Create simple version

hydra_flash/conf/config1.yaml

 preprocess:
   _target_: flash.core.data.utils.download_data
   url: "https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip"
-  path: "./data"
+  path: "/tmp"
 
 datamodule:
   _target_: flash.image.ImageClassificationData.from_folders
@@ -26,7 +26,10 @@ trainer:
 predict_datamodule:
   _target_: flash.image.ImageClassificationData.from_files
   predict_files:
-    - "data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg"
-    - "data/hymenoptera_data/val/bees/590318879_68cf112861.jpg"
-    - "data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg"
+    - "${preprocess.path}/hymenoptera_data/val/bees/65038344_52a45d090d.jpg"
+    - "${preprocess.path}/hymenoptera_data/val/bees/590318879_68cf112861.jpg"
+    - "${preprocess.path}/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg"
   batch_size: 3
+
+show: true
+save: false

hydra_flash/conf/datamodule/lyft.yaml

-_target_: flash.image.SemanticSegmentationData.from_folders
-train_folder: "${preprocess.path}/CameraRGB"
-train_target_folder: "${preprocess.path}/CameraSeg"
-val_split: 0.1
-transform_kwargs:
-  image_size: [256, 256]
-num_classes: 21
-batch_size: 4

hydra_flash/conf/datamodule/lyft_test.yaml

-_target_: flash.image.SemanticSegmentationData.from_files
-predict_files:
-  - "${preprocess.path}/CameraRGB/F61-1.png"
-  - "${preprocess.path}/CameraRGB/F62-1.png"
-  - "${preprocess.path}/CameraRGB/F63-1.png"
-batch_size: 3

hydra_flash/conf/datamodule/movie_posters.yaml

-_target_: flash.image.ImageClassificationData.from_csv
-input_field: "Id"
-target_fields: ["Action", "Romance", "Crime", "Thriller", "Adventure"]
-train_file: ${preprocess.path}/movie_posters/train/metadata.csv
-train_resolver:
-  _target_: hydra_flash.utils.resolver
-  _partial_: true
-val_file: ${preprocess.path}/movie_posters/val/metadata.csv
-val_resolver:
-  _target_: hydra_flash.utils.resolver
-  _partial_: true
-transform_kwargs:
-  image_size: [128, 128]
-batch_size: 1

hydra_flash/conf/datamodule/movie_posters_test.yaml

-_target_: flash.image.ImageClassificationData.from_files
-predict_files:
-  - "${preprocess.path}/movie_posters/predict/tt0085318.jpg"
-  - "${preprocess.path}/movie_posters/predict/tt0089461.jpg"
-  - "${preprocess.path}/movie_posters/predict/tt0097179.jpg"
-batch_size: 3

hydra_flash/conf/experiment/hymenoptera.yaml

-# @package _global_
-defaults:
-  - _self_
-  - /preprocess: hymenoptera
-  - /datamodule: hymenoptera
-  - /model: resnet18
-  - /datamodule@predict_datamodule: hymenoptera_test
-  - /server: local
-
-trainer:
-  _target_: flash.Trainer
-  max_epochs: 3
-  gpus: 1

hydra_flash/conf/experiment/lyft.yaml

-# @package _global_
-defaults:
-  - _self_
-  - /preprocess: lyft
-  - /datamodule: lyft
-  - /model: seg_mobilenet
-  - /datamodule@predict_datamodule: lyft_test
-  - /server: local
-
-trainer:
-  _target_: flash.Trainer
-  max_epochs: 3
-  gpus: 1

hydra_flash/conf/experiment/movie_poster.yaml

-# @package _global_
-defaults:
-  - _self_
-  - /preprocess: movie_poster
-  - /datamodule: movie_posters
-  - /model: resnet18
-  - /datamodule@predict_datamodule: movie_posters_test
-  - /server: local
-
-trainer:
-  _target_: flash.Trainer
-  max_epochs: 3
-  gpus: 1

hydra_flash/conf/model/resnet18.yaml

 _target_: flash.image.ImageClassifier
 backbone: resnet18
 learning_rate: 1.e-3
-complete:
-  - labels
-  - multi_label

hydra_flash/conf/model/seg_mobilenet.yaml

-_target_: flash.image.SemanticSegmentation
-backbone: "mobilenetv3_large_100"
-head: fpn
-num_classes: ${datamodule.num_classes}
-complete: []

hydra_flash/conf/preprocess/lyft.yaml

-defaults:
-  - download
-
-url: "https://github.com/ongchinkiat/LyftPerceptionChallenge/releases/download/v0.1/carla-capture-20180513A.zip"

hydra_flash/conf/preprocess/movie_poster.yaml

-defaults:
-  - download
-
-url: "https://pl-flash-data.s3.amazonaws.com/movie_posters.zip"

hydra_flash/train.py

@@ -11,7 +11,7 @@ log = logging.getLogger(__name__)
 instantiate = partial(hydra_instantiate, _convert_="all")
 
 
-@hydra.main(version_base="1.1", config_path="conf", config_name="config")
+@hydra.main(version_base="1.1", config_path="conf", config_name="config2")
 def main(cfg):
     # Preprocess : download and/or unzip data
     call(cfg.preprocess)
@@ -20,11 +20,9 @@ def main(cfg):
     datamodule = instantiate(cfg.datamodule)
 
     # Create model
-    model_kwargs = {}
-    for kw in cfg.model.complete:
-        model_kwargs[kw] = getattr(datamodule, kw)
-    del cfg.model.complete
-    model = instantiate(cfg.model, **model_kwargs)
+    model = instantiate(
+        cfg.model, labels=datamodule.labels, multi_label=datamodule.multi_label
+    )
 
     # Finetune model
     trainer = instantiate(cfg.trainer)