fix logic for wandb

delft/sequenceLabelling/trainer.py

@@ -79,22 +79,19 @@ def compile_model(self, local_model, train_size):
             if local_model.config.use_chain_crf:
                 local_model.compile(
                     optimizer=optimizer,
-                    loss=local_model.crf.sparse_crf_loss_bert_masked,
-                    metrics = ["accuracy"] if self.enable_wandb else []
+                    loss=local_model.crf.sparse_crf_loss_bert_masked
                 )
             elif local_model.config.use_crf:
                 # loss is calculated by the custom CRF wrapper
                 local_model.compile(
                     optimizer=optimizer,
-                    metrics = ["accuracy"] if self.enable_wandb else []
                 )
             else:
                 # we apply a mask on the predicted labels so that the weights 
                 # corresponding to special symbols are neutralized
                 local_model.compile(
                     optimizer=optimizer,
                     loss=sparse_crossentropy_masked,
-                    metrics=["accuracy"] if self.enable_wandb else []
                 )
         else:
 
@@ -109,14 +106,12 @@ def compile_model(self, local_model, train_size):
                 local_model.compile(
                     optimizer=optimizer,
                     loss=local_model.crf.loss,
-                    metrics = ["accuracy"] if self.enable_wandb else []
                 )
             elif local_model.config.use_crf:
                 if tf.executing_eagerly():
                     # loss is calculated by the custom CRF wrapper, no need to specify a loss function here
                     local_model.compile(
                         optimizer=optimizer,
-                        metrics = ["accuracy"] if self.enable_wandb else []
                     )
                 else:
                     print("compile model, graph mode")
@@ -128,15 +123,13 @@ def compile_model(self, local_model, train_size):
                     local_model.compile(
                         optimizer=optimizer,
                         loss='sparse_categorical_crossentropy',
-                        metrics = ["accuracy"] if self.enable_wandb else []
                     )
                     #local_model.compile(optimizer=optimizer, loss=InnerLossPusher(local_model))
             else:
                 # only sparse label encoding is used (no one-hot encoded labels as it was the case in DeLFT < 0.3)
                 local_model.compile(
                     optimizer=optimizer,
                     loss='sparse_categorical_crossentropy',
-                    metrics = ["accuracy"] if self.enable_wandb else []
                 )
 
         return local_model

delft/sequenceLabelling/wrapper.py

@@ -108,10 +108,13 @@ def __init__(self,
         word_emb_size = 0
         self.embeddings = None
         self.model_local_path = None
-        if wandb_config is not None and 'project' not in wandb_config:
-            self.wandb_config = wandb_config
-        else:
-            raise ValueError("The wandb_config should be a dictionary with at least the string parameter 'project'. ")
+
+        self.wandb_config = None
+        if wandb_config is not None:
+            if 'project' in wandb_config:
+                self.wandb_config = wandb_config
+            else:
+                raise ValueError("The wandb_config should be a dictionary with at least the string parameter 'project'. ")
 
         self.registry = load_resource_registry("delft/resources-registry.json")