Cluster

docs/source/index.rst

@@ -21,6 +21,7 @@ Lightning Flash
 
    reference/task
    reference/image_classification
+   reference/image_embedder
    reference/text_classification
    reference/tabular_classification
 

docs/source/reference/image_classification.rst

@@ -23,39 +23,39 @@ Use the :class:`~flash.text.ImageClassificatier` pretrained model for inference
 
 .. code-block:: python
 
-	# import our libraries
-	from flash.text import TextClassifier
-
-	# Load finetuned task
-	model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
-
-	# 2. Perform inference from list of sequences
-	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)
+    # import our libraries
+    from flash.text import TextClassifier
+
+    # Load finetuned task
+    model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+
+    # 2. Perform inference from list of sequences
+    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)
 
 Or on a given dataset:
 
 .. code-block:: python
 
-	# import our libraries
-	from flash import download_data
-	from flash.text import TextClassifier
+    # import our libraries
+    from flash import download_data
+    from flash.text import TextClassifier
 
-	# 1. Download dataset, save it under 'data' dir
-	download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
+    # 1. Download dataset, save it under 'data' dir
+    download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
 
-	# 2. Load finetuned task
-	model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
+    # 2. Load finetuned task
+    model = ImageClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
 
-	# 3. Perform inference from a csv file
-	predictions = model.predict("data/imdb/test.csv")
-	print(predictions)
+    # 3. Perform inference from a csv file
+    predictions = model.predict("data/imdb/test.csv")
+    print(predictions)
 
 For more advanced inference options, see :ref:`predictions`.
 
@@ -97,16 +97,16 @@ Now all we need is three lines of code to build to train our task!
 
 .. code-block:: python
 
-	import flash
-	from flash import download_data
-	from flash.vision import ImageClassificationData, ImageClassifier
+    import flash
+    from flash import download_data
+    from flash.vision import ImageClassificationData, ImageClassifier
 
     # 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(
-    	backbone="resnet18",
+        backbone="resnet18",
         train_folder="data/hymenoptera_data/train/",
         valid_folder="data/hymenoptera_data/val/",
         test_folder="data/hymenoptera_data/test/",
@@ -140,7 +140,7 @@ By default, we use a `ResNet-18 <https://arxiv.org/abs/1512.03385>`_ for image c
 
     # 1. organize the data
     data = ImageClassificationData.from_folders(
-    	backbone="resnet34",
+        backbone="resnet34",
         train_folder="data/hymenoptera_data/train/",
         valid_folder="data/hymenoptera_data/val/"
     )

