fix wandb parameters

Author: lfoppiano

delft/applications/grobidTagger.py

@@ -168,7 +168,7 @@ def configure(model, architecture, output_path=None, max_sequence_length=-1, bat
 def train(model, embeddings_name=None, architecture=None, transformer=None, input_path=None,
           output_path=None, features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1,
           use_ELMo=False, incremental=False, input_model_path=None, patience=-1, learning_rate=None, early_stop=None, multi_gpu=False,
-          wandb_config=None):
+          report_to_wandb=False):
 
     print('Loading data...')
     if input_path == None:
@@ -216,7 +216,7 @@ def train(model, embeddings_name=None, architecture=None, transformer=None, inpu
         early_stop=early_stop,
         patience=patience,
         learning_rate=learning_rate,
-        report_to_wandb=wandb_config
+        report_to_wandb=report_to_wandb
     )
 
     if incremental:
@@ -243,9 +243,10 @@ def train(model, embeddings_name=None, architecture=None, transformer=None, inpu
 # split data, train a GROBID model and evaluate it
 def train_eval(model, embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,
                input_path=None, output_path=None, fold_count=1,
-               features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1, 
+               features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1,
                use_ELMo=False, incremental=False, input_model_path=None, patience=-1,
-               learning_rate=None, early_stop=None, multi_gpu=False, wandb_config=None):
+               learning_rate=None, early_stop=None, multi_gpu=False,
+               report_to_wandb=False):
 
     print('Loading data...')
     if input_path is None:
@@ -274,11 +275,12 @@ def train_eval(model, embeddings_name=None, architecture='BidLSTM_CRF', transfor
                                                                             use_ELMo,
                                                                             patience,
                                                                             early_stop)
+
     model = Sequence(model_name, architecture=architecture, embeddings_name=embeddings_name,
                      max_sequence_length=max_sequence_length, recurrent_dropout=0.50, batch_size=batch_size,
                      learning_rate=learning_rate, max_epoch=max_epoch, early_stop=early_stop, patience=patience,
                      use_ELMo=use_ELMo, fold_number=fold_count, multiprocessing=multiprocessing,
-                     features_indices=features_indices, transformer_name=transformer, report_to_wandb=wandb_config)
+                     features_indices=features_indices, transformer_name=transformer, report_to_wandb=report_to_wandb)
 
     if incremental:
         if input_model_path != None:
@@ -310,7 +312,7 @@ def train_eval(model, embeddings_name=None, architecture='BidLSTM_CRF', transfor
 
 
 # split data, train a GROBID model and evaluate it
-def eval_(model, input_path=None, architecture='BidLSTM_CRF', use_ELMo=False):
+def eval_(model, input_path=None, architecture='BidLSTM_CRF', use_ELMo=False, report_to_wandb=False):
     print('Loading data...')
     if input_path is None:
         # it should never be the case
@@ -331,7 +333,7 @@ def eval_(model, input_path=None, architecture='BidLSTM_CRF', use_ELMo=False):
     start_time = time.time()
 
     # load the model
-    model = Sequence(model_name)
+    model = Sequence(model_name, report_to_wandb=report_to_wandb)
     model.load()
 
     # evaluation
@@ -478,12 +480,6 @@ class Tasks:
         # default word embeddings
         embeddings_name = "glove-840B"
 
-    wandb_config = None
-    if wandb:
-        wandb_config = {
-            "project": "delft-grobidTagger"
-        }
-
     if action == Tasks.TRAIN:
             train(
                 model,
@@ -502,7 +498,7 @@ class Tasks:
                 max_epoch=max_epoch,
                 early_stop=early_stop,
                 multi_gpu=multi_gpu,
-                wandb_config=wandb_config
+                report_to_wandb=wandb
             )
 
     if action == Tasks.EVAL:
@@ -516,23 +512,23 @@ class Tasks:
     if action == Tasks.TRAIN_EVAL:
         if args.fold_count < 1:
             raise ValueError("fold-count should be equal or more than 1")
-        train_eval(model, 
-                embeddings_name=embeddings_name, 
-                architecture=architecture, 
-                transformer=transformer,
-                input_path=input_path, 
-                output_path=output, 
-                fold_count=args.fold_count,
-                max_sequence_length=max_sequence_length,
-                batch_size=batch_size,
-                use_ELMo=use_ELMo, 
-                incremental=incremental,
-                input_model_path=input_model_path,
-                learning_rate=learning_rate,
-                max_epoch=max_epoch,
-                early_stop=early_stop,
-                multi_gpu=multi_gpu,
-                wandb_config=wandb_config)
+        train_eval(model,
+                   embeddings_name=embeddings_name,
+                   architecture=architecture,
+                   transformer=transformer,
+                   input_path=input_path,
+                   output_path=output,
+                   fold_count=args.fold_count,
+                   max_sequence_length=max_sequence_length,
+                   batch_size=batch_size,
+                   use_ELMo=use_ELMo,
+                   incremental=incremental,
+                   input_model_path=input_model_path,
+                   learning_rate=learning_rate,
+                   max_epoch=max_epoch,
+                   early_stop=early_stop,
+                   multi_gpu=multi_gpu,
+                   report_to_wandb=wandb)
 
     if action == Tasks.TAG:
         someTexts = []