[Refactor] Support progressive test with fewer memory cost

configs/_base_/schedules/schedule_160k.py

@@ -6,4 +6,4 @@
 # runtime settings
 runner = dict(type='IterBasedRunner', max_iters=160000)
 checkpoint_config = dict(by_epoch=False, interval=16000)
-evaluation = dict(interval=16000, metric='mIoU')
+evaluation = dict(interval=16000, metric='mIoU', pre_eval=True)

configs/_base_/schedules/schedule_20k.py

@@ -6,4 +6,4 @@
 # runtime settings
 runner = dict(type='IterBasedRunner', max_iters=20000)
 checkpoint_config = dict(by_epoch=False, interval=2000)
-evaluation = dict(interval=2000, metric='mIoU')
+evaluation = dict(interval=2000, metric='mIoU', pre_eval=True)

configs/_base_/schedules/schedule_40k.py

@@ -6,4 +6,4 @@
 # runtime settings
 runner = dict(type='IterBasedRunner', max_iters=40000)
 checkpoint_config = dict(by_epoch=False, interval=4000)
-evaluation = dict(interval=4000, metric='mIoU')
+evaluation = dict(interval=4000, metric='mIoU', pre_eval=True)

configs/_base_/schedules/schedule_80k.py

@@ -6,4 +6,4 @@
 # runtime settings
 runner = dict(type='IterBasedRunner', max_iters=80000)
 checkpoint_config = dict(by_epoch=False, interval=8000)
-evaluation = dict(interval=8000, metric='mIoU')
+evaluation = dict(interval=8000, metric='mIoU', pre_eval=True)

docs/inference.md

@@ -21,11 +21,11 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [-
 
 Optional arguments:
 
-- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
+- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file. (After mmseg v0.17, the output results become pre-evaluation results or format result paths)
 - `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `mIoU` is available for all dataset. Cityscapes could be evaluated by `cityscapes` as well as standard `mIoU` metrics.
 - `--show`: If specified, segmentation results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment, otherwise you may encounter the error like `cannot connect to X server`.
 - `--show-dir`: If specified, segmentation results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option.
-- `--eval-options`: Optional parameters during evaluation. When `efficient_test=True`, it will save intermediate results to local files to save CPU memory. Make sure that you have enough local storage space (more than 20GB).
+- `--eval-options`: Optional parameters for `dataset.format_results` and `dataset.evaluate` during evaluation. When `efficient_test=True`, it will save intermediate results to local files to save CPU memory. Make sure that you have enough local storage space (more than 20GB). (`efficient_test` argument does not have effect after mmseg v0.17, we use a progressive mode to evaluation and format results which can largely save memory cost and evaluation time.)
 
 Examples:
 
@@ -98,4 +98,4 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
     --eval mIoU
     ```
 
-    Using ```pmap``` to view CPU memory footprint, it used 2.25GB CPU memory with ```efficient_test=True``` and 11.06GB CPU memory with ```efficient_test=False``` . This optional parameter can save a lot of memory.
+    Using ```pmap``` to view CPU memory footprint, it used 2.25GB CPU memory with ```efficient_test=True``` and 11.06GB CPU memory with ```efficient_test=False``` . This optional parameter can save a lot of memory. (After mmseg v0.17, efficient_test has not effect and we use a progressive mode to evaluation and format results efficiently by default.)

docs_zh-CN/inference.md

@@ -20,11 +20,11 @@ python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}]
 
 可选参数:
 
