[Feature] Support inference and visualization of VPD (open-mmlab#3331)

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## Motivation

Support inference and visualization of VPD

## Modification

1. add a new VPD model that does not generate black border in
predictions
2. update `SegLocalVisualizer` to support depth visualization
3. update `MMSegInferencer` to support save predictions of depth
estimation in method `postprocess`

## BC-breaking (Optional)

Does the modification introduce changes that break the
backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the
downstream projects should modify their code to keep compatibility with
this PR.

## Use cases (Optional)

Run inference with VPD using the this command

```sh
python demo/image_demo_with_inferencer.py demo/classroom__rgb_00283.jpg vpd_depth --out-dir vis_results
```

The following image will be saved under `vis_results/vis`


![classroom__rgb_00283](https://github.com/open-mmlab/mmsegmentation/assets/26127467/051e8c4b-8f92-495f-8c3e-f249aac888e3)




## Checklist

1. Pre-commit or other linting tools are used to fix the potential lint
issues.
4. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
5. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
6. The documentation has been modified accordingly, like docstring or
example tutorials.

Author: Ben-Louis

.readthedocs.yml

@@ -1,10 +1,14 @@
 version: 2
 
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.7"
+
 formats:
     - epub
 
 python:
-  version: 3.7
   install:
     - requirements: requirements/docs.txt
     - requirements: requirements/readthedocs.txt

configs/_base_/datasets/nyu.py

@@ -24,6 +24,7 @@
 
 test_pipeline = [
     dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(2000, 480), keep_ratio=True),
     dict(dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3)),
     dict(
         type='PackSegInputs',

configs/_base_/datasets/nyu_512x512.py

@@ -0,0 +1,72 @@
+# dataset settings
+dataset_type = 'NYUDataset'
+data_root = 'data/nyu'
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3),
+    dict(type='RandomDepthMix', prob=0.25),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandomResize',
+        scale=(768, 512),
+        ratio_range=(0.8, 1.5),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(512, 512)),
+    dict(
+        type='Albu',
+        transforms=[
+            dict(type='RandomBrightnessContrast'),
+            dict(type='RandomGamma'),
+            dict(type='HueSaturationValue'),
+        ]),
+    dict(
+        type='PackSegInputs',
+        meta_keys=('img_path', 'depth_map_path', 'ori_shape', 'img_shape',
+                   'pad_shape', 'scale_factor', 'flip', 'flip_direction',
+                   'category_id')),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(2048, 512), keep_ratio=True),
+    dict(dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3)),
+    dict(
+        type='PackSegInputs',
+        meta_keys=('img_path', 'depth_map_path', 'ori_shape', 'img_shape',
+                   'pad_shape', 'scale_factor', 'flip', 'flip_direction',
+                   'category_id'))
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='images/train', depth_map_path='annotations/train'),
+        pipeline=train_pipeline))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        test_mode=True,
+        data_prefix=dict(
+            img_path='images/test', depth_map_path='annotations/test'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='DepthMetric',
+    min_depth_eval=0.001,
+    max_depth_eval=10.0,
+    crop_type='nyu_crop')
+test_evaluator = val_evaluator

configs/_base_/models/vpd_sd.py

@@ -5,7 +5,7 @@
     std=[127.5, 127.5, 127.5],
     bgr_to_rgb=True,
     pad_val=0,
-    seg_pad_val=255)
+    seg_pad_val=0)
 
