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mmdet.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: mmdet
+Version: 2.11.0
+Summary: OpenMMLab Detection Toolbox and Benchmark
+Home-page: https://github.com/open-mmlab/mmdetection
+Author: OpenMMLab
+Author-email: openmmlab@gmail.com
+License: Apache License 2.0
+Keywords: computer vision,object detection
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Description-Content-Type: text/markdown
+Provides-Extra: all
+Provides-Extra: tests
+Provides-Extra: build
+Provides-Extra: optional
+License-File: LICENSE
+
+# Swin Transformer for Object Detection
+
+This repo contains the supported code and configuration files to reproduce object detection results of [Swin Transformer](https://arxiv.org/pdf/2103.14030.pdf). It is based on [mmdetection](https://github.com/open-mmlab/mmdetection).
+
+## Updates
+
+***05/11/2021*** Models for [MoBY](https://github.com/SwinTransformer/Transformer-SSL) are released
+
+***04/12/2021*** Initial commits
+
+## Results and Models
+
+### Mask R-CNN
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
+| Swin-T | ImageNet-1K | 1x | 43.7 | 39.8 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1bYZk7BIeFEozjRNUesxVWg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/19UOW0xl0qc-pXQ59aFKU5w) |
+| Swin-T | ImageNet-1K | 3x | 46.0 | 41.6 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1Te-Ovk4yaavmE4jcIOPAaw) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1YpauXYAFOohyMi3Vkb6DBg) |
+| Swin-S | ImageNet-1K | 3x | 48.5 | 43.3 | 69M | 359G | [config](configs/swin/mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_small_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1ymCK7378QS91yWlxHMf1yw) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_small_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1V4w4aaV7HSjXNFTOSA6v6w) |
+
+### Cascade Mask R-CNN
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
+| Swin-T | ImageNet-1K | 1x | 48.1 | 41.7 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/cascade_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1x4vnorYZfISr-d_VUSVQCA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/cascade_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1vFwbN1iamrtwnQSxMIW4BA) |
+| Swin-T | ImageNet-1K | 3x | 50.4 | 43.7 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_tiny_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1GW_ic617Ak_NpRayOqPSOA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_tiny_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1i-izBrODgQmMwTv6F6-x3A) |
+| Swin-S | ImageNet-1K | 3x | 51.9 | 45.0 | 107M | 838G | [config](configs/swin/cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_small_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/17Vyufk85vyocxrBT1AbavQ) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_small_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1Sv9-gP1Qpl6SGOF6DBhUbw) |
+| Swin-B | ImageNet-1K | 3x | 51.9 | 45.0 | 145M | 982G | [config](configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_base_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1UZAR39g-0kE_aGrINwfVHg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_base_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1tHoC9PMVnldQUAfcF6FT3A) |
+
+### RepPoints V2
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| Swin-T | ImageNet-1K | 3x | 50.0 | - | 45M | 283G |
+
+### Mask RepPoints V2
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| Swin-T | ImageNet-1K | 3x | 50.3 | 43.6 | 47M | 292G |
+
+**Notes**: 
+
+- **Pre-trained models can be downloaded from [Swin Transformer for ImageNet Classification](https://github.com/microsoft/Swin-Transformer)**.
+- Access code for `baidu` is `swin`.
+
+## Results of MoBY with Swin Transformer
+
+### Mask R-CNN
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
+| Swin-T | ImageNet-1K | 1x | 43.6 | 39.6 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1P5gCIfLUQ64jbVMOom0H3w) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1xGRihuIrGVreFKn5eJ6oTg) |
+| Swin-T | ImageNet-1K | 3x | 46.0 | 41.7 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_3x.log.json)/[baidu](https://pan.baidu.com/s/17WAhUmhAam1of3hXOu-wtA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_3x.pth)/[baidu](https://pan.baidu.com/s/1MSj8cC1wlQU1QaXCdKrzeA) |
+
+### Cascade Mask R-CNN
+
+| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
+| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
+| Swin-T | ImageNet-1K | 1x | 48.1 | 41.5 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1eOdq1rvi0QoXjc7COgiM7A) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1-gbY-LExbf0FgYxWWs8OPg) |
+| Swin-T | ImageNet-1K | 3x | 50.2 | 43.5 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_3x.log.json)/[baidu](https://pan.baidu.com/s/1zEFXHYjEiXUCWF1U7HR5Zg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_3x.pth)/[baidu](https://pan.baidu.com/s/1FMmW0GOpT4MKsKUrkJRgeg) |
+
+**Notes:**
+
+- The drop path rate needs to be tuned for best practice.
+- MoBY pre-trained models can be downloaded from [MoBY with Swin Transformer](https://github.com/SwinTransformer/Transformer-SSL).
+
+## Usage
+
+### Installation
+
+Please refer to [get_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) for installation and dataset preparation.
+
+### Inference
+```
+# single-gpu testing
+python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm
+
+# multi-gpu testing
+tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm
+```
+
+### Training
+
+To train a detector with pre-trained models, run:
+```
+# single-gpu training
+python tools/train.py <CONFIG_FILE> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]
+
+# multi-gpu training
+tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments] 
+```
+For example, to train a Cascade Mask R-CNN model with a `Swin-T` backbone and 8 gpus, run:
+```
+tools/dist_train.sh configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py 8 --cfg-options model.pretrained=<PRETRAIN_MODEL> 
+```
+
+**Note:** `use_checkpoint` is used to save GPU memory. Please refer to [this page](https://pytorch.org/docs/stable/checkpoint.html) for more details.
+
+
+### Apex (optional):
+We use apex for mixed precision training by default. To install apex, run:
+```
+git clone https://github.com/NVIDIA/apex
+cd apex
+pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+```
+If you would like to disable apex, modify the type of runner as `EpochBasedRunner` and comment out the following code block in the [configuration files](configs/swin):
+```
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+    type="DistOptimizerHook",
+    update_interval=1,
+    grad_clip=None,
+    coalesce=True,
+    bucket_size_mb=-1,
+    use_fp16=True,
+)
+```
+
+## Citing Swin Transformer
+```
+@article{liu2021Swin,
+  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
+  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
+  journal={arXiv preprint arXiv:2103.14030},
+  year={2021}
+}
+```
+
+## Other Links
+
+> **Image Classification**: See [Swin Transformer for Image Classification](https://github.com/microsoft/Swin-Transformer).
+
+> **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
+
+> **Self-Supervised Learning**: See [MoBY with Swin Transformer](https://github.com/SwinTransformer/Transformer-SSL).
+
+> **Video Recognition**, See [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).
+
+
+

