simplify examples

flash/core/data/datamodule.py

@@ -11,7 +11,8 @@
 # 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 os
+import platform
 from typing import Any, Optional
 
 import pytorch_lightning as pl
@@ -63,11 +64,11 @@ def __init__(
         self.batch_size = batch_size
 
         # TODO: figure out best solution for setting num_workers
-        # if num_workers is None:
-        #    num_workers = os.cpu_count()
         if num_workers is None:
-            #    warnings.warn("Could not infer cpu count automatically, setting it to zero")
-            num_workers = 0
+            if platform.system() == "Darwin":
+                num_workers = 0
+            else:
+                num_workers = os.cpu_count()
         self.num_workers = num_workers
 
         self._data_pipeline = None

flash_examples/finetuning/image_classification.py

@@ -3,29 +3,27 @@
 from flash.core.finetuning import FreezeUnfreeze
 from flash.vision import ImageClassificationData, ImageClassifier
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
 
-    # 1. Download the data
-    download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
+# 2. Load the data
+datamodule = ImageClassificationData.from_folders(
+    train_folder="data/hymenoptera_data/train/",
+    valid_folder="data/hymenoptera_data/val/",
+    test_folder="data/hymenoptera_data/test/",
+)
 
-    # 2. Load the data
-    datamodule = ImageClassificationData.from_folders(
-        train_folder="data/hymenoptera_data/train/",
-        valid_folder="data/hymenoptera_data/val/",
-        test_folder="data/hymenoptera_data/test/",
-    )
+# 3. Build the model
+model = ImageClassifier(num_classes=datamodule.num_classes)
 
-    # 3. Build the model
-    model = ImageClassifier(num_classes=datamodule.num_classes)
+# 4. Create the trainer. Run twice on data
+trainer = flash.Trainer(max_epochs=2)
 
-    # 4. Create the trainer. Run twice on data
-    trainer = flash.Trainer(max_epochs=2)
+# 5. Train the model
+trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
 
-    # 5. Train the model
-    trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
+# 6. Test the model
+trainer.test()
 
-    # 6. Test the model
-    trainer.test()
-
-    # 7. Save it!
-    trainer.save_checkpoint("image_classification_model.pt")
+# 7. Save it!
+trainer.save_checkpoint("image_classification_model.pt")

flash_examples/finetuning/summarization.py

@@ -2,30 +2,29 @@
 from flash import download_data
 from flash.text import SummarizationData, SummarizationTask
 
-if __name__ == "__main__":
-    # 1. Download the data
-    download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
 
-    # 2. Load the data
-    datamodule = SummarizationData.from_files(
-        train_file="data/xsum/train.csv",
-        valid_file="data/xsum/valid.csv",
-        test_file="data/xsum/test.csv",
-        input="input",
-        target="target"
-    )
+# 2. Load the data
+datamodule = SummarizationData.from_files(
+    train_file="data/xsum/train.csv",
+    valid_file="data/xsum/valid.csv",
+    test_file="data/xsum/test.csv",
+    input="input",
+    target="target"
+)
 
-    # 3. Build the model
-    model = SummarizationTask()
+# 3. Build the model
+model = SummarizationTask()
 
-    # 4. Create the trainer. Run once on data
-    trainer = flash.Trainer(max_epochs=1)
+# 4. Create the trainer. Run once on data
+trainer = flash.Trainer(max_epochs=1)
 
-    # 5. Fine-tune the model
-    trainer.finetune(model, datamodule=datamodule)
+# 5. Fine-tune the model
+trainer.finetune(model, datamodule=datamodule)
 
-    # 6. Test model
-    trainer.test()
+# 6. Test model
+trainer.test()
 
-    # 7. Save it!
-    trainer.save_checkpoint("summarization_model_xsum.pt")
+# 7. Save it!
+trainer.save_checkpoint("summarization_model_xsum.pt")

flash_examples/finetuning/tabular_classification.py

@@ -4,32 +4,30 @@
 from flash.core.data import download_data
 from flash.tabular import TabularClassifier, TabularData
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')
 
