Summary:
[export_model.py](https://github.com/facebookresearch/detectron2/blob/master/tools/deploy/export_model.py)
 requires to process sample input, and current implementation requires whole dataset to be imported, it is not a easy way for custom models, so instead of dataset, user can pass a sample image.

Pull Request resolved: https://github.com/facebookresearch/detectron2/pull/2931

Reviewed By: alexander-kirillov

Differential Revision: D27903407

Pulled By: ppwwyyxx

fbshipit-source-id: 1e5d9b5770d2a8200abc46aad01121f0012cf5e7

Summary: Add Confidence Based CSE data samplers (coarse segm only for the moment)

Reviewed By: vkhalidov

Differential Revision: D27188563

fbshipit-source-id: f220448e87c0f31e25c1c3f98d7061c16aa68b6c

Summary: Adapt CSE Bootstrapping to use ground truth category data

Reviewed By: vkhalidov

Differential Revision: D27045679

fbshipit-source-id: 7130931a4cb66e8a9a015309814410f5f8ded171

Summary: Create CSE Base and Uniform Data Samplers

Reviewed By: vkhalidov

Differential Revision: D26691586

fbshipit-source-id: 2688698d612220b4047322ecd91df24186f2efb5

Summary:
Refactor image transform so it does nothing else than the resizing.

Before it was a bit confusing because it was resizing AND assuming that the input data was RGB and in NHWC format, and applying corresponding transforms, which depends on the input dataset

Reviewed By: vkhalidov

Differential Revision: D27081805

fbshipit-source-id: e12a23ff843e24f86feccf9f0dfd00cf628a5a5b

Summary: Correct Image List Dataset to use one image with 3 channels instead of 3 images with 1 channel

Reviewed By: vkhalidov

Differential Revision: D27080295

fbshipit-source-id: 4b90f02302858af800d6e0514972a002f92a3a3d

Summary:
In the bootstrapping datasets, we might not have annotated images, but we often know very well to which animal categories they belong. This diff aims to pass down this information.

For the details of the Instagram Datasets and `build_fb`, check the following diff D27045763

Category to class mapping should look like that and aims to convert "layman's terms" categories, used in the datasets, to category classes (and then to mesh names with CLASS_TO_MESH_NAME_MAPPING)

{F506421983}

Reviewed By: vkhalidov

Differential Revision: D27045669

fbshipit-source-id: 3865c36e622813b7591a3a969a2fa58295a48744

Summary:
Hi~
We propose a new object detection method: Sparse R-CNN. The code is based on detectron2.
I will appreciate it if detectron2 could include it.

Thanks very much.

Pull Request resolved: https://github.com/facebookresearch/detectron2/pull/2911

Reviewed By: alexander-kirillov

Differential Revision: D27899736

Pulled By: ppwwyyxx

fbshipit-source-id: 6b2032f6870e6b0643513fe4829aef1397a4bd02

Summary: This diff fixes a bug in the visualization framework, which image_inference and video_inference debugging tools utilize to visualize RCNN model outputs.

Reviewed By: sampepose

Differential Revision: D27875273

fbshipit-source-id: 4afc88b11b22c902a8956a7d6600a0dbf2550cde

Summary:
Pull Request resolved: https://github.com/facebookresearch/detectron2/pull/2932

Pull Request resolved: https://github.com/fairinternal/detectron2/pull/533

Actually only the one between backbone and proposal generator is redundant unless
we implement a quantized version for roi_align (currently roi align is expecting
a float32 input, so we have to dequantize it in the code)

Reviewed By: vkuzo, ppwwyyxx

Differential Revision: D27837648

fbshipit-source-id: 74fd87b31d6f701dd2917e90efad558e9d5c5b21

Summary: Pull Request resolved: https://github.com/fairinternal/detectron2/pull/534

Reviewed By: alexander-kirillov

Differential Revision: D27877352

Pulled By: ppwwyyxx

fbshipit-source-id: 03e6aa99385115014b02952aa9f97282ed152752