Version pretrained models (#931)

Lightning-Universe · Nov 4, 2021 · f2b08da · f2b08da
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diff --git a/docs/source/general/predictions.rst b/docs/source/general/predictions.rst
@@ -23,7 +23,9 @@ You can pass in a sample of data (image file path, a string of text, etc) to the
     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")
+    model = ImageClassifier.load_from_checkpoint(
+        "https://flash-weights.s3.amazonaws.com/0.5.2/image_classification_model.pt"
+    )
 
     # 3. Predict whether the image contains an ant or a bee
     predictions = model.predict("data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg")
@@ -43,7 +45,9 @@ Predict on a csv file
     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")
+    model = TabularClassifier.load_from_checkpoint(
+        "https://flash-weights.s3.amazonaws.com/0.5.2/tabular_classification_model.pt"
+    )
 
     # 3. Generate predictions from a csv file! Who would survive?
     predictions = model.predict("data/titanic/titanic.csv")
@@ -69,7 +73,9 @@ reference below).
     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")
+    model = ImageClassifier.load_from_checkpoint(
+        "https://flash-weights.s3.amazonaws.com/0.5.2/image_classification_model.pt"
+    )
 
     # 3. Attach the Serializer
     model.serializer = Probabilities()

diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
@@ -91,7 +91,7 @@ Here's an example of inference:
     from flash.text import TextClassifier
 
     # 1. Init the finetuned task from URL
-    model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")
+    model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/0.5.2/text_classification_model.pt")
 
     # 2. Perform inference from list of sequences
     predictions = model.predict(

diff --git a/flash_examples/integrations/fiftyone/image_classification.py b/flash_examples/integrations/fiftyone/image_classification.py
@@ -53,7 +53,9 @@
 trainer.save_checkpoint("image_classification_model.pt")
 
 # 4 Predict from checkpoint
-model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+model = ImageClassifier.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/image_classification_model.pt"
+)
 model.serializer = FiftyOneLabels(return_filepath=True)  # output FiftyOne format
 predictions = trainer.predict(model, datamodule=datamodule)
 predictions = list(chain.from_iterable(predictions))  # flatten batches

diff --git a/flash_examples/integrations/fiftyone/image_classification_fiftyone_datasets.py b/flash_examples/integrations/fiftyone/image_classification_fiftyone_datasets.py
@@ -66,7 +66,9 @@
 trainer.save_checkpoint("image_classification_model.pt")
 
 # 5 Predict from checkpoint on data with ground truth
-model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+model = ImageClassifier.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/image_classification_model.pt"
+)
 model.serializer = FiftyOneLabels(return_filepath=False)  # output FiftyOne format
 datamodule = ImageClassificationData.from_fiftyone(predict_dataset=test_dataset)
 predictions = trainer.predict(model, datamodule=datamodule)

diff --git a/flash_examples/serve/image_classification/inference_server.py b/flash_examples/serve/image_classification/inference_server.py
@@ -13,5 +13,7 @@
 # limitations under the License.
 from flash.image import ImageClassifier
 
-model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+model = ImageClassifier.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/image_classification_model.pt"
+)
 model.serve()
diff --git a/flash_examples/serve/semantic_segmentation/inference_server.py b/flash_examples/serve/semantic_segmentation/inference_server.py
@@ -15,7 +15,7 @@
 from flash.image.segmentation.serialization import SegmentationLabels
 
 model = SemanticSegmentation.load_from_checkpoint(
-    "https://flash-weights.s3.amazonaws.com/semantic_segmentation_model.pt"
+    "https://flash-weights.s3.amazonaws.com/0.5.2/semantic_segmentation_model.pt"
 )
 model.serializer = SegmentationLabels(visualize=False)
 model.serve()
diff --git a/flash_examples/serve/speech_recognition/inference_server.py b/flash_examples/serve/speech_recognition/inference_server.py
@@ -13,5 +13,7 @@
 # limitations under the License.
 from flash.audio import SpeechRecognition
 
-model = SpeechRecognition.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/speech_recognition_model.pt")
+model = SpeechRecognition.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/speech_recognition_model.pt"
+)
 model.serve()
diff --git a/flash_examples/serve/summarization/inference_server.py b/flash_examples/serve/summarization/inference_server.py
@@ -13,5 +13,7 @@
 # limitations under the License.
 from flash.text import SummarizationTask
 
-model = SummarizationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/summarization_model_xsum.pt")
+model = SummarizationTask.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/summarization_model_xsum.pt"
+)
 model.serve()
diff --git a/flash_examples/serve/tabular_classification/inference_server.py b/flash_examples/serve/tabular_classification/inference_server.py
@@ -14,6 +14,8 @@
 from flash.core.classification import Labels
 from flash.tabular import TabularClassifier
 
