Merge branch 'master' into optional_input_108

CHANGELOG.md

@@ -5,19 +5,56 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
-## [0.1.0] - 02/02/2021
+## [Unreleased] - 2021-MM-DD
 
 ### Added
 
-- Added flash_notebook examples ([#9](https://github.com/PyTorchLightning/lightning-flash/pull/9))
-- Added `strategy` to `trainer.finetune` with `NoFreeze`, `Freeze`, `FreezeUnfreeze`, `UnfreezeMilestones` Callbacks([#39](https://github.com/PyTorchLightning/lightning-flash/pull/39))
-- Added `SummarizationData`, `SummarizationTask` and `TranslationData`, `TranslationTask` ([#37](https://github.com/PyTorchLightning/lightning-flash/pull/37))
-- Added `ImageEmbedder` ([#36](https://github.com/PyTorchLightning/lightning-flash/pull/36))
+
+
+### Changed
+
+
+
+### Fixed
+
+
+
+### Removed
+
+
+
+
+## [0.2.0] - 2021-02-12
+
+### Added
+
+- Added `ObjectDetector` Task ([#56](https://github.com/PyTorchLightning/lightning-flash/pull/56))
+- Added TabNet for tabular classification ([#101](https://github.com/PyTorchLightning/lightning-flash/pull/#101))
+- Added support for more backbones(mobilnet, vgg, densenet, resnext) ([#45](https://github.com/PyTorchLightning/lightning-flash/pull/45))
+- Added backbones for image embedding model ([#63](https://github.com/PyTorchLightning/lightning-flash/pull/63))
+- Added SWAV and SimCLR models to `imageclassifier` + backbone reorg ([#68](https://github.com/PyTorchLightning/lightning-flash/pull/68))
 
 ### Changed
 
+- Applied transform in `FilePathDataset` ([#97](https://github.com/PyTorchLightning/lightning-flash/pull/97))
+- Moved classification integration from vision root to folder ([#86](https://github.com/PyTorchLightning/lightning-flash/pull/86))
 
 ### Fixed
 
+- Unfreeze default number of workers in datamodule ([#57](https://github.com/PyTorchLightning/lightning-flash/pull/57))
+- Fixed wrong label in `FilePathDataset` ([#94](https://github.com/PyTorchLightning/lightning-flash/pull/94))
 
 ### Removed
+
+- Removed `densenet161` duplicate in `DENSENET_MODELS` ([#76](https://github.com/PyTorchLightning/lightning-flash/pull/76))
+- Removed redundant `num_features` arg from Classification model ([#88](https://github.com/PyTorchLightning/lightning-flash/pull/88))
+
+
+## [0.1.0] - 2021-02-02
+
+### Added
+
+- Added flash_notebook examples ([#9](https://github.com/PyTorchLightning/lightning-flash/pull/9))
+- Added `strategy` to `trainer.finetune` with `NoFreeze`, `Freeze`, `FreezeUnfreeze`, `UnfreezeMilestones` Callbacks([#39](https://github.com/PyTorchLightning/lightning-flash/pull/39))
+- Added `SummarizationData`, `SummarizationTask` and `TranslationData`, `TranslationTask` ([#37](https://github.com/PyTorchLightning/lightning-flash/pull/37))
+- Added `ImageEmbedder` ([#36](https://github.com/PyTorchLightning/lightning-flash/pull/36))

docs/source/conf.py

@@ -44,6 +44,7 @@
     'sphinx.ext.intersphinx',
     # 'sphinx.ext.todo',
     # 'sphinx.ext.coverage',
+    'sphinx.ext.viewcode',
     'sphinx.ext.autosummary',
     'sphinx.ext.napoleon',
     'sphinx.ext.imgmath',

docs/source/index.rst

@@ -26,6 +26,7 @@ Lightning Flash
    reference/text_classification
    reference/tabular_classification
    reference/translation
+   reference/object_detection
 
 .. toctree::
    :maxdepth: 1

docs/source/reference/image_embedder.rst

@@ -29,7 +29,7 @@ Use the :class:`~flash.vision.ImageEmbedder` pretrained model for inference on a
 	embedder = ImageEmbedder(backbone="resnet18")
 
 	# 2. Perform inference on an image file
-	embeddings = model.predict("path/to/image.png")
+	embeddings = embedder.predict("path/to/image.png")
 	print(embeddings)
 
 Or on a random image tensor
@@ -91,13 +91,12 @@ By default, we use the encoder from `SwAV <https://arxiv.org/pdf/2006.09882.pdf>
 
 .. note::
 
-   When changing the backbone, make sure you pass in the same backbone to the Task and the Data object!
+   When changing the backbone, make sure you pass in the same backbone to the Task!
 
