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Remove fileio from mmcv and use mmengine.fileio instead (#2179)

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docs/en/api.rst
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fileio
-------
.. automodule:: mmcv.fileio
:members:
image
------
.. automodule:: mmcv.image
......
docs/en/index.rst
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......@@ -18,7 +18,6 @@ You can switch between Chinese and English documents in the lower-left corner of
understand_mmcv/config.md
understand_mmcv/registry.md
understand_mmcv/runner.md
understand_mmcv/io.md
understand_mmcv/data_process.md
understand_mmcv/visualization.md
understand_mmcv/cnn.md
......
docs/en/understand_mmcv/io.md deleted 100644 → 0
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## File IO
This module provides two universal API to load and dump files of different formats.
```{note}
Since v1.3.16, the IO modules support loading (dumping) data from (to) different backends, respectively. More details are in PR [#1330](https://github.com/open-mmlab/mmcv/pull/1330).
```
### Load and dump data
`mmcv` provides a universal api for loading and dumping data, currently
supported formats are json, yaml and pickle.
#### Load from disk or dump to disk
```python
import mmcv
# load data from a file
data = mmcv.load('test.json')
data = mmcv.load('test.yaml')
data = mmcv.load('test.pkl')
# load data from a file-like object
with open('test.json', 'r') as f:
data = mmcv.load(f, file_format='json')
# dump data to a string
json_str = mmcv.dump(data, file_format='json')
# dump data to a file with a filename (infer format from file extension)
mmcv.dump(data, 'out.pkl')
# dump data to a file with a file-like object
with open('test.yaml', 'w') as f:
data = mmcv.dump(data, f, file_format='yaml')
```
#### Load from other backends or dump to other backends
```python
import mmcv
# load data from a file
data = mmcv.load('s3://bucket-name/test.json')
data = mmcv.load('s3://bucket-name/test.yaml')
data = mmcv.load('s3://bucket-name/test.pkl')
# dump data to a file with a filename (infer format from file extension)
mmcv.dump(data, 's3://bucket-name/out.pkl')
```
It is also very convenient to extend the api to support more file formats.
All you need to do is to write a file handler inherited from `BaseFileHandler`
and register it with one or several file formats.
You need to implement at least 3 methods.
```python
import mmcv
# To register multiple file formats, a list can be used as the argument.
# @mmcv.register_handler(['txt', 'log'])
@mmcv.register_handler('txt')
class TxtHandler1(mmcv.BaseFileHandler):
def load_from_fileobj(self, file):
return file.read()
def dump_to_fileobj(self, obj, file):
file.write(str(obj))
def dump_to_str(self, obj, **kwargs):
return str(obj)
```
Here is an example of `PickleHandler`.
```python
import pickle
class PickleHandler(mmcv.BaseFileHandler):
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return super(PickleHandler, self).load_from_path(
filepath, mode='rb', **kwargs)
def dump_to_str(self, obj, **kwargs):
kwargs.setdefault('protocol', 2)
return pickle.dumps(obj, **kwargs)
def dump_to_fileobj(self, obj, file, **kwargs):
kwargs.setdefault('protocol', 2)
pickle.dump(obj, file, **kwargs)
def dump_to_path(self, obj, filepath, **kwargs):
super(PickleHandler, self).dump_to_path(
obj, filepath, mode='wb', **kwargs)
```
### Load a text file as a list or dict
For example `a.txt` is a text file with 5 lines.
```
a
b
c
d
e
```
#### Load from disk
Use `list_from_file` to load the list from a.txt.
```python
>>> mmcv.list_from_file('a.txt')
['a', 'b', 'c', 'd', 'e']
>>> mmcv.list_from_file('a.txt', offset=2)
['c', 'd', 'e']
>>> mmcv.list_from_file('a.txt', max_num=2)
['a', 'b']
>>> mmcv.list_from_file('a.txt', prefix='/mnt/')
['/mnt/a', '/mnt/b', '/mnt/c', '/mnt/d', '/mnt/e']
```
For example `b.txt` is a text file with 3 lines.
