Routine updates

.gitattributes

@@ -40,3 +40,10 @@ checkpoints/Model_80.pth filter=lfs diff=lfs merge=lfs -text
 checkpoints/AEMatter/AEM_RWA.ckpt filter=lfs diff=lfs merge=lfs -text
 checkpoints/StableDiffusion/90c7c97574f8db765509b6a5d2e7b2551b430a10cac03e37d368654eac5e8169cd149644d188be4b5b2f1b9f29e66b64a02535f622f2bf284c319b076224cb2b filter=lfs diff=lfs merge=lfs -text
 checkpoints/StableDiffusion/b970812225cfb95427c13e73b75eef66430e2a525876dddac494d70fe4ed0524cb197043e0ac3dc3026b32a45cd1d6d126ec2fe74a5bc3ef5df21836ca022b30 filter=lfs diff=lfs merge=lfs -text
+checkpoints/MVANet/skin.pth filter=lfs diff=lfs merge=lfs -text
+checkpoints/MVANet/garment.pth filter=lfs diff=lfs merge=lfs -text
+demo/demo_lip.png filter=lfs diff=lfs merge=lfs -text
+demo/lip-visualization.jpg filter=lfs diff=lfs merge=lfs -text
+demo/demo_pascal.png filter=lfs diff=lfs merge=lfs -text
+demo/demo_atr.png filter=lfs diff=lfs merge=lfs -text
+demo/demo.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,28 +1,30 @@
-/ComfyUI_MVANet/download.sh
+/ComfyUI_MVANet/__pycache__/__init__.cpython-310.pyc
+/ComfyUI_MVANet/#README.org#
+/ComfyUI_MVANet/.#README.org
+/ComfyUI_MVANet/README.org~
+/ComfyUI_MVANet/.README.org.~undo-tree~
+/#main.org#
+/.#main.org
+/main.org~
+/.main.org.~undo-tree~
+/.README.md.~undo-tree~
+/ComfyUI_MVANet/.#README.org
+/ComfyUI_AEMatter/__pycache__/__init__.cpython-310.pyc
+/ComfyUI_AEMatter/AEMatter.class.py
+/ComfyUI_AEMatter/AEMatter.execute.py
+/ComfyUI_AEMatter/AEMatter.function.py
+/ComfyUI_AEMatter/AEMatter.import.py
 /ComfyUI_MVANet/MVANet_inference.class.py
 /ComfyUI_MVANet/MVANet_inference.execute.py
 /ComfyUI_MVANet/MVANet_inference.function.py
 /ComfyUI_MVANet/MVANet_inference.import.py
-/ComfyUI_MVANet/MVANet_inference.run.sh
 /ComfyUI_MVANet/MVANet_inference.unify.sh
-/ComfyUI_MVANet/.#README.org
-data/
-demo/demo_atr.png
-demo/demo.jpg
-demo/demo_lip.png
-demo/demo_pascal.png
-demo/lip-visualization.jpg
+/ComfyUI_AEMatter/AEMatter.unify.sh
 /git_add.txt
+/git_lfs_track.txt
+/gitignore.txt
+/rm.txt
+/work.sh
 log/
-/main.org
 pretrain_model/
-**/__pycache__
-/rm.txt
-/waste.txt
-ComfyUI_AEMatter/AEMatter.execute.py
-ComfyUI_AEMatter/__pycache__/__init__.cpython-310.pyc
-ComfyUI_AEMatter/AEMatter.run.sh
-ComfyUI_AEMatter/AEMatter.class.py
-ComfyUI_AEMatter/AEMatter.import.py
-ComfyUI_AEMatter/AEMatter.function.py
-ComfyUI_AEMatter/AEMatter.unify.sh
+commit_and_push.sh

ComfyUI_AEMatter/AEMatter.run.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+. "${HOME}/dbnew.sh"
+python3 './AEMatter.py'

ComfyUI_MVANet/MVANet_inference.run.sh

@@ -0,0 +1,3 @@
+#!/bin/sh
+. "${HOME}/dbnew.sh"
+python3 './MVANet_inference.py'

ComfyUI_MVANet/download.sh

@@ -0,0 +1,13 @@
+#!/bin/sh
+get_repo(){
+    DIR_REPO="${HOME}/GITHUB/$('echo' "${1}" | 'sed' 's/^git@github.com://g ; s@^https://github.com/@@g ; s@.git$@@g' )"
+    DIR_BASE="$('dirname' '--' "${DIR_REPO}")"
+    mkdir -pv -- "${DIR_BASE}"
+    cd "${DIR_BASE}"
+    git clone "${1}"
+    cd "${DIR_REPO}"
+    git pull
+    git submodule update --recursive --init
+}
+
+get_repo 'https://github.com/qianyu-dlut/MVANet.git'

checkpoints/MVANet/garment.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7604ed46e06fbcff3b8f38c8934d253617171d02aecdd028f0f01086d9344893
+size 380785263

checkpoints/MVANet/skin.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c71afcdd9cb1be73e43d84f5ffc2ae12b4964cc13c8460fc0adb6d52a0603cd4
+size 380782803

main.org

@@ -0,0 +1,680 @@
+* COMMENT WORK SPACE
+cd $HOME/HUGGINGFACE/aravindhv10/Self-Correction-Human-Parsing
+
+** ELISP
+#+begin_src elisp
+  (save-buffer)
+  (org-babel-tangle)
+  (shell-command "./work.sh")
+#+end_src
+
+#+RESULTS:
+: 0
+
+** ELISP
+#+begin_src elisp
+  (shell-command "./commit_and_push.sh")
+#+end_src
+
+** SHELL
+#+begin_src sh :shebang #!/bin/sh :results output 
+  git status
+#+end_src
+
+#+RESULTS:
+#+begin_example
+On branch main
+Your branch is up to date with 'origin/main'.
+
+Changes to be committed:
+  (use "git restore --staged <file>..." to unstage)
+	modified:   .gitattributes
+	modified:   .gitignore
+	new file:   ComfyUI_AEMatter/AEMatter.run.sh
+	new file:   ComfyUI_MVANet/MVANet_inference.run.sh
+	new file:   ComfyUI_MVANet/download.sh
+	new file:   checkpoints/MVANet/garment.pth
+	new file:   checkpoints/MVANet/skin.pth
+	new file:   demo/demo.jpg
+	new file:   demo/demo_atr.png
+	new file:   demo/demo_lip.png
+	new file:   demo/demo_pascal.png
+	new file:   demo/lip-visualization.jpg
+	new file:   main.org
+	new file:   training_code/MVANet/README.org
+
+#+end_example
+
+* Commit and push
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./commit_and_push.sh
+  git commit -m 'Routine updates'
+  git push
+#+end_src
+
+* List of large files
+#+begin_src conf :tangle ./git_lfs_track.txt
+  checkpoints/AEMatter/AEM_RWA.ckpt
+  checkpoints/atr.pth
+  checkpoints/lip.pth
+  checkpoints/Model_80.pth
+  checkpoints/MVANet/garment.pth
+  checkpoints/MVANet/skin.pth
+  checkpoints/pascal.pth
+  checkpoints/StableDiffusion/90c7c97574f8db765509b6a5d2e7b2551b430a10cac03e37d368654eac5e8169cd149644d188be4b5b2f1b9f29e66b64a02535f622f2bf284c319b076224cb2b
+  checkpoints/StableDiffusion/b970812225cfb95427c13e73b75eef66430e2a525876dddac494d70fe4ed0524cb197043e0ac3dc3026b32a45cd1d6d126ec2fe74a5bc3ef5df21836ca022b30
+  demo/demo_atr.png
+  demo/demo.jpg
+  demo/demo_lip.png
+  demo/demo_pascal.png
+  demo/lip-visualization.jpg
+#+end_src
+
+* List of source files to add
+#+begin_src conf :tangle ./git_add.txt
+  checkpoints/StableDiffusion/hash
+  ComfyUI_AEMatter/AEMatter.py
+  ComfyUI_AEMatter/AEMatter.run.sh
+  ComfyUI_AEMatter/__init__.py
+  ComfyUI_AEMatter/README.org
+  ComfyUI_MVANet/download.sh
+  ComfyUI_MVANet/__init__.py
+  ComfyUI_MVANet/MVANet_inference.py
+  ComfyUI_MVANet/MVANet_inference.run.sh
+  ComfyUI_MVANet/README.org
+  ComfyUI_MVANet/requirements.txt
+  datasets/datasets.py
+  datasets/__init__.py
+  datasets/simple_extractor_dataset.py
+  datasets/target_generation.py
+  environment.yaml
+  evaluate.py
+  .gitattributes
+  .gitignore
+  LICENSE
+  main.org
+  mhp_extension/coco_style_annotation_creator/human_to_coco.py
+  mhp_extension/coco_style_annotation_creator/pycococreatortools.py
+  mhp_extension/coco_style_annotation_creator/test_human2coco_format.py
+  mhp_extension/demo.ipynb
+  mhp_extension/detectron2/.circleci/config.yml
+  mhp_extension/detectron2/.clang-format
+  mhp_extension/detectron2/configs/Base-RCNN-C4.yaml
+  mhp_extension/detectron2/configs/Base-RCNN-DilatedC5.yaml
+  mhp_extension/detectron2/configs/Base-RCNN-FPN.yaml
+  mhp_extension/detectron2/configs/Base-RetinaNet.yaml
+  mhp_extension/detectron2/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml
+  mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml
+  mhp_extension/detectron2/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml
+  mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml
+  mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml
+  mhp_extension/detectron2/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml
+  mhp_extension/detectron2/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml
+  mhp_extension/detectron2/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml
+  mhp_extension/detectron2/configs/Detectron1-Comparisons/README.md
+  mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml
+  mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml
+  mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml
+  mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv_parsing.yaml
+  mhp_extension/detectron2/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml
+  mhp_extension/detectron2/configs/Misc/demo.yaml
+  mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml
+  mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml
+  mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml
+  mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml
+  mhp_extension/detectron2/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml
+  mhp_extension/detectron2/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml
+  mhp_extension/detectron2/configs/Misc/parsing_finetune_cihp.yaml
+  mhp_extension/detectron2/configs/Misc/parsing_inference.yaml
+  mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml
+  mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml
+  mhp_extension/detectron2/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml
+  mhp_extension/detectron2/configs/Misc/semantic_R_50_FPN_1x.yaml
+  mhp_extension/detectron2/configs/my_Base-RCNN-FPN.yaml
+  mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml
+  mhp_extension/detectron2/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml
+  mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/README.md
+  mhp_extension/detectron2/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml
+  mhp_extension/detectron2/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml
+  mhp_extension/detectron2/demo/demo.py
+  mhp_extension/detectron2/demo/predictor.py
+  mhp_extension/detectron2/demo/README.md
+  mhp_extension/detectron2/detectron2/checkpoint/c2_model_loading.py
+  mhp_extension/detectron2/detectron2/checkpoint/catalog.py
+  mhp_extension/detectron2/detectron2/checkpoint/detection_checkpoint.py
+  mhp_extension/detectron2/detectron2/checkpoint/__init__.py
+  mhp_extension/detectron2/detectron2/config/compat.py
+  mhp_extension/detectron2/detectron2/config/config.py
+  mhp_extension/detectron2/detectron2/config/defaults.py
+  mhp_extension/detectron2/detectron2/config/__init__.py
+  mhp_extension/detectron2/detectron2/data/build.py
+  mhp_extension/detectron2/detectron2/data/catalog.py
+  mhp_extension/detectron2/detectron2/data/common.py
+  mhp_extension/detectron2/detectron2/data/dataset_mapper.py
+  mhp_extension/detectron2/detectron2/data/datasets/builtin_meta.py
+  mhp_extension/detectron2/detectron2/data/datasets/builtin.py
+  mhp_extension/detectron2/detectron2/data/datasets/cityscapes.py
+  mhp_extension/detectron2/detectron2/data/datasets/coco.py
+  mhp_extension/detectron2/detectron2/data/datasets/__init__.py
+  mhp_extension/detectron2/detectron2/data/datasets/lvis.py
+  mhp_extension/detectron2/detectron2/data/datasets/lvis_v0_5_categories.py
+  mhp_extension/detectron2/detectron2/data/datasets/pascal_voc.py
+  mhp_extension/detectron2/detectron2/data/datasets/README.md
+  mhp_extension/detectron2/detectron2/data/datasets/register_coco.py
+  mhp_extension/detectron2/detectron2/data/detection_utils.py
+  mhp_extension/detectron2/detectron2/data/__init__.py
+  mhp_extension/detectron2/detectron2/data/samplers/distributed_sampler.py
+  mhp_extension/detectron2/detectron2/data/samplers/grouped_batch_sampler.py
