[Feature] Add model ensemble tools (open-mmlab#2218)

* [Feature] Add model ensemble tool

* [Enhance] Add en and zh_cn instructions for model_ensemble

* [Enhance] Add default-value for --out and modify instruction

* [Enhance] Add arg-type for --out

* [Enhance] Delete redundant code

docs/en/useful_tools.md

@@ -424,3 +424,32 @@ result/pred_result.pkl \
 result/confusion_matrix \
 --show
 ```
+
+## Model ensemble
+
+To complete the integration of prediction probabilities for multiple models, we provide 'tools/model_ensemble.py'
+
+### Usage
+
+```bash
+python tools/model_ensemble.py \
+  --config ${CONFIG_FILE1} ${CONFIG_FILE2} ... \
+  --checkpoint ${CHECKPOINT_FILE1} ${CHECKPOINT_FILE2} ...\
+  --aug-test \
+  --out ${OUTPUT_DIR}\
+  --gpus ${GPU_USED}\
+```
+
+### Description of all arguments
+
+- `--config`: Path to the config file for the ensemble model
+- `--checkpoint`: Path to the checkpoint file for the ensemble model
+- `--aug-test`: Whether to use flip and multi-scale test
+- `--out`: Save folder for model ensemble results
+- `--gpus`: Gpu-id used for model ensemble
+
+### Result of model ensemble
+
+- The model ensemble will generate an unrendered segmentation mask for each input, the input shape is `[H, W]`, the segmentation mask shape is `[H, W]`, and each pixel-value in the segmentation mask represents the pixel category after segmentation at that position.
+
+- The filename of the model ensemble result will be named in the same filename as `Ground Truth`. If the filename of `Ground Truth` is called `1.png`, the model ensemble result file will also be named `1.png` and placed in the folder specified by `--out`.

docs/zh_cn/useful_tools.md

@@ -366,3 +366,31 @@ configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py \
 checkpoint/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth \
 fcn
 ```
+
+## 模型集成
+
+我们提供了`tools/model_ensemble.py` 完成对多个模型的预测概率进行集成的脚本
+
+### 使用方法
+
+```bash
+python tools/model_ensemble.py \
+  --config ${CONFIG_FILE1} ${CONFIG_FILE2} ... \
+  --checkpoint ${CHECKPOINT_FILE1} ${CHECKPOINT_FILE2} ...\
+  --aug-test \
+  --out ${OUTPUT_DIR}\
+  --gpus ${GPU_USED}\
+```
+
+### 各个参数的描述:
+
+- `--config`: 集成模型的配置文件的路径
+- `--checkpoint`: 集成模型的权重文件的路径
+- `--aug-test`: 是否使用翻转和多尺度预测
+- `--out`: 模型集成结果的保存文件夹路径
+- `--gpus`: 模型集成使用的gpu-id
+
+### 模型集成结果
+
+- 模型集成会对每一张输入，形状为`[H, W]`，产生一张未渲染的分割掩膜文件(segmentation mask)，形状为`[H, W]`，分割掩膜中的每个像素点的值代表该位置分割后的像素类别.
+- 模型集成结果的文件名会采用和`Ground Truth`一致的文件命名，如`Ground Truth`文件名称为`1.png`，则模型集成结果文件也会被命名为`1.png`，并放置在`--out`指定的文件夹中.

mmseg/models/segmentors/encoder_decoder.py

@@ -278,6 +278,15 @@ def simple_test(self, img, img_meta, rescale=True):
         seg_pred = list(seg_pred)
         return seg_pred
 
+    def simple_test_logits(self, img, img_metas, rescale=True):
+        """Test without augmentations.
+
+        Return numpy seg_map logits.
+        """
+        seg_logit = self.inference(img[0], img_metas[0], rescale)
+        seg_logit = seg_logit.cpu().numpy()
+        return seg_logit
+
     def aug_test(self, imgs, img_metas, rescale=True):
         """Test with augmentations.
 
