

@@ -17,7 +17,7 @@
 Code for plotting trajectories in the top view, and also plot first person views
 from saved trajectories. Does not run the network but only loads the mesh data
 to plot the view points.
-  CUDA_VISIBLE_DEVICES=0 LD_LIBRARY_PATH=/opt/cuda-8.0/lib64:/opt/cudnnv51/lib64 
+  CUDA_VISIBLE_DEVICES=0 LD_LIBRARY_PATH=/opt/cuda-8.0/lib64:/opt/cudnnv51/lib64
   PYTHONPATH='.' PYOPENGL_PLATFORM=egl python scripts/script_plot_trajectory.py \
       --first_person --num_steps 40 \
       --config_name cmp.lmap_Msc.clip5.sbpd_d_r2r \
@@ -36,13 +36,13 @@
 import cv2
 import logging
 from tensorflow.python.platform import gfile
-from tensorflow.python.platform import app 
-from tensorflow.python.platform import flags 
+from tensorflow.python.platform import app
+from tensorflow.python.platform import flags
 
 from datasets import nav_env
-import scripts.script_nav_agent_release as sna 
+import scripts.script_nav_agent_release as sna
 import src.file_utils as fu
-from src import graph_utils 
+from src import graph_utils
 from src import utils
 FLAGS = flags.FLAGS
 
@@ -95,7 +95,7 @@ def _compute_hardness():
     # Initialize the agent.
     init_env_state = e.reset(rng_data)
 
-    gt_dist_to_goal = [e.episode.dist_to_goal[0][j][s] 
+    gt_dist_to_goal = [e.episode.dist_to_goal[0][j][s]
                        for j, s in enumerate(e.episode.start_node_ids)]
 
     for j in range(args.navtask.task_params.batch_size):
@@ -120,15 +120,15 @@ def plot_trajectory_first_person(dt, orig_maps, out_dir):
   out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(),
                          FLAGS.imset)
   fu.makedirs(out_dir)
-  
+
   # Load the model so that we can render.
   plt.set_cmap('gray')
   samples_per_action = 8; wait_at_action = 0;
-  
+
   Writer = animation.writers['mencoder']
-  writer = Writer(fps=3*(samples_per_action+wait_at_action), 
+  writer = Writer(fps=3*(samples_per_action+wait_at_action),
                   metadata=dict(artist='anonymous'), bitrate=1800)
-  
+
   args = sna.get_args_for_config(FLAGS.config_name + '+bench_'+FLAGS.imset)
   args.navtask.logdir = None
   navtask_ = copy.deepcopy(args.navtask)
@@ -142,10 +142,10 @@ def plot_trajectory_first_person(dt, orig_maps, out_dir):
   R = lambda: nav_env.get_multiplexer_class(navtask_, 0)
   R = R()
   b = R.buildings[0]
-  
+
   f = [0 for _ in range(wait_at_action)] + \
       [float(_)/samples_per_action for _ in range(samples_per_action)];
-  
+
   # Generate things for it to render.
   inds_to_do = []
   inds_to_do += [1, 4, 10] #1291, 1268, 1273, 1289, 1302, 1426, 1413, 1449, 1399, 1390]
@@ -163,7 +163,7 @@ def plot_trajectory_first_person(dt, orig_maps, out_dir):
     # axes = [ax]
     for ax in axes:
       ax.set_axis_off()
-    
+
     node_ids = dt['all_node_ids'][i, :, 0]*1
     # Prune so that last node is not repeated more than 3 times?
     if np.all(node_ids[-4:] == node_ids[-1]):
@@ -185,15 +185,15 @@ def plot_trajectory_first_person(dt, orig_maps, out_dir):
     node_ids_all = np.reshape(node_ids_all[:-1,:], -1)
     perturbs_all = np.reshape(perturbs_all, [-1, 4])
     imgs = b.render_nodes(b.task.nodes[node_ids_all,:], perturb=perturbs_all)
-  
+
     # Get action at each node.
     actions = []
     _, action_to_nodes = b.get_feasible_actions(node_ids)
     for j in range(num_steps-1):
       action_to_node = action_to_nodes[j]
       node_to_action = dict(zip(action_to_node.values(), action_to_node.keys()))
       actions.append(node_to_action[node_ids[j+1]])
-    
+
     def init_fn():
       return fig,
     gt_dist_to_goal = []
@@ -205,8 +205,8 @@ def worker(j):
       img = imgs[j]; ax = axes[0]; ax.clear(); ax.set_axis_off();
       img = img.astype(np.uint8); ax.imshow(img);
       tt = ax.set_title(
-          "First Person View\n" + 
-          "Top corners show diagnostics (distance, agents' action) not input to agent.", 
+          "First Person View\n" +
+          "Top corners show diagnostics (distance, agents' action) not input to agent.",
           fontsize=12)
       plt.setp(tt, color='white')
 