```
1 cat
2 dog cow
3 panda
```
Then use `dict_from_file` to load the dict from `b.txt`.
```python
>>> mmcv.dict_from_file('b.txt')
{'1': 'cat', '2': ['dog', 'cow'], '3': 'panda'}
>>> mmcv.dict_from_file('b.txt', key_type=int)
{1: 'cat', 2: ['dog', 'cow'], 3: 'panda'}
```
#### Load from other backends
Use `list_from_file` to load the list from `s3://bucket-name/a.txt`.
```python
>>> mmcv.list_from_file('s3://bucket-name/a.txt')
['a', 'b', 'c', 'd', 'e']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', offset=2)
['c', 'd', 'e']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', max_num=2)
['a', 'b']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', prefix='/mnt/')
['/mnt/a', '/mnt/b', '/mnt/c', '/mnt/d', '/mnt/e']
```
Use `dict_from_file` to load the dict from `s3://bucket-name/b.txt`.
```python
>>> mmcv.dict_from_file('s3://bucket-name/b.txt')
{'1': 'cat', '2': ['dog', 'cow'], '3': 'panda'}
>>> mmcv.dict_from_file('s3://bucket-name/b.txt', key_type=int)
{1: 'cat', 2: ['dog', 'cow'], 3: 'panda'}
```
### Load and dump checkpoints
#### Load checkpoints from disk or save to disk
We can read the checkpoints from disk or save to disk in the following way.
```python
import torch
filepath1 = '/path/of/your/checkpoint1.pth'
filepath2 = '/path/of/your/checkpoint2.pth'
# read from filepath1
checkpoint = torch.load(filepath1)
# save to filepath2
torch.save(checkpoint, filepath2)
```
MMCV provides many backends. `HardDiskBackend` is one of them and we can use it to read or save checkpoints.
```python
import io
from mmcv.fileio.file_client import HardDiskBackend
disk_backend = HardDiskBackend()
with io.BytesIO(disk_backend.get(filepath1)) as buffer:
checkpoint = torch.load(buffer)
with io.BytesIO() as buffer:
torch.save(checkpoint, buffer)
disk_backend.put(buffer.getvalue(), filepath2)
```
If we want to implement an interface which automatically select the corresponding
backend based on the file path, we can use the `FileClient`.
For example, we want to implement two methods for reading checkpoints as well as saving checkpoints,
which need to support different types of file paths, either disk paths, network paths or other paths.
```python
from mmcv.fileio.file_client import FileClient
def load_checkpoint(path):
file_client = FileClient.infer(uri=path)
with io.BytesIO(file_client.get(path)) as buffer:
checkpoint = torch.load(buffer)
return checkpoint
def save_checkpoint(checkpoint, path):
with io.BytesIO() as buffer:
torch.save(checkpoint, buffer)
file_client.put(buffer.getvalue(), path)
file_client = FileClient.infer_client(uri=filepath1)
checkpoint = load_checkpoint(filepath1)
save_checkpoint(checkpoint, filepath2)
```
#### Load checkpoints from the Internet
```{note}
Currently, it only supports reading checkpoints from the Internet, and does not support saving checkpoints to the Internet.
```
```python
import io
import torch
from mmcv.fileio.file_client import HTTPBackend, FileClient
filepath = 'http://path/of/your/checkpoint.pth'
checkpoint = torch.utils.model_zoo.load_url(filepath)
http_backend = HTTPBackend()
with io.BytesIO(http_backend.get(filepath)) as buffer:
checkpoint = torch.load(buffer)
file_client = FileClient.infer_client(uri=filepath)
with io.BytesIO(file_client.get(filepath)) as buffer:
checkpoint = torch.load(buffer)
```
docs/zh_cn/api.rst
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fileio
-------
.. automodule:: mmcv.fileio
:members:
image
------
.. automodule:: mmcv.image
......
