[Feature] add DPT head (#605)

* add DPT head

* [fix] fix init error

* use mmcv function

* delete code

* remove transpose clas

* support NLC output shape

* Delete post_process_layer.py

* add unittest and docstring

* rename variables

* fix project error and add unittest

* match dpt weights

* add configs

* fix vit pos_embed bug and dpt feature fusion bug

* match vit output

* fix gelu

* minor change

* update unitest

* fix configs error

* inference test

* remove auxilary

* use local pretrain

* update training results

* update yml

* update fps and memory test

* update doc

* update readme

* add yml

* update doc

* remove with_cp

* update config

* update docstring

* remove dpt-l

* add init_cfg and modify readme.md

* Update dpt_vit-b16.py

* zh-n README

* use constructor instead of build function

* prevent tensor being modified by ConvModule

* fix unittest

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>

configs/_base_/models/dpt_vit-b16.py

@@ -0,0 +1,31 @@
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa
+    backbone=dict(
+        type='VisionTransformer',
+        img_size=224,
+        embed_dims=768,
+        num_layers=12,
+        num_heads=12,
+        out_indices=(2, 5, 8, 11),
+        final_norm=False,
+        with_cls_token=True,
+        output_cls_token=True),
+    decode_head=dict(
+        type='DPTHead',
+        in_channels=(768, 768, 768, 768),
+        channels=256,
+        embed_dims=768,
+        post_process_channels=[96, 192, 384, 768],
+        num_classes=150,
+        readout_type='project',
+        input_transform='multiple_select',
+        in_index=(0, 1, 2, 3),
+        norm_cfg=norm_cfg,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=None,
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))  # yapf: disable

configs/dpt/README.md

@@ -0,0 +1,47 @@
+# Vision Transformer for Dense Prediction
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@article{dosoViTskiy2020,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
+  journal={arXiv preprint arXiv:2010.11929},
+  year={2020}
+}
+
+@article{Ranftl2021,
+  author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
+  title     = {Vision Transformers for Dense Prediction},
+  journal   = {ArXiv preprint},
+  year      = {2021},
+}
+```
+
+## Usage
+
+To use other repositories' pre-trained models, it is necessary to convert keys.
+
+We provide a script [`vit2mmseg.py`](../../tools/model_converters/vit2mmseg.py) in the tools directory to convert the key of models from [timm](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to MMSegmentation style.
+
+```shell
+python tools/model_converters/vit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}
+```
+
+E.g.
+
+```shell
+python tools/model_converters/vit2mmseg.py https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth pretrain/jx_vit_base_p16_224-80ecf9dd.pth
+```
+
+This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
+
+## Results and models
+
+### ADE20K
+
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                 | download                                                                                                                                                                                                                                                                                                                               |
+| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DPT | ViT-B | 512x512  | 160000  | 8.09 | 10.41 | 46.97 | 48.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-20210809_172025.log.json) |

configs/dpt/dpt.yml

@@ -0,0 +1,28 @@
+Collections:
+- Metadata:
+    Training Data:
+    - ADE20K
+  Name: dpt
+Models:
+- Config: configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py
+  In Collection: dpt
+  Metadata:
+    backbone: ViT-B
+    crop size: (512,512)
+    inference time (ms/im):
+    - backend: PyTorch
+      batch size: 1
+      hardware: V100
+      mode: FP32
+      resolution: (512,512)
+      value: 96.06
+    lr schd: 160000
+    memory (GB): 8.09
+  Name: dpt_vit-b16_512x512_160k_ade20k
+  Results:
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.97
+      mIoU(ms+flip): 48.34
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth

configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py

@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/dpt_vit-b16.py', '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+
+# AdamW optimizer, no weight decay for position embedding & layer norm
+# in backbone
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.00006,
+    betas=(0.9, 0.999),
+    weight_decay=0.01,
+    paramwise_cfg=dict(
+        custom_keys={
+            'pos_embed': dict(decay_mult=0.),
+            'cls_token': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.)
+        }))
+
+lr_config = dict(
+    _delete_=True,
+    policy='poly',
+    warmup='linear',
+    warmup_iters=1500,
+    warmup_ratio=1e-6,
+    power=1.0,
+    min_lr=0.0,
+    by_epoch=False)
+
+# By default, models are trained on 8 GPUs with 2 images per GPU
+data = dict(samples_per_gpu=2, workers_per_gpu=2)

mmseg/models/decode_heads/__init__.py

@@ -6,6 +6,7 @@
 from .da_head import DAHead
 from .dm_head import DMHead
 from .dnl_head import DNLHead
+from .dpt_head import DPTHead
 from .ema_head import EMAHead
 from .enc_head import EncHead
 from .fcn_head import FCNHead
@@ -29,5 +30,5 @@
     'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
     'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
     'PointHead', 'APCHead', 'DMHead', 'LRASPPHead', 'SETRUPHead',
-    'SETRMLAHead', 'SegformerHead'
+    'SETRMLAHead', 'DPTHead', 'SETRMLAHead', 'SegformerHead'
 ]