@@ -218,7 +218,7 @@ def worker(j):
           fontsize=20, color='red',
           transform=ax.transAxes, alpha=1.0)
       t.set_bbox(dict(color='white', alpha=0.85, pad=-0.1))
-      
+
       # Action to take.
       action_latex = ['$\odot$ ', '$\curvearrowright$ ', '$\curvearrowleft$ ', '$\Uparrow$ ']
       t = ax.text(0.99, 0.99, action_latex[actions[step_number]],
@@ -256,7 +256,7 @@ def worker(j):
       locs = np.expand_dims(locs, axis=0)
       ax.plot(locs[:,0], locs[:,1], 'r.', alpha=1.0, linewidth=0, markersize=4)
       tt = ax.set_title('Trajectory in topview', fontsize=14)
-      plt.setp(tt, color='white') 
+      plt.setp(tt, color='white')
       return fig,
 
     line_ani = animation.FuncAnimation(fig, worker,
@@ -265,7 +265,7 @@ def worker(j):
     tmp_file_name = 'tmp.mp4'
     line_ani.save(tmp_file_name, writer=writer, savefig_kwargs={'facecolor':'black'})
     out_file_name = os.path.join(out_dir, 'vis_{:04d}.mp4'.format(i))
-    print out_file_name
+    print(out_file_name)
 
     if fu.exists(out_file_name):
       gfile.Remove(out_file_name)
@@ -280,12 +280,12 @@ def plot_trajectory(dt, hardness, orig_maps, out_dir):
   out_file = os.path.join(out_dir, 'all_locs_at_t.pkl')
   dt['hardness'] = hardness
   utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True)
-  
+
   #Plot trajectories onto the maps
   plt.set_cmap('gray')
   for i in range(4000):
     goal_loc = dt['all_goal_locs'][i, :, :]
-    locs = np.concatenate((dt['all_locs'][i,:,:], 
+    locs = np.concatenate((dt['all_locs'][i,:,:],
                            dt['all_locs'][i,:,:]), axis=0)
     xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0))
     xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0))
@@ -305,35 +305,35 @@ def plot_trajectory(dt, hardness, orig_maps, out_dir):
     uniq = np.array(uniq)
     all_locs = all_locs[uniq, :]
 
-    ax.plot(dt['all_locs'][i, 0, 0], 
+    ax.plot(dt['all_locs'][i, 0, 0],
             dt['all_locs'][i, 0, 1], 'b.', markersize=24)
-    ax.plot(dt['all_goal_locs'][i, 0, 0], 
+    ax.plot(dt['all_goal_locs'][i, 0, 0],
             dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19)
     ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2)
     ax.scatter(all_locs[:,0], all_locs[:,1],
-               c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], 
+               c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0],
                cmap='Reds', s=30, linewidth=0)
     ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal')
     ax.set_xlim([xy1[0], xy2[0]])
     ax.set_ylim([xy1[1], xy2[1]])
-    
+
     file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i))
-    print file_name
-    with fu.fopen(file_name, 'w') as f: 
+    print(file_name)
+    with fu.fopen(file_name, 'w') as f:
       plt.savefig(f)
     plt.close(fig)
-  
+
 
 def main(_):
   a = _load_trajectory()
   h_dists, gt_dists, orig_maps = _compute_hardness()
   hardness = 1.-h_dists*1./ gt_dists
-  
+
   if FLAGS.top_view:
     plot_trajectory(a, hardness, orig_maps, out_dir=FLAGS.out_dir)
 
   if FLAGS.first_person:
     plot_trajectory_first_person(a, orig_maps, out_dir=FLAGS.out_dir)
-  
+
 if __name__ == '__main__':
   app.run()

research/cognitive_mapping_and_planning/src/utils.py

@@ -17,6 +17,7 @@
 """
 
 import numpy as np, cPickle, os, time
+from six.moves import xrange
 import src.file_utils as fu
 import logging
 
@@ -93,12 +94,12 @@ def tic_toc_print(interval, string):
   global tic_toc_print_time_old
   if 'tic_toc_print_time_old' not in globals():
     tic_toc_print_time_old = time.time()
-    print string
+    print(string)
   else:
     new_time = time.time()
     if new_time - tic_toc_print_time_old > interval:
       tic_toc_print_time_old = new_time;
-      print string
+      print(string)
 
 def mkdir_if_missing(output_dir):
   if not fu.exists(output_dir):
@@ -126,7 +127,7 @@ def load_variables(pickle_file_name):
 def voc_ap(rec, prec):
   rec = rec.reshape((-1,1))
   prec = prec.reshape((-1,1))
-  z = np.zeros((1,1)) 
+  z = np.zeros((1,1))
   o = np.ones((1,1))
   mrec = np.vstack((z, rec, o))
   mpre = np.vstack((z, prec, z))
@@ -165,4 +166,3 @@ def calc_pr(gt, out, wt=None):
 
   ap = voc_ap(rec, prec)
   return ap, rec, prec
-

research/compression/entropy_coder/core/entropy_coder_single.py

@@ -58,7 +58,7 @@ def main(_):
   #iteration = FLAGS.iteration
 
   if not tf.gfile.Exists(FLAGS.input_codes):
-    print '\nInput codes not found.\n'
+    print('\nInput codes not found.\n')
     return
 
   with tf.gfile.FastGFile(FLAGS.input_codes, 'rb') as code_file:

research/compression/entropy_coder/core/entropy_coder_train.py

@@ -171,7 +171,7 @@ def train():
               'code_length': model.average_code_length
           }
           np_tensors = sess.run(tf_tensors, feed_dict=feed_dict)
-          print np_tensors['code_length']
+          print(np_tensors['code_length'])
 
       sv.Stop()