docs/zh_cn/index.rst
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......@@ -18,7 +18,6 @@
understand_mmcv/config.md
understand_mmcv/registry.md
understand_mmcv/runner.md
understand_mmcv/io.md
understand_mmcv/data_process.md
understand_mmcv/data_transform.md
understand_mmcv/visualization.md
......
docs/zh_cn/understand_mmcv/io.md deleted 100644 → 0
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## 文件输入输出
文件输入输出模块提供了两个通用的 API 接口用于读取和保存不同格式的文件。
```{note}
在 v1.3.16 及之后的版本中，IO 模块支持从不同后端读取数据并支持将数据至不同后端。更多细节请访问 PR [#1330](https://github.com/open-mmlab/mmcv/pull/1330)。
```
### 读取和保存数据
`mmcv` 提供了一个通用的 api 用于读取和保存数据，目前支持的格式有 json、yaml 和 pickle。
#### 从硬盘读取数据或者将数据保存至硬盘
```python
import mmcv
# 从文件中读取数据
data = mmcv.load('test.json')
data = mmcv.load('test.yaml')
data = mmcv.load('test.pkl')
# 从文件对象中读取数据
with open('test.json', 'r') as f:
data = mmcv.load(f, file_format='json')
# 将数据序列化为字符串
json_str = mmcv.dump(data, file_format='json')
# 将数据保存至文件 (根据文件名后缀反推文件类型)
mmcv.dump(data, 'out.pkl')
# 将数据保存至文件对象
with open('test.yaml', 'w') as f:
data = mmcv.dump(data, f, file_format='yaml')
```
#### 从其他后端加载或者保存至其他后端
```python
import mmcv
# 从 s3 文件读取数据
data = mmcv.load('s3://bucket-name/test.json')
data = mmcv.load('s3://bucket-name/test.yaml')
data = mmcv.load('s3://bucket-name/test.pkl')
# 将数据保存至 s3 文件 (根据文件名后缀反推文件类型)
mmcv.dump(data, 's3://bucket-name/out.pkl')
```
我们提供了易于拓展的方式以支持更多的文件格式。我们只需要创建一个继承自 `BaseFileHandler` 的
文件句柄类并将其注册到 `mmcv` 中即可。句柄类至少需要重写三个方法。
```python
import mmcv
# 支持为文件句柄类注册多个文件格式
# @mmcv.register_handler(['txt', 'log'])
@mmcv.register_handler('txt')
class TxtHandler1(mmcv.BaseFileHandler):
def load_from_fileobj(self, file):
return file.read()
def dump_to_fileobj(self, obj, file):
file.write(str(obj))
def dump_to_str(self, obj, **kwargs):
return str(obj)
```
以 `PickleHandler` 为例
```python
import pickle
class PickleHandler(mmcv.BaseFileHandler):
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return super(PickleHandler, self).load_from_path(
filepath, mode='rb', **kwargs)
def dump_to_str(self, obj, **kwargs):
kwargs.setdefault('protocol', 2)
return pickle.dumps(obj, **kwargs)
def dump_to_fileobj(self, obj, file, **kwargs):
kwargs.setdefault('protocol', 2)
pickle.dump(obj, file, **kwargs)
def dump_to_path(self, obj, filepath, **kwargs):
super(PickleHandler, self).dump_to_path(
obj, filepath, mode='wb', **kwargs)
```
### 读取文件并返回列表或字典
例如， `a.txt` 是文本文件，一共有5行内容。
```
a
b
c
d
e
```
#### 从硬盘读取
使用 `list_from_file` 读取 `a.txt`
```python
>>> mmcv.list_from_file('a.txt')
['a', 'b', 'c', 'd', 'e']
>>> mmcv.list_from_file('a.txt', offset=2)
['c', 'd', 'e']
>>> mmcv.list_from_file('a.txt', max_num=2)
['a', 'b']
>>> mmcv.list_from_file('a.txt', prefix='/mnt/')
['/mnt/a', '/mnt/b', '/mnt/c', '/mnt/d', '/mnt/e']
```
同样， `b.txt` 也是文本文件，一共有3行内容
```
1 cat
2 dog cow
3 panda
```
使用 `dict_from_file` 读取 `b.txt`
```python