 .. 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/"
     )

docs/source/reference/object_detection.rst

@@ -0,0 +1,132 @@
+
+.. _object_detection:
+
+################
+Object Detection
+################
+
+********
+The task
+********
+
+The object detection task identifies instances of objects of a certain class within an image.
+
+------
+
+*********
+Inference
+*********
+
+The :class:`~flash.vision.ObjectDetector` is already pre-trained on `COCO train2017 <https://cocodataset.org/>`_, a dataset with `91 classes <https://cocodataset.org/#explore>`_ (123,287 images, 886,284 instances).
+
+.. code-block::
+
+    annotation{
+        "id": int, 
+        "image_id": int, 
+        "category_id": int, 
+        "segmentation": RLE or [polygon], 
+        "area": float, 
+        "bbox": [x,y,width,height], 
+        "iscrowd": 0 or 1,
+    }
+
+    categories[{
+        "id": int, 
+        "name": str, 
+        "supercategory": str,
+    }]
+
+Use the :class:`~flash.vision.ObjectDetector` pretrained model for inference on any image tensor or image path using :func:`~flash.vision.ObjectDetector.predict`:
+
+.. code-block:: python
+
+	from flash.vision import ObjectDetector
+
+	# 1. Load the model
+	detector = ObjectDetector()
+
+	# 2. Perform inference on an image file
+	predictions = detector.predict("path/to/image.png")
+	print(predictions)
+
+Or on a random image tensor
+
+.. code-block:: python
+
+    # Perform inference on a random image tensor
+    import torch
+    images = torch.rand(32, 3, 1080, 1920)
+    predictions = detector.predict(images)
+    print(predictions)
+
+For more advanced inference options, see :ref:`predictions`.
+
+------
+
+**********
+Finetuning
+**********
+
+To tailor the object detector to your dataset, you would need to have it in `COCO Format <https://cocodataset.org/#format-data>`_, and then finetune the model.
+
+.. code-block:: python
+
+    import flash
+    from flash.core.data import download_data
+    from flash.vision import ObjectDetectionData, ObjectDetector
+
+    # 1. Download the data
+    # Dataset Credit: https://www.kaggle.com/ultralytics/coco128
+    download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")
+
+    # 2. Load the Data
+    datamodule = ObjectDetectionData.from_coco(
+        train_folder="data/coco128/images/train2017/",
+        train_ann_file="data/coco128/annotations/instances_train2017.json",
+        batch_size=2
+    )
+
+    # 3. Build the model
+    model = ObjectDetector(num_classes=datamodule.num_classes)
+
+    # 4. Create the trainer. Run thrice on data
+    trainer = flash.Trainer(max_epochs=3)
+
+    # 5. Finetune the model
+    trainer.finetune(model, datamodule)
+
+    # 6. Save it!
+    trainer.save_checkpoint("object_detection_model.pt")
+
+------
+
+*****
+Model
+*****
+
+By default, we use the `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_ model with a ResNet-50 FPN backbone. The inputs could be images of different sizes. The model behaves differently for training and evaluation. For training, it expects both the input tensors as well as the targets. And during evaluation, it expects only the input tensors and returns predictions for each image. The predictions are a list of boxes, labels and scores.
+
+------
+
+*************
+API reference
+*************
+
+.. _object_detector:
+
+ObjectDetector
+--------------
+
+.. autoclass:: flash.vision.ObjectDetector
+    :members:
+    :exclude-members: forward
+
+.. _object_detection_data:
+
+ObjectDetectionData
+-------------------
+
+.. autoclass:: flash.vision.ObjectDetectionData
+
+.. automethod:: flash.vision.ObjectDetectionData.from_coco

flash/__init__.py

@@ -14,7 +14,7 @@
 """Root package info."""
 import os
 
-__version__ = "0.2.0rc1"
+__version__ = "0.2.1-dev"
 __author__ = "PyTorchLightning et al."
 __author_email__ = "name@pytorchlightning.ai"
 __license__ = 'Apache-2.0'
@@ -56,5 +56,13 @@
     from flash.core.trainer import Trainer
 
