diff --git a/mmengine/data/sampler.py b/mmengine/data/sampler.py
index 47b2c3b4c1e3e5ae5a5f1941263f598c3a6af2f2..ff1d13ec9954e315fddd1005ce02488ccce50fa1 100644
--- a/mmengine/data/sampler.py
+++ b/mmengine/data/sampler.py
@@ -2,18 +2,13 @@
import itertools
import math
from typing import Iterator, Optional, Sized
-# from mmengine.dist import get_dist_info, sync_random_seed
-from unittest.mock import MagicMock
import torch
from torch.utils.data import Sampler
+from mmengine.dist import get_dist_info, sync_random_seed
from mmengine.registry import DATA_SAMPLERS
-# TODO, need to remove those lines after implementing dist module
-get_dist_info = MagicMock(return_value=(0, 1))
-sync_random_seed = MagicMock(return_value=0)
-
@DATA_SAMPLERS.register_module()
class DefaultSampler(Sampler):
diff --git a/mmengine/data/utils.py b/mmengine/data/utils.py
index 0f569d39803a4f24a6564eb7301749a7ae5b2dc6..c284a336dd47f2cb8b56de89dd6637e763a20219 100644
--- a/mmengine/data/utils.py
+++ b/mmengine/data/utils.py
@@ -1,13 +1,11 @@
# Copyright (c) OpenMMLab. All rights reserved.
import random
-from typing import Any, Sequence, Tuple
+from typing import Sequence
import numpy as np
import torch
-from .base_data_element import BaseDataElement
-
-DATA_BATCH = Sequence[Tuple[Any, BaseDataElement]]
+DATA_BATCH = Sequence[dict]
def worker_init_fn(worker_id: int, num_workers: int, rank: int,
@@ -36,10 +34,10 @@ def pseudo_collate(data_batch: DATA_BATCH) -> DATA_BATCH:
nothing just returns ``data_batch``.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): Batch of data from
+ data_batch (Sequence[dict]): Batch of data from
dataloader.
Returns:
- Sequence[Tuple[Any, BaseDataElement]]: Return input ``data_batch``.
+ Sequence[dict]: Return input ``data_batch``.
"""
return data_batch
diff --git a/mmengine/evaluator/evaluator.py b/mmengine/evaluator/evaluator.py
index c653fb563aa90ce447ad0be964b14901b9aed657..34bb02e51a4953a31eb5d930de23ef67f339f7de 100644
--- a/mmengine/evaluator/evaluator.py
+++ b/mmengine/evaluator/evaluator.py
@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Any, Iterator, List, Optional, Sequence, Tuple, Union
+from typing import Iterator, List, Optional, Sequence, Union
from mmengine.data import BaseDataElement
from ..registry.root import METRICS
@@ -37,23 +37,25 @@ class Evaluator:
for metric in self.metrics:
metric.dataset_meta = dataset_meta
- def process(self, data_batch: Sequence[Tuple[Any, BaseDataElement]],
+ def process(self, data_batch: Sequence[dict],
predictions: Sequence[BaseDataElement]):
"""Convert ``BaseDataSample`` to dict and invoke process method of each
metric.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): A batch of data
- from the dataloader.
+ data_batch (Sequence[dict]): A batch of data from the dataloader.
predictions (Sequence[BaseDataElement]): A batch of outputs from
the model.
"""
_data_batch = []
- for input, data in data_batch:
- if isinstance(data, BaseDataElement):
- _data_batch.append((input, data.to_dict()))
+ for data in data_batch:
+ if isinstance(data['data_sample'], BaseDataElement):
+ _data_batch.append(
+ dict(
+ inputs=data['inputs'],
+ data_sample=data['data_sample'].to_dict()))
else:
- _data_batch.append((input, data))
+ _data_batch.append(data)
_predictions = []
for pred in predictions:
if isinstance(pred, BaseDataElement):
diff --git a/mmengine/evaluator/metric.py b/mmengine/evaluator/metric.py
index e8a71488023a344792acfc22726c3b9582e9e276..4bcf163be41c7f625aacc879675742286a0b6e1d 100644
--- a/mmengine/evaluator/metric.py
+++ b/mmengine/evaluator/metric.py
@@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
-from typing import Any, List, Optional, Sequence, Tuple, Union
+from typing import Any, List, Optional, Sequence, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
@@ -50,15 +50,14 @@ class BaseMetric(metaclass=ABCMeta):
self._dataset_meta = dataset_meta
@abstractmethod
- def process(self, data_batch: Sequence[Tuple[Any, dict]],
+ def process(self, data_batch: Sequence[dict],
predictions: Sequence[dict]) -> None:
"""Process one batch of data samples and predictions. The processed
results should be stored in ``self.results``, which will be used to
compute the metrics when all batches have been processed.