@@ -300,3 +309,21 @@ def aug_test(self, imgs, img_metas, rescale=True):
         # unravel batch dim
         seg_pred = list(seg_pred)
         return seg_pred
+
+    def aug_test_logits(self, img, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Return seg_map logits. Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+
+        imgs = img
+        seg_logit = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale)
+            seg_logit += cur_seg_logit
+
+        seg_logit /= len(imgs)
+        seg_logit = seg_logit.cpu().numpy()
+        return seg_logit

tools/model_ensemble.py

@@ -0,0 +1,121 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.parallel import MMDataParallel
+from mmcv.parallel.scatter_gather import scatter_kwargs
+from mmcv.runner import load_checkpoint, wrap_fp16_model
+from PIL import Image
+
+from mmseg.datasets import build_dataloader, build_dataset
+from mmseg.models import build_segmentor
+
+
+@torch.no_grad()
+def main(args):
+
+    models = []
+    gpu_ids = args.gpus
+    configs = args.config
+    ckpts = args.checkpoint
+
+    cfg = mmcv.Config.fromfile(configs[0])
+
+    if args.aug_test:
+        cfg.data.test.pipeline[1].img_ratios = [
+            0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0
+        ]
+        cfg.data.test.pipeline[1].flip = True
+    else:
+        cfg.data.test.pipeline[1].img_ratios = [1.0]
+        cfg.data.test.pipeline[1].flip = False
+
+    torch.backends.cudnn.benchmark = True
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=1,
+        workers_per_gpu=4,
+        dist=False,
+        shuffle=False,
+    )
+
+    for idx, (config, ckpt) in enumerate(zip(configs, ckpts)):
+        cfg = mmcv.Config.fromfile(config)
+        cfg.model.pretrained = None
+        cfg.data.test.test_mode = True
+
+        model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
+        if cfg.get('fp16', None):
+            wrap_fp16_model(model)
+        load_checkpoint(model, ckpt, map_location='cpu')
+        torch.cuda.empty_cache()
+        tmpdir = args.out
+        mmcv.mkdir_or_exist(tmpdir)
+        model = MMDataParallel(model, device_ids=[gpu_ids[idx % len(gpu_ids)]])
+        model.eval()
+        models.append(model)
+
+    dataset = data_loader.dataset
+    prog_bar = mmcv.ProgressBar(len(dataset))
+    loader_indices = data_loader.batch_sampler
+    for batch_indices, data in zip(loader_indices, data_loader):
+        result = []
+
+        for model in models:
+            x, _ = scatter_kwargs(
+                inputs=data, kwargs=None, target_gpus=model.device_ids)
+            if args.aug_test:
+                logits = model.module.aug_test_logits(**x[0])
+            else:
+                logits = model.module.simple_test_logits(**x[0])
+            result.append(logits)
+
+        result_logits = 0
+        for logit in result:
+            result_logits += logit
+
+        pred = result_logits.argmax(axis=1).squeeze()
+        img_info = dataset.img_infos[batch_indices[0]]
+        file_name = os.path.join(
+            tmpdir, img_info['ann']['seg_map'].split(os.path.sep)[-1])
+        Image.fromarray(pred.astype(np.uint8)).save(file_name)
+        prog_bar.update()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Model Ensemble with logits result')
+    parser.add_argument(
+        '--config', type=str, nargs='+', help='ensemble config files path')
+    parser.add_argument(
+        '--checkpoint',
+        type=str,
+        nargs='+',
+        help='ensemble checkpoint files path')
+    parser.add_argument(
+        '--aug-test',
+        action='store_true',
+        help='control ensemble aug-result or single-result (default)')
+    parser.add_argument(
+        '--out', type=str, default='results', help='the dir to save result')
+    parser.add_argument(
+        '--gpus', type=int, nargs='+', default=[0], help='id of gpu to use')
+
+    args = parser.parse_args()
+    assert len(args.config) == len(args.checkpoint), \
+        f'len(config) must equal len(checkpoint), ' \
+        f'but len(config) = {len(args.config)} and' \
+        f'len(checkpoint) = {len(args.checkpoint)}'
+    assert args.out, "ensemble result out-dir can't be None"
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)