-model = TabularClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/tabular_classification_model.pt")
+model = TabularClassifier.load_from_checkpoint(
+    "https://flash-weights.s3.amazonaws.com/0.5.2/tabular_classification_model.pt"
+)
 model.serializer = Labels(["Did not survive", "Survived"])
 model.serve()
diff --git a/flash_examples/serve/text_classification/inference_server.py b/flash_examples/serve/text_classification/inference_server.py
@@ -13,5 +13,5 @@
 # limitations under the License.
 from flash.text import TextClassifier
 
-model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")
+model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/0.5.2/text_classification_model.pt")
 model.serve()
diff --git a/flash_examples/serve/translation/inference_server.py b/flash_examples/serve/translation/inference_server.py
@@ -13,5 +13,5 @@
 # limitations under the License.
 from flash.text import TranslationTask
 
-model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")
+model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/0.5.2/translation_model_en_ro.pt")
 model.serve()
diff --git a/flash_notebooks/tabular_classification.ipynb b/flash_notebooks/tabular_classification.ipynb
@@ -222,7 +222,7 @@
    "outputs": [],
    "source": [
     "model = TabularClassifier.load_from_checkpoint(\n",
-    "    \"https://flash-weights.s3.amazonaws.com/tabular_classification_model.pt\")"
+    "    \"https://flash-weights.s3.amazonaws.com/0.5.2/tabular_classification_model.pt\")"
    ]
   },
   {

diff --git a/tests/core/test_model.py b/tests/core/test_model.py
@@ -32,18 +32,15 @@
 from torchmetrics import Accuracy
 
 import flash
+from flash.audio import SpeechRecognition
 from flash.core.adapter import Adapter
 from flash.core.classification import ClassificationTask
 from flash.core.data.process import DefaultPreprocess, Postprocess
-from flash.core.utilities.imports import _TABULAR_AVAILABLE, _TORCH_OPTIMIZER_AVAILABLE, _TRANSFORMERS_AVAILABLE, Image
-from flash.image import ImageClassificationData, ImageClassifier
-from tests.helpers.utils import _IMAGE_TESTING, _TABULAR_TESTING
-
-if _TABULAR_AVAILABLE:
-    from flash.tabular import TabularClassifier
-else:
-    TabularClassifier = None
-
+from flash.core.utilities.imports import _TORCH_OPTIMIZER_AVAILABLE, _TRANSFORMERS_AVAILABLE, Image
+from flash.image import ImageClassificationData, ImageClassifier, SemanticSegmentation
+from flash.tabular import TabularClassifier
+from flash.text import SummarizationTask, TextClassifier, TranslationTask
+from tests.helpers.utils import _AUDIO_TESTING, _IMAGE_TESTING, _TABULAR_TESTING, _TEXT_TESTING
 
 # ======== Mock functions ========
 
@@ -251,23 +248,62 @@ def test_task_datapipeline_save(tmpdir):
 @pytest.mark.parametrize(
     ["cls", "filename"],
     [
-        # needs to be updated.
-        # pytest.param(
-        #    ImageClassifier,
-        #    "image_classification_model.pt",
-        #    marks=pytest.mark.skipif(
-        #        not _IMAGE_TESTING,
-        #        reason="image packages aren't installed",
-        #    ),
-        # ),
+        pytest.param(
+            ImageClassifier,
+            "0.5.2/image_classification_model.pt",
+            marks=pytest.mark.skipif(
+                not _IMAGE_TESTING,
+                reason="image packages aren't installed",
+            ),
+        ),
+        pytest.param(
+            SemanticSegmentation,
+            "0.5.2/semantic_segmentation_model.pt",
+            marks=pytest.mark.skipif(
+                not _IMAGE_TESTING,
+                reason="image packages aren't installed",
+            ),
+        ),
+        pytest.param(
+            SpeechRecognition,
+            "0.5.2/speech_recognition_model.pt",
+            marks=pytest.mark.skipif(
+                not _AUDIO_TESTING,
+                reason="audio packages aren't installed",
+            ),
+        ),
         pytest.param(
             TabularClassifier,
-            "tabular_classification_model.pt",
+            "0.5.2/tabular_classification_model.pt",
             marks=pytest.mark.skipif(
                 not _TABULAR_TESTING,
                 reason="tabular packages aren't installed",
             ),
         ),
+        pytest.param(
+            TextClassifier,
+            "0.5.2/text_classification_model.pt",
+            marks=pytest.mark.skipif(
+                not _TEXT_TESTING,
+                reason="text packages aren't installed",
+            ),
+        ),
+        pytest.param(
+            SummarizationTask,
+            "0.5.2/summarization_model_xsum.pt",
+            marks=pytest.mark.skipif(
+                not _TEXT_TESTING,
+                reason="text packages aren't installed",
+            ),
+        ),
+        pytest.param(
+            TranslationTask,
+            "0.5.2/translation_model_en_ro.pt",
+            marks=pytest.mark.skipif(
+                not _TEXT_TESTING,
+                reason="text packages aren't installed",
+            ),
+        ),
     ],
 )
 def test_model_download(tmpdir, cls, filename):