docs/source/reference/image_embedder.rst

@@ -0,0 +1,150 @@
+
+.. _image_embedder:
+
+##############
+Image Embedder
+##############
+
+********
+The task
+********
+Image embedding encodes an image into a vector of image features which can be used for anything like clustering, similarity
+search or classification.
+
+------
+
+*********
+Inference
+*********
+
+The :class:`~flash.vision.ImageEmbedder` is already pre-trained on [ImageNet](http://www.image-net.org/), a dataset of over 14 million images.
+
+Use the :class:`~flash.vision.ImageEmbedder` pretrained model for inference on any image tensor or image path using :func:`~flash.vision.ImageEmbedder.predict`:
+
+.. testcode:: python
+
+	from flash.vision import ImageEmbedder
+
+	# Load finetuned task
+	embedder = ImageEmbedder(backbone='swav-imagenet')
+
+	# 2. Perform inference on an image file
+	embeddings = model.predict('path/to/image.png')
+	print(predictions)
+
+Or on a random image tensor
+
+.. testcode:: python
+
+	# 2. Perform inference on an image file
+	images = torch.rand(32, 3, 224, 224)
+	embeddings = model.predict(images)
+	print(predictions)
+
+For more advanced inference options, see :ref:`predictions`.
+
+------
+
+**********
+Finetuning
+**********
+To tailor this image embedder to your dataset, finetune first.
+
+.. code-block:: python
+
+    import flash
+    from flash import download_data
+    from flash.vision import ImageClassificationData, ImageClassifier
+
+    # 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/",
+    )
+
+    # 3. Build the model
+    embedder = ImageEmbedder(backbone="resnet18")
+
+    # 4. Create the trainer. Run once on data
+    trainer = flash.Trainer(max_epochs=1)
+
+    # 5. Train the model
+    trainer.finetune(model, datamodule=datamodule)
+
+    # 6. Test the model
+    trainer.test()
+
+    # 7. Save it!
+    trainer.save_checkpoint("image_embedder_model.pt")
+
+------
+
+*********************
+Changing the backbone
+*********************
+By default, we use the encoder from `Swav <https://arxiv.org/pdf/2006.09882.pdf>`_ pretrained on Imagenet via contrastive learning. You can change the model run by the task by passing in a different backbone.
+
+.. note::
+
+   When changing the backbone, make sure you pass in the same backbone to the Task and the Data object!
+
+.. code-block:: python
+
+    # 1. organize the data
+    data = ImageClassificationData.from_folders(
+    	backbone="resnet34",
+        train_folder="data/hymenoptera_data/train/",
+        valid_folder="data/hymenoptera_data/val/"
+    )
+
+    # 2. build the task
+    task = ImageClassifier(num_classes=2, backbone="resnet34")
+
+Backbones available
+
+.. list-table:: Backbones
+   :widths: 50 20 20
+   :header-rows: 1
+
+   * - backbone
+     - dataset
+     - training method
+   * - resnet18
+     - Imagenet
+     - supervised
+   * - resnet34
+     - Imagenet
+     - supervised
+   * - resnet50
+     - Imagenet
+     - supervised
+   * - resnet101
+     - Imagenet
+     - supervised
+   * - resnet152
+     - Imagenet
+     - supervised
+   * - swav-imagenet
+     - Imagenet
+     - self-supervised (clustering)
+
+------
+
+*************
+API reference
+*************
+
+.. _image_embedder_class:
+
+ImageEmbedder
+---------------
+
+.. autoclass:: flash.vision.ImageEmbedder
+    :members:
+    :exclude-members: forward
+
+.. _image_embedder_data:

flash/core/model.py

@@ -11,6 +11,7 @@
 # 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 contextlib import contextmanager
 from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Type, Union
 
 import pytorch_lightning as pl
@@ -21,6 +22,25 @@
 from flash.core.utils import get_callable_dict
 
 
+def predict_context(func: Callable) -> Callable:
+    """
+    This decorator is used as context manager
+    to put model in eval mode before running predict and reset to train after.
+    """
+
+    def wrapper(self, *args, **kwargs) -> Any:
+        self.eval()
+        torch.set_grad_enabled(False)
+
+        result = func(self, *args, **kwargs)
+
+        self.train()
+        torch.set_grad_enabled(True)
+        return result
+
+    return wrapper
+
+
 class Task(pl.LightningModule):
     """A general Task.
 
@@ -91,6 +111,7 @@ def test_step(self, batch: Any, batch_idx: int) -> None:
         output = self.step(batch, batch_idx)
         self.log_dict({f"test_{k}": v for k, v in output["logs"].items()}, on_step=False, on_epoch=True, prog_bar=True)
 
+    @predict_context
     def predict(
         self,
         x: Any,
@@ -119,7 +140,8 @@ def predict(
         batch = x if skip_collate_fn else data_pipeline.collate_fn(x)
         batch_x, batch_y = batch if len(batch) == 2 else (batch, None)
         predictions = self.forward(batch_x)
-        return data_pipeline.uncollate_fn(predictions)  # TODO: pass batch and x
+        output = data_pipeline.uncollate_fn(predictions)  # TODO: pass batch and x
+        return output
 
     def configure_optimizers(self) -> torch.optim.Optimizer:
         return self.optimizer_cls(filter(lambda p: p.requires_grad, self.parameters()), lr=self.learning_rate)

flash/vision/__init__.py

@@ -1,2 +1,3 @@
 from flash.vision.classification import ImageClassificationData, ImageClassifier
 from flash.vision.detection import ImageDetector
+from flash.vision.embedding import ImageEmbedder

flash/vision/embedding/__init__.py

@@ -0,0 +1 @@
+from flash.vision.embedding.image_embedder_model import ImageEmbedder