-- `RESULT_FILE`: pickle 格式的输出结果的文件名，如果不专门指定，结果将不会被专门保存成文件
-- `EVAL_METRICS`: 在结果里将被评估的指标，这主要取决于数据集，  `mIoU`  对于所有数据集都可获得，像 Cityscapes 数据集可以通过 `cityscapes` 命令来专门评估，就像标准的 `mIoU`一样
-- `--show`: 如果被指定，分割结果将会在一张图像里画出来并且在另一个窗口展示，它仅仅是用来调试与可视化，并且仅针对单卡 GPU 测试，请确认 GUI 在您的环境里可用，否则您也许会遇到报错 `cannot connect to X server`
-- `--show-dir`: 如果被指定，分割结果将会在一张图像里画出来并且保存在指定文件夹里，它仅仅是用来调试与可视化，并且仅针对单卡GPU测试，使用该参数时，您的环境不需要 GUI
-- `--eval-options`: 评估时的可选参数，当设置 `efficient_test=True` 时，它将会保存中间结果至本地文件里以节约 CPU 内存，请确认您本地硬盘有足够的存储空间（大于20GB）
+- `RESULT_FILE`: pickle 格式的输出结果的文件名，如果不专门指定，结果将不会被专门保存成文件。（MMseg v0.17 之后，args.out 将只会保存评估时的中间结果或者是分割图的保存路径。）
+- `EVAL_METRICS`: 在结果里将被评估的指标。这主要取决于数据集，  `mIoU`  对于所有数据集都可获得，像 Cityscapes 数据集可以通过 `cityscapes` 命令来专门评估，就像标准的 `mIoU`一样。
+- `--show`: 如果被指定，分割结果将会在一张图像里画出来并且在另一个窗口展示。它仅仅是用来调试与可视化，并且仅针对单卡 GPU 测试。请确认 GUI 在您的环境里可用，否则您也许会遇到报错 `cannot connect to X server`
+- `--show-dir`: 如果被指定，分割结果将会在一张图像里画出来并且保存在指定文件夹里。它仅仅是用来调试与可视化，并且仅针对单卡GPU测试。使用该参数时，您的环境不需要 GUI。
+- `--eval-options`: 评估时的可选参数，当设置 `efficient_test=True` 时，它将会保存中间结果至本地文件里以节约 CPU 内存。请确认您本地硬盘有足够的存储空间（大于20GB）。（MMseg v0.17 之后，`efficient_test` 不再生效，我们重构了 test api，通过使用一种渐近式的方式来提升评估和保存结果的效率。）
 
 例子:
 
@@ -96,4 +96,4 @@ python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}]
     --eval mIoU
     ```
 
-    使用 ```pmap``` 可查看 CPU 内存情况,  ```efficient_test=True``` 会使用约 2.25GB 的 CPU 内存， ```efficient_test=False``` 会使用约 11.06GB 的 CPU 内存。 这个可选参数可以节约很多 CPU 内存。
+    使用 ```pmap``` 可查看 CPU 内存情况,  ```efficient_test=True``` 会使用约 2.25GB 的 CPU 内存， ```efficient_test=False``` 会使用约 11.06GB 的 CPU 内存。 这个可选参数可以节约很多 CPU 内存。（MMseg v0.17 之后, `efficient_test` 参数将不再生效, 我们使用了一种渐近的方式来更加有效快速地评估和保存结果。）

mmseg/apis/test.py

@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import os.path as osp
 import tempfile
+import warnings
 
 import mmcv
 import numpy as np
@@ -19,7 +20,6 @@ def np2tmp(array, temp_file_name=None, tmpdir=None):
             function will generate a file name with tempfile.NamedTemporaryFile
             to save ndarray. Default: None.
         tmpdir (str): Temporary directory to save Ndarray files. Default: None.
-
     Returns:
         str: The numpy file name.
     """
@@ -36,8 +36,11 @@ def single_gpu_test(model,
                     show=False,
                     out_dir=None,
                     efficient_test=False,
-                    opacity=0.5):
-    """Test with single GPU.
+                    opacity=0.5,
+                    pre_eval=False,
+                    format_only=False,
+                    format_args={}):
+    """Test with single GPU by progressive mode.
 
     Args:
         model (nn.Module): Model to be tested.
@@ -46,24 +49,60 @@ def single_gpu_test(model,
         out_dir (str, optional): If specified, the results will be dumped into
             the directory to save output results.
         efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
+            save CPU memory during evaluation. Mutually exclusive with
+            pre_eval and format_results. Default: False.
         opacity(float): Opacity of painted segmentation map.
             Default 0.5.
             Must be in (0, 1] range.
+        pre_eval (bool): Use dataset.pre_eval() function to generate
+            pre_results for metric evaluation. Mutually exclusive with
+            efficient_test and format_results. Default: False.
+        format_only (bool): Only format result for results commit.
+            Mutually exclusive with pre_eval and efficient_test.
+            Default: False.
+        format_args (dict): The args for format_results. Default: {}.
     Returns:
-        list: The prediction results.
+        list: list of evaluation pre-results or list of save file names.
     """
+    if efficient_test:
+        warnings.warn(
+            'DeprecationWarning: ``efficient_test`` will be deprecated, the '
+            'evaluation is CPU memory friendly with pre_eval=True')
+        mmcv.mkdir_or_exist('.efficient_test')
+    # when none of them is set true, return segmentation results as
+    # a list of np.array.
+    assert [efficient_test, pre_eval, format_only].count(True) <= 1, \
+        '``efficient_test``, ``pre_eval`` and ``format_only`` are mutually ' \
+        'exclusive, only one of them could be true .'
 