+  mhp_extension/detectron2/detectron2/data/samplers/__init__.py
+  mhp_extension/detectron2/detectron2/data/transforms/__init__.py
+  mhp_extension/detectron2/detectron2/data/transforms/transform_gen.py
+  mhp_extension/detectron2/detectron2/data/transforms/transform.py
+  mhp_extension/detectron2/detectron2/engine/defaults.py
+  mhp_extension/detectron2/detectron2/engine/hooks.py
+  mhp_extension/detectron2/detectron2/engine/__init__.py
+  mhp_extension/detectron2/detectron2/engine/launch.py
+  mhp_extension/detectron2/detectron2/engine/train_loop.py
+  mhp_extension/detectron2/detectron2/evaluation/cityscapes_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/coco_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/evaluator.py
+  mhp_extension/detectron2/detectron2/evaluation/__init__.py
+  mhp_extension/detectron2/detectron2/evaluation/lvis_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/panoptic_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/pascal_voc_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/rotated_coco_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/sem_seg_evaluation.py
+  mhp_extension/detectron2/detectron2/evaluation/testing.py
+  mhp_extension/detectron2/detectron2/export/api.py
+  mhp_extension/detectron2/detectron2/export/c10.py
+  mhp_extension/detectron2/detectron2/export/caffe2_export.py
+  mhp_extension/detectron2/detectron2/export/caffe2_inference.py
+  mhp_extension/detectron2/detectron2/export/caffe2_modeling.py
+  mhp_extension/detectron2/detectron2/export/__init__.py
+  mhp_extension/detectron2/detectron2/export/patcher.py
+  mhp_extension/detectron2/detectron2/export/README.md
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+  mhp_extension/detectron2/tests/test_checkpoint.py
+  mhp_extension/detectron2/tests/test_config.py
+  mhp_extension/detectron2/tests/test_export_caffe2.py
+  mhp_extension/detectron2/tests/test_model_analysis.py
+  mhp_extension/detectron2/tests/test_model_zoo.py
+  mhp_extension/detectron2/tests/test_visualizer.py
+  mhp_extension/detectron2/tools/analyze_model.py
+  mhp_extension/detectron2/tools/benchmark.py
+  mhp_extension/detectron2/tools/convert-torchvision-to-d2.py
+  mhp_extension/detectron2/tools/deploy/caffe2_converter.py
+  mhp_extension/detectron2/tools/deploy/caffe2_mask_rcnn.cpp
+  mhp_extension/detectron2/tools/deploy/README.md
+  mhp_extension/detectron2/tools/deploy/torchscript_traced_mask_rcnn.cpp
+  mhp_extension/detectron2/tools/finetune_net.py
+  mhp_extension/detectron2/tools/inference.sh
+  mhp_extension/detectron2/tools/plain_train_net.py
+  mhp_extension/detectron2/tools/README.md
+  mhp_extension/detectron2/tools/run.sh
+  mhp_extension/detectron2/tools/train_net.py
+  mhp_extension/detectron2/tools/visualize_data.py
+  mhp_extension/detectron2/tools/visualize_json_results.py
+  mhp_extension/global_local_parsing/global_local_datasets.py
+  mhp_extension/global_local_parsing/global_local_evaluate.py
+  mhp_extension/global_local_parsing/global_local_train.py
+  mhp_extension/global_local_parsing/make_id_list.py
+  mhp_extension/logits_fusion.py
+  mhp_extension/make_crop_and_mask_w_mask_nms.py
+  mhp_extension/README.md
+  mhp_extension/scripts/make_coco_style_annotation.sh
+  mhp_extension/scripts/make_crop.sh
+  mhp_extension/scripts/parsing_fusion.sh
+  modules/bn.py
+  modules/deeplab.py
+  modules/dense.py
+  modules/functions.py
+  modules/__init__.py
+  modules/misc.py
+  modules/residual.py
+  modules/src/checks.h
+  modules/src/inplace_abn.cpp
+  modules/src/inplace_abn_cpu.cpp
+  modules/src/inplace_abn_cuda.cu
+  modules/src/inplace_abn_cuda_half.cu
+  modules/src/inplace_abn.h
+  modules/src/utils/checks.h
+  modules/src/utils/common.h
+  modules/src/utils/cuda.cuh
+  networks/AugmentCE2P.py
+  networks/backbone/mobilenetv2.py
+  networks/backbone/resnet.py
+  networks/backbone/resnext.py
+  networks/context_encoding/aspp.py
+  networks/context_encoding/ocnet.py
+  networks/context_encoding/psp.py
+  networks/__init__.py
+  README.md
+  requirements.txt
+  simple_extractor.py
+  training_code/MVANet/README.org
+  train.py
+  utils/consistency_loss.py
+  utils/criterion.py
+  utils/encoding.py
+  utils/__init__.py
+  utils/kl_loss.py
+  utils/lovasz_softmax.py
+  utils/miou.py
+  utils/schp.py
+  utils/soft_dice_loss.py
+  utils/transforms.py
+  utils/warmup_scheduler.py
+#+end_src
+
+* List of files to remove
+#+begin_src conf :tangle ./rm.txt
+  ComfyUI_MVANet/__pycache__/__init__.cpython-310.pyc
+  ComfyUI_MVANet/#README.org#
+  ComfyUI_MVANet/.#README.org
+  ComfyUI_MVANet/README.org~
+  ComfyUI_MVANet/.README.org.~undo-tree~
+  #main.org#
+  .#main.org
+  main.org~
+  .main.org.~undo-tree~
+  .README.md.~undo-tree~
+  ComfyUI_MVANet/.#README.org
+  ComfyUI_AEMatter/__pycache__/__init__.cpython-310.pyc
+  ComfyUI_AEMatter/AEMatter.class.py
+  ComfyUI_AEMatter/AEMatter.execute.py
+  ComfyUI_AEMatter/AEMatter.function.py
+  ComfyUI_AEMatter/AEMatter.import.py
+  ComfyUI_MVANet/MVANet_inference.class.py
+  ComfyUI_MVANet/MVANet_inference.execute.py
+  ComfyUI_MVANet/MVANet_inference.function.py
+  ComfyUI_MVANet/MVANet_inference.import.py
+  ComfyUI_MVANet/MVANet_inference.unify.sh
+  ComfyUI_AEMatter/AEMatter.unify.sh
+  git_add.txt
+  git_lfs_track.txt
+  gitignore.txt
+  rm.txt
+  work.sh
+#+end_src
+
+* List of patterns to ignore
+#+begin_src conf :tangle ./gitignore.txt
+  log/
+  pretrain_model/
+  commit_and_push.sh
+#+end_src
+
+* Main script to do everything
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./work.sh
+  do_ignore(){
+      'sed' 's@^@/@g' './rm.txt';
+      'cat' './gitignore.txt';
+  }
+
+  do_add(){
+      'sed' 's@^@("git" "lfs" "track" "./@g;s@$@");@g' './git_lfs_track.txt' ;
+      'cat' './git_add.txt' './git_lfs_track.txt' | \
+          'sed' 's@^@("git" "add" "./@g;s@$@");@g' ;
+  }
+
+  do_rm(){
+      'sed' 's@^@("rm" "-vf" "--" "./@g ; s@$@");@g' './rm.txt' ;
+  }
+
+  all_commands(){
+      do_add
+      do_rm
+  }
+
+  do_all(){
+      do_ignore > './.gitignore'
+      all_commands | sh
+  }
+
+  do_all
+#+end_src
+

training_code/MVANet/README.org

@@ -0,0 +1,2338 @@
+* Requirements
+#+begin_src conf :tangle ./requirements.txt
+  einops
+  pillow
+  prodigyopt
+  tensorboard
+  timm
+  torch
+  torchvision
+#+end_src
+
+* Download trained model
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./download.sh
+  "efficient_download.sh" \
+      'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth' \
+      'Model_80.pth' \
+      '6ca28df33ba8476ac13be329a1b1b8b390da5d8042638fb124df3c067c2fe45bccde4366643b830066cbe0164ddbb978a1987a398b4a987f99d908903b44774f' \
+      "${HOME}/GITHUB/aravind-h-v/dreambooth_experiments/cloth_segmentation/MVANet_Train/pretrained_model/Model_80.pth" \
+  ;
+#+end_src
+
+* Swin code
+
+** swin.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py
+  import  os
+  os.environ["CUDA_VISIBLE_DEVICES"] ='0'
+#+end_src
+
+** swin.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py
+  import numpy as np
+#+end_src
+
+** swin.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py
+  import torch
+  import torch.nn as nn
+  import torch.nn.functional as F
+  import torch.utils.checkpoint as checkpoint
+#+end_src
+
+** swin.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py
+  from timm.models import load_checkpoint
+  from timm.models.layers import DropPath
+  from timm.models.layers import to_2tuple
+  from timm.models.layers import trunc_normal_
+
+  # from mmdet.utils import get_root_logger
+#+end_src
+
+** swin.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.function.py
+  def window_partition(x, window_size):
+      """
+      Args:
+          x: (B, H, W, C)
+          window_size (int): window size
+
+      Returns:
+          windows: (num_windows*B, window_size, window_size, C)
+      """
+      B, H, W, C = x.shape
+      x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+                 C)
+      windows = x.permute(0, 1, 3, 2, 4,
+                          5).contiguous().view(-1, window_size, window_size, C)
+      return windows
+
+
+  def window_reverse(windows, window_size, H, W):
+      """
+      Args:
+          windows: (num_windows*B, window_size, window_size, C)
+          window_size (int): Window size
+          H (int): Height of image
+          W (int): Width of image
+
+      Returns:
+          x: (B, H, W, C)
+      """
+      B = int(windows.shape[0] / (H * W / window_size / window_size))
+      x = windows.view(B, H // window_size, W // window_size, window_size,
+                       window_size, -1)
+      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+      return x
+
+
+  def SwinT(pretrained=True):
+      model = SwinTransformer(embed_dim=96,
+                              depths=[2, 2, 6, 2],
+                              num_heads=[3, 6, 12, 24],
+                              window_size=7)
+      # if pretrained is True:
+      #     model.load_state_dict(torch.load(
+      #         'data/backbone_ckpt/swin_tiny_patch4_window7_224.pth',
+      #         map_location='cpu')['model'],
+      #                           strict=False)
+
+      return model
+
+
+  def SwinS(pretrained=True):
+      model = SwinTransformer(embed_dim=96,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[3, 6, 12, 24],
+                              window_size=7)
+      # if pretrained is True:
+      #     model.load_state_dict(torch.load(
+      #         'data/backbone_ckpt/swin_small_patch4_window7_224.pth',
+      #         map_location='cpu')['model'],
+      #                           strict=False)
+
+      return model
+
+
+  def SwinB(pretrained=True):
+      model = SwinTransformer(embed_dim=128,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[4, 8, 16, 32],
+                              window_size=12)
+      # if pretrained is True:
+      #     model.load_state_dict(
+      #         torch.load('./swin_base_patch4_window12_384_22kto1k.pth',
+      #                    map_location='cpu')['model'],
+      #         strict=False)
+
+      return model
+
+
+  def SwinL(pretrained=True):
+      model = SwinTransformer(embed_dim=192,
+                              depths=[2, 2, 18, 2],
+                              num_heads=[6, 12, 24, 48],
+                              window_size=12)
+      # if pretrained is True:
+      #     model.load_state_dict(torch.load(
+      #         'data/backbone_ckpt/swin_large_patch4_window12_384_22kto1k.pth',
+      #         map_location='cpu')['model'],
+      #                           strict=False)
+
+      return model
+#+end_src
+
+** swin.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.class.py
+  class Mlp(nn.Module):
+      """ Multilayer perceptron."""
