parameterize lr_decay_factor, step_boundaries and log_period (#34)

plsc/entry.py

@@ -136,6 +136,9 @@ class Entry(object):
             self.model_save_dir = os.path.abspath(self.model_save_dir)
         if self.dataset_dir:
             self.dataset_dir = os.path.abspath(self.dataset_dir)
+        
+        self.lr_decay_factor = 0.1
+        self.log_period = 200
 
         logger.info('=' * 30)
         logger.info("Default configuration:")
@@ -143,6 +146,8 @@ class Entry(object):
             logger.info('\t' + str(key) + ": " + str(self.config[key]))
         logger.info('trainer_id: {}, num_trainers: {}'.format(
             trainer_id, num_trainers))
+        logger.info('default lr_decay_factor: {}'.format(self.lr_decay_factor))
+        logger.info('default log period: {}'.format(self.log_period))
         logger.info('=' * 30)
 
     def set_val_targets(self, targets):
@@ -157,6 +162,20 @@ class Entry(object):
         self.global_train_batch_size = batch_size * self.num_trainers
         logger.info("Set train batch size to {}.".format(batch_size))
 
+    def set_log_period(self, period):
+        self.log_period = period
+        logger.info("Set log period to {}.".format(period))
+
+    def set_lr_decay_factor(self, factor):
+        self.lr_decay_factor = factor
+        logger.info("Set lr decay factor to {}.".format(factor))
+
+    def set_step_boundaries(self, boundaries):
+        if not isinstance(boundaries, list):
+            raise ValueError("The parameter must be of type list.")
+        self.lr_steps = boundaries
+        logger.info("Set step boundaries to {}.".format(boundaries))
+
     def set_mixed_precision(self,
                             use_fp16,
                             init_loss_scaling=1.0,
@@ -332,7 +351,8 @@ class Entry(object):
             warmup_steps = steps_per_pass * self.warmup_epochs
             batch_denom = 1024
             base_lr = start_lr * global_batch_size / batch_denom
-            lr = [base_lr * (0.1 ** i) for i in range(len(bd) + 1)]
+            lr_decay_factor = self.lr_decay_factor
+            lr = [base_lr * (lr_decay_factor ** i) for i in range(len(bd) + 1)]
             logger.info("LR boundaries: {}".format(bd))
             logger.info("lr_step: {}".format(lr))
             if self.warmup_epochs:
@@ -938,7 +958,7 @@ class Entry(object):
     
         local_time = 0.0
         nsamples = 0
-        inspect_steps = 200
+        inspect_steps = self.log_period
         global_batch_size = self.global_train_batch_size
         for pass_id in range(self.train_epochs):
             self.train_pass_id = pass_id