mmdet.egg-info/SOURCES.txt

@@ -0,0 +1,978 @@
+.gitignore
+.pre-commit-config.yaml
+.readthedocs.yml
+LICENSE
+README.md
+pytest.ini
+requirements.txt
+setup.cfg
+setup.py
+.dev_scripts/batch_test.py
+.dev_scripts/batch_test.sh
+.dev_scripts/benchmark_filter.py
+.dev_scripts/convert_benchmark_script.py
+.dev_scripts/gather_benchmark_metric.py
+.dev_scripts/gather_models.py
+.dev_scripts/linter.sh
+.github/CODE_OF_CONDUCT.md
+.github/CONTRIBUTING.md
+.github/ISSUE_TEMPLATE/config.yml
+.github/ISSUE_TEMPLATE/error-report.md
+.github/ISSUE_TEMPLATE/feature_request.md
+.github/ISSUE_TEMPLATE/general_questions.md
+.github/ISSUE_TEMPLATE/reimplementation_questions.md
+.github/workflows/build.yml
+.github/workflows/build_pat.yml
+.github/workflows/deploy.yml
+configs/_base_/default_runtime.py
+configs/_base_/datasets/cityscapes_detection.py
+configs/_base_/datasets/cityscapes_instance.py
+configs/_base_/datasets/coco_detection.py
+configs/_base_/datasets/coco_instance.py
+configs/_base_/datasets/coco_instance_semantic.py
+configs/_base_/datasets/deepfashion.py
+configs/_base_/datasets/lvis_v0.5_instance.py
+configs/_base_/datasets/lvis_v1_instance.py
+configs/_base_/datasets/voc0712.py
+configs/_base_/datasets/wider_face.py
+configs/_base_/models/cascade_mask_rcnn_r50_fpn.py
+configs/_base_/models/cascade_mask_rcnn_swin_fpn.py
+configs/_base_/models/cascade_rcnn_r50_fpn.py
+configs/_base_/models/fast_rcnn_r50_fpn.py
+configs/_base_/models/faster_rcnn_r50_caffe_c4.py
+configs/_base_/models/faster_rcnn_r50_caffe_dc5.py
+configs/_base_/models/faster_rcnn_r50_fpn.py
+configs/_base_/models/mask_rcnn_r50_caffe_c4.py
+configs/_base_/models/mask_rcnn_r50_fpn.py
+configs/_base_/models/mask_rcnn_swin_fpn.py
+configs/_base_/models/retinanet_r50_fpn.py
+configs/_base_/models/rpn_r50_caffe_c4.py
+configs/_base_/models/rpn_r50_fpn.py
+configs/_base_/models/ssd300.py
+configs/_base_/schedules/schedule_1x.py
+configs/_base_/schedules/schedule_20e.py
+configs/_base_/schedules/schedule_2x.py
+configs/albu_example/README.md
+configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py
+configs/atss/README.md
+configs/atss/atss_r101_fpn_1x_coco.py
+configs/atss/atss_r50_fpn_1x_coco.py
+configs/carafe/README.md
+configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
+configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
+configs/cascade_rcnn/README.md
+configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
+configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
+configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
+configs/cascade_rpn/README.md
+configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
+configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
+configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py
+configs/centripetalnet/README.md
+configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
+configs/cityscapes/README.md
+configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
+configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
+configs/cornernet/README.md
+configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
+configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
+configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
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+configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
+configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
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+requirements/build.txt
+requirements/docs.txt
+requirements/optional.txt
+requirements/readthedocs.txt
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+requirements/tests.txt
+resources/coco_test_12510.jpg
+resources/corruptions_sev_3.png
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+resources/mmdet-logo.png
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+tests/test_runtime/test_fp16.py
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+tests/test_utils/test_masks.py
+tests/test_utils/test_misc.py
+tests/test_utils/test_version.py
+tests/test_utils/test_visualization.py
+tools/dist_test.sh
+tools/dist_train.sh
+tools/slurm_test.sh
+tools/slurm_train.sh
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+tools/analysis_tools/analyze_results.py
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+tools/analysis_tools/coco_error_analysis.py
+tools/analysis_tools/eval_metric.py
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+tools/analysis_tools/test_robustness.py
+tools/dataset_converters/cityscapes.py
+tools/dataset_converters/pascal_voc.py
+tools/deployment/mmdet2torchserve.py
+tools/deployment/mmdet_handler.py
+tools/deployment/onnx2tensorrt.py
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+tools/model_converters/upgrade_model_version.py