Args:
- data_batch (Sequence[Tuple[Any, dict]]): A batch of data
- from the dataloader.
+ data_batch (Sequence[dict]): A batch of data from the dataloader.
predictions (Sequence[dict]): A batch of outputs from
the model.
"""
diff --git a/mmengine/hooks/checkpoint_hook.py b/mmengine/hooks/checkpoint_hook.py
index a373bbb5cad27e423c556ece9b045ac87bc3c3cb..017784b9a7cdfb49c81e1ae7a97840406ef59060 100644
--- a/mmengine/hooks/checkpoint_hook.py
+++ b/mmengine/hooks/checkpoint_hook.py
@@ -2,15 +2,14 @@
import os.path as osp
import warnings
from pathlib import Path
-from typing import Any, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Union
-from mmengine.data import BaseDataElement
from mmengine.dist import master_only
from mmengine.fileio import FileClient
from mmengine.registry import HOOKS
from .hook import Hook
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -185,8 +184,8 @@ class CheckpointHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict, optional): Outputs from model.
Defaults to None.
"""
diff --git a/mmengine/hooks/empty_cache_hook.py b/mmengine/hooks/empty_cache_hook.py
index c793f01b1ea62189e92459cb9b742dc26a985fe2..be6b5c2c3dded63b45c39c95fa71ec4e8d994e9e 100644
--- a/mmengine/hooks/empty_cache_hook.py
+++ b/mmengine/hooks/empty_cache_hook.py
@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Any, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Union
import torch
@@ -7,7 +7,7 @@ from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -46,8 +46,8 @@ class EmptyCacheHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict or sequence, optional): Outputs from model.
Defaults to None.
mode (str): Current mode of runner. Defaults to 'train'.
diff --git a/mmengine/hooks/hook.py b/mmengine/hooks/hook.py
index 84060334f1bcd6bd382b51f0e04cb89882728c59..49995334c6ea6fa78464bc543482a56e5db0e47c 100644
--- a/mmengine/hooks/hook.py
+++ b/mmengine/hooks/hook.py
@@ -1,9 +1,9 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Any, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Union
from mmengine.data import BaseDataElement
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
class Hook:
@@ -174,8 +174,8 @@ class Hook:
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
"""
self._before_iter(
runner, batch_idx=batch_idx, data_batch=data_batch, mode='train')
@@ -190,8 +190,8 @@ class Hook:
Args:
runner (Runner): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
"""
self._before_iter(
runner, batch_idx=batch_idx, data_batch=data_batch, mode='val')
@@ -206,8 +206,8 @@ class Hook:
Args:
runner (Runner): The runner of the testing process.
batch_idx (int): The index of the current batch in the test loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
"""
self._before_iter(
runner, batch_idx=batch_idx, data_batch=data_batch, mode='test')
@@ -223,8 +223,8 @@ class Hook:
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict, optional): Outputs from model.
Defaults to None.
"""
@@ -247,8 +247,8 @@ class Hook:
Args:
runner (Runner): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict or sequence, optional): Outputs from
model. Defaults to None.
"""
@@ -271,8 +271,8 @@ class Hook:
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the test loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict, optional): Outputs from model.
Defaults to None.
"""
@@ -317,8 +317,8 @@ class Hook:
runner (Runner): The runner of the training, validation or testing
process.
batch_idx (int): The index of the current batch in the loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
mode (str): Current mode of runner. Defaults to 'train'.
"""
pass
@@ -337,8 +337,8 @@ class Hook:
runner (Runner): The runner of the training, validation or testing
process.
batch_idx (int): The index of the current batch in the loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (Sequence[BaseDataElement], optional): Outputs from model.
Defaults to None.
mode (str): Current mode of runner. Defaults to 'train'.
@@ -387,19 +387,19 @@ class Hook:
"""
return (runner.iter + 1) % n == 0 if n > 0 else False
- def end_of_epoch(self, runner, batch_idx: int) -> bool:
+ def end_of_epoch(self, dataloader, batch_idx: int) -> bool:
"""Check whether the current iteration reaches the last iteration of
- current dataloader.
+ the dataloader.
Args:
- runner (Runner): The runner of the training, validation or testing
- process.
+ dataloader (Dataloader): The dataloader of the training,
+ validation or testing process.
batch_idx (int): The index of the current batch in the loop.
Returns:
bool: Whether reaches the end of current epoch or not.