     __all__ = [
-        "Task", "ClassificationTask", "DataModule", "vision", "text", "tabular", "data", "utils", "download_data"
+        "Task",
+        "ClassificationTask",
+        "DataModule",
+        "vision",
+        "text",
+        "tabular",
+        "data",
+        "utils",
+        "download_data",
     ]

flash/tabular/classification/model.py

@@ -11,12 +11,11 @@
 # 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 typing import Any, Callable, List, Optional, Tuple, Type, Union
+from typing import Any, Callable, List, Optional, Tuple, Type
 
 import torch
 from pytorch_lightning.metrics import Metric
 from pytorch_tabnet.tab_network import TabNet
-from torch import nn
 from torch.nn import functional as F
 
 from flash.core.classification import ClassificationTask

flash/vision/detection/model.py

@@ -11,7 +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 typing import Any, Callable, Mapping, Optional, Sequence, Type, Union
+from typing import Any, Callable, Mapping, Sequence, Type, Union
 
 import torch
 import torchvision
@@ -20,7 +20,6 @@
 from torchvision.ops import box_iou
 
 from flash.core import Task
-from flash.core.data import DataPipeline
 from flash.vision.detection.data import ObjectDetectionDataPipeline
 from flash.vision.detection.finetuning import ObjectDetectionFineTuning
 
@@ -29,8 +28,7 @@
 
 def _evaluate_iou(target, pred):
     """
-    Evaluate intersection over union (IOU) for target from dataset and output prediction
-    from model
+    Evaluate intersection over union (IOU) for target from dataset and output prediction from model
     """
     if pred["boxes"].shape[0] == 0:
         # no box detected, 0 IOU
@@ -42,17 +40,16 @@ class ObjectDetector(Task):
     """Image detection task
 
     Ref: Lightning Bolts https://github.com/PyTorchLightning/pytorch-lightning-bolts
+
     Args:
         num_classes: the number of classes for detection, including background
         model: either a string of :attr`_models` or a custom nn.Module.
             Defaults to 'fasterrcnn_resnet50_fpn'.
         loss: the function(s) to update the model with. Has no effect for torchvision detection models.
         metrics: The provided metrics. All metrics here will be logged to progress bar and the respective logger.
-            Defaults to None.
         optimizer: The optimizer to use for training. Can either be the actual class or the class name.
-            Defaults to Adam.
         pretrained: Whether the model from torchvision should be loaded with it's pretrained weights.
-            Has no effect for custom models. Defaults to True.
+            Has no effect for custom models.
         learning_rate: The learning rate to use for training
 
     """
@@ -89,8 +86,7 @@ def __init__(
         )
 
     def training_step(self, batch, batch_idx) -> Any:
-        """The training step.
-        Overrides Task.training_step
+        """The training step. Overrides ``Task.training_step``
         """
         images, targets = batch
         targets = [{k: v for k, v in t.items()} for t in targets]

flash/vision/embedding/image_embedder_model.py

@@ -14,7 +14,6 @@
 from typing import Any, Callable, Mapping, Optional, Sequence, Type, Union
 
 import torch
-import torchvision
 from pytorch_lightning.metrics import Accuracy
 from pytorch_lightning.utilities.distributed import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException

tests/vision/classification/test_model.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 import pytest
 import torch
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 from flash import Trainer
 from flash.vision import ImageClassifier

tests/vision/detection/test_data.py

@@ -3,16 +3,12 @@
 from pathlib import Path
 
 import pytest
-import torch
 from PIL import Image
 from pytorch_lightning.utilities import _module_available
-from torchvision import transforms as T
 
 from flash.vision.detection.data import ObjectDetectionData
 
 _COCO_AVAILABLE = _module_available("pycocotools")
-if _COCO_AVAILABLE:
-    from pycocotools.coco import COCO
 
 
 def _create_dummy_coco_json(dummy_json_path):

tests/vision/detection/test_data_model_integration.py

@@ -23,8 +23,6 @@
 from tests.vision.detection.test_data import _create_synth_coco_dataset
 
 _COCO_AVAILABLE = _module_available("pycocotools")
-if _COCO_AVAILABLE:
-    from pycocotools.coco import COCO
 
 
 @pytest.mark.skipif(not _COCO_AVAILABLE, reason="pycocotools is not installed for testing")