     model.eval()
     results = []
     dataset = data_loader.dataset
     prog_bar = mmcv.ProgressBar(len(dataset))
-    if efficient_test:
-        mmcv.mkdir_or_exist('.efficient_test')
-    for i, data in enumerate(data_loader):
+    # The pipeline about how the data_loader retrieval samples from dataset:
+    # sampler -> batch_sampler -> indices
+    # The indices are passed to dataset_fetcher to get data from dataset.
+    # data_fetcher -> collate_fn(dataset[index]) -> data_sample
+    # we use batch_sampler to get correct data idx
+    loader_indices = data_loader.batch_sampler
+
+    for batch_indices, data in zip(loader_indices, data_loader):
         with torch.no_grad():
             result = model(return_loss=False, **data)
 
+        if efficient_test:
+            result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
+
+        if format_only:
+            result = dataset.format_results(
+                result, indices=batch_indices, **format_args)
+        if pre_eval:
+            # TODO: adapt samples_per_gpu > 1.
+            # only samples_per_gpu=1 valid now
+            result = dataset.pre_eval(result, indices=batch_indices)
+
+        results.extend(result)
+
         if show or out_dir:
             img_tensor = data['img'][0]
             img_metas = data['img_metas'][0].data[0]
@@ -90,27 +129,22 @@ def single_gpu_test(model,
                     out_file=out_file,
                     opacity=opacity)
 
-        if isinstance(result, list):
-            if efficient_test:
-                result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
-            results.extend(result)
-        else:
-            if efficient_test:
-                result = np2tmp(result, tmpdir='.efficient_test')
-            results.append(result)
-
         batch_size = len(result)
         for _ in range(batch_size):
             prog_bar.update()
+
     return results
 
 
 def multi_gpu_test(model,
                    data_loader,
                    tmpdir=None,
                    gpu_collect=False,
-                   efficient_test=False):
-    """Test model with multiple gpus.
+                   efficient_test=False,
+                   pre_eval=False,
+                   format_only=False,
+                   format_args={}):
+    """Test model with multiple gpus by progressive mode.
 
     This method tests model with multiple gpus and collects the results
     under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
@@ -123,39 +157,71 @@ def multi_gpu_test(model,
         data_loader (utils.data.Dataloader): Pytorch data loader.
         tmpdir (str): Path of directory to save the temporary results from
             different gpus under cpu mode. The same path is used for efficient
-            test.
+            test. Default: None.
         gpu_collect (bool): Option to use either gpu or cpu to collect results.
+            Default: False.
         efficient_test (bool): Whether save the results as local numpy files to
-            save CPU memory during evaluation. Default: False.
+            save CPU memory during evaluation. Mutually exclusive with
+            pre_eval and format_results. Default: False.
+        pre_eval (bool): Use dataset.pre_eval() function to generate
+            pre_results for metric evaluation. Mutually exclusive with
+            efficient_test and format_results. Default: False.
+        format_only (bool): Only format result for results commit.
+            Mutually exclusive with pre_eval and efficient_test.
+            Default: False.
+        format_args (dict): The args for format_results. Default: {}.
 
     Returns:
-        list: The prediction results.
+        list: list of evaluation pre-results or list of save file names.
     """
+    if efficient_test:
+        warnings.warn(
+            'DeprecationWarning: ``efficient_test`` will be deprecated, the '
+            'evaluation is CPU memory friendly with pre_eval=True')
+        mmcv.mkdir_or_exist('.efficient_test')
+    # when none of them is set true, return segmentation results as
+    # a list of np.array.
+    assert [efficient_test, pre_eval, format_only].count(True) <= 1, \
+        '``efficient_test``, ``pre_eval`` and ``format_only`` are mutually ' \
+        'exclusive, only one of them could be true .'
 
     model.eval()
     results = []
     dataset = data_loader.dataset
+    # The pipeline about how the data_loader retrieval samples from dataset:
+    # sampler -> batch_sampler -> indices
+    # The indices are passed to dataset_fetcher to get data from dataset.
+    # data_fetcher -> collate_fn(dataset[index]) -> data_sample
+    # we use batch_sampler to get correct data idx
+
+    # batch_sampler based on DistributedSampler, the indices only point to data
+    # samples of related machine.
+    loader_indices = data_loader.batch_sampler
+
     rank, world_size = get_dist_info()
     if rank == 0:
         prog_bar = mmcv.ProgressBar(len(dataset))
-    if efficient_test:
-        mmcv.mkdir_or_exist('.efficient_test')
-    for i, data in enumerate(data_loader):
+
+    for batch_indices, data in zip(loader_indices, data_loader):
         with torch.no_grad():
             result = model(return_loss=False, rescale=True, **data)
 