"""
- return batch_idx + 1 == len(runner.cur_dataloader)
+ return batch_idx + 1 == len(dataloader)
def is_last_train_epoch(self, runner) -> bool:
"""Test whether current epoch is the last train epoch.
diff --git a/mmengine/hooks/iter_timer_hook.py b/mmengine/hooks/iter_timer_hook.py
index bf123cae3d07dd420b2ea113177d55344be6d44c..26abf17e1ea4badaea5d2d231fae96cec2baff6b 100644
--- a/mmengine/hooks/iter_timer_hook.py
+++ b/mmengine/hooks/iter_timer_hook.py
@@ -1,12 +1,12 @@
# Copyright (c) OpenMMLab. All rights reserved.
import time
-from typing import Any, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Union
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -37,8 +37,8 @@ class IterTimerHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
mode (str): Current mode of runner. Defaults to 'train'.
"""
# TODO: update for new logging system
@@ -57,8 +57,8 @@ class IterTimerHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict or sequence, optional): Outputs from model. Defaults
to None.
mode (str): Current mode of runner. Defaults to 'train'.
diff --git a/mmengine/hooks/logger_hook.py b/mmengine/hooks/logger_hook.py
index 786fd31150daf8a452dc13c418ee7748dff1a34d..16adedaaa9a1f1972af7af04cb833c459169556f 100644
--- a/mmengine/hooks/logger_hook.py
+++ b/mmengine/hooks/logger_hook.py
@@ -5,18 +5,17 @@ import os
import os.path as osp
from collections import OrderedDict
from pathlib import Path
-from typing import Any, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Union
import torch
-from mmengine.data import BaseDataElement
from mmengine.dist import master_only
from mmengine.fileio import FileClient
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils import is_tuple_of, scandir
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -183,15 +182,16 @@ class LoggerHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[BaseDataElement], optional): Data from
- dataloader. Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (dict, optional): Outputs from model.
Defaults to None.
"""
self._inner_iter = batch_idx
if runner.meta is not None and 'exp_name' in runner.meta:
if (self.every_n_iters(runner, self.interval_exp_name)) or (
- self.by_epoch and self.end_of_epoch(runner, batch_idx)):
+ self.by_epoch and self.end_of_epoch(
+ runner.train_loop.dataloader, batch_idx)):
exp_info = f'Exp name: {runner.meta["exp_name"]}'
runner.logger.info(exp_info)
if self.by_epoch and self.every_n_inner_iters(batch_idx,
@@ -199,7 +199,8 @@ class LoggerHook(Hook):
self._log_train(runner)
elif not self.by_epoch and self.every_n_iters(runner, self.interval):
self._log_train(runner)
- elif self.end_of_epoch(runner, batch_idx) and not self.ignore_last:
+ elif self.end_of_epoch(runner.train_loop.dataloader,
+ batch_idx) and not self.ignore_last:
# `runner.max_iters` may not be divisible by `self.interval`. if
# `self.ignore_last==True`, the log of remaining iterations will
# be recorded (Epoch [4][1000/1007], the logs of 998-1007
@@ -271,7 +272,7 @@ class LoggerHook(Hook):
# by iter: Iter [100/100000]
if self.by_epoch:
log_str = f'Epoch [{cur_epoch}]' \
- f'[{cur_iter}/{len(runner.cur_dataloader)}]\t'
+ f'[{cur_iter}/{len(runner.train_loop.dataloader)}]\t'
else:
log_str = f'Iter [{cur_iter}/{runner.train_loop.max_iters}]\t'
log_str += f'{lr_momentum_str}, '
@@ -311,7 +312,7 @@ class LoggerHook(Hook):
"""
tag = self._collect_info(runner, 'val')
# Compatible with function `log` https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/logger/text.py # noqa E501
- eval_iter = len(runner.cur_dataloader)
+ eval_iter = len(runner.val_loop.dataloader)
cur_iter = self._get_iter(runner)
cur_epoch = self._get_epoch(runner, 'val')
# val/test time
diff --git a/mmengine/hooks/naive_visualization_hook.py b/mmengine/hooks/naive_visualization_hook.py
index 2e05fc5970dd8637a3487d99a984085a07077537..e8bd383473b4b50b46c24ea615c90421652e3dcf 100644
--- a/mmengine/hooks/naive_visualization_hook.py
+++ b/mmengine/hooks/naive_visualization_hook.py
@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
-from typing import Any, Optional, Sequence, Tuple
+from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
@@ -41,26 +41,25 @@ class NaiveVisualizationHook(Hook):
self,
runner,
batch_idx: int,
- data_batch: Optional[Sequence[Tuple[Any, BaseDataElement]]] = None,
+ data_batch: Optional[Sequence[dict]] = None,
outputs: Optional[Sequence[BaseDataElement]] = None) -> None:
"""Show or Write the predicted results.