>>> mmcv.dict_from_file('b.txt')
{'1': 'cat', '2': ['dog', 'cow'], '3': 'panda'}
>>> mmcv.dict_from_file('b.txt', key_type=int)
{1: 'cat', 2: ['dog', 'cow'], 3: 'panda'}
```
#### 从其他后端读取
使用 `list_from_file` 读取 `s3://bucket-name/a.txt`
```python
>>> mmcv.list_from_file('s3://bucket-name/a.txt')
['a', 'b', 'c', 'd', 'e']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', offset=2)
['c', 'd', 'e']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', max_num=2)
['a', 'b']
>>> mmcv.list_from_file('s3://bucket-name/a.txt', prefix='/mnt/')
['/mnt/a', '/mnt/b', '/mnt/c', '/mnt/d', '/mnt/e']
```
使用 `dict_from_file` 读取 `b.txt`
```python
>>> mmcv.dict_from_file('s3://bucket-name/b.txt')
{'1': 'cat', '2': ['dog', 'cow'], '3': 'panda'}
>>> mmcv.dict_from_file('s3://bucket-name/b.txt', key_type=int)
{1: 'cat', 2: ['dog', 'cow'], 3: 'panda'}
```
### 读取和保存权重文件
#### 从硬盘读取权重文件或者将权重文件保存至硬盘
我们可以通过下面的方式从磁盘读取权重文件或者将权重文件保存至磁盘
```python
import torch
filepath1 = '/path/of/your/checkpoint1.pth'
filepath2 = '/path/of/your/checkpoint2.pth'
# 从 filepath1 读取权重文件
checkpoint = torch.load(filepath1)
# 将权重文件保存至 filepath2
torch.save(checkpoint, filepath2)
```
MMCV 提供了很多后端，`HardDiskBackend` 是其中一个，我们可以通过它来读取或者保存权重文件。
```python
import io
from mmcv.fileio.file_client import HardDiskBackend
disk_backend = HardDiskBackend()
with io.BytesIO(disk_backend.get(filepath1)) as buffer:
checkpoint = torch.load(buffer)
with io.BytesIO() as buffer:
torch.save(checkpoint, f)
disk_backend.put(f.getvalue(), filepath2)
```
如果我们想在接口中实现根据文件路径自动选择对应的后端，我们可以使用 `FileClient`。
例如，我们想实现两个方法，分别是读取权重以及保存权重，它们需支持不同类型的文件路径，可以是磁盘路径，也可以是网络路径或者其他路径。
```python
from mmcv.fileio.file_client import FileClient
def load_checkpoint(path):
file_client = FileClient.infer(uri=path)
with io.BytesIO(file_client.get(path)) as buffer:
checkpoint = torch.load(buffer)
return checkpoint
def save_checkpoint(checkpoint, path):
with io.BytesIO() as buffer:
torch.save(checkpoint, buffer)
file_client.put(buffer.getvalue(), path)
file_client = FileClient.infer_client(uri=filepath1)
checkpoint = load_checkpoint(filepath1)
save_checkpoint(checkpoint, filepath2)
```
#### 从网络远端读取权重文件
```{note}
目前只支持从网络远端读取权重文件，暂不支持将权重文件写入网络远端
```
```python
import io
import torch
from mmcv.fileio.file_client import HTTPBackend, FileClient
filepath = 'http://path/of/your/checkpoint.pth'
checkpoint = torch.utils.model_zoo.load_url(filepath)
http_backend = HTTPBackend()
with io.BytesIO(http_backend.get(filepath)) as buffer:
checkpoint = torch.load(buffer)
file_client = FileClient.infer_client(uri=filepath)
with io.BytesIO(file_client.get(filepath)) as buffer:
checkpoint = torch.load(buffer)
```
mmcv/__init__.py
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# Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .arraymisc import *
from .fileio import *
from .image import *
from .transforms import *
from .utils import *
......