-    # 1. Download the data
-    download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')
+# 2. Load the data
+datamodule = TabularData.from_csv(
+    "./data/titanic/titanic.csv",
+    test_csv="./data/titanic/test.csv",
+    categorical_input=["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
+    numerical_input=["Fare"],
+    target="Survived",
+    val_size=0.25,
+)
 
-    # 2. Load the data
-    datamodule = TabularData.from_csv(
-        "./data/titanic/titanic.csv",
-        test_csv="./data/titanic/test.csv",
-        categorical_input=["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
-        numerical_input=["Fare"],
-        target="Survived",
-        val_size=0.25,
-    )
+# 3. Build the model
+model = TabularClassifier.from_data(datamodule, metrics=[Accuracy(), Precision(), Recall()])
 
-    # 3. Build the model
-    model = TabularClassifier.from_data(datamodule, metrics=[Accuracy(), Precision(), Recall()])
+# 4. Create the trainer. Run 10 times on data
+trainer = flash.Trainer(max_epochs=10)
 
-    # 4. Create the trainer. Run 10 times on data
-    trainer = flash.Trainer(max_epochs=10)
+# 5. Train the model
+trainer.fit(model, datamodule=datamodule)
 
-    # 5. Train the model
-    trainer.fit(model, datamodule=datamodule)
+# 6. Test model
+trainer.test()
 
-    # 6. Test model
-    trainer.test()
-
-    # 7. Save it!
-    trainer.save_checkpoint("tabular_classification_model.pt")
+# 7. Save it!
+trainer.save_checkpoint("tabular_classification_model.pt")

flash_examples/finetuning/text_classification.py

@@ -2,32 +2,30 @@
 from flash.core.data import download_data
 from flash.text import TextClassificationData, TextClassifier
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
 
-    # 1. Download the data
-    download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
+# 2. Load the data
+datamodule = TextClassificationData.from_files(
+    train_file="data/imdb/train.csv",
+    valid_file="data/imdb/valid.csv",
+    test_file="data/imdb/test.csv",
+    input="review",
+    target="sentiment",
+    batch_size=512
+)
 
-    # 2. Load the data
-    datamodule = TextClassificationData.from_files(
-        train_file="data/imdb/train.csv",
-        valid_file="data/imdb/valid.csv",
-        test_file="data/imdb/test.csv",
-        input="review",
-        target="sentiment",
-        batch_size=512
-    )
+# 3. Build the model
+model = TextClassifier(num_classes=datamodule.num_classes)
 
-    # 3. Build the model
-    model = TextClassifier(num_classes=datamodule.num_classes)
+# 4. Create the trainer. Run once on data
+trainer = flash.Trainer(max_epochs=1)
 
-    # 4. Create the trainer. Run once on data
-    trainer = flash.Trainer(max_epochs=1)
+# 5. Fine-tune the model
+trainer.finetune(model, datamodule=datamodule, strategy='freeze')
 
-    # 5. Fine-tune the model
-    trainer.finetune(model, datamodule=datamodule, strategy='freeze')
+# 6. Test model
+trainer.test()
 
-    # 6. Test model
-    trainer.test()
-
-    # 7. Save it!
-    trainer.save_checkpoint("text_classification_model.pt")
+# 7. Save it!
+trainer.save_checkpoint("text_classification_model.pt")

flash_examples/finetuning/translation.py

@@ -2,30 +2,29 @@
 from flash import download_data
 from flash.text import TranslationData, TranslationTask
 
-if __name__ == "__main__":
-    # 1. Download the data
-    download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", 'data/')
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip", 'data/')
 
-    # 2. Load the data
-    datamodule = TranslationData.from_files(
-        train_file="data/wmt_en_ro/train.csv",
-        valid_file="data/wmt_en_ro/valid.csv",
-        test_file="data/wmt_en_ro/test.csv",
-        input="input",
-        target="target",
-    )
+# 2. Load the data
+datamodule = TranslationData.from_files(
+    train_file="data/wmt_en_ro/train.csv",
+    valid_file="data/wmt_en_ro/valid.csv",
+    test_file="data/wmt_en_ro/test.csv",
+    input="input",
+    target="target",
+)
 
-    # 3. Build the model
-    model = TranslationTask()
+# 3. Build the model
+model = TranslationTask()
 