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the test loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
+ data_batch (Sequence[dict], optional): Data
from dataloader. Defaults to None.
outputs (Sequence[BaseDataElement], optional): Outputs from model.
Defaults to None.
"""
if self.every_n_iters(runner, self._interval):
- inputs, data_samples = data_batch # type: ignore
- inputs = tensor2imgs(inputs,
- **data_samples[0].get('img_norm_cfg', dict()))
- for input, data_sample, output in zip(
- inputs,
- data_samples, # type: ignore
- outputs): # type: ignore
+ for data, output in zip(data_batch, outputs): # type: ignore
+ input = data['inputs']
+ data_sample = data['data_sample']
+ input = tensor2imgs(input,
+ **data_sample.get('img_norm_cfg',
+ dict()))[0]
# TODO We will implement a function to revert the augmentation
# in the future.
ori_shape = (data_sample.ori_width, data_sample.ori_height)
diff --git a/mmengine/hooks/optimizer_hook.py b/mmengine/hooks/optimizer_hook.py
index ff33b54a7de343bb2681398d2ca661a5e59f55c5..9107dbf02500e24d471271fd99a7fc1b29ad12fe 100644
--- a/mmengine/hooks/optimizer_hook.py
+++ b/mmengine/hooks/optimizer_hook.py
@@ -1,16 +1,15 @@
# Copyright (c) OpenMMLab. All rights reserved.
import logging
-from typing import Any, List, Optional, Sequence, Tuple
+from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
-from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -77,10 +76,9 @@ class OptimizerHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. In order to keep this interface consistent
- with other hooks, we keep ``data_batch`` here.
- Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ In order to keep this interface consistent with other hooks,
+ we keep ``data_batch`` here. Defaults to None.
outputs (dict, optional): Outputs from model.
In order to keep this interface consistent with other hooks,
we keep ``outputs`` here. Defaults to None.
diff --git a/mmengine/hooks/param_scheduler_hook.py b/mmengine/hooks/param_scheduler_hook.py
index 9522abcf8ae4321d16d6bed102d101c31449677e..c4e7af58df9b38978eedb4b45dc0b803efc0a964 100644
--- a/mmengine/hooks/param_scheduler_hook.py
+++ b/mmengine/hooks/param_scheduler_hook.py
@@ -1,11 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Any, Optional, Sequence, Tuple
+from typing import Optional, Sequence
-from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
-DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
+DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
@@ -25,10 +24,9 @@ class ParamSchedulerHook(Hook):
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional): Data
- from dataloader. In order to keep this interface consistent
- with other hooks, we keep ``data_batch`` here.
- Defaults to None.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ In order to keep this interface consistent with other hooks,
+ we keep ``data_batch`` here. Defaults to None.
outputs (dict, optional): Outputs from model.
In order to keep this interface consistent with other hooks, we
keep ``data_batch`` here. Defaults to None.
diff --git a/mmengine/hooks/sampler_seed_hook.py b/mmengine/hooks/sampler_seed_hook.py
index eed3fa90d46f5abd98bec0bb2c60978c82795855..b90657c851bf3d51c5ffe0c2cae80a09020ff316 100644
--- a/mmengine/hooks/sampler_seed_hook.py
+++ b/mmengine/hooks/sampler_seed_hook.py
@@ -20,9 +20,11 @@ class DistSamplerSeedHook(Hook):
Args:
runner (Runner): The runner of the training process.
"""
- if hasattr(runner.cur_dataloader.sampler, 'set_epoch'):
+ if hasattr(runner.train_loop.dataloader.sampler, 'set_epoch'):
# in case the data loader uses `SequentialSampler` in Pytorch
- runner.cur_dataloader.sampler.set_epoch(runner.epoch)
- elif hasattr(runner.cur_dataloader.batch_sampler.sampler, 'set_epoch'):
+ runner.train_loop.dataloader.sampler.set_epoch(runner.epoch)
+ elif hasattr(runner.train_loop.dataloader.batch_sampler.sampler,
+ 'set_epoch'):