mmcv/engine/test.py
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......@@ -6,6 +6,7 @@ import tempfile
import time
from typing import Optional
import mmengine
import torch
import torch.distributed as dist
import torch.nn as nn
......@@ -135,7 +136,7 @@ def collect_results_cpu(result_part: list,
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
part_file = osp.join(tmpdir, f'part_{rank}.pkl') # type: ignore
mmcv.dump(result_part, part_file)
mmengine.dump(result_part, part_file)
dist.barrier()
# collect all parts
if rank != 0:
......@@ -145,7 +146,7 @@ def collect_results_cpu(result_part: list,
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, f'part_{i}.pkl') # type: ignore
part_result = mmcv.load(part_file)
part_result = mmengine.load(part_file)
# When data is severely insufficient, an empty part_result
# on a certain gpu could makes the overall outputs empty.
if part_result:
......
mmcv/fileio/__init__.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from .file_client import BaseStorageBackend, FileClient
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
from .io import dump, load, register_handler
from .parse import dict_from_file, list_from_file
__all__ = [
'BaseStorageBackend', 'FileClient', 'load', 'dump', 'register_handler',
'BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler',
'list_from_file', 'dict_from_file'
]
mmcv/fileio/file_client.py deleted 100644 → 0
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This diff is collapsed.
mmcv/fileio/handlers/__init__.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseFileHandler
from .json_handler import JsonHandler
from .pickle_handler import PickleHandler
from .yaml_handler import YamlHandler
__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler']
mmcv/fileio/handlers/base.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
class BaseFileHandler(metaclass=ABCMeta):
# `str_like` is a flag to indicate whether the type of file object is
# str-like object or bytes-like object. Pickle only processes bytes-like
# objects but json only processes str-like object. If it is str-like
# object, `StringIO` will be used to process the buffer.
str_like = True
@abstractmethod
def load_from_fileobj(self, file, **kwargs):
pass
@abstractmethod
def dump_to_fileobj(self, obj, file, **kwargs):
pass
@abstractmethod
def dump_to_str(self, obj, **kwargs):
pass
def load_from_path(self, filepath: str, mode: str = 'r', **kwargs):
with open(filepath, mode) as f:
return self.load_from_fileobj(f, **kwargs)
def dump_to_path(self, obj, filepath: str, mode: str = 'w', **kwargs):
with open(filepath, mode) as f:
self.dump_to_fileobj(obj, f, **kwargs)
mmcv/fileio/handlers/json_handler.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
import json
import numpy as np
from .base import BaseFileHandler
def set_default(obj):
"""Set default json values for non-serializable values.
It helps convert ``set``, ``range`` and ``np.ndarray`` data types to list.
It also converts ``np.generic`` (including ``np.int32``, ``np.float32``,
etc.) into plain numbers of plain python built-in types.