mmdet.egg-info/dependency_links.txt

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+

mmdet.egg-info/not-zip-safe

@@ -0,0 +1 @@
+

mmdet.egg-info/requires.txt

@@ -0,0 +1,59 @@
+matplotlib
+mmpycocotools
+numpy
+six
+terminaltables
+timm
+
+[all]
+cython
+numpy
+albumentations>=0.3.2
+cityscapesscripts
+imagecorruptions
+mmlvis
+scipy
+sklearn
+matplotlib
+mmpycocotools
+six
+terminaltables
+timm
+asynctest
+codecov
+flake8
+interrogate
+isort==4.3.21
+kwarray
+onnx==1.7.0
+onnxruntime==1.5.1
+pytest
+ubelt
+xdoctest>=0.10.0
+yapf
+
+[build]
+cython
+numpy
+
+[optional]
+albumentations>=0.3.2
+cityscapesscripts
+imagecorruptions
+mmlvis
+scipy
+sklearn
+
+[tests]
+asynctest
+codecov
+flake8
+interrogate
+isort==4.3.21
+kwarray
+onnx==1.7.0
+onnxruntime==1.5.1
+pytest
+ubelt
+xdoctest>=0.10.0
+yapf

mmdet.egg-info/top_level.txt

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+mmcv_custom
+mmdet

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+size 538113079

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work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py

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+model = dict(
+    type='MaskRCNN',
+    pretrained=None,
+    backbone=dict(
+        type='SwinTransformer',
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        use_checkpoint=False),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 384, 768],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=12,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[{
+            'type': 'Resize',
+            'img_scale': [(224, 224)],
+            'multiscale_mode': 'value',
+            'keep_ratio': True
+        }],
+                  [{
+                      'type': 'Resize',
+                      'img_scale': [(224, 224)],
+                      'multiscale_mode': 'value',
+                      'keep_ratio': True
+                  }, {
+                      'type': 'RandomCrop',
+                      'crop_type': 'absolute_range',
+                      'crop_size': (384, 600),
+                      'allow_negative_crop': True
+                  }, {
+                      'type': 'Resize',
+                      'img_scale': [(224, 224)],
+                      'multiscale_mode': 'value',
+                      'override': True,
+                      'keep_ratio': True
+                  }]]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=[(224, 224)],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=8,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/train.json',
+        img_prefix='data/coco/train/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='AutoAugment',
+                policies=[[{
+                    'type': 'Resize',
+                    'img_scale': [(224, 224)],
+                    'multiscale_mode': 'value',
+                    'keep_ratio': True
+                }],
+                          [{
+                              'type': 'Resize',
+                              'img_scale': [(224, 224)],
+                              'multiscale_mode': 'value',
+                              'keep_ratio': True
+                          }, {
+                              'type': 'RandomCrop',
+                              'crop_type': 'absolute_range',
+                              'crop_size': (384, 600),
+                              'allow_negative_crop': True
+                          }, {
+                              'type': 'Resize',
+                              'img_scale': [(224, 224)],
+                              'multiscale_mode': 'value',
+                              'override': True,
+                              'keep_ratio': True
+                          }]]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/val.json',
+        img_prefix='data/coco/val/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=[(224, 224)],
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/val.json',
+        img_prefix='data/coco/val/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=[(224, 224)],
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(interval=1, metric='bbox')
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg=dict(
+        custom_keys=dict(
+            absolute_pos_embed=dict(decay_mult=0.0),
+            relative_position_bias_table=dict(decay_mult=0.0),
+            norm=dict(decay_mult=0.0))))
+optimizer_config = dict(
+    grad_clip=None,
+    type='DistOptimizerHook',
+    update_interval=1,
+    coalesce=True,
+    bucket_size_mb=-1,
+    use_fp16=True)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[27, 33])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=200)
+checkpoint_config = dict(interval=25)
+log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [dict(type='NumClassCheckHook')]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = 'checkpoints/epoch_75.pth'
+workflow = [('train', 1)]
+fp16 = None
+work_dir = './work_dirs\mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco'
+gpu_ids = range(0, 1)