# batch sampler in pytorch warps the sampler as its attributes.
- runner.cur_dataloader.batch_sampler.sampler.set_epoch(runner.epoch)
+ runner.train_loop.dataloader.batch_sampler.sampler.set_epoch(
+ runner.epoch)
diff --git a/mmengine/runner/base_loop.py b/mmengine/runner/base_loop.py
index 0a0e3ca772415dd3e9f61ee13c4bbde523be4281..ec3f880dd42b539ecf484f55152525154292bb5c 100644
--- a/mmengine/runner/base_loop.py
+++ b/mmengine/runner/base_loop.py
@@ -25,9 +25,6 @@ class BaseLoop(metaclass=ABCMeta):
else:
self.dataloader = dataloader
- # TODO, used by `end_of_epoch` of `Hook`
- self._runner.data_loader = self.dataloader
-
@property
def runner(self):
return self._runner
diff --git a/mmengine/runner/loops.py b/mmengine/runner/loops.py
index eb5c3454bb38c1b686b57518f205715a55ce2001..d791c52c691e8d45d2ece9f4279aa73c4b69608e 100644
--- a/mmengine/runner/loops.py
+++ b/mmengine/runner/loops.py
@@ -1,10 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Any, Dict, List, Sequence, Tuple, Union
+import warnings
+from typing import Dict, List, Sequence, Union
import torch
from torch.utils.data import DataLoader
-from mmengine.data import BaseDataElement
from mmengine.evaluator import Evaluator
from mmengine.registry import LOOPS
from mmengine.utils import is_list_of
@@ -40,7 +40,6 @@ class EpochBasedTrainLoop(BaseLoop):
def run(self) -> None:
"""Launch training."""
- self.runner.cur_dataloader = self.dataloader
self.runner.call_hook('before_train')
while self.runner._epoch < self._max_epochs:
@@ -62,13 +61,11 @@ class EpochBasedTrainLoop(BaseLoop):
self.runner.call_hook('after_train_epoch')
self.runner._epoch += 1
- def run_iter(self, idx,
- data_batch: Sequence[Tuple[Any, BaseDataElement]]) -> None:
+ def run_iter(self, idx, data_batch: Sequence[dict]) -> None:
"""Iterate one min-batch.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): Batch of data
- from dataloader.
+ data_batch (Sequence[dict]): Batch of data from dataloader.
"""
self.runner.call_hook(
'before_train_iter', batch_idx=idx, data_batch=data_batch)
@@ -112,7 +109,6 @@ class IterBasedTrainLoop(BaseLoop):
def run(self) -> None:
"""Launch training."""
- self.runner.cur_dataloader = self.dataloader
self.runner.call_hook('before_train')
# In iteration-based training loop, we treat the whole training process
# as a big epoch and execute the corresponding hook.
@@ -130,13 +126,11 @@ class IterBasedTrainLoop(BaseLoop):
self.runner.call_hook('after_train_epoch')
self.runner.call_hook('after_train')
- def run_iter(self, data_batch: Sequence[Tuple[Any,
- BaseDataElement]]) -> None:
+ def run_iter(self, data_batch: Sequence[dict]) -> None:
"""Iterate one mini-batch.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): Batch of data
- from dataloader.
+ data_batch (Sequence[dict]): Batch of data from dataloader.
"""
self.runner.call_hook(
'before_train_iter',
@@ -180,12 +174,17 @@ class ValLoop(BaseLoop):
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
else:
self.evaluator = evaluator # type: ignore
-
+ if hasattr(self.dataloader.dataset, 'metainfo'):
+ self.evaluator.dataset_meta = self.dataloader.dataset.metainfo
+ else:
+ warnings.warn(
+ f'Dataset {self.dataloader.dataset.__class__.__name__} has no '
+ 'metainfo. ``dataset_meta`` in evaluator and metric will be '
+ 'None.')
self.interval = interval
def run(self):
"""Launch validation."""
- self.runner.cur_dataloader = self.dataloader
self.runner.call_hook('before_val')
self.runner.call_hook('before_val_epoch')
self.runner.model.eval()
@@ -201,11 +200,11 @@ class ValLoop(BaseLoop):
self.runner.call_hook('after_val')
@torch.no_grad()
- def run_iter(self, idx, data_batch: Sequence[Tuple[Any, BaseDataElement]]):
+ def run_iter(self, idx, data_batch: Sequence[dict]):
"""Iterate one mini-batch.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): Batch of data
+ data_batch (Sequence[dict]): Batch of data
from dataloader.
"""
self.runner.call_hook(
@@ -239,10 +238,16 @@ class TestLoop(BaseLoop):
self.evaluator = runner.build_evaluator(evaluator) # type: ignore
else:
self.evaluator = evaluator # type: ignore
+ if hasattr(self.dataloader.dataset, 'metainfo'):
+ self.evaluator.dataset_meta = self.dataloader.dataset.metainfo
+ else:
+ warnings.warn(
+ f'Dataset {self.dataloader.dataset.__class__.__name__} has no '
+ 'metainfo. ``dataset_meta`` in evaluator and metric will be '
+ 'None.')
def run(self) -> None:
"""Launch test."""
