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‎=6.2.2‎

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Defaulting to user installation because normal site-packages is not writeable
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Requirement already satisfied: Pillow in /opt/conda/lib/python3.7/site-packages (7.0.0)

‎MODEL_ZOO.md‎

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OCR | ResNet-101 | Train | Val | 40000 | 8 | No | No | No | 79.63 | 80.68 | [Log](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/spatial_ocrnet_deepbase_resnet101_dilated8_1.log) / [Model](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/spatial_ocrnet_deepbase_resnet101_dilated8_1_latest.pth) | scripts/cityscapes/ocrnet/run_r_101_d_8_ocrnet_train.sh |
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ASP-OCR | ResNet-101 | Train | Val | 40000 | 8 | No | No | No | 79.89 | 80.69 | [Log](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/spatial_asp_ocrnet_deepbase_resnet101_dilated8_1.log) / [Model](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/spatial_asp_ocrnet_deepbase_resnet101_dilated8_1_latest.pth) | scripts/cityscapes/ocrnet/run_r_101_d_8_asp_ocrnet_train.sh |
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OCR | HRNet-W48 | Train | Val | 80000 | 8 | No | No | No | 81.09 | 81.73 | [Log](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/hrnet_w48_ocr_1.log) / [Model](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.cityscapes/hrnet_w48_ocr_1_latest.pth) | scripts/cityscapes/hrnet/run_h_48_d_4_ocr.sh |
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OCR | HRNet-W48 (Paddle) | Train | Val | 40000 | 16 | No | No | No | 81.53 | ---- | [Log]() / [Model]() | scripts/cityscapes/hrnet/run_h_48_d_4_ocr_paddle.sh |
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OCR | HRNet-W48 (Paddle) | Train | Val | 40000 | 16 | No | No | No | 81.53 | 82.78 | [Log]() / [Model]() | scripts/cityscapes/hrnet/run_h_48_d_4_ocr_paddle.sh |
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### How to reproduce the HRNet + OCR with Mapillary pretraining
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| :---- | :----: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |
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OCR | HRNet-W48 | Train | Val | 60000 | 16 | No | No | No | 55.11 | [Log](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.pascal_context/hrnet_w48_ocr_hrnet48_2.log) / [Model](https://github.com/hsfzxjy/models.storage/releases/download/openseg.pytorch.pascal_context/hrnet_w48_ocr_hrnet48_2_latest.pth) | scripts/pascal_context/run_h_48_d_4_ocr_train.sh |
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OCR | HRNet-W48 (Paddle) | Train | Val | 60000 | 16 | No | No | No | 57.82 | [Log]() / [Model]() | scripts/pascal_context/run_h_48_d_4_ocr_train_paddle.sh |
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OCR | HRNet-W48 (Paddle) | Train | Val | 60000 | 16 | No | Yes | Yes | 59.13 | [Log]() / [Model]() | scripts/pascal_context/run_h_48_d_4_ocr_train_paddle.sh |
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## LIP
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‎README.md‎

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# openseg.pytorch
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# openseg.pytorch [pytorch-1.7]
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/object-contextual-representations-for/semantic-segmentation-on-coco-stuff-test)](https://paperswithcode.com/sota/semantic-segmentation-on-coco-stuff-test?p=object-contextual-representations-for)
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## News
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- 2021/02/14 Support **pytorch-1.7, mixed-precision, and distributed training**. We also will add more segmentation models that achieve better performance based on the paddlepaddle image pretrained weights soon. Please check [Model Zoo](https://github.com/openseg-group/openseg.pytorch/blob/pytorch-1.7/MODEL_ZOO.md) for more details. For example, we gain **2.61%** on PASCAL-Context and **2.89%** on COCO-Stuff with single-scale test.
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- 2021/02/14 Support **pytorch-1.7, mixed-precision, and distributed training**. Based on the [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) ImageNet pretrained weights, "HRNet + OCR" achieves **82.78%** on Cityscapes val, **59.13%** on PASCAL-Context val and **43.26%** on COCO-Stuff val. Check [Model Zoo](https://github.com/openseg-group/openseg.pytorch/blob/pytorch-1.7/MODEL_ZOO.md) for more details.
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- 2020/08/16 [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) has supported our HRNet + OCR.
2020

