first commit

LICENSE

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+MIT License
+
+Copyright (c) 2017 Jianwei Yang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# A Pytorch Implementation of Strong-Weak Distribution Alignment for Adaptive Object Detection
+
+<img src='./docs/swda.png' width=900/>
+## Introduction
+Follow [original repository](https://github.com/jwyang/faster-rcnn.pytorch).
+ to setup the environment. When installing pytorch-faster-rcnn, you may encounter some issues.
+Many issues have been reported there to setup the environment.
+### Data Preparation
+
+* **PASCAL_VOC 07+12**: Please follow the instructions in [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models) to prepare VOC datasets. Actually, you can refer to any others. After downloading the data, creat softlinks in the folder data/.
+* **Clipart, WaterColor**: Dataset preparation instruction link [Cross Domain Detection ](https://github.com/naoto0804/cross-domain-detection/tree/master/datasets). Images translated by Cyclegan are available in the website.
+* **Sim10k**: Website [Sim10k](https://fcav.engin.umich.edu/sim-dataset/)
+* **Cityscape-Translated Sim10k**: TBA
+* **CitysScape, FoggyCityscape**: Download website [Cityscape](https://www.cityscapes-dataset.com/)
+
+All codes are written to fit for the format of PASCAL_VOC.
+For example, the dataset [Sim10k](https://fcav.engin.umich.edu/sim-dataset/) is stored as follows.
+
+```
+$ cd Sim10k/VOC2012/
+$ ls
+Annotations  ImageSets  JPEGImages
+$ cat ImageSets/Main/val.txt
+3384827.jpg
+3384828.jpg
+3384829.jpg
+.
+.
+.
+```
+If you want to test the code on your own dataset, arange the dataset
+ in the format of PASCAL, make dataset class in lib/datasets/. and add
+ it to  lib/datasets/factory.py, lib/datasets/config_dataset.py. Then, add the dataset option to lib/model/utils/parser_func.py.
+### Data Path
+Write your dataset directories' paths in lib/datasets/config_dataset.py.
+
+### Pretrained Model
+
+We used two pretrained models in our experiments, VGG and ResNet101. You can download these two models from:
+
+* VGG16: [Dropbox](https://www.dropbox.com/s/s3brpk0bdq60nyb/vgg16_caffe.pth?dl=0), [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/vgg16_caffe.pth)
+
+* ResNet101: [Dropbox](https://www.dropbox.com/s/iev3tkbz5wyyuz9/resnet101_caffe.pth?dl=0), [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/resnet101_caffe.pth)
+
+Download them and write the path in __C.VGG_PATH and __C.RESNET_PATH at lib/model/utils/config.py.
+
+## Train
+* With only local alignment loss,
+```
+ CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_local.py \
+                    --dataset source_dataset --dataset_t target_dataset --net vgg16 \
+                    --cuda
+```
+Add --lc when using context-vector based regularization loss.
+
+* With only global alignment loss,
+```
+ CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_global.py \
+                    --dataset source_dataset --dataset_t target_dataset --net vgg16 \
+                    --cuda
+```
+Add --gc when using context-vector based regularization loss.
+* With global and local alignment loss,
+```
+ CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_global_local.py \
+                    --dataset source_dataset --dataset_t target_dataset --net vgg16 \
+                    --cuda
+```
+Add --lc and --gc when using context-vector based regularization loss.
+## Test
+
+```
+ CUDA_VISIBLE_DEVICES=$GPU_ID python test_net_global_local.py \
+                    --dataset target_dataset --net vgg16 \
+                    --cuda --lc --gc --load_name path_to_model
+```

_init_paths.py

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+import os.path as osp
+import sys
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+this_dir = osp.dirname(__file__)
+
+# Add lib to PYTHONPATH
+lib_path = osp.join(this_dir, 'lib')
+add_path(lib_path)
+
+coco_path = osp.join(this_dir, 'data', 'coco', 'PythonAPI')
+add_path(coco_path)

cfgs/res101.yml

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+EXP_DIR: res101
+TRAIN:
+  HAS_RPN: True
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+  DISPLAY: 20
+  BATCH_SIZE: 128
+  RPN_POST_NMS_TOP_N_TARGET: 128
+  WEIGHT_DECAY: 0.0001
+  DOUBLE_BIAS: False
+  LEARNING_RATE: 0.001
+TEST:
+  HAS_RPN: True
+POOLING_SIZE: 7
+POOLING_MODE: align
+CROP_RESIZE_WITH_MAX_POOL: False

cfgs/res101_ls.yml

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+EXP_DIR: res101
+TRAIN:
+  HAS_RPN: True
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+  DISPLAY: 20
+  BATCH_SIZE: 128
+  WEIGHT_DECAY: 0.0001
+  SCALES: [800]
+  DOUBLE_BIAS: False
+  LEARNING_RATE: 0.001
+TEST:
+  HAS_RPN: True
+  SCALES: [800]
+  MAX_SIZE: 1200
+  RPN_POST_NMS_TOP_N: 1000
+POOLING_SIZE: 7
+POOLING_MODE: align
+CROP_RESIZE_WITH_MAX_POOL: False

cfgs/res50.yml

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+EXP_DIR: res50
+TRAIN:
+  HAS_RPN: True
+  # IMS_PER_BATCH: 1
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+  DISPLAY: 20
+  BATCH_SIZE: 256
+  WEIGHT_DECAY: 0.0001
+  DOUBLE_BIAS: False
+  SNAPSHOT_PREFIX: res50_faster_rcnn
+TEST:
+  HAS_RPN: True
+POOLING_MODE: crop

cfgs/vgg16.yml

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+EXP_DIR: vgg16
+TRAIN:
+  HAS_RPN: True
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+  BATCH_SIZE: 256
+  RPN_POST_NMS_TOP_N_TARGET: 256
+  LEARNING_RATE: 0.001
+TEST:
+  HAS_RPN: True
+POOLING_MODE: align
+CROP_RESIZE_WITH_MAX_POOL: False