- self.runner.cur_dataloader = self.dataloader
self.runner.call_hook('before_test')
self.runner.call_hook('before_test_epoch')
self.runner.model.eval()
@@ -258,13 +263,11 @@ class TestLoop(BaseLoop):
self.runner.call_hook('after_test')
@torch.no_grad()
- def run_iter(self, idx,
- data_batch: Sequence[Tuple[Any, BaseDataElement]]) -> None:
+ def run_iter(self, idx, data_batch: Sequence[dict]) -> None:
"""Iterate one mini-batch.
Args:
- data_batch (Sequence[Tuple[Any, BaseDataElement]]): Batch of data
- from dataloader.
+ data_batch (Sequence[dict]): Batch of data from dataloader.
"""
self.runner.call_hook(
'before_test_iter', batch_idx=idx, data_batch=data_batch)
diff --git a/mmengine/runner/runner.py b/mmengine/runner/runner.py
index 9d364b34ac129429b9a78b512e6e103b506172cd..28d339ca86acda4344333401b4aa4f0d5d74b6dd 100644
--- a/mmengine/runner/runner.py
+++ b/mmengine/runner/runner.py
@@ -1215,6 +1215,8 @@ class Runner:
+----------------------+-------------------------+
| IterTimerHook | NORMAL (40) |
+----------------------+-------------------------+
+ | DistSamplerSeedHook | NORMAL (40) |
+ +----------------------+-------------------------+
| LoggerHook | BELOW_NORMAL (60) |
+----------------------+-------------------------+
| ParamSchedulerHook | LOW (70) |
@@ -1228,6 +1230,7 @@ class Runner:
default_hooks = dict(
optimizer=dict(type='OptimizerHook', grad_clip=None),
timer=dict(type='IterTimerHook'),
+ sampler_seed=dict(type='DistSamplerSeedHook'),
logger=dict(type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
@@ -1252,6 +1255,7 @@ class Runner:
logger=dict(type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
+ sampler_seed=dict(type='DistSamplerSeedHook'),
)
if hooks is not None:
for name, hook in hooks.items():
diff --git a/tests/test_evaluator/test_evaluator.py b/tests/test_evaluator/test_evaluator.py
index 61be034be2d5d3934f3d23eb60fd7eaa0eec143b..5364c0679997d8dfba7630056f3b8ab93e955cd4 100644
--- a/tests/test_evaluator/test_evaluator.py
+++ b/tests/test_evaluator/test_evaluator.py
@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
import math
-from typing import Any, Dict, List, Optional, Sequence, Tuple
+from typing import Dict, List, Optional, Sequence
from unittest import TestCase
import numpy as np
@@ -40,7 +40,7 @@ class ToyMetric(BaseMetric):
def process(self, data_batch, predictions):
results = [{
'pred': pred.get('pred'),
- 'label': data[1].get('label')
+ 'label': data['data_sample'].get('label')
} for pred, data in zip(predictions, data_batch)]
self.results.extend(results)
@@ -66,7 +66,7 @@ class NonPrefixedMetric(BaseMetric):
"""Evaluator with unassigned `default_prefix` to test the warning
information."""
- def process(self, data_batch: Sequence[Tuple[Any, dict]],
+ def process(self, data_batch: Sequence[dict],
predictions: Sequence[dict]) -> None:
pass
@@ -79,8 +79,11 @@ def generate_test_results(size, batch_size, pred, label):
bs_residual = size % batch_size
for i in range(num_batch):
bs = bs_residual if i == num_batch - 1 else batch_size
- data_batch = [(np.zeros((3, 10, 10)), BaseDataElement(label=label))
- for _ in range(bs)]
+ data_batch = [
+ dict(
+ inputs=np.zeros((3, 10, 10)),
+ data_sample=BaseDataElement(label=label)) for _ in range(bs)
+ ]
predictions = [BaseDataElement(pred=pred) for _ in range(bs)]
yield (data_batch, predictions)
@@ -228,7 +231,10 @@ class TestEvaluator(TestCase):
size = 10
- all_data = [(np.zeros((3, 10, 10)), BaseDataElement(label=1))
- for _ in range(size)]
+ all_data = [
+ dict(
+ inputs=np.zeros((3, 10, 10)),
+ data_sample=BaseDataElement(label=1)) for _ in range(size)
+ ]
all_predictions = [BaseDataElement(pred=0) for _ in range(size)]
evaluator.offline_evaluate(all_data, all_predictions)
diff --git a/tests/test_hook/test_hook.py b/tests/test_hook/test_hook.py
index db80ed4a6301b469128ec07168655d5ca83a7dc3..771c54f615079adfffccfd7ea058aa90c0d6ad74 100644
--- a/tests/test_hook/test_hook.py
+++ b/tests/test_hook/test_hook.py
@@ -157,18 +157,17 @@ class TestHook:
def test_end_of_epoch(self):
hook = Hook()
- runner = Mock()
# last inner iter
batch_idx = 1
- runner.cur_dataloader.__len__ = Mock(return_value=2)
- runner.cur_dataloader.__len__ = Mock(return_value=2)
- return_val = hook.end_of_epoch(runner, batch_idx)
+ dataloader = Mock()
+ dataloader.__len__ = Mock(return_value=2)
+ return_val = hook.end_of_epoch(dataloader, batch_idx)
assert return_val
# not the last inner iter
batch_idx = 0
- return_val = hook.end_of_epoch(runner, batch_idx)
+ return_val = hook.end_of_epoch(dataloader, batch_idx)
assert not return_val
def test_is_last_train_epoch(self):
diff --git a/tests/test_hook/test_logger_hook.py b/tests/test_hook/test_logger_hook.py
index 6fac1a93b596031ef7cd85997be89b20bb5f9609..024716f2b4323e5fd318b1c53abe5e03252ba3ea 100644
--- a/tests/test_hook/test_logger_hook.py
+++ b/tests/test_hook/test_logger_hook.py
@@ -111,7 +111,7 @@ class TestLoggerHook:
# Test end of the epoch.
logger_hook = LoggerHook(by_epoch=True, ignore_last=False)
logger_hook._log_train = MagicMock()
- runner.cur_dataloader = [0] * 5
+ runner.train_loop.dataloader = [0] * 5
batch_idx = 4
logger_hook.after_train_iter(runner, batch_idx=batch_idx)
logger_hook._log_train.assert_called()
@@ -341,7 +341,9 @@ class TestLoggerHook:
def _setup_runner(self):
runner = MagicMock()
runner.epoch = 1
- runner.cur_dataloader = [0] * 5
+ runner.train_loop.dataloader = [0] * 5
+ runner.val_loop.dataloader = [0] * 5
+ runner.test_loop.dataloader = [0] * 5
runner.iter = 10
runner.train_loop.max_iters = 50
logger = logging.getLogger()
diff --git a/tests/test_hook/test_naive_visualization_hook.py b/tests/test_hook/test_naive_visualization_hook.py
index 4d75fedb1588b865071a88846ee7bbfae880ab88..0bbe47dfb84996f89d9377126f9c428942468a3c 100644
--- a/tests/test_hook/test_naive_visualization_hook.py
+++ b/tests/test_hook/test_naive_visualization_hook.py
@@ -16,70 +16,56 @@ class TestNaiveVisualizationHook:
inputs = torch.randn(1, 3, 15, 15)
batch_idx = 10
# test with normalize, resize, pad
- gt_datasamples = [
- BaseDataElement(
- metainfo=dict(
- img_norm_cfg=dict(
- mean=(0, 0, 0), std=(0.5, 0.5, 0.5), to_bgr=True),
- scale=(10, 10),
- pad_shape=(15, 15, 3),
- ori_height=5,
- ori_width=5,
- img_path='tmp.jpg'))
- ]
+ gt_datasamples = BaseDataElement(
+ metainfo=dict(
+ img_norm_cfg=dict(
+ mean=(0, 0, 0), std=(0.5, 0.5, 0.5), to_bgr=True),
+ scale=(10, 10),
+ pad_shape=(15, 15, 3),
+ ori_height=5,
+ ori_width=5,
+ img_path='tmp.jpg'))
pred_datasamples = [BaseDataElement()]
- data_batch = (inputs, gt_datasamples)
+ data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
pred_datasamples)
# test with resize, pad
- gt_datasamples = [
- BaseDataElement(
- metainfo=dict(
- scale=(10, 10),
- pad_shape=(15, 15, 3),
- ori_height=5,
- ori_width=5,
- img_path='tmp.jpg')),
- ]
+ gt_datasamples = BaseDataElement(
+ metainfo=dict(
+ scale=(10, 10),
+ pad_shape=(15, 15, 3),
+ ori_height=5,
+ ori_width=5,
+ img_path='tmp.jpg'))
pred_datasamples = [BaseDataElement()]
- data_batch = (inputs, gt_datasamples)
+ data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
pred_datasamples)
# test with only resize
- gt_datasamples = [
- BaseDataElement(
- metainfo=dict(
- scale=(15, 15),
- ori_height=5,
- ori_width=5,
- img_path='tmp.jpg')),
- ]
+ gt_datasamples = BaseDataElement(
+ metainfo=dict(
+ scale=(15, 15), ori_height=5, ori_width=5, img_path='tmp.jpg'))
pred_datasamples = [BaseDataElement()]
- data_batch = (inputs, gt_datasamples)
+ data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
pred_datasamples)
# test with only pad
- gt_datasamples = [
- BaseDataElement(
- metainfo=dict(
- pad_shape=(15, 15, 3),
- ori_height=5,
- ori_width=5,
- img_path='tmp.jpg')),
- ]
+ gt_datasamples = BaseDataElement(
+ metainfo=dict(
+ pad_shape=(15, 15, 3),
+ ori_height=5,
+ ori_width=5,
+ img_path='tmp.jpg'))
pred_datasamples = [BaseDataElement()]
- data_batch = (inputs, gt_datasamples)
+ data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
pred_datasamples)
# test no transform
- gt_datasamples = [