-    # 4. Create the trainer. Run once on data
-    trainer = flash.Trainer(max_epochs=1, precision=16, gpus=1)
+# 4. Create the trainer. Run once on data
+trainer = flash.Trainer(max_epochs=1, precision=16, gpus=1)
 
-    # 5. Fine-tune the model
-    trainer.finetune(model, datamodule=datamodule)
+# 5. Fine-tune the model
+trainer.finetune(model, datamodule=datamodule)
 
-    # 6. Test model
-    trainer.test()
+# 6. Test model
+trainer.test()
 
-    # 7. Save it!
-    trainer.save_checkpoint("translation_model_en_ro.pt")
+# 7. Save it!
+trainer.save_checkpoint("translation_model_en_ro.pt")

flash_examples/predict/classify_image.py

@@ -2,23 +2,21 @@
 from flash.core.data import download_data
 from flash.vision import ImageClassificationData, ImageClassifier
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
 
-    # 1. Download the 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")
 
-    # 2. Load the model from a checkpoint
-    model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+# 3a. Predict what's on a few images! ants or bees?
+predictions = model.predict([
+    "data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg",
+    "data/hymenoptera_data/val/bees/590318879_68cf112861.jpg",
+    "data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg",
+])
+print(predictions)
 
-    # 3a. Predict what's on a few images! ants or bees?
-    predictions = model.predict([
-        "data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg",
-        "data/hymenoptera_data/val/bees/590318879_68cf112861.jpg",
-        "data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg",
-    ])
-    print(predictions)
-
-    # 3b. Or generate predictions with a whole folder!
-    datamodule = ImageClassificationData.from_folder(folder="data/hymenoptera_data/predict/")
-    predictions = Trainer().predict(model, datamodule=datamodule)
-    print(predictions)
+# 3b. Or generate predictions with a whole folder!
+datamodule = ImageClassificationData.from_folder(folder="data/hymenoptera_data/predict/")
+predictions = Trainer().predict(model, datamodule=datamodule)
+print(predictions)

flash_examples/predict/classify_tabular.py

@@ -1,16 +1,12 @@
 from flash.core.data import download_data
 from flash.tabular import TabularClassifier
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", 'data/')
 
-    # 1. Download the 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/tabular_classification_model.pt")
 
-    # 2. Load the model from a checkpoint
-    model = TabularClassifier.load_from_checkpoint(
-        "https://flash-weights.s3.amazonaws.com/tabular_classification_model.pt"
-    )
-
-    # 3. Generate predictions from a sheet file! Who would survive?
-    predictions = model.predict("data/titanic/titanic.csv")
-    print(predictions)
+# 3. Generate predictions from a sheet file! Who would survive?
+predictions = model.predict("data/titanic/titanic.csv")
+print(predictions)

flash_examples/predict/classify_text.py

@@ -3,28 +3,26 @@
 from flash.core.data import download_data
 from flash.text import TextClassificationData, TextClassifier
 
-if __name__ == "__main__":
+# 1. Download the data
+download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
 
-    # 1. Download the 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")
 
-    # 2. Load the model from a checkpoint
-    model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")
+# 2a. Classify a few sentences! How was the movie?
+predictions = model.predict([
+    "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
+    "The worst movie in the history of cinema.",
+    "I come from Bulgaria where it 's almost impossible to have a tornado."
+    "Very, very afraid"
+    "This guy has done a great job with this movie!",
+])
+print(predictions)
 
-    # 2a. Classify a few sentences! How was the movie?
-    predictions = model.predict([
-        "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
-        "The worst movie in the history of cinema.",
-        "I come from Bulgaria where it 's almost impossible to have a tornado."
-        "Very, very afraid"
-        "This guy has done a great job with this movie!",
-    ])
-    print(predictions)
-
-    # 2b. Or generate predictions from a sheet file!
-    datamodule = TextClassificationData.from_file(
-        predict_file="data/imdb/predict.csv",
-        input="review",
-    )
-    predictions = Trainer().predict(model, datamodule=datamodule)
-    print(predictions)
+# 2b. Or generate predictions from a sheet file!
+datamodule = TextClassificationData.from_file(
+    predict_file="data/imdb/predict.csv",
+    input="review",
+)
+predictions = Trainer().predict(model, datamodule=datamodule)
+print(predictions)