Enable tests for examples (#123)

flash_examples/finetuning/image_classification.py

@@ -17,7 +17,7 @@
 from flash.vision import ImageClassificationData, ImageClassifier
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "data/")
 
 # 2. Load the data
 datamodule = ImageClassificationData.from_folders(

flash_examples/finetuning/summarization.py

@@ -16,7 +16,7 @@
 from flash.text import SummarizationData, SummarizationTask
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "data/")
 
 # 2. Load the data
 datamodule = SummarizationData.from_files(

flash_examples/finetuning/tabular_classification.py

@@ -1,7 +1,3 @@
-from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall
-
-import flash
-from flash.core.data import download_data
 # Copyright The PyTorch Lightning team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,10 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall
+
+import flash
+from flash.core.data import download_data
 from flash.tabular import TabularClassifier, TabularData
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "data/")
 
 # 2. Load the data
 datamodule = TabularData.from_csv(

flash_examples/finetuning/text_classification.py

@@ -1,5 +1,3 @@
-import flash
-from flash.core.data import download_data
 # Copyright The PyTorch Lightning team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,10 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import flash
+from flash.core.data import download_data
 from flash.text import TextClassificationData, TextClassifier
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "data/")
 
 # 2. Load the data
 datamodule = TextClassificationData.from_files(
@@ -35,7 +35,7 @@
 trainer = flash.Trainer(max_epochs=1)
 
 # 5. Fine-tune the model
-trainer.finetune(model, datamodule=datamodule, strategy='freeze')
+trainer.finetune(model, datamodule=datamodule, strategy="freeze")
 
 # 6. Test model
 trainer.test()

flash_examples/finetuning/translation.py

@@ -1,5 +1,3 @@
-import flash
-from flash import download_data
 # Copyright The PyTorch Lightning team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,10 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import flash
+from flash import download_data
 from flash.text import TranslationData, TranslationTask
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", "data/")
 
 # 2. Load the data
 datamodule = TranslationData.from_files(

flash_examples/predict/classify_image.py

@@ -16,7 +16,7 @@
 from flash.vision import ImageClassificationData, ImageClassifier
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "data/")
 
 # 2. Load the model from a checkpoint
 model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")

flash_examples/predict/classify_tabular.py

@@ -15,7 +15,7 @@
 from flash.tabular import TabularClassifier
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "data/")
 
 # 2. Load the model from a checkpoint
 model = TabularClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/tabnet_classification_model.pt")

flash_examples/predict/classify_text.py

@@ -17,7 +17,7 @@
 from flash.text import TextClassificationData, TextClassifier
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "data/")
 
 # 2. Load the model from a checkpoint
 model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")

flash_examples/predict/image_embedder.py

@@ -17,14 +17,14 @@
 from flash.vision import ImageEmbedder
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "data/")
 
 # 2. Create an ImageEmbedder with swav trained on imagenet.
 # Check out SWAV: https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#swav
 embedder = ImageEmbedder(backbone="swav-imagenet", embedding_dim=128)
 
 # 3. Generate an embedding from an image path.
-embeddings = embedder.predict('data/hymenoptera_data/predict/153783656_85f9c3ac70.jpg')
+embeddings = embedder.predict(["data/hymenoptera_data/predict/153783656_85f9c3ac70.jpg"])
 
 # 4. Print embeddings shape
 print(embeddings.shape)

flash_examples/predict/summarize.py

@@ -17,7 +17,7 @@
 from flash.text import SummarizationData, SummarizationTask
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "data/")
 
 # 2. Load the model from a checkpoint
 model = SummarizationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/summarization_model_xsum.pt")

flash_examples/predict/translate.py

@@ -17,7 +17,7 @@
 from flash.text import TranslationData, TranslationTask
 
 # 1. Download the data
-download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", 'data/')
+download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", "data/")
 
 # 2. Load the model from a checkpoint
 model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")

tests/examples/test_examples.py

@@ -53,13 +53,20 @@ def run_test(filepath):
     "step,file",
     [
         ("finetuning", "image_classification.py"),
+        # ("finetuning", "object_detection.py"),  # TODO: takes too long.
+        # ("finetuning", "summarization.py"),  # TODO: takes too long.
         ("finetuning", "tabular_classification.py"),
+        ("finetuning", "text_classification.py"),
+        # ("finetuning", "translation.py"),  # TODO: takes too long.
         ("predict", "classify_image.py"),
         ("predict", "classify_tabular.py"),
-        # "classify_text.py" TODO: takes too long
+        ("predict", "classify_text.py"),
+        ("predict", "image_embedder.py"),
+        ("predict", "summarize.py"),
+        # ("predict", "translate.py"),  # TODO: takes too long
     ]
 )
-def test_finetune_example(tmpdir, step, file):
+def test_example(tmpdir, step, file):
     run_test(str(root / "flash_examples" / step / file))