-        if isinstance(result, list):
-            if efficient_test:
-                result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
-            results.extend(result)
-        else:
-            if efficient_test:
-                result = np2tmp(result, tmpdir='.efficient_test')
-            results.append(result)
+        if efficient_test:
+            result = [np2tmp(_, tmpdir='.efficient_test') for _ in result]
+
+        if format_only:
+            result = dataset.format_results(
+                result, indices=batch_indices, **format_args)
+        if pre_eval:
+            # TODO: adapt samples_per_gpu > 1.
+            # only samples_per_gpu=1 valid now
+            result = dataset.pre_eval(result, indices=batch_indices)
+
+        results.extend(result)
 
         if rank == 0:
-            batch_size = len(result)
-            for _ in range(batch_size * world_size):
+            batch_size = len(result) * world_size
+            for _ in range(batch_size):
                 prog_bar.update()
 
     # collect results from all ranks

mmseg/core/evaluation/__init__.py

@@ -1,9 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .class_names import get_classes, get_palette
 from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
+from .metrics import (eval_metrics, intersect_and_union, mean_dice,
+                      mean_fscore, mean_iou, pre_eval_to_metrics)
 
 __all__ = [
     'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
-    'eval_metrics', 'get_classes', 'get_palette'
+    'eval_metrics', 'get_classes', 'get_palette', 'pre_eval_to_metrics',
+    'intersect_and_union'
 ]

mmseg/core/evaluation/eval_hooks.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import os.path as osp
+import warnings
 
 import torch.distributed as dist
 from mmcv.runner import DistEvalHook as _DistEvalHook
@@ -16,15 +17,28 @@ class EvalHook(_EvalHook):
             Default: False.
         efficient_test (bool): Whether save the results as local numpy files to
             save CPU memory during evaluation. Default: False.
+        pre_eval (bool): Whether to use progressive mode to evaluate model.
+            Default: False.
     Returns:
         list: The prediction results.
     """
 
     greater_keys = ['mIoU', 'mAcc', 'aAcc']
 
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
+    def __init__(self,
+                 *args,
+                 by_epoch=False,
+                 efficient_test=False,
+                 pre_eval=False,
+                 **kwargs):
         super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
+        self.pre_eval = pre_eval
+        if efficient_test:
+            warnings.warn(
+                'DeprecationWarning: ``efficient_test`` for evaluation hook '
+                'is deprecated, the evaluation hook is CPU memory friendly '
+                'with ``pre_eval=True`` as argument for ``single_gpu_test()`` '
+                'function')
 
     def _do_evaluate(self, runner):
         """perform evaluation and save ckpt."""
@@ -33,10 +47,8 @@ def _do_evaluate(self, runner):
 
         from mmseg.apis import single_gpu_test
         results = single_gpu_test(
-            runner.model,
-            self.dataloader,
-            show=False,
-            efficient_test=self.efficient_test)
+            runner.model, self.dataloader, show=False, pre_eval=self.pre_eval)
+        runner.log_buffer.clear()
         runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
         key_score = self.evaluate(runner, results)
         if self.save_best:
@@ -52,15 +64,28 @@ class DistEvalHook(_DistEvalHook):
             Default: False.
         efficient_test (bool): Whether save the results as local numpy files to
             save CPU memory during evaluation. Default: False.
+        pre_eval (bool): Whether to use progressive mode to evaluate model.
+            Default: False.
     Returns:
         list: The prediction results.
     """
 
     greater_keys = ['mIoU', 'mAcc', 'aAcc']
 
-    def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
+    def __init__(self,
+                 *args,
+                 by_epoch=False,
+                 efficient_test=False,
+                 pre_eval=False,
+                 **kwargs):
         super().__init__(*args, by_epoch=by_epoch, **kwargs)
-        self.efficient_test = efficient_test
+        self.pre_eval = pre_eval
+        if efficient_test:
+            warnings.warn(
+                'DeprecationWarning: ``efficient_test`` for evaluation hook '
+                'is deprecated, the evaluation hook is CPU memory friendly '
+                'with ``pre_eval=True`` as argument for ``multi_gpu_test()`` '
+                'function')
 
     def _do_evaluate(self, runner):
         """perform evaluation and save ckpt."""
@@ -90,7 +115,10 @@ def _do_evaluate(self, runner):
             self.dataloader,
             tmpdir=tmpdir,
             gpu_collect=self.gpu_collect,
-            efficient_test=self.efficient_test)
+            pre_eval=self.pre_eval)
+
+        runner.log_buffer.clear()
+
         if runner.rank == 0:
             print('\n')
             runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)