+
+      def __init__(self,
+                   in_features,
+                   hidden_features=None,
+                   out_features=None,
+                   act_layer=nn.GELU,
+                   drop=0.):
+          super().__init__()
+          out_features = out_features or in_features
+          hidden_features = hidden_features or in_features
+          self.fc1 = nn.Linear(in_features, hidden_features)
+          self.act = act_layer()
+          self.fc2 = nn.Linear(hidden_features, out_features)
+          self.drop = nn.Dropout(drop)
+
+      def forward(self, x):
+          x = self.fc1(x)
+          x = self.act(x)
+          x = self.drop(x)
+          x = self.fc2(x)
+          x = self.drop(x)
+          return x
+
+
+  class WindowAttention(nn.Module):
+      """ Window based multi-head self attention (W-MSA) module with relative position bias.
+      It supports both of shifted and non-shifted window.
+
+      Args:
+          dim (int): Number of input channels.
+          window_size (tuple[int]): The height and width of the window.
+          num_heads (int): Number of attention heads.
+          qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+          attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+          proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+      """
+
+      def __init__(self,
+                   dim,
+                   window_size,
+                   num_heads,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   attn_drop=0.,
+                   proj_drop=0.):
+
+          super().__init__()
+          self.dim = dim
+          self.window_size = window_size  # Wh, Ww
+          self.num_heads = num_heads
+          head_dim = dim // num_heads
+          self.scale = qk_scale or head_dim**-0.5
+
+          # define a parameter table of relative position bias
+          self.relative_position_bias_table = nn.Parameter(
+              torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                          num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+          # get pair-wise relative position index for each token inside the window
+          coords_h = torch.arange(self.window_size[0])
+          coords_w = torch.arange(self.window_size[1])
+          coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+          coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+          relative_coords = coords_flatten[:, :,
+                                           None] - coords_flatten[:,
+                                                                  None, :]  # 2, Wh*Ww, Wh*Ww
+          relative_coords = relative_coords.permute(
+              1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+          relative_coords[:, :,
+                          0] += self.window_size[0] - 1  # shift to start from 0
+          relative_coords[:, :, 1] += self.window_size[1] - 1
+          relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+          relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+          self.register_buffer("relative_position_index",
+                               relative_position_index)
+
+          self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+          self.attn_drop = nn.Dropout(attn_drop)
+          self.proj = nn.Linear(dim, dim)
+          self.proj_drop = nn.Dropout(proj_drop)
+
+          trunc_normal_(self.relative_position_bias_table, std=.02)
+          self.softmax = nn.Softmax(dim=-1)
+
+      def forward(self, x, mask=None):
+          """ Forward function.
+
+          Args:
+              x: input features with shape of (num_windows*B, N, C)
+              mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+          """
+          B_, N, C = x.shape
+          qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                    C // self.num_heads).permute(2, 0, 3, 1, 4)
+          q, k, v = qkv[0], qkv[1], qkv[
+              2]  # make torchscript happy (cannot use tensor as tuple)
+
+          q = q * self.scale
+          attn = (q @ k.transpose(-2, -1))
+
+          relative_position_bias = self.relative_position_bias_table[
+              self.relative_position_index.view(-1)].view(
+                  self.window_size[0] * self.window_size[1],
+                  self.window_size[0] * self.window_size[1],
+                  -1)  # Wh*Ww,Wh*Ww,nH
+          relative_position_bias = relative_position_bias.permute(
+              2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+          attn = attn + relative_position_bias.unsqueeze(0)
+
+          if mask is not None:
+              nW = mask.shape[0]
+              attn = attn.view(B_ // nW, nW, self.num_heads, N,
+                               N) + mask.unsqueeze(1).unsqueeze(0)
+              attn = attn.view(-1, self.num_heads, N, N)
+              attn = self.softmax(attn)
+          else:
+              attn = self.softmax(attn)
+
+          attn = self.attn_drop(attn)
+
+          x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+          x = self.proj(x)
+          x = self.proj_drop(x)
+          return x
+
+
+  class SwinTransformerBlock(nn.Module):
+      """ Swin Transformer Block.
+
+      Args:
+          dim (int): Number of input channels.
+          num_heads (int): Number of attention heads.
+          window_size (int): Window size.
+          shift_size (int): Shift size for SW-MSA.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float, optional): Stochastic depth rate. Default: 0.0
+          act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self,
+                   dim,
+                   num_heads,
+                   window_size=7,
+                   shift_size=0,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   act_layer=nn.GELU,
+                   norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.num_heads = num_heads
+          self.window_size = window_size
+          self.shift_size = shift_size
+          self.mlp_ratio = mlp_ratio
+          assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+          self.norm1 = norm_layer(dim)
+          self.attn = WindowAttention(dim,
+                                      window_size=to_2tuple(self.window_size),
+                                      num_heads=num_heads,
+                                      qkv_bias=qkv_bias,
+                                      qk_scale=qk_scale,
+                                      attn_drop=attn_drop,
+                                      proj_drop=drop)
+
+          self.drop_path = DropPath(
+              drop_path) if drop_path > 0. else nn.Identity()
+          self.norm2 = norm_layer(dim)
+          mlp_hidden_dim = int(dim * mlp_ratio)
+          self.mlp = Mlp(in_features=dim,
+                         hidden_features=mlp_hidden_dim,
+                         act_layer=act_layer,
+                         drop=drop)
+
+          self.H = None
+          self.W = None
+
+      def forward(self, x, mask_matrix):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+              mask_matrix: Attention mask for cyclic shift.
+          """
+          B, L, C = x.shape
+          H, W = self.H, self.W
+          assert L == H * W, "input feature has wrong size"
+
+          shortcut = x
+          x = self.norm1(x)
+          x = x.view(B, H, W, C)
+
+          # pad feature maps to multiples of window size
+          pad_l = pad_t = 0
+          pad_r = (self.window_size - W % self.window_size) % self.window_size
+          pad_b = (self.window_size - H % self.window_size) % self.window_size
+          x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+          _, Hp, Wp, _ = x.shape
+
+          # cyclic shift
+          if self.shift_size > 0:
+              shifted_x = torch.roll(x,
+                                     shifts=(-self.shift_size, -self.shift_size),
+                                     dims=(1, 2))
+              attn_mask = mask_matrix
+          else:
+              shifted_x = x
+              attn_mask = None
+
+          # partition windows
+          x_windows = window_partition(
+              shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+          x_windows = x_windows.view(-1, self.window_size * self.window_size,
+                                     C)  # nW*B, window_size*window_size, C
+
+          # W-MSA/SW-MSA
+          attn_windows = self.attn(
+              x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+          # merge windows
+          attn_windows = attn_windows.view(-1, self.window_size,
+                                           self.window_size, C)
+          shifted_x = window_reverse(attn_windows, self.window_size, Hp,
+                                     Wp)  # B H' W' C
+
+          # reverse cyclic shift
+          if self.shift_size > 0:
+              x = torch.roll(shifted_x,
+                             shifts=(self.shift_size, self.shift_size),
+                             dims=(1, 2))
+          else:
+              x = shifted_x
+
+          if pad_r > 0 or pad_b > 0:
+              x = x[:, :H, :W, :].contiguous()
+
+          x = x.view(B, H * W, C)
+
+          # FFN
+          x = shortcut + self.drop_path(x)
+          x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+          return x
+
+
+  class PatchMerging(nn.Module):
+      """ Patch Merging Layer
+
+      Args:
+          dim (int): Number of input channels.
+          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+      """
+
+      def __init__(self, dim, norm_layer=nn.LayerNorm):
+          super().__init__()
+          self.dim = dim
+          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+          self.norm = norm_layer(4 * dim)
+
+      def forward(self, x, H, W):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+          B, L, C = x.shape
+          assert L == H * W, "input feature has wrong size"
+
+          x = x.view(B, H, W, C)
+
+          # padding
+          pad_input = (H % 2 == 1) or (W % 2 == 1)
+          if pad_input:
+              x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+          x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+          x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+          x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+          x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+          x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+          x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+          x = self.norm(x)
+          x = self.reduction(x)
+
+          return x
+
+
+  class BasicLayer(nn.Module):
+      """ A basic Swin Transformer layer for one stage.
+
+      Args:
+          dim (int): Number of feature channels
+          depth (int): Depths of this stage.
+          num_heads (int): Number of attention head.
+          window_size (int): Local window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+          drop (float, optional): Dropout rate. Default: 0.0
+          attn_drop (float, optional): Attention dropout rate. Default: 0.0
+          drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+          norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+          downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   dim,
+                   depth,
+                   num_heads,
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop=0.,
+                   attn_drop=0.,
+                   drop_path=0.,
+                   norm_layer=nn.LayerNorm,
+                   downsample=None,
+                   use_checkpoint=False):
+          super().__init__()
+          self.window_size = window_size
+          self.shift_size = window_size // 2
+          self.depth = depth
+          self.use_checkpoint = use_checkpoint
+
+          # build blocks
+          self.blocks = nn.ModuleList([
+              SwinTransformerBlock(dim=dim,
+                                   num_heads=num_heads,
+                                   window_size=window_size,
+                                   shift_size=0 if
+                                   (i % 2 == 0) else window_size // 2,
+                                   mlp_ratio=mlp_ratio,
+                                   qkv_bias=qkv_bias,
+                                   qk_scale=qk_scale,
+                                   drop=drop,
+                                   attn_drop=attn_drop,
+                                   drop_path=drop_path[i] if isinstance(
+                                       drop_path, list) else drop_path,
+                                   norm_layer=norm_layer) for i in range(depth)
+          ])
+
+          # patch merging layer
+          if downsample is not None:
+              self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+          else:
+              self.downsample = None
+
+      def forward(self, x, H, W):
+          """ Forward function.
