DI WU first commit

.flake8

@@ -1,8 +1,8 @@
-# This is an example .flake8 config, used when developing *Black* itself.
-# Keep in sync with setup.cfg which is used for source packages.
-
-[flake8]
-ignore = E203, E266, E501, W503
-max-line-length = 80
-max-complexity = 18
-select = B,C,E,F,W,T4,B9
+# This is an example .flake8 config, used when developing *Black* itself.
+# Keep in sync with setup.cfg which is used for source packages.
+
+[flake8]
+ignore = E203, E266, E501, W503
+max-line-length = 80
+max-complexity = 18
+select = B,C,E,F,W,T4,B9

.github/ISSUE_TEMPLATE/bug-report.md

@@ -1,49 +1,49 @@
----
-name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve Mask R-CNN Benchmark
-
----
-
-## 🐛 Bug
-
-<!-- A clear and concise description of what the bug is. -->
-
-## To Reproduce
-
-Steps to reproduce the behavior:
-
-1.
-1.
-1.
-
-<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
-
-## Expected behavior
-
-<!-- A clear and concise description of what you expected to happen. -->
-
-## Environment
-
-Please copy and paste the output from the
-[environment collection script from PyTorch](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
-(or fill out the checklist below manually).
-
-You can get the script and run it with:
-```
-wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
-# For security purposes, please check the contents of collect_env.py before running it.
-python collect_env.py
-```
-
- - PyTorch Version (e.g., 1.0):
- - OS (e.g., Linux):
- - How you installed PyTorch (`conda`, `pip`, source):
- - Build command you used (if compiling from source):
- - Python version:
- - CUDA/cuDNN version:
- - GPU models and configuration:
- - Any other relevant information:
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
+---
+name: "\U0001F41B Bug Report"
+about: Submit a bug report to help us improve Mask R-CNN Benchmark
+
+---
+
+## 🐛 Bug
+
+<!-- A clear and concise description of what the bug is. -->
+
+## To Reproduce
+
+Steps to reproduce the behavior:
+
+1.
+1.
+1.
+
+<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
+
+## Expected behavior
+
+<!-- A clear and concise description of what you expected to happen. -->
+
+## Environment
+
+Please copy and paste the output from the
+[environment collection script from PyTorch](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
+(or fill out the checklist below manually).
+
+You can get the script and run it with:
+```
+wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
+# For security purposes, please check the contents of collect_env.py before running it.
+python collect_env.py
+```
+
+ - PyTorch Version (e.g., 1.0):
+ - OS (e.g., Linux):
+ - How you installed PyTorch (`conda`, `pip`, source):
+ - Build command you used (if compiling from source):
+ - Python version:
+ - CUDA/cuDNN version:
+ - GPU models and configuration:
+ - Any other relevant information:
+
+## Additional context
+
+<!-- Add any other context about the problem here. -->

.github/ISSUE_TEMPLATE/feature-request.md

@@ -1,24 +1,24 @@
----
-name: "\U0001F680Feature Request"
-about: Submit a proposal/request for a new Mask R-CNN Benchmark feature
-
----
-
-## 🚀 Feature
-<!-- A clear and concise description of the feature proposal -->
-
-## Motivation
-
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
-
-## Pitch
-
-<!-- A clear and concise description of what you want to happen. -->
-
-## Alternatives
-
-<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
-
-## Additional context
-
-<!-- Add any other context or screenshots about the feature request here. -->
+---
+name: "\U0001F680Feature Request"
+about: Submit a proposal/request for a new Mask R-CNN Benchmark feature
+
+---
+
+## 🚀 Feature
+<!-- A clear and concise description of the feature proposal -->
+
+## Motivation
+
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
+
+## Pitch
+
+<!-- A clear and concise description of what you want to happen. -->
+
+## Alternatives
+
+<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
+
+## Additional context
+
+<!-- Add any other context or screenshots about the feature request here. -->

.github/ISSUE_TEMPLATE/questions-help-support.md

@@ -1,7 +1,7 @@
----
-name: "❓Questions/Help/Support"
-about: Do you need support?
-
----
-
-## ❓ Questions and Help
+---
+name: "❓Questions/Help/Support"
+about: Do you need support?
+
+---
+
+## ❓ Questions and Help

.gitignore

@@ -1,30 +1,30 @@
-# compilation and distribution
-__pycache__
-_ext
-*.pyc
-*.so
-maskrcnn_benchmark.egg-info/
-build/
-dist/
-
-# pytorch/python/numpy formats
-*.pth
-*.pkl
-*.npy
-
-# ipython/jupyter notebooks
-*.ipynb
-**/.ipynb_checkpoints/
-
-# Editor temporaries
-*.swn
-*.swo
-*.swp
-*~
-
-# Pycharm editor settings
-.idea
-
-# project dirs
-/datasets
-/models
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
+*.so
+maskrcnn_benchmark.egg-info/
+build/
+dist/
+
+# pytorch/python/numpy formats
+*.pth
+*.pkl
+*.npy
+
+# ipython/jupyter notebooks
+*.ipynb
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# Pycharm editor settings
+.idea
+
+# project dirs
+/datasets
+/models