 # adapted from stable-diffusion/configs/stable-diffusion/v1-inference.yaml
 stable_diffusion_cfg = dict(

configs/vpd/README.md

@@ -4,7 +4,7 @@
 
 ## Introduction
 
-<!-- [BACKBONE] -->
+<!-- [ALGORITHM] -->
 
 <a href = "https://github.com/wl-zhao/VPD">Official Repo</a>
 
@@ -36,6 +36,7 @@ pip install -r requirements/optional.txt
 | Method | Backbone              | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | RMSE  | d1    | d2    | d3    | REL   | log_10 | config                                                                                                      | download                                                                                                                                                                                                                     |
 | ------ | --------------------- | --------- | ------- | -------- | -------------- | ------ | ----- | ----- | ----- | ----- | ----- | ------ | ----------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | VPD    | Stable-Diffusion-v1-5 | 480x480   | 25000   | -        | -              | A100   | 0.253 | 0.964 | 0.995 | 0.999 | 0.069 | 0.030  | [config](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/vpd/vpd_sd_4xb8-25k_nyu-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-480x480_20230908-66144bc4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-480x480_20230908.json) |
+| VPD    | Stable-Diffusion-v1-5 | 512x512   | 25000   | -        | -              | A100   | 0.258 | 0.963 | 0.995 | 0.999 | 0.072 | 0.031  | [config](https://github.com/open-mmlab/mmsegmentation/tree/main/configs/vpd/vpd_sd_4xb8-25k_nyu-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-512x512_20230918-60cefcff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-512x512_20230918.json) |
 
 ## Citation
 

configs/vpd/metafile.yaml

@@ -32,3 +32,25 @@ Models:
     URL: https://arxiv.org/abs/2112.10752
   Code: https://github.com/open-mmlab/mmsegmentation/tree/main/mmseg/models/backbones/vpd.py#L333
   Framework: PyTorch
+- Name: vpd_sd_4xb8-25k_nyu-512x512
+  In Collection: VPD
+  Alias: vpd_depth
+  Results:
+    Task: Depth Estimation
+    Dataset: NYU
+    Metrics:
+      RMSE: 0.258
+  Config: configs/vpd/vpd_sd_4xb8-25k_nyu-512x512.py
+  Metadata:
+    Training Data: NYU
+    Batch Size: 32
+    Architecture:
+    - Stable-Diffusion
+    Training Resources: 8x A100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-512x512_20230918-60cefcff.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vpd/vpd_sd_4xb8-25k_nyu-512x512_20230918.json
+  Paper:
+    Title: 'High-Resolution Image Synthesis with Latent Diffusion Models'
+    URL: https://arxiv.org/abs/2112.10752
+  Code: https://github.com/open-mmlab/mmsegmentation/tree/main/mmseg/models/backbones/vpd.py#L333
+  Framework: PyTorch

configs/vpd/vpd_sd_4xb8-25k_nyu-480x480.py

@@ -23,7 +23,8 @@
     ),
     test_cfg=dict(mode='slide_flip', crop_size=crop_size, stride=(160, 160)))
 
-default_hooks = dict(checkpoint=dict(save_best='rmse', rule='less'))
+default_hooks = dict(
+    checkpoint=dict(save_best='rmse', rule='less', max_keep_ckpts=1))
 
 # custom optimizer
 optim_wrapper = dict(

configs/vpd/vpd_sd_4xb8-25k_nyu-512x512.py

@@ -0,0 +1,37 @@
+_base_ = [
+    '../_base_/models/vpd_sd.py', '../_base_/datasets/nyu_512x512.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_25k.py'
+]
+
+crop_size = (512, 512)
+
+model = dict(
+    type='DepthEstimator',
+    data_preprocessor=dict(size=crop_size),
+    backbone=dict(
+        class_embed_path='https://download.openmmlab.com/mmsegmentation/'
+        'v0.5/vpd/nyu_class_embeddings.pth',
+        class_embed_select=True,
+        pad_shape=512,
+        unet_cfg=dict(use_attn=False),
+    ),
+    decode_head=dict(
+        type='VPDDepthHead',
+        in_channels=[320, 640, 1280, 1280],
+        max_depth=10,
+    ),
+    test_cfg=dict(mode='slide_flip', crop_size=crop_size, stride=(128, 128)))
+
+default_hooks = dict(
+    checkpoint=dict(save_best='rmse', rule='less', max_keep_ckpts=1))
+
+# custom optimizer
+optim_wrapper = dict(
+    constructor='ForceDefaultOptimWrapperConstructor',
+    paramwise_cfg=dict(
+        bias_decay_mult=0,
+        force_default_settings=True,
+        custom_keys={
+            'backbone.encoder_vq': dict(lr_mult=0),
+            'backbone.unet': dict(lr_mult=0.01),
+        }))