+
+          Args:
+              x: Input feature, tensor size (B, H*W, C).
+              H, W: Spatial resolution of the input feature.
+          """
+
+          # calculate attention mask for SW-MSA
+          Hp = int(np.ceil(H / self.window_size)) * self.window_size
+          Wp = int(np.ceil(W / self.window_size)) * self.window_size
+          img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+          h_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          w_slices = (slice(0, -self.window_size),
+                      slice(-self.window_size,
+                            -self.shift_size), slice(-self.shift_size, None))
+          cnt = 0
+          for h in h_slices:
+              for w in w_slices:
+                  img_mask[:, h, w, :] = cnt
+                  cnt += 1
+
+          mask_windows = window_partition(
+              img_mask, self.window_size)  # nW, window_size, window_size, 1
+          mask_windows = mask_windows.view(-1,
+                                           self.window_size * self.window_size)
+          attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+          attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                            float(-100.0)).masked_fill(
+                                                attn_mask == 0, float(0.0))
+
+          for blk in self.blocks:
+              blk.H, blk.W = H, W
+              if self.use_checkpoint:
+                  x = checkpoint.checkpoint(blk, x, attn_mask)
+              else:
+                  x = blk(x, attn_mask)
+          if self.downsample is not None:
+              x_down = self.downsample(x, H, W)
+              Wh, Ww = (H + 1) // 2, (W + 1) // 2
+              return x, H, W, x_down, Wh, Ww
+          else:
+              return x, H, W, x, H, W
+
+
+  class PatchEmbed(nn.Module):
+      """ Image to Patch Embedding
+
+      Args:
+          patch_size (int): Patch token size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          norm_layer (nn.Module, optional): Normalization layer. Default: None
+      """
+
+      def __init__(self,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   norm_layer=None):
+          super().__init__()
+          patch_size = to_2tuple(patch_size)
+          self.patch_size = patch_size
+
+          self.in_chans = in_chans
+          self.embed_dim = embed_dim
+
+          self.proj = nn.Conv2d(in_chans,
+                                embed_dim,
+                                kernel_size=patch_size,
+                                stride=patch_size)
+          if norm_layer is not None:
+              self.norm = norm_layer(embed_dim)
+          else:
+              self.norm = None
+
+      def forward(self, x):
+          """Forward function."""
+          # padding
+          _, _, H, W = x.size()
+          if W % self.patch_size[1] != 0:
+              x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+          if H % self.patch_size[0] != 0:
+              x = F.pad(x,
+                        (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+          x = self.proj(x)  # B C Wh Ww
+          if self.norm is not None:
+              Wh, Ww = x.size(2), x.size(3)
+              x = x.flatten(2).transpose(1, 2)
+              x = self.norm(x)
+              x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+          return x
+
+
+  class SwinTransformer(nn.Module):
+      """ Swin Transformer backbone.
+          A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+            https://arxiv.org/pdf/2103.14030
+
+      Args:
+          pretrain_img_size (int): Input image size for training the pretrained model,
+              used in absolute postion embedding. Default 224.
+          patch_size (int | tuple(int)): Patch size. Default: 4.
+          in_chans (int): Number of input image channels. Default: 3.
+          embed_dim (int): Number of linear projection output channels. Default: 96.
+          depths (tuple[int]): Depths of each Swin Transformer stage.
+          num_heads (tuple[int]): Number of attention head of each stage.
+          window_size (int): Window size. Default: 7.
+          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+          qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+          qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+          drop_rate (float): Dropout rate.
+          attn_drop_rate (float): Attention dropout rate. Default: 0.
+          drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+          norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+          ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+          patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+          out_indices (Sequence[int]): Output from which stages.
+          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+              -1 means not freezing any parameters.
+          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+      """
+
+      def __init__(self,
+                   pretrain_img_size=224,
+                   patch_size=4,
+                   in_chans=3,
+                   embed_dim=96,
+                   depths=[2, 2, 6, 2],
+                   num_heads=[3, 6, 12, 24],
+                   window_size=7,
+                   mlp_ratio=4.,
+                   qkv_bias=True,
+                   qk_scale=None,
+                   drop_rate=0.,
+                   attn_drop_rate=0.,
+                   drop_path_rate=0.2,
+                   norm_layer=nn.LayerNorm,
+                   ape=False,
+                   patch_norm=True,
+                   out_indices=(0, 1, 2, 3),
+                   frozen_stages=-1,
+                   use_checkpoint=False):
+          super().__init__()
+
+          self.pretrain_img_size = pretrain_img_size
+          self.num_layers = len(depths)
+          self.embed_dim = embed_dim
+          self.ape = ape
+          self.patch_norm = patch_norm
+          self.out_indices = out_indices
+          self.frozen_stages = frozen_stages
+
+          # split image into non-overlapping patches
+          self.patch_embed = PatchEmbed(
+              patch_size=patch_size,
+              in_chans=in_chans,
+              embed_dim=embed_dim,
+              norm_layer=norm_layer if self.patch_norm else None)
+
+          # absolute position embedding
+          if self.ape:
+              pretrain_img_size = to_2tuple(pretrain_img_size)
+              patch_size = to_2tuple(patch_size)
+              patches_resolution = [
+                  pretrain_img_size[0] // patch_size[0],
+                  pretrain_img_size[1] // patch_size[1]
+              ]
+
+              self.absolute_pos_embed = nn.Parameter(
+                  torch.zeros(1, embed_dim, patches_resolution[0],
+                              patches_resolution[1]))
+              trunc_normal_(self.absolute_pos_embed, std=.02)
+
+          self.pos_drop = nn.Dropout(p=drop_rate)
+
+          # stochastic depth
+          dpr = [
+              x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+          ]  # stochastic depth decay rule
+
+          # build layers
+          self.layers = nn.ModuleList()
+          for i_layer in range(self.num_layers):
+              layer = BasicLayer(
+                  dim=int(embed_dim * 2**i_layer),
+                  depth=depths[i_layer],
+                  num_heads=num_heads[i_layer],
+                  window_size=window_size,
+                  mlp_ratio=mlp_ratio,
+                  qkv_bias=qkv_bias,
+                  qk_scale=qk_scale,
+                  drop=drop_rate,
+                  attn_drop=attn_drop_rate,
+                  drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                  norm_layer=norm_layer,
+                  downsample=PatchMerging if
+                  (i_layer < self.num_layers - 1) else None,
+                  use_checkpoint=use_checkpoint)
+              self.layers.append(layer)
+
+          num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+          self.num_features = num_features
+
+          # add a norm layer for each output
+          for i_layer in out_indices:
+              layer = norm_layer(num_features[i_layer])
+              layer_name = f'norm{i_layer}'
+              self.add_module(layer_name, layer)
+
+          self._freeze_stages()
+
+      def _freeze_stages(self):
+          if self.frozen_stages >= 0:
+              self.patch_embed.eval()
+              for param in self.patch_embed.parameters():
+                  param.requires_grad = False
+
+          if self.frozen_stages >= 1 and self.ape:
+              self.absolute_pos_embed.requires_grad = False
+
+          if self.frozen_stages >= 2:
+              self.pos_drop.eval()
+              for i in range(0, self.frozen_stages - 1):
+                  m = self.layers[i]
+                  m.eval()
+                  for param in m.parameters():
+                      param.requires_grad = False
+
+      def init_weights(self, pretrained=None):
+          """Initialize the weights in backbone.
+
+          Args:
+              pretrained (str, optional): Path to pre-trained weights.
+                  Defaults to None.
+          """
+
+          def _init_weights(m):
+              if isinstance(m, nn.Linear):
+                  trunc_normal_(m.weight, std=.02)
+                  if isinstance(m, nn.Linear) and m.bias is not None:
+                      nn.init.constant_(m.bias, 0)
+              elif isinstance(m, nn.LayerNorm):
+                  nn.init.constant_(m.bias, 0)
+                  nn.init.constant_(m.weight, 1.0)
+
+          if isinstance(pretrained, str):
+              self.apply(_init_weights)
+              # logger = get_root_logger()
+              load_checkpoint(self, pretrained, strict=False, logger=None)
+          elif pretrained is None:
+              self.apply(_init_weights)
+          else:
+              raise TypeError('pretrained must be a str or None')
+
+      def forward(self, x):
+          x = self.patch_embed(x)
+
+          Wh, Ww = x.size(2), x.size(3)
+          if self.ape:
+              # interpolate the position embedding to the corresponding size
+              absolute_pos_embed = F.interpolate(self.absolute_pos_embed,
+                                                 size=(Wh, Ww),
+                                                 mode='bicubic')
+              x = (x + absolute_pos_embed)  # B Wh*Ww C
+
+          outs = [x.contiguous()]
+          x = x.flatten(2).transpose(1, 2)
+          x = self.pos_drop(x)
+          for i in range(self.num_layers):
+              layer = self.layers[i]
+              x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+              if i in self.out_indices:
+                  norm_layer = getattr(self, f'norm{i}')
+                  x_out = norm_layer(x_out)
+
+                  out = x_out.view(-1, H, W,
+                                   self.num_features[i]).permute(0, 3, 1,
+                                                                 2).contiguous()
+                  outs.append(out)
+
+          return tuple(outs)
+
+      def train(self, mode=True):
+          """Convert the model into training mode while keep layers freezed."""
