[scripts] xconfig: make scripts work when LDA-like preconditioning layer is not used (#1447)

egs/wsj/s5/steps/libs/nnet3/train/common.py

@@ -444,12 +444,20 @@ def smooth_presoftmax_prior_scale_vector(pdf_counts,
 
 
 def prepare_initial_network(dir, run_opts, srand=-3):
+    if os.path.exists(dir+"/configs/init.config"):
         common_lib.run_job(
             """{command} {dir}/log/add_first_layer.log \
                     nnet3-init --srand={srand} {dir}/init.raw \
                     {dir}/configs/layer1.config {dir}/0.raw""".format(
                         command=run_opts.command, srand=srand,
                         dir=dir))
+    else:
+        common_lib.run_job(
+            """{command} {dir}/log/add_first_layer.log \
+                    nnet3-init --srand={srand} \
+                    {dir}/configs/layer1.config {dir}/0.raw""".format(
+                        command=run_opts.command, srand=srand,
+                        dir=dir))
 
 
 def verify_iterations(num_iters, num_epochs, num_hidden_layers,

egs/wsj/s5/steps/libs/nnet3/xconfig/basic_layers.py

@@ -786,7 +786,8 @@ class XconfigFixedAffineLayer(XconfigLayerBase):
         # the most recent layer.
         self.config = { 'input':'[-1]',
                         'dim':-1,
-                        'affine-transform-file':''}
+                        'affine-transform-file':'',
+                        'write-init-config':True}
 
     def check_configs(self):
         if self.config['affine-transform-file'] is None:
@@ -817,7 +818,7 @@ class XconfigFixedAffineLayer(XconfigLayerBase):
         output_dim = self.output_dim()
         transform_file = self.config['affine-transform-file']
 
-
+        if self.config['write-init-config']:
             # to init.config we write an output-node with the name 'output' and
             # with a Descriptor equal to the descriptor that's the input to this
             # layer.  This will be used to accumulate stats to learn the LDA transform.

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

@@ -199,7 +199,7 @@ def train(args, run_opts, background_process_handler):
     # we do this as it's a convenient way to get the stats for the 'lda-like'
     # transform.
 
-    if (args.stage <= -5):
+    if (args.stage <= -5) and os.path.exists(args.dir+"/configs/init.config"):
         logger.info("Initializing a basic network for estimating "
                     "preconditioning matrix")
         common_lib.run_job(
@@ -245,7 +245,7 @@ def train(args, run_opts, background_process_handler):
     # use during decoding
     common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir)
 
-    if (args.stage <= -3):
+    if (args.stage <= -3) and os.path.exists(args.dir+"/configs/init.config"):
         logger.info('Computing the preconditioning matrix for input features')
 
         train_lib.common.compute_preconditioning_matrix(

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

@@ -170,7 +170,24 @@ def write_config_files(config_dir, all_layers):
             # preserves the backtrace
             raise
 
+    # remove previous init.config
+    try:
+        os.remove(config_dir + '/init.config')
+    except OSError:
+        pass
+
     for basename, lines in config_basename_to_lines.items():
+        # check the lines num start with 'output-node':
+        num_output_node_lines = sum( [ 1 if line.startswith('output-node' ) else 0
+                                       for line in lines ] )
+        if num_output_node_lines == 0:
+            if basename == 'init':
+                continue # do not write the init.config
+            else:
+                print('{0}: error in xconfig file {1}: may be lack of a output layer'.format(
+                    sys.argv[0], sys.argv[2]), file=sys.stderr)
+                raise
+
         header = config_basename_to_header[basename]
         filename = '{0}/{1}.config'.format(config_dir, basename)
         try: