changing the integer division operator from / to //, which is necessa…

egs/wsj/s5/steps/nnet3/chain/train.py

@@ -341,16 +341,16 @@ def train(args, run_opts):
             {dir}/init.raw""".format(command=run_opts.command,
                                      dir=args.dir))
 
-    egs_left_context = left_context + args.frame_subsampling_factor / 2
-    egs_right_context = right_context + args.frame_subsampling_factor / 2
+    egs_left_context = left_context + args.frame_subsampling_factor // 2
+    egs_right_context = right_context + args.frame_subsampling_factor // 2
     # note: the '+ args.frame_subsampling_factor / 2' is to allow for the
     # fact that we'll be shifting the data slightly during training to give
     # variety to the training data.
     egs_left_context_initial = (left_context_initial +
-                                args.frame_subsampling_factor / 2 if
+                                args.frame_subsampling_factor // 2 if
                                 left_context_initial >= 0 else -1)
     egs_right_context_final = (right_context_final +
-                               args.frame_subsampling_factor / 2 if
+                               args.frame_subsampling_factor // 2 if
                                right_context_final >= 0 else -1)
 
     default_egs_dir = '{0}/egs'.format(args.dir)
@@ -432,7 +432,7 @@ def train(args, run_opts):
     num_archives_to_process = int(args.num_epochs * num_archives_expanded)
     num_archives_processed = 0
     num_iters = ((num_archives_to_process * 2)
-                 / (args.num_jobs_initial + args.num_jobs_final))
+                 // (args.num_jobs_initial + args.num_jobs_final))
 
     # If do_final_combination is True, compute the set of models_to_combine.
     # Otherwise, models_to_combine will be none.

egs/wsj/s5/steps/nnet3/components.py

@@ -168,8 +168,8 @@ def AddConvolutionLayer(config_lines, name, input,
     components.append(conv_init_string)
     component_nodes.append("component-node name={0}_conv_t component={0}_conv input={1}".format(name, input['descriptor']))
 
-    num_x_steps = (1 + (input_x_dim - filt_x_dim) / filt_x_step)
-    num_y_steps = (1 + (input_y_dim - filt_y_dim) / filt_y_step)
+    num_x_steps = (1 + (input_x_dim - filt_x_dim) // filt_x_step)
+    num_y_steps = (1 + (input_y_dim - filt_y_dim) // filt_y_step)
     output_dim = num_x_steps * num_y_steps * num_filters;
     return {'descriptor':  '{0}_conv_t'.format(name),
             'dimension': output_dim,
@@ -204,9 +204,9 @@ def AddMaxpoolingLayer(config_lines, name, input,
 
     component_nodes.append('component-node name={0}_maxp_t component={0}_maxp input={1}'.format(name, input['descriptor']))
 
-    num_pools_x = 1 + (input_x_dim - pool_x_size) / pool_x_step;
-    num_pools_y = 1 + (input_y_dim - pool_y_size) / pool_y_step;
-    num_pools_z = 1 + (input_z_dim - pool_z_size) / pool_z_step;
+    num_pools_x = 1 + (input_x_dim - pool_x_size) // pool_x_step;
+    num_pools_y = 1 + (input_y_dim - pool_y_size) // pool_y_step;
+    num_pools_z = 1 + (input_z_dim - pool_z_size) // pool_z_step;
     output_dim = num_pools_x * num_pools_y * num_pools_z;
 
     return {'descriptor':  '{0}_maxp_t'.format(name),

egs/wsj/s5/steps/nnet3/multilingual/allocate_multilingual_examples.py

@@ -198,15 +198,15 @@ def process_multilingual_egs(args):
     lang_len = lang2len[:]
     # total num of egs in all languages
     tot_num_egs = sum(lang2len[i] for i in range(len(lang2len)))
-    num_archives = max(1, min(args.max_archives, tot_num_egs / args.samples_per_iter))
+    num_archives = max(1, min(args.max_archives, tot_num_egs // args.samples_per_iter))
 
     num_arch_file = open("{0}/info/{1}num_archives".format(
                             args.egs_dir,
                             args.egs_prefix),
                          "w")
     print("{0}".format(num_archives), file=num_arch_file)
     num_arch_file.close()
-    this_num_egs_per_archive = tot_num_egs / (num_archives * args.num_jobs)
+    this_num_egs_per_archive = tot_num_egs // (num_archives * args.num_jobs)
 
     logger.info("Generating {0}scp.<job>.<archive_index> temporary files used to "
                 "generate {0}<archive_index>.scp.".format(args.egs_prefix))

egs/wsj/s5/steps/segmentation/internal/arc_info_to_targets.py

@@ -144,7 +144,7 @@ def run(args):
 
     logger.info("Wrote {num_utts} targets; failed with {num_err}"
                 "".format(num_utts=num_utts, num_err=num_err))
-    if num_utts == 0 or num_err >= num_utts / 2:
+    if num_utts == 0 or num_err >= num_utts // 2:
         raise RuntimeError
 
 

egs/wsj/s5/steps/segmentation/internal/get_default_targets_for_out_of_segments.py

@@ -163,7 +163,7 @@ def run(args):
                 "".format(num_reco=num_reco, num_utt=num_utt,
                           num_utt_err=num_utt_err))
 
-    if num_utt == 0 or num_utt_err > num_utt / 2 or num_reco == 0:
+    if num_utt == 0 or num_utt_err > num_utt // 2 or num_reco == 0:
         raise RuntimeError
 
 

egs/wsj/s5/steps/segmentation/internal/merge_segment_targets_to_recording.py

@@ -259,7 +259,7 @@ def run(args):
                 "".format(num_utt=num_utt, num_reco=num_reco,
                           num_utt_err=num_utt_err))
 
-    if num_utt == 0 or num_utt_err > num_utt / 2 or num_reco == 0:
+    if num_utt == 0 or num_utt_err > num_utt // 2 or num_reco == 0:
         raise RuntimeError
 
 

egs/wsj/s5/steps/segmentation/internal/merge_targets.py

@@ -120,7 +120,7 @@ def should_remove_frame(row, dim):
         # Remove frame.
         return True
 
-    best_source = max_idx / dim
+    best_source = max_idx // dim
     best_class = max_idx % dim
 
     confident_in_source = []  # List of length num_sources
@@ -171,7 +171,7 @@ def run(args):
                     "is not a multiple of dim {dim}"
                     "".format(utt=key, f=args.pasted_targets.name,
                               nc=mat.shape[1], dim=args.dim))
-            num_sources = mat.shape[1] / args.dim
+            num_sources = mat.shape[1] // args.dim
 
             out_mat = np.matrix(np.zeros([mat.shape[0], args.dim]))