+          super(SwinTransformer, self).train(mode)
+          self._freeze_stages()
+#+end_src
+
+* Main code
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  import os
+
+  os.environ["CUDA_VISIBLE_DEVICES"] = '0'
+  HOME_DIR = os.environ.get('HOME', '/root')
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  import sys
+
+  sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  from datetime import datetime
+  import argparse
+  import numpy as np
+  import random
+  import math
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  import cv2
+  from PIL import Image
+  from PIL import ImageEnhance
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  from einops import rearrange
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  import torch
+  import torch.nn as nn
+  import torch.nn.functional as F
+  import torch.utils.data as data
+
+  from torch.autograd import Variable
+  from torch.backends import cudnn
+  from torch.cuda import amp
+  from torch.utils.tensorboard import SummaryWriter
+
+  from torchvision import transforms
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  from prodigyopt import Prodigy
+#+end_src
+
+** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+  # from model.MVANet import MVANet
+  from swin import SwinB
+#+end_src
+
+** train.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.function.py
+  def get_activation_fn(activation):
+      """Return an activation function given a string"""
+      if activation == "relu":
+          return F.relu
+      if activation == "gelu":
+          return F.gelu
+      if activation == "glu":
+          return F.glu
+      raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+  def make_cbr(in_dim, out_dim):
+      return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                           nn.BatchNorm2d(out_dim), nn.PReLU())
+
+
+  def make_cbg(in_dim, out_dim):
+      return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
+                           nn.BatchNorm2d(out_dim), nn.GELU())
+
+
+  def rescale_to(x, scale_factor: float = 2, interpolation='nearest'):
+      return F.interpolate(x, scale_factor=scale_factor, mode=interpolation)
+
+
+  def resize_as(x, y, interpolation='bilinear'):
+      return F.interpolate(x, size=y.shape[-2:], mode=interpolation)
+
+
+  def image2patches(x):
+      """b c (hg h) (wg w) -> (hg wg b) c h w"""
+      x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2)
+      return x
+
+
+  def patches2image(x):
+      """(hg wg b) c h w -> b c (hg h) (wg w)"""
+      x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2)
+      return x
+
+
+  def structure_loss(pred, mask):
+      weit = 1 + 5 * torch.abs(
+          F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)
+      wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
+      wbce = (weit * wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))
+
+      pred = torch.sigmoid(pred)
+      inter = ((pred * mask) * weit).sum(dim=(2, 3))
+
+      union = ((pred + mask) * weit).sum(dim=(2, 3))
+      wiou = 1 - (inter + 1) / (union - inter + 1)
+
+      return (wbce + wiou).mean()
+
+
+  def clip_gradient(optimizer, grad_clip):
+      for group in optimizer.param_groups:
+          for param in group['params']:
+              if param.grad is not None:
+                  param.grad.data.clamp_(-grad_clip, grad_clip)
+
+
+  def adjust_lr(optimizer, init_lr, epoch, decay_rate=0.1, decay_epoch=5):
+      decay = decay_rate**(epoch // decay_epoch)
+      for param_group in optimizer.param_groups:
+          param_group['lr'] *= decay
+
+
+  def truncated_normal_(tensor, mean=0, std=1):
+      size = tensor.shape
+      tmp = tensor.new_empty(size + (4, )).normal_()
+      valid = (tmp < 2) & (tmp > -2)
+      ind = valid.max(-1, keepdim=True)[1]
+      tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
+      tensor.data.mul_(std).add_(mean)
+
+
+  def init_weights(m):
+      if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
+          nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
+          #nn.init.normal_(m.weight, std=0.001)
+          #nn.init.normal_(m.bias, std=0.001)
+          truncated_normal_(m.bias, mean=0, std=0.001)
+
+
+  def init_weights_orthogonal_normal(m):
+      if type(m) == nn.Conv2d or type(m) == nn.ConvTranspose2d:
+          nn.init.orthogonal_(m.weight)
+          truncated_normal_(m.bias, mean=0, std=0.001)
+          #nn.init.normal_(m.bias, std=0.001)
+
+
+  def l2_regularisation(m):
+      l2_reg = None
+
+      for W in m.parameters():
+          if l2_reg is None:
+              l2_reg = W.norm(2)
+          else:
+              l2_reg = l2_reg + W.norm(2)
+      return l2_reg
+
+
+  def check_mkdir(dir_name):
+      if not os.path.isdir(dir_name):
+          os.makedirs(dir_name)
+
+
+  # several data augumentation strategies
+  def cv_random_flip(img, label):
+      flip_flag = random.randint(0, 1)
+      flip_flag2 = random.randint(0, 1)
+
+      # left right flip
+      if flip_flag == 1:
+          img = img.transpose(Image.FLIP_LEFT_RIGHT)
+          label = label.transpose(Image.FLIP_LEFT_RIGHT)
+
+      # top bottom flip
+      if flip_flag2 == 1:
+          img = img.transpose(Image.FLIP_TOP_BOTTOM)
+          label = label.transpose(Image.FLIP_TOP_BOTTOM)
+
+      return img, label
+
+
+  def random_crop_full(image, X, Y, TX, TY):
+      image_width = image.size[0]
+      image_height = image.size[1]
+      final_width = image_width * TX
+      final_height = image_height * TY
+
+      start_x = (1.0 - TX) * X * image_width
+      start_y = (1.0 - TY) * Y * image_height
+
+      random_region = (start_x, start_y, start_x + final_width,
+                       start_y + final_height)
+
+      return image.crop(random_region)
+
+
+  def random_crop(image, X, Y, T):
+      image_width = image.size[0]
+      image_height = image.size[1]
+      final_width = image_width * T
+      final_height = image_height * T
+
+      start_x = (1.0 - T) * X * image_width
+      start_y = (1.0 - T) * Y * image_height
+
+      random_region = (start_x, start_y, start_x + final_width,
+                       start_y + final_height)
+
+      return image.crop(random_region)
+
+
+  def garment_color_jitter(image, mask):
+      image = np.array(image)
+      mask = np.array(mask)
+      mask = (mask > 127).astype(dtype=np.uint8)
+      image = cv2.cvtColor(src=image, code=cv2.COLOR_RGB2HSV_FULL)
+      image[:, :, 0] += mask * np.random.randint(0, 255)
+      image = cv2.cvtColor(src=image, code=cv2.COLOR_HSV2RGB_FULL)
+      image = Image.fromarray(image)
+      return image
+
+
+  def garment_color_jitter_rotate(image, mask, rotate_index=0, shift_amount=0):
+      image = np.array(image)
+      mask = np.array(mask)
+
+      if rotate_index == 1:
+
+          image = cv2.rotate(src=image, rotateCode=cv2.ROTATE_90_CLOCKWISE)
+          mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_90_CLOCKWISE)
+
+      elif rotate_index == 2:
+
+          image = cv2.rotate(src=image, rotateCode=cv2.ROTATE_180)
+          mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_180)
+
+      elif rotate_index == 3:
+
+          image = cv2.rotate(src=image,
+                             rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE)
+
+          mask = cv2.rotate(src=mask, rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE)
+
+      image = cv2.cvtColor(src=image,
+                           code=cv2.COLOR_RGB2HSV_FULL).astype(dtype=np.int32)
+      # image[:, :, 0] += mask_tmp * shift_amount
+      image[:, :, 0] += shift_amount
+      image[:, :, 0] %= 255
+      image = cv2.cvtColor(src=image.astype(np.uint8),
+                           code=cv2.COLOR_HSV2RGB_FULL)
+
+      image = Image.fromarray(image)
+      mask = Image.fromarray(mask)
+
+      return image, mask
+
+
+  def randomCrop_Both(image, label):
+
+      image, label = garment_color_jitter_rotate(
+          image=image,
+          mask=label,
+          rotate_index=np.random.randint(0, 4),
+          shift_amount=np.random.randint(-4, +4),
+      )
+
+      TX = (np.random.rand() * 0.6) + 0.4
+      TY = (np.random.rand() * 0.6) + 0.4
+      X = np.random.rand()
+      Y = np.random.rand()
+      return random_crop_full(image, X, Y, TX,
+                              TY), random_crop_full(label, X, Y, TX, TY)
+
+
+  def randomCrop_Old(image, label):
+
+      # image, label = garment_color_jitter_rotate(
+      #     image=image,
+      #     mask=label,
+      #     rotate_index=np.random.randint(0, 4),
+      #     shift_amount=np.random.randint(0, 256))
+
+      # image, label = garment_color_jitter_rotate(
+      #     image=image,
+      #     mask=label,
+      #     rotate_index=np.random.randint(0, 4),
+      #     shift_amount=0,
+      # )
+
+      T = (np.random.rand() * 0.6) + 0.4
+      X = np.random.rand()
+      Y = np.random.rand()
+      return random_crop(image, X, Y, T), random_crop(label, X, Y, T)
+
+
+  def randomCrop(image, label):
+      return randomCrop_Both(image, label)
+
+
+  def randomCrop_original(image, label):
+      image_width = image.size[0]
+      image_height = image.size[1]
+      border = min(image_width, image_height) // 2
+
+      crop_win_width = np.random.randint(image_width - border, image_width)
+      crop_win_height = np.random.randint(image_height - border, image_height)
+
+      random_region = ((image_width - crop_win_width) >> 1,
+                       (image_height - crop_win_height) >> 1,
+                       (image_width + crop_win_width) >> 1,
+                       (image_height + crop_win_height) >> 1)
+
+      return image.crop(random_region), label.crop(random_region)
+
+
+  def randomRotation(image, label):
+      mode = Image.BICUBIC
+      if random.random() > 0.8:
+          random_angle = np.random.randint(-15, 15)
+          image = image.rotate(random_angle, mode)
+          label = label.rotate(random_angle, mode)
+      return image, label
+
+
+  def colorEnhance(image):
+      bright_intensity = random.randint(5, 15) / 10.0
+      image = ImageEnhance.Brightness(image).enhance(bright_intensity)
+      contrast_intensity = random.randint(5, 15) / 10.0
+      image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
+      color_intensity = random.randint(0, 20) / 10.0
+      image = ImageEnhance.Color(image).enhance(color_intensity)
+      sharp_intensity = random.randint(0, 30) / 10.0
+      image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
+      return image
+
+
+  def randomGaussian(image, mean=0.1, sigma=0.35):
+
+      def gaussianNoisy(im, mean=mean, sigma=sigma):
+          for _i in range(len(im)):
+              im[_i] += random.gauss(mean, sigma)
+          return im
+
+      img = np.asarray(image)
+      width, height = img.shape
+      img = gaussianNoisy(img[:].flatten(), mean, sigma)
+      img = img.reshape([width, height])
+      return Image.fromarray(np.uint8(img))
+
+
+  def randomPeper(img):
+      img = np.array(img)
+      noiseNum = int(0.0015 * img.shape[0] * img.shape[1])
+      for i in range(noiseNum):
+
+          randX = random.randint(0, img.shape[0] - 1)
+
+          randY = random.randint(0, img.shape[1] - 1)
+
+          if random.randint(0, 1) == 0:
+
+              img[randX, randY] = 0
+
+          else:
+
+              img[randX, randY] = 255
+      return Image.fromarray(img)
+
+
+  # dataloader for training
+  def get_loader(image_root,
+                 gt_root,
+                 batchsize,
+                 trainsize,
+                 shuffle=True,
+                 num_workers=12,
+                 pin_memory=False):
+      print('DEBUG 6')
+      dataset = DISDataset(image_root, gt_root, trainsize)
+      print('DEBUG 7')
+      data_loader = data.DataLoader(dataset=dataset,
+                                    batch_size=batchsize,
+                                    shuffle=shuffle,
+                                    num_workers=num_workers,
+                                    pin_memory=pin_memory)
+      print('DEBUG 8')
+      return data_loader
+#+end_src
+
+** train.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py
+  class AvgMeter(object):
+
+      def __init__(self, num=40):
+          self.num = num
+          self.reset()
+
+      def reset(self):
+          self.val = 0
+          self.avg = 0
+          self.sum = 0
+          self.count = 0
+          self.losses = []
+
+      def update(self, val, n=1):
+          self.val = val
+          self.sum += val * n
+          self.count += n
+          self.avg = self.sum / self.count
+          self.losses.append(val)
+
+      def show(self):
+          a = len(self.losses)
+          b = np.maximum(a - self.num, 0)
+          c = self.losses[b:]
+          #print(c)
+          #d = torch.mean(torch.stack(c))
+          #print(d)
+          return torch.mean(torch.stack(c))
+
+
+  class Running_Avg(object):
+
+      def __init__(self, weight=0.999):
+          self.weight = weight
+          self.reset()
+
+      def reset(self):
+          self.n = 0
+          self.val = 0
+
+      def update(self, val, n=1):
+          self.val = (self.weight * self.val) + ((1 - self.weight) * val)
+          self.n = (self.weight * self.n) + ((1 - self.weight) * n)
+
+      def show(self):
+          if self.n == 0:
+              return 0
+          else:
+              return self.val / self.n
+#+end_src
+
+** Main training dataset
+
+*** COMMENT Original
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py
+  # dataset for training
+  # The current loader is not using the normalized depth maps for training and test. If you use the normalized depth maps
+  # (e.g., 0 represents background and 1 represents foreground.), the performance will be further improved.