‎configs/cityscapes/H_48_D_4_TEST.json‎

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{
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"dataset": "cityscapes",
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"method": "fcn_segmentor",
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"data": {
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"image_tool": "cv2",
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"input_mode": "BGR",
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"num_classes": 19,
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"label_list": [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33],
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"data_dir": "/msravcshare/dataset/cityscapes",
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"workers": 8
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},
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"train": {
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"batch_size": 16,
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"data_transformer": {
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"size_mode": "fix_size",
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"input_size": [1024, 512],
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"align_method": "only_pad",
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"pad_mode": "random"
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}
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},
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"val": {
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"batch_size": 8,
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"scale_search": [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
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"mode": "ms_test",
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"data_transformer": {
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"size_mode": "fix_size",
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"input_size": [2048, 1024],
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"align_method": "only_pad"
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}
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},
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"test": {
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"batch_size": 8,
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"scale_search": [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
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"mode": "ms_test",
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"data_transformer": {
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"size_mode": "fix_size",
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"input_size": [2048, 1024],
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"align_method": "only_pad"
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}
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},
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"train_trans": {
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"trans_seq": ["random_resize", "random_crop", "random_hflip", "random_brightness"],
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"random_brightness": {
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"ratio": 1.0,
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"shift_value": 10
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},
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"random_hflip": {
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"ratio": 0.5,
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"swap_pair": []
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},
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"random_resize": {
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"ratio": 1.0,
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"method": "random",
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"scale_range": [0.5, 2.0],
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"aspect_range": [0.9, 1.1]
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},
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"random_crop":{
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"ratio": 1.0,
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"crop_size": [1024, 512],
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"method": "random",
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"allow_outside_center": false
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}
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},
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"val_trans": {
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"trans_seq": []
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},
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"normalize": {
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"div_value": 255.0,
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"mean_value": [0.485, 0.456, 0.406],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225]
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},
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"checkpoints": {
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"checkpoints_name": "fs_baseocnet_cityscapes_seg",
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"checkpoints_dir": "./checkpoints/cityscapes",
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"save_iters": 1000
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},
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"network":{
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"backbone": "deepbase_resnet101_dilated8",
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"multi_grid": [1, 1, 1],
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"model_name": "base_ocnet",
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"bn_type": "torchsyncbn",
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"stride": 8,
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"factors": [[8, 8]],
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"loss_weights": {
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"corr_loss": 0.01,
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"aux_loss": 0.4,
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"seg_loss": 1.0
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}
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},
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"logging": {
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"logfile_level": "info",
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"stdout_level": "info",
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"log_file": "./log/cityscapes/fs_baseocnet_cityscapes_seg.log",
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"log_format": "%(asctime)s %(levelname)-7s %(message)s",
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"rewrite": true
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},
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"lr": {
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"base_lr": 0.01,
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"metric": "iters",
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"lr_policy": "lambda_poly",
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"step": {
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"gamma": 0.5,
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"step_size": 100
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}
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},
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"solver": {
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"display_iter": 10,
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"test_interval": 1000,
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"max_iters": 40000
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},
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"optim": {
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"optim_method": "sgd",
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"adam": {
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"betas": [0.9, 0.999],
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"eps": 1e-08,
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"weight_decay": 0.0001
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},
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"sgd": {
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"weight_decay": 0.0005,
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"momentum": 0.9,
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"nesterov": false
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}
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},
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"loss": {
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"loss_type": "fs_auxce_loss",
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"params": {
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"ce_weight": [0.8373, 0.9180, 0.8660, 1.0345, 1.0166, 0.9969, 0.9754,
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1.0489, 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037,
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1.0865, 1.0955, 1.0865, 1.1529, 1.0507],
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"ce_reduction": "elementwise_mean",
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"ce_ignore_index": -1,
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"ohem_minkeep": 100000,
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"ohem_thresh": 0.9
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}
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}
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}

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