- BaseDataElement(
- metainfo=dict(ori_height=15, ori_width=15,
- img_path='tmp.jpg')),
- ]
+ gt_datasamples = BaseDataElement(
+ metainfo=dict(ori_height=15, ori_width=15, img_path='tmp.jpg'))
pred_datasamples = [BaseDataElement()]
- data_batch = (inputs, gt_datasamples)
+ data_batch = [dict(inputs=inputs, data_sample=gt_datasamples)]
naive_visualization_hook.after_test_iter(runner, batch_idx, data_batch,
pred_datasamples)
diff --git a/tests/test_hook/test_sampler_seed_hook.py b/tests/test_hook/test_sampler_seed_hook.py
index 9d19edf713cafc665a8b75a8a800b3ecbd6a4f68..c1bf8b543aa1a7e5bf8216a32b55692b9b4d9bb8 100644
--- a/tests/test_hook/test_sampler_seed_hook.py
+++ b/tests/test_hook/test_sampler_seed_hook.py
@@ -12,17 +12,18 @@ class TestDistSamplerSeedHook:
# Test dataset sampler
runner = Mock()
runner.epoch = 1
- runner.cur_dataloader = Mock()
- runner.cur_dataloader.sampler = Mock()
- runner.cur_dataloader.sampler.set_epoch = Mock()
+ runner.train_loop.dataloader = Mock()
+ runner.train_loop.dataloader.sampler = Mock()
+ runner.train_loop.dataloader.sampler.set_epoch = Mock()
hook.before_train_epoch(runner)
- runner.cur_dataloader.sampler.set_epoch.assert_called()
+ runner.train_loop.dataloader.sampler.set_epoch.assert_called()
# Test batch sampler
runner = Mock()
- runner.cur_dataloader = Mock()
- runner.cur_dataloader.sampler = Mock(spec_set=True)
- runner.cur_dataloader.batch_sampler = Mock()
- runner.cur_dataloader.batch_sampler.sampler = Mock()
- runner.cur_dataloader.batch_sampler.sampler.set_epoch = Mock()
+ runner.train_loop.dataloader = Mock()
+ runner.train_loop.dataloader.sampler = Mock(spec_set=True)
+ runner.train_loop.dataloader.batch_sampler = Mock()
+ runner.train_loop.dataloader.batch_sampler.sampler = Mock()
+ runner.train_loop.dataloader.batch_sampler.sampler.set_epoch = Mock()
hook.before_train_epoch(runner)
- runner.cur_dataloader.batch_sampler.sampler.set_epoch.assert_called()
+ runner.train_loop.dataloader.\
+ batch_sampler.sampler.set_epoch.assert_called()
diff --git a/tests/test_runner/test_runner.py b/tests/test_runner/test_runner.py
index 0fdd855312a23973708754bd84829d15c43ab792..fa1b9e5b5f91ca51f8bfb1a5e80d4d6fa752366a 100644
--- a/tests/test_runner/test_runner.py
+++ b/tests/test_runner/test_runner.py
@@ -36,7 +36,8 @@ class ToyModel(nn.Module):
self.linear = nn.Linear(2, 1)
def forward(self, data_batch, return_loss=False):
- inputs, labels = zip(*data_batch)
+ inputs, labels = zip(
+ *map(lambda x: (x['inputs'], x['data_sample']), data_batch))
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
inputs = torch.stack(inputs).to(device)
labels = torch.stack(labels).to(device)
@@ -67,7 +68,7 @@ class CustomModelWrapper(nn.Module):
@DATASETS.register_module()
class ToyDataset(Dataset):
- META = dict() # type: ignore
+ METAINFO = dict() # type: ignore
data = torch.randn(12, 2)
label = torch.ones(12)
@@ -75,7 +76,7 @@ class ToyDataset(Dataset):
return self.data.size(0)
def __getitem__(self, index):
- return self.data[index], self.label[index]
+ return dict(inputs=self.data[index], data_sample=self.label[index])
@METRICS.register_module()