+  class DISDataset(data.Dataset):
+
+      def __init__(self, image_root, gt_root, trainsize):
+          self.trainsize = trainsize
+          self.images = [
+              image_root + f for f in os.listdir(image_root)
+              if f.endswith('.jpg') or f.endswith('.png') or f.endswith('tif')
+          ]
+          self.gts = [
+              gt_root + f for f in os.listdir(gt_root)
+              if f.endswith('.jpg') or f.endswith('.png') or f.endswith('tif')
+          ]
+          self.images = sorted(self.images)
+          self.gts = sorted(self.gts)
+          self.filter_files()
+          self.size = len(self.images)
+          self.img_transform = transforms.Compose([
+              transforms.Resize((self.trainsize, self.trainsize)),
+              transforms.ToTensor(),
+              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+          ])
+          self.gt_transform = transforms.Compose([
+              transforms.Resize((self.trainsize, self.trainsize)),
+              transforms.ToTensor()
+          ])
+
+      def __getitem__(self, index):
+          image = self.rgb_loader(self.images[index])
+          gt = self.binary_loader(self.gts[index])
+          image, gt = cv_random_flip(image, gt)
+          image, gt = randomCrop(image, gt)
+          image, gt = randomRotation(image, gt)
+          image = colorEnhance(image)
+          image = self.img_transform(image)
+          gt = self.gt_transform(gt)
+
+          return image, gt
+
+      def filter_files(self):
+          assert len(self.images) == len(self.gts) and len(self.gts) == len(
+              self.images)
+          images = []
+          gts = []
+          for img_path, gt_path in zip(self.images, self.gts):
+              img = Image.open(img_path)
+              gt = Image.open(gt_path)
+              if img.size == gt.size:
+                  images.append(img_path)
+                  gts.append(gt_path)
+          self.images = images
+          self.gts = gts
+
+      def rgb_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('RGB')
+
+      def binary_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('L')
+
+      def resize(self, img, gt):
+          assert img.size == gt.size
+          w, h = img.size
+          if h < self.trainsize or w < self.trainsize:
+              h = max(h, self.trainsize)
+              w = max(w, self.trainsize)
+              return img.resize((w, h), Image.BILINEAR), gt.resize((w, h),
+                                                                   Image.NEAREST)
+          else:
+              return img, gt
+
+      def __len__(self):
+          return self.size
+#+end_src
+
+*** Changed
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py
+  # dataset for training
+  # The current loader is not using the normalized depth maps for training and test. If you use the normalized depth maps
+  # (e.g., 0 represents background and 1 represents foreground.), the performance will be further improved.
+  class DISDataset(data.Dataset):
+
+      def __init__(self, image_root, gt_root, trainsize):
+          self.trainsize = trainsize
+          end_pattern = '_segm.png'
+          files = list(f for f in os.listdir(gt_root) if f.endswith(end_pattern))
+          files.sort()
+
+          self.gts = list(gt_root + f for f in files)
+
+          self.images = list(image_root + f[0:-len(end_pattern)] + '.jpg'
+                             for f in files)
+
+          self.size = len(self.images)
+
+          self.img_transform = transforms.Compose([
+              transforms.Resize((self.trainsize, self.trainsize)),
+              transforms.ToTensor(),
+              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+          ])
+
+          self.gt_transform = transforms.Compose([
+              transforms.Resize((self.trainsize, self.trainsize)),
+              transforms.ToTensor()
+          ])
+
+      def __getitem__(self, index):
+          image = self.rgb_loader(self.images[index])
+          gt = self.binary_loader(self.gts[index])
+          image, gt = cv_random_flip(image, gt)
+          image, gt = randomCrop(image, gt)
+          image, gt = randomRotation(image, gt)
+          image = colorEnhance(image)
+          image = self.img_transform(image)
+          gt = self.gt_transform(gt)
+
+          return image, gt
+
+      def filter_files(self):
+          assert len(self.images) == len(self.gts) and len(self.gts) == len(
+              self.images)
+          images = []
+          gts = []
+          for img_path, gt_path in zip(self.images, self.gts):
+              img = Image.open(img_path)
+              gt = Image.open(gt_path)
+              if img.size == gt.size:
+                  images.append(img_path)
+                  gts.append(gt_path)
+          self.images = images
+          self.gts = gts
+
+      def rgb_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('RGB')
+
+      def binary_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('L')
+
+      def resize(self, img, gt):
+          assert img.size == gt.size
+          w, h = img.size
+          if h < self.trainsize or w < self.trainsize:
+              h = max(h, self.trainsize)
+              w = max(w, self.trainsize)
+              return img.resize((w, h), Image.BILINEAR), gt.resize((w, h),
+                                                                   Image.NEAREST)
+          else:
+              return img, gt
+
+      def __len__(self):
+          return self.size
+#+end_src
+
+** train.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py
+  # test dataset and loader
+  class test_dataset:
+
+      def __init__(self, image_root, depth_root, testsize):
+          self.testsize = testsize
+          self.images = [
+              image_root + f for f in os.listdir(image_root)
+              if f.endswith('.jpg')
+          ]
+          self.depths = [
+              depth_root + f for f in os.listdir(depth_root)
+              if f.endswith('.bmp') or f.endswith('.png')
+          ]
+          self.images = sorted(self.images)
+          self.depths = sorted(self.depths)
+          self.transform = transforms.Compose([
+              transforms.Resize((self.testsize, self.testsize)),
+              transforms.ToTensor(),
+              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+          ])
+          # self.gt_transform = transforms.Compose([
+          #     transforms.Resize((self.trainsize, self.trainsize)),
+          #     transforms.ToTensor()])
+          self.depths_transform = transforms.Compose([
+              transforms.Resize((self.testsize, self.testsize)),
+              transforms.ToTensor()
+          ])
+          self.size = len(self.images)
+          self.index = 0
+
+      def load_data(self):
+          image = self.rgb_loader(self.images[self.index])
+          HH = image.size[0]
+          WW = image.size[1]
+          image = self.transform(image).unsqueeze(0)
+          depth = self.rgb_loader(self.depths[self.index])
+          depth = self.depths_transform(depth).unsqueeze(0)
+
+          name = self.images[self.index].split('/')[-1]
+          # image_for_post=self.rgb_loader(self.images[self.index])
+          # image_for_post=image_for_post.resize(gt.size)
+          if name.endswith('.jpg'):
+              name = name.split('.jpg')[0] + '.png'
+          self.index += 1
+          self.index = self.index % self.size
+          return image, depth, HH, WW, name
+
+      def rgb_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('RGB')
+
+      def binary_loader(self, path):
+          with open(path, 'rb') as f:
+              img = Image.open(f)
+              return img.convert('L')
+
+      def __len__(self):
+          return self.size
+
+
+  class PositionEmbeddingSine:
+
+      def __init__(self,
+                   num_pos_feats=64,
+                   temperature=10000,
+                   normalize=False,
+                   scale=None):
+
+          super().__init__()
+
+          self.num_pos_feats = num_pos_feats
+          self.temperature = temperature
+          self.normalize = normalize
+          if scale is not None and normalize is False:
+              raise ValueError("normalize should be True if scale is passed")
+          if scale is None:
+              scale = 2 * math.pi
+          self.scale = scale
+          self.dim_t = torch.arange(0,
+                                    self.num_pos_feats,
+                                    dtype=torch.float32,
+                                    device='cuda')
+
+      def __call__(self, b, h, w):
+          mask = torch.zeros([b, h, w], dtype=torch.bool, device='cuda')
+          assert mask is not None
+          not_mask = ~mask
+          y_embed = not_mask.cumsum(dim=1, dtype=torch.float32)
+          x_embed = not_mask.cumsum(dim=2, dtype=torch.float32)
+          if self.normalize:
+              eps = 1e-6
+              y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) *
+                         self.scale).cuda()
+              x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) *
+                         self.scale).cuda()
+
+          dim_t = self.temperature**(2 * (self.dim_t // 2) / self.num_pos_feats)
+
+          pos_x = x_embed[:, :, :, None] / dim_t
+          pos_y = y_embed[:, :, :, None] / dim_t
+          pos_x = torch.stack(
+              (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+              dim=4).flatten(3)
+          pos_y = torch.stack(
+              (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+              dim=4).flatten(3)
+          return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+
+  class MCLM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+          super(MCLM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear1 = nn.Linear(d_model, d_model * 2)
+          self.linear2 = nn.Linear(d_model * 2, d_model)
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.activation = get_activation_fn('relu')
+          self.pool_ratios = pool_ratios
+          self.p_poses = []
+          self.g_pos = None
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, l, g):
+          """
+          l: 4,c,h,w
+          g: 1,c,h,w
+          """
+          b, c, h, w = l.size()
+          # 4,c,h,w -> 1,c,2h,2w
+          concated_locs = rearrange(l,
+                                    '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                    hg=2,
+                                    wg=2)
+
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              # b,c,h,w
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+              pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+              if self.g_pos is None:
+                  pos_emb = self.positional_encoding(pool.shape[0],
+                                                     pool.shape[2],
+                                                     pool.shape[3])
+                  pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+                  self.p_poses.append(pos_emb)
+          pools = torch.cat(pools, 0)
+          if self.g_pos is None:
+              self.p_poses = torch.cat(self.p_poses, dim=0)
+              pos_emb = self.positional_encoding(g.shape[0], g.shape[2],
+                                                 g.shape[3])
+              self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+          # attention between glb (q) & multisensory concated-locs (k,v)
+          g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+          g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+              g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+          g_hw_b_c = self.norm1(g_hw_b_c)
+          g_hw_b_c = g_hw_b_c + self.dropout2(
+              self.linear2(
+                  self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+          g_hw_b_c = self.norm2(g_hw_b_c)
+
+          # attention between origin locs (q) & freashed glb (k,v)
+          l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+          _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+          _g_hw_b_c = rearrange(_g_hw_b_c,
+                                "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                                ng=2,
+                                nw=2)
+          outputs_re = []
+          for i, (_l, _g) in enumerate(
+                  zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+              outputs_re.append(self.attention[i + 1](_l, _g,
+                                                      _g)[0])  # (h w) 1 c
+          outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+          l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+          l_hw_b_c = self.norm1(l_hw_b_c)
+          l_hw_b_c = l_hw_b_c + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+          l_hw_b_c = self.norm2(l_hw_b_c)
+
+          l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+          return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+  class inf_MCLM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