ABSTRACTIONS.md

@@ -1,65 +1,65 @@
-## Abstractions
-The main abstractions introduced by `maskrcnn_benchmark` that are useful to
-have in mind are the following:
-
-### ImageList
-In PyTorch, the first dimension of the input to the network generally represents
-the batch dimension, and thus all elements of the same batch have the same
-height / width.
-In order to support images with different sizes and aspect ratios in the same
-batch, we created the `ImageList` class, which holds internally a batch of
-images (os possibly different sizes). The images are padded with zeros such that
-they have the same final size and batched over the first dimension. The original
-sizes of the images before padding are stored in the `image_sizes` attribute,
-and the batched tensor in `tensors`.
-We provide a convenience function `to_image_list` that accepts a few different
-input types, including a list of tensors, and returns an `ImageList` object.
-
-```python
-from maskrcnn_benchmark.structures.image_list import to_image_list
-
-images = [torch.rand(3, 100, 200), torch.rand(3, 150, 170)]
-batched_images = to_image_list(images)
-
-# it is also possible to make the final batched image be a multiple of a number
-batched_images_32 = to_image_list(images, size_divisible=32)
-```
-
-### BoxList
-The `BoxList` class holds a set of bounding boxes (represented as a `Nx4` tensor) for
-a specific image, as well as the size of the image as a `(width, height)` tuple.
-It also contains a set of methods that allow to perform geometric
-transformations to the bounding boxes (such as cropping, scaling and flipping).
-The class accepts bounding boxes from two different input formats:
-- `xyxy`, where each box is encoded as a `x1`, `y1`, `x2` and `y2` coordinates, and
-- `xywh`, where each box is encoded as `x1`, `y1`, `w` and `h`.
-
-Additionally, each `BoxList` instance can also hold arbitrary additional information
-for each bounding box, such as labels, visibility, probability scores etc.
-
-Here is an example on how to create a `BoxList` from a list of coordinates:
-```python
-from maskrcnn_benchmark.structures.bounding_box import BoxList, FLIP_LEFT_RIGHT
-
-width = 100
-height = 200
-boxes = [
-  [0, 10, 50, 50],
-  [50, 20, 90, 60],
-  [10, 10, 50, 50]
-]
-# create a BoxList with 3 boxes
-bbox = BoxList(boxes, image_size=(width, height), mode='xyxy')
-
-# perform some box transformations, has similar API as PIL.Image
-bbox_scaled = bbox.resize((width * 2, height * 3))
-bbox_flipped = bbox.transpose(FLIP_LEFT_RIGHT)
-
-# add labels for each bbox
-labels = torch.tensor([0, 10, 1])
-bbox.add_field('labels', labels)
-
-# bbox also support a few operations, like indexing
-# here, selects boxes 0 and 2
-bbox_subset = bbox[[0, 2]]
-```
+## Abstractions
+The main abstractions introduced by `maskrcnn_benchmark` that are useful to
+have in mind are the following:
+
+### ImageList
+In PyTorch, the first dimension of the input to the network generally represents
+the batch dimension, and thus all elements of the same batch have the same
+height / width.
+In order to support images with different sizes and aspect ratios in the same
+batch, we created the `ImageList` class, which holds internally a batch of
+images (os possibly different sizes). The images are padded with zeros such that
+they have the same final size and batched over the first dimension. The original
+sizes of the images before padding are stored in the `image_sizes` attribute,
+and the batched tensor in `tensors`.
+We provide a convenience function `to_image_list` that accepts a few different
+input types, including a list of tensors, and returns an `ImageList` object.
+
+```python
+from maskrcnn_benchmark.structures.image_list import to_image_list
+
+images = [torch.rand(3, 100, 200), torch.rand(3, 150, 170)]
+batched_images = to_image_list(images)
+
+# it is also possible to make the final batched image be a multiple of a number
+batched_images_32 = to_image_list(images, size_divisible=32)
+```
+
+### BoxList
+The `BoxList` class holds a set of bounding boxes (represented as a `Nx4` tensor) for
+a specific image, as well as the size of the image as a `(width, height)` tuple.
+It also contains a set of methods that allow to perform geometric
+transformations to the bounding boxes (such as cropping, scaling and flipping).
+The class accepts bounding boxes from two different input formats:
+- `xyxy`, where each box is encoded as a `x1`, `y1`, `x2` and `y2` coordinates, and
+- `xywh`, where each box is encoded as `x1`, `y1`, `w` and `h`.
+
+Additionally, each `BoxList` instance can also hold arbitrary additional information
+for each bounding box, such as labels, visibility, probability scores etc.
+
+Here is an example on how to create a `BoxList` from a list of coordinates:
+```python
+from maskrcnn_benchmark.structures.bounding_box import BoxList, FLIP_LEFT_RIGHT
+
+width = 100
+height = 200
+boxes = [
+  [0, 10, 50, 50],
+  [50, 20, 90, 60],
+  [10, 10, 50, 50]
+]
+# create a BoxList with 3 boxes
+bbox = BoxList(boxes, image_size=(width, height), mode='xyxy')
+
+# perform some box transformations, has similar API as PIL.Image
+bbox_scaled = bbox.resize((width * 2, height * 3))
+bbox_flipped = bbox.transpose(FLIP_LEFT_RIGHT)
+
+# add labels for each bbox
+labels = torch.tensor([0, 10, 1])
+bbox.add_field('labels', labels)
+
+# bbox also support a few operations, like indexing
+# here, selects boxes 0 and 2
+bbox_subset = bbox[[0, 2]]
+```

CODE_OF_CONDUCT.md

@@ -1,5 +1,5 @@
-# Code of Conduct
-
-Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
-Please read the [full text](https://code.fb.com/codeofconduct/)
-so that you can understand what actions will and will not be tolerated.
+# Code of Conduct
+
+Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
+Please read the [full text](https://code.fb.com/codeofconduct/)
+so that you can understand what actions will and will not be tolerated.