mmseg/apis/mmseg_inferencer.py

@@ -306,17 +306,28 @@ def postprocess(self,
         results_dict['visualization'] = []
 
         for i, pred in enumerate(preds):
-            pred_data = pred.pred_sem_seg.numpy().data[0]
-            results_dict['predictions'].append(pred_data)
+            pred_data = dict()
+            if 'pred_sem_seg' in pred.keys():
+                pred_data['sem_seg'] = pred.pred_sem_seg.numpy().data[0]
+            elif 'pred_depth_map' in pred.keys():
+                pred_data['depth_map'] = pred.pred_depth_map.numpy().data[0]
+
             if visualization is not None:
                 vis = visualization[i]
                 results_dict['visualization'].append(vis)
             if pred_out_dir != '':
                 mmengine.mkdir_or_exist(pred_out_dir)
-                img_name = str(self.num_pred_imgs).zfill(8) + '_pred.png'
-                img_path = osp.join(pred_out_dir, img_name)
-                output = Image.fromarray(pred_data.astype(np.uint8))
-                output.save(img_path)
+                for key, data in pred_data.items():
+                    post_fix = '_pred.png' if key == 'sem_seg' else '_pred.npy'
+                    img_name = str(self.num_pred_imgs).zfill(8) + post_fix
+                    img_path = osp.join(pred_out_dir, img_name)
+                    if key == 'sem_seg':
+                        output = Image.fromarray(data.astype(np.uint8))
+                        output.save(img_path)
+                    else:
+                        np.save(img_path, data)
+            pred_data = next(iter(pred_data.values()))
+            results_dict['predictions'].append(pred_data)
             self.num_pred_imgs += 1
 
         if len(results_dict['predictions']) == 1:
@@ -344,12 +355,13 @@ def preprocess(self, inputs, batch_size, **kwargs):
         """
         pipeline_cfg = cfg.test_dataloader.dataset.pipeline
         # Loading annotations is also not applicable
-        idx = self._get_transform_idx(pipeline_cfg, 'LoadAnnotations')
-        if idx != -1:
-            del pipeline_cfg[idx]
+        for transform in ('LoadAnnotations', 'LoadDepthAnnotation'):
+            idx = self._get_transform_idx(pipeline_cfg, transform)
+            if idx != -1:
+                del pipeline_cfg[idx]
+
         load_img_idx = self._get_transform_idx(pipeline_cfg,
                                                'LoadImageFromFile')
-
         if load_img_idx == -1:
             raise ValueError(
                 'LoadImageFromFile is not found in the test pipeline')

mmseg/datasets/transforms/__init__.py

@@ -11,7 +11,7 @@
                          BioMedicalRandomGamma, ConcatCDInput, GenerateEdge,
                          PhotoMetricDistortion, RandomCrop, RandomCutOut,
                          RandomDepthMix, RandomFlip, RandomMosaic,
-                         RandomRotate, RandomRotFlip, Rerange,
+                         RandomRotate, RandomRotFlip, Rerange, Resize,
                          ResizeShortestEdge, ResizeToMultiple, RGB2Gray,
                          SegRescale)
 
@@ -26,5 +26,5 @@
     'BioMedical3DRandomFlip', 'BioMedicalRandomGamma', 'BioMedical3DPad',
     'RandomRotFlip', 'Albu', 'LoadSingleRSImageFromFile', 'ConcatCDInput',
     'LoadMultipleRSImageFromFile', 'LoadDepthAnnotation', 'RandomDepthMix',
-    'RandomFlip'
+    'RandomFlip', 'Resize'
 ]