+          super(inf_MCLM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear1 = nn.Linear(d_model, d_model * 2)
+          self.linear2 = nn.Linear(d_model * 2, d_model)
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.activation = get_activation_fn('relu')
+          self.pool_ratios = pool_ratios
+          self.p_poses = []
+          self.g_pos = None
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, l, g):
+          """
+          l: 4,c,h,w
+          g: 1,c,h,w
+          """
+          b, c, h, w = l.size()
+          # 4,c,h,w -> 1,c,2h,2w
+          concated_locs = rearrange(l,
+                                    '(hg wg b) c h w -> b c (hg h) (wg w)',
+                                    hg=2,
+                                    wg=2)
+          self.p_poses = []
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              # b,c,h,w
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
+              pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
+              # if self.g_pos is None:
+              pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2],
+                                                 pool.shape[3])
+              pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
+              self.p_poses.append(pos_emb)
+          pools = torch.cat(pools, 0)
+          # if self.g_pos is None:
+          self.p_poses = torch.cat(self.p_poses, dim=0)
+          pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3])
+          self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
+
+          # attention between glb (q) & multisensory concated-locs (k,v)
+          g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
+          g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
+              g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
+          g_hw_b_c = self.norm1(g_hw_b_c)
+          g_hw_b_c = g_hw_b_c + self.dropout2(
+              self.linear2(
+                  self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
+          g_hw_b_c = self.norm2(g_hw_b_c)
+
+          # attention between origin locs (q) & freashed glb (k,v)
+          l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
+          _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
+          _g_hw_b_c = rearrange(_g_hw_b_c,
+                                "(ng h) (nw w) b c -> (h w) (ng nw b) c",
+                                ng=2,
+                                nw=2)
+          outputs_re = []
+          for i, (_l, _g) in enumerate(
+                  zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
+              outputs_re.append(self.attention[i + 1](_l, _g,
+                                                      _g)[0])  # (h w) 1 c
+          outputs_re = torch.cat(outputs_re, 1)  # (h w) 4 c
+
+          l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
+          l_hw_b_c = self.norm1(l_hw_b_c)
+          l_hw_b_c = l_hw_b_c + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
+          l_hw_b_c = self.norm2(l_hw_b_c)
+
+          l = torch.cat((l_hw_b_c, g_hw_b_c), 1)  # hw,b(5),c
+          return rearrange(l, "(h w) b c -> b c h w", h=h, w=w)  ## (5,c,h*w)
+
+
+  class MCRM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+          super(MCRM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.sigmoid = nn.Sigmoid()
+          self.activation = get_activation_fn('relu')
+          self.sal_conv = nn.Conv2d(d_model, 1, 1)
+          self.pool_ratios = pool_ratios
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, x):
+          b, c, h, w = x.size()
+          loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+          # b(4),c,h,w
+          patched_glb = rearrange(glb,
+                                  'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                  hg=2,
+                                  wg=2)
+
+          # generate token attention map
+          token_attention_map = self.sigmoid(self.sal_conv(glb))
+          token_attention_map = F.interpolate(token_attention_map,
+                                              size=patches2image(loc).shape[-2:],
+                                              mode='nearest')
+          loc = loc * rearrange(token_attention_map,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+              pools.append(rearrange(pool,
+                                     'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+          # nl(4),c,nphw -> nl(4),nphw,1,c
+          pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+          loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+          outputs = []
+          for i, q in enumerate(
+                  loc_.unbind(dim=0)):  # traverse all local patches
+              # np*hw,1,c
+              v = pools[i]
+              k = v
+              outputs.append(self.attention[i](q, k, v)[0])
+          outputs = torch.cat(outputs, 1)
+          src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+          src = self.norm1(src)
+          src = src + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(src)).clone())))
+          src = self.norm2(src)
+
+          src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+          glb = glb + F.interpolate(patches2image(src),
+                                    size=glb.shape[-2:],
+                                    mode='nearest')  # freshed glb
+          return torch.cat((src, glb), 0), token_attention_map
+
+
+  class inf_MCRM(nn.Module):
+
+      def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
+          super(inf_MCRM, self).__init__()
+          self.attention = nn.ModuleList([
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
+              nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
+          ])
+
+          self.linear3 = nn.Linear(d_model, d_model * 2)
+          self.linear4 = nn.Linear(d_model * 2, d_model)
+          self.norm1 = nn.LayerNorm(d_model)
+          self.norm2 = nn.LayerNorm(d_model)
+          self.dropout = nn.Dropout(0.1)
+          self.dropout1 = nn.Dropout(0.1)
+          self.dropout2 = nn.Dropout(0.1)
+          self.sigmoid = nn.Sigmoid()
+          self.activation = get_activation_fn('relu')
+          self.sal_conv = nn.Conv2d(d_model, 1, 1)
+          self.pool_ratios = pool_ratios
+          self.positional_encoding = PositionEmbeddingSine(
+              num_pos_feats=d_model // 2, normalize=True)
+
+      def forward(self, x):
+          b, c, h, w = x.size()
+          loc, glb = x.split([4, 1], dim=0)  # 4,c,h,w; 1,c,h,w
+          # b(4),c,h,w
+          patched_glb = rearrange(glb,
+                                  'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                  hg=2,
+                                  wg=2)
+
+          # generate token attention map
+          token_attention_map = self.sigmoid(self.sal_conv(glb))
+          token_attention_map = F.interpolate(token_attention_map,
+                                              size=patches2image(loc).shape[-2:],
+                                              mode='nearest')
+          loc = loc * rearrange(token_attention_map,
+                                'b c (hg h) (wg w) -> (hg wg b) c h w',
+                                hg=2,
+                                wg=2)
+          pools = []
+          for pool_ratio in self.pool_ratios:
+              tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
+              pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
+              pools.append(rearrange(pool,
+                                     'nl c h w -> nl c (h w)'))  # nl(4),c,hw
+          # nl(4),c,nphw -> nl(4),nphw,1,c
+          pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
+          loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
+          outputs = []
+          for i, q in enumerate(
+                  loc_.unbind(dim=0)):  # traverse all local patches
+              # np*hw,1,c
+              v = pools[i]
+              k = v
+              outputs.append(self.attention[i](q, k, v)[0])
+          outputs = torch.cat(outputs, 1)
+          src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
+          src = self.norm1(src)
+          src = src + self.dropout2(
+              self.linear4(
+                  self.dropout(self.activation(self.linear3(src)).clone())))
+          src = self.norm2(src)
+
+          src = src.permute(1, 2, 0).reshape(4, c, h, w)  # freshed loc
+          glb = glb + F.interpolate(patches2image(src),
+                                    size=glb.shape[-2:],
+                                    mode='nearest')  # freshed glb
+          return torch.cat((src, glb), 0)
+
+
+  # model for single-scale training
+  class MVANet(nn.Module):
+
+      def __init__(self):
+          super().__init__()
+          self.backbone = SwinB(pretrained=True)
+          emb_dim = 128
+          self.sideout5 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout4 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout3 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout2 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+          self.sideout1 = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          self.output5 = make_cbr(1024, emb_dim)
+          self.output4 = make_cbr(512, emb_dim)
+          self.output3 = make_cbr(256, emb_dim)
+          self.output2 = make_cbr(128, emb_dim)
+          self.output1 = make_cbr(128, emb_dim)
+
+          self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8])
+          self.conv1 = make_cbr(emb_dim, emb_dim)
+          self.conv2 = make_cbr(emb_dim, emb_dim)
+          self.conv3 = make_cbr(emb_dim, emb_dim)
+          self.conv4 = make_cbr(emb_dim, emb_dim)
+          self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8])
+
+          self.insmask_head = nn.Sequential(
+              nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+              nn.BatchNorm2d(384), nn.PReLU(),
+              nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+              nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+          self.shallow = nn.Sequential(
+              nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+          self.upsample1 = make_cbg(emb_dim, emb_dim)
+          self.upsample2 = make_cbg(emb_dim, emb_dim)
+          self.output = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          for m in self.modules():
+              if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                  m.inplace = True
+
+      def forward(self, x):
+          shallow = self.shallow(x)
+          glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+          loc = image2patches(x)
+          input = torch.cat((loc, glb), dim=0)
+          feature = self.backbone(input)
+          e5 = self.output5(feature[4])  # (5,128,16,16)
+          e4 = self.output4(feature[3])  # (5,128,32,32)
+          e3 = self.output3(feature[2])  # (5,128,64,64)
+          e2 = self.output2(feature[1])  # (5,128,128,128)
+          e1 = self.output1(feature[0])  # (5,128,128,128)
+          loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+          e5 = self.multifieldcrossatt(loc_e5, glb_e5)  # (4,128,16,16)
+
+          e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4))
+          e4 = self.conv4(e4)
+          e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3))
+          e3 = self.conv3(e3)
+          e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2))
+          e2 = self.conv2(e2)
+          e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1))
+          e1 = self.conv1(e1)
+          loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+          output1_cat = patches2image(loc_e1)  # (1,128,256,256)
+          # add glb feat in
+          output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+          # merge
+          final_output = self.insmask_head(output1_cat)  # (1,128,256,256)
+          # shallow feature merge
+          final_output = final_output + resize_as(shallow, final_output)
+          final_output = self.upsample1(rescale_to(final_output))
+          final_output = rescale_to(final_output +
+                                    resize_as(shallow, final_output))
+          final_output = self.upsample2(final_output)
+          final_output = self.output(final_output)
+          ####
+          sideout5 = self.sideout5(e5).cuda()
+          sideout4 = self.sideout4(e4)
+          sideout3 = self.sideout3(e3)
+          sideout2 = self.sideout2(e2)
+          sideout1 = self.sideout1(e1)
+          #######glb_sideouts ######
+          glb5 = self.sideout5(glb_e5)
+          glb4 = sideout4[-1, :, :, :].unsqueeze(0)
+          glb3 = sideout3[-1, :, :, :].unsqueeze(0)
+          glb2 = sideout2[-1, :, :, :].unsqueeze(0)
+          glb1 = sideout1[-1, :, :, :].unsqueeze(0)
+          ####### concat 4 to 1 #######
+          sideout1 = patches2image(sideout1[:-1]).cuda()
+          sideout2 = patches2image(
+              sideout2[:-1]).cuda()  ####(5,c,h,w) -> (1 c 2h,2w)
+          sideout3 = patches2image(sideout3[:-1]).cuda()
+          sideout4 = patches2image(sideout4[:-1]).cuda()
