Fixes

flash/core/model.py

@@ -391,7 +391,8 @@ def build_data_pipeline(
             else:
                 data_source = preprocess.data_source_of_name(data_source)
 
-        deserializer = deserializer or getattr(preprocess, "deserializer", None)
+        if type(deserializer) == Deserializer:
+            deserializer = getattr(preprocess, "deserializer", deserializer)
 
         data_pipeline = DataPipeline(data_source, preprocess, postprocess, deserializer, serializer)
         self._data_pipeline_state = data_pipeline.initialize(self._data_pipeline_state)
@@ -400,7 +401,7 @@ def build_data_pipeline(
     @torch.jit.unused
     @property
     def is_servable(self) -> bool:
-        return self.build_data_pipeline()._deserializer is not None
+        return type(self.build_data_pipeline()._deserializer) != Deserializer
 
     @torch.jit.unused
     @property
@@ -608,7 +609,7 @@ def run_serve_sanity_check(self):
 
         from flash.core.serve.flash_components import build_flash_serve_model_component
 
-        print("Running sanity check")
+        print("Running serve sanity check")
         comp = build_flash_serve_model_component(self)
         composition = Composition(predict=comp, TESTING=True, DEBUG=True)
         app = composition.serve(host="0.0.0.0", port=8000)

flash/image/classification/model.py

@@ -19,7 +19,7 @@
 from torch import nn
 from torch.optim.lr_scheduler import _LRScheduler
 
-from flash.core.classification import ClassificationTask
+from flash.core.classification import ClassificationTask, Labels
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.data.process import Serializer
 from flash.core.registry import FlashRegistry
@@ -91,7 +91,7 @@ def __init__(
             metrics=metrics,
             learning_rate=learning_rate,
             multi_label=multi_label,
-            serializer=serializer,
+            serializer=serializer or Labels(),
         )
 
         self.save_hyperparameters()

flash_examples/finetuning/image_classification.py

@@ -48,7 +48,7 @@ def fn_resnet(pretrained: bool = True):
 print(ImageClassifier.available_backbones())
 
 # 4. Build the model
-model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes, serializer=Labels())
+model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
 
 # 5. Create the trainer
 trainer = flash.Trainer(max_epochs=3)