+          sideout5 = patches2image(sideout5[:-1]).cuda()
+          if self.training:
+              return sideout5, sideout4, sideout3, sideout2, sideout1, final_output, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1
+          else:
+              return final_output
+
+
+  # model for multi-scale testing
+  class inf_MVANet(nn.Module):
+
+      def __init__(self):
+          super().__init__()
+          self.backbone = SwinB(pretrained=True)
+
+          emb_dim = 128
+          self.output5 = make_cbr(1024, emb_dim)
+          self.output4 = make_cbr(512, emb_dim)
+          self.output3 = make_cbr(256, emb_dim)
+          self.output2 = make_cbr(128, emb_dim)
+          self.output1 = make_cbr(128, emb_dim)
+
+          self.multifieldcrossatt = inf_MCLM(emb_dim, 1, [1, 4, 8])
+          self.conv1 = make_cbr(emb_dim, emb_dim)
+          self.conv2 = make_cbr(emb_dim, emb_dim)
+          self.conv3 = make_cbr(emb_dim, emb_dim)
+          self.conv4 = make_cbr(emb_dim, emb_dim)
+          self.dec_blk1 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk2 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk3 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+          self.dec_blk4 = inf_MCRM(emb_dim, 1, [2, 4, 8])
+
+          self.insmask_head = nn.Sequential(
+              nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
+              nn.BatchNorm2d(384), nn.PReLU(),
+              nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
+              nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
+
+          self.shallow = nn.Sequential(
+              nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
+          self.upsample1 = make_cbg(emb_dim, emb_dim)
+          self.upsample2 = make_cbg(emb_dim, emb_dim)
+          self.output = nn.Sequential(
+              nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
+
+          for m in self.modules():
+              if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
+                  m.inplace = True
+
+      def forward(self, x):
+          shallow = self.shallow(x)
+          glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
+          loc = image2patches(x)
+          input = torch.cat((loc, glb), dim=0)
+          feature = self.backbone(input)
+          e5 = self.output5(feature[4])
+          e4 = self.output4(feature[3])
+          e3 = self.output3(feature[2])
+          e2 = self.output2(feature[1])
+          e1 = self.output1(feature[0])
+          print(e5.shape)
+          loc_e5, glb_e5 = e5.split([4, 1], dim=0)
+          e5_cat = self.multifieldcrossatt(loc_e5, glb_e5)
+
+          e4 = self.conv4(self.dec_blk4(e4 + resize_as(e5_cat, e4)))
+          e3 = self.conv3(self.dec_blk3(e3 + resize_as(e4, e3)))
+          e2 = self.conv2(self.dec_blk2(e2 + resize_as(e3, e2)))
+          e1 = self.conv1(self.dec_blk1(e1 + resize_as(e2, e1)))
+          loc_e1, glb_e1 = e1.split([4, 1], dim=0)
+          # after decoder, concat loc features to a whole one, and merge
+          output1_cat = patches2image(loc_e1)
+          # add glb feat in
+          output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
+          # merge
+          final_output = self.insmask_head(output1_cat)
+          # shallow feature merge
+          final_output = final_output + resize_as(shallow, final_output)
+          final_output = self.upsample1(rescale_to(final_output))
+          final_output = rescale_to(final_output +
+                                    resize_as(shallow, final_output))
+          final_output = self.upsample2(final_output)
+          final_output = self.output(final_output)
+          return final_output
+#+end_src
+
+** train.execute.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.execute.py
+  writer = SummaryWriter()
+
+  cudnn.benchmark = True
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--epoch', type=int, default=80, help='epoch number')
+  parser.add_argument('--lr_gen', type=float, default=1e-5, help='learning rate')
+  parser.add_argument('--batchsize',
+                      type=int,
+                      default=1,
+                      help='training batch size')
+  parser.add_argument('--trainsize',
+                      type=int,
+                      default=1024,
+                      help='training dataset size')
+  parser.add_argument('--decay_rate',
+                      type=float,
+                      default=0.9,
+                      help='decay rate of learning rate')
+  parser.add_argument('--decay_epoch',
+                      type=int,
+                      default=80,
+                      help='every n epochs decay learning rate')
+
+  opt = parser.parse_args()
+  print('Generator Learning Rate: {}'.format(opt.lr_gen))
+  # build models
+  if hasattr(torch.cuda, 'empty_cache'):
+      torch.cuda.empty_cache()
+  generator = MVANet()
+  generator.cuda()
+  print('DEBUG 3')
+
+  pretrained_dict = torch.load(
+      HOME_DIR +
+      '/GITHUB/aravind-h-v/dreambooth_experiments/cloth_segmentation/MVANet_Train/pretrained_model/Model_80.pth',
+      map_location='cuda')
+
+  model_dict = generator.state_dict()
+  pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
+  model_dict.update(pretrained_dict)
+  generator.load_state_dict(model_dict)
+
+  generator_params = generator.parameters()
+  # generator_optimizer = torch.optim.Adam(generator_params, opt.lr_gen)
+  generator_optimizer = Prodigy(generator_params, lr=1., weight_decay=0.01)
+
+  print('DEBUG 4')
+
+  image_root = './data/image/'
+  gt_root = './data/mask/'
+
+  train_loader = get_loader(image_root,
+                            gt_root,
+                            batchsize=opt.batchsize,
+                            trainsize=opt.trainsize)
+
+  print('DEBUG 5')
+
+  total_step = len(train_loader)
+  to_pil = transforms.ToPILImage()
+  ## define loss
+  print('DEBUG 2')
+
+  CE = torch.nn.BCELoss()
+  mse_loss = torch.nn.MSELoss(size_average=True, reduce=True)
+  size_rates = [1]
+  criterion = nn.BCEWithLogitsLoss().cuda()
+  criterion_mae = nn.L1Loss().cuda()
+  criterion_mse = nn.MSELoss().cuda()
+  use_fp16 = True
+  scaler = amp.GradScaler(enabled=use_fp16)
+  print('DEBUG 1')
+
+  for epoch in range(1, opt.epoch + 1):
+      torch.cuda.empty_cache()
+      generator.train()
+      # loss_record = AvgMeter()
+      loss_record = Running_Avg()
+      print('Generator Learning Rate: {}'.format(
+          generator_optimizer.param_groups[0]['lr']))
+      for i, pack in enumerate(train_loader, start=1):
+          torch.cuda.empty_cache()
+          for rate in size_rates:
+              torch.cuda.empty_cache()
+              generator_optimizer.zero_grad()
+              images, gts = pack
+              images = Variable(images)
+              gts = Variable(gts)
+              images = images.cuda()
+              gts = gts.cuda()
+              trainsize = int(round(opt.trainsize * rate / 32) * 32)
+              if rate != 1:
+                  images = F.upsample(images,
+                                      size=(trainsize, trainsize),
+                                      mode='bilinear',
+                                      align_corners=True)
+                  gts = F.upsample(gts,
+                                   size=(trainsize, trainsize),
+                                   mode='bilinear',
+                                   align_corners=True)
+
+              b, c, h, w = gts.size()
+              target_1 = F.upsample(gts, size=h // 4, mode='nearest')
+              target_2 = F.upsample(gts, size=h // 8, mode='nearest').cuda()
+              target_3 = F.upsample(gts, size=h // 16, mode='nearest').cuda()
+              target_4 = F.upsample(gts, size=h // 32, mode='nearest').cuda()
+              target_5 = F.upsample(gts, size=h // 64, mode='nearest').cuda()
+
+              with amp.autocast(enabled=use_fp16):
+                  sideout5, sideout4, sideout3, sideout2, sideout1, final, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1 = generator.forward(
+                      images)
+                  loss1 = structure_loss(sideout5, target_4)
+                  loss2 = structure_loss(sideout4, target_3)
+                  loss3 = structure_loss(sideout3, target_2)
+                  loss4 = structure_loss(sideout2, target_1)
+                  loss5 = structure_loss(sideout1, target_1)
+                  loss6 = structure_loss(final, gts)
+                  loss7 = structure_loss(glb5, target_5)
+                  loss8 = structure_loss(glb4, target_4)
+                  loss9 = structure_loss(glb3, target_3)
+                  loss10 = structure_loss(glb2, target_2)
+                  loss11 = structure_loss(glb1, target_2)
+                  loss12 = structure_loss(tokenattmap4, target_3)
+                  loss13 = structure_loss(tokenattmap3, target_2)
+                  loss14 = structure_loss(tokenattmap2, target_1)
+                  loss15 = structure_loss(tokenattmap1, target_1)
+                  loss = loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + 0.3 * (
+                      loss7 + loss8 + loss9 + loss10 +
+                      loss11) + 0.3 * (loss12 + loss13 + loss14 + loss15)
+                  Loss_loc = loss1 + loss2 + loss3 + loss4 + loss5 + loss6
+                  Loss_glb = loss7 + loss8 + loss9 + loss10 + loss11
+                  Loss_map = loss12 + loss13 + loss14 + loss15
+                  writer.add_scalar('loss', loss.item(),
+                                    epoch * len(train_loader) + i)
+
+              generator_optimizer.zero_grad()
+              scaler.scale(loss).backward()
+              scaler.step(generator_optimizer)
+              scaler.update()
+
+              if rate == 1:
+                  loss_record.update(loss.data, opt.batchsize)
+
+          if i % 10 == 0 or i == total_step:
+              print(
+                  '{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], gen Loss: {:.4f}'
+                  .format(datetime.now(), epoch, opt.epoch, i, total_step,
+                          loss_record.show()))
+
+          if i % 8000 == 0 or i == total_step:
+              save_path = './saved_model/'
+              if not os.path.exists(save_path):
+                  os.mkdir(save_path)
+              torch.save(
+                  generator.state_dict(),
+                  save_path + 'Model' + '_%d' % epoch + '_%d' % i + '.pth')
+
+      # adjust_lr(generator_optimizer, opt.lr_gen, epoch, opt.decay_rate,
+      #           opt.decay_epoch)
+      # save checkpoints every 20 epochs
+      # if epoch % 20 == 0:
+      if True:
+
+          save_path = './saved_model/'
+          if not os.path.exists(save_path):
+              os.mkdir(save_path)
+
+          save_path = './saved_model/MVANet/'
+          if not os.path.exists(save_path):
+              os.mkdir(save_path)
+
+          torch.save(generator.state_dict(),
+                     save_path + 'Model' + '_%d' % epoch + '.pth')
+#+end_src
+
+* SAMPLE
+
+** train
+
+*** train.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.import.py
+#+end_src
+
+*** train.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.function.py
+#+end_src
+
+*** train.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.class.py
+#+end_src
+
+*** train.execute.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./train.execute.py
+#+end_src
+
+** swin
+
+*** swin.import.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.import.py
+#+end_src
+
+*** swin.function.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.function.py
+#+end_src
+
+*** swin.class.py
+#+begin_src python :shebang #!/usr/bin/python3 :results output :tangle ./swin.class.py
+#+end_src
+
+* UNIFY
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./train.unify.sh
+  . "${HOME}/dbnew.sh"
+
+  echo '#!/usr/bin/python3' > './train.py' 
+
+  cat \
+          './train.import.py' \
+          './train.function.py' \
+          './train.class.py' \
+          './train.execute.py' \
+      | expand | yapf3 \
+      | grep -v '^#!/usr/bin/python3$' \
+      >> './train.py' \
+  ;
+
+  echo '#!/usr/bin/python3' > './swin.py' 
+
+  cat \
+          './swin.import.py' \
+          './swin.function.py' \
+          './swin.class.py' \
+      | expand | yapf3 \
+      | grep -v '^#!/usr/bin/python3$' \
+      >> './swin.py' \
+  ;
+
+  rm -vf -- \
+      './swin.class.py' \
+      './swin.function.py' \
+      './swin.import.py' \
+      './train.class.py' \
+      './train.execute.py' \
+      './train.function.py' \
+      './train.import.py' \
+      './train.unify.sh' \
+  ;
+#+end_src
+
+* Run
+#+begin_src sh :shebang #!/bin/sh :results output :tangle ./run.sh
+  . "${HOME}/dbnew.sh"
+
+  cd "$('dirname' '--' "${0}")"
+
+  pip3 install -r './requirements.txt'
+
+  python3 ./train.py --batchsize 4
+#+end_src
+
+* WORK SPACE
+
+** ELISP
+#+begin_src elisp
+  (save-buffer)
+  (org-babel-tangle)
+  (shell-command "./train.unify.sh")
+#+end_src
+
+#+RESULTS:
+: 0
+
+** SHELL
+#+begin_src sh :shebang #!/bin/sh :results output 
+  realpath .
+  cd /home/asd/GITHUB/aravind-h-v/dreambooth_experiments/cloth_segmentation/MVANet_Train
+#+end_src