"""
if isinstance(obj, (set, range)):
return list(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
elif isinstance(obj, np.generic):
return obj.item()
raise TypeError(f'{type(obj)} is unsupported for json dump')
class JsonHandler(BaseFileHandler):
def load_from_fileobj(self, file):
return json.load(file)
def dump_to_fileobj(self, obj, file, **kwargs):
kwargs.setdefault('default', set_default)
json.dump(obj, file, **kwargs)
def dump_to_str(self, obj, **kwargs):
kwargs.setdefault('default', set_default)
return json.dumps(obj, **kwargs)
mmcv/fileio/handlers/pickle_handler.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
import pickle
from .base import BaseFileHandler
class PickleHandler(BaseFileHandler):
str_like = False
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return super().load_from_path(filepath, mode='rb', **kwargs)
def dump_to_str(self, obj, **kwargs):
kwargs.setdefault('protocol', 2)
return pickle.dumps(obj, **kwargs)
def dump_to_fileobj(self, obj, file, **kwargs):
kwargs.setdefault('protocol', 2)
pickle.dump(obj, file, **kwargs)
def dump_to_path(self, obj, filepath, **kwargs):
super().dump_to_path(obj, filepath, mode='wb', **kwargs)
mmcv/fileio/handlers/yaml_handler.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
import yaml
try:
from yaml import CDumper as Dumper
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader, Dumper # type: ignore
from .base import BaseFileHandler # isort:skip
class YamlHandler(BaseFileHandler):
def load_from_fileobj(self, file, **kwargs):
kwargs.setdefault('Loader', Loader)
return yaml.load(file, **kwargs)
def dump_to_fileobj(self, obj, file, **kwargs):
kwargs.setdefault('Dumper', Dumper)
yaml.dump(obj, file, **kwargs)
def dump_to_str(self, obj, **kwargs):
kwargs.setdefault('Dumper', Dumper)
return yaml.dump(obj, **kwargs)
mmcv/fileio/io.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from io import BytesIO, StringIO
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, TextIO, Union
from ..utils import is_list_of
from .file_client import FileClient
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
FileLikeObject = Union[TextIO, StringIO, BytesIO]
file_handlers = {
'json': JsonHandler(),
'yaml': YamlHandler(),
'yml': YamlHandler(),
'pickle': PickleHandler(),
'pkl': PickleHandler()
}
def load(file: Union[str, Path, FileLikeObject],
file_format: Optional[str] = None,
file_client_args: Optional[Dict] = None,
**kwargs):
"""Load data from json/yaml/pickle files.
This method provides a unified api for loading data from serialized files.
Note:
In v1.3.16 and later, ``load`` supports loading data from serialized
files those can be storaged in different backends.
Args:
file (str or :obj:`Path` or file-like object): Filename or a file-like
object.
file_format (str, optional): If not specified, the file format will be
inferred from the file extension, otherwise use the specified one.
Currently supported formats include "json", "yaml/yml" and
"pickle/pkl".
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
Examples:
>>> load('/path/of/your/file') # file is storaged in disk
>>> load('https://path/of/your/file') # file is storaged in Internet
>>> load('s3://path/of/your/file') # file is storaged in petrel
Returns:
The content from the file.
"""
if isinstance(file, Path):
file = str(file)
if file_format is None and isinstance(file, str):
file_format = file.split('.')[-1]
if file_format not in file_handlers:
raise TypeError(f'Unsupported format: {file_format}')
handler = file_handlers[file_format]
f: FileLikeObject
if isinstance(file, str):
file_client = FileClient.infer_client(file_client_args, file)
if handler.str_like:
with StringIO(file_client.get_text(file)) as f:
obj = handler.load_from_fileobj(f, **kwargs)
else:
with BytesIO(file_client.get(file)) as f:
obj = handler.load_from_fileobj(f, **kwargs)
elif hasattr(file, 'read'):
obj = handler.load_from_fileobj(file, **kwargs)
else:
raise TypeError('"file" must be a filepath str or a file-object')
return obj
def dump(obj: Any,
file: Optional[Union[str, Path, FileLikeObject]] = None,
file_format: Optional[str] = None,
file_client_args: Optional[Dict] = None,
**kwargs):
"""Dump data to json/yaml/pickle strings or files.
This method provides a unified api for dumping data as strings or to files,
and also supports custom arguments for each file format.
Note:
In v1.3.16 and later, ``dump`` supports dumping data as strings or to
files which is saved to different backends.
Args:
obj (any): The python object to be dumped.
file (str or :obj:`Path` or file-like object, optional): If not
specified, then the object is dumped to a str, otherwise to a file
specified by the filename or file-like object.
file_format (str, optional): Same as :func:`load`.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
Examples:
>>> dump('hello world', '/path/of/your/file') # disk
>>> dump('hello world', 's3://path/of/your/file') # ceph or petrel
Returns:
bool: True for success, False otherwise.
"""
if isinstance(file, Path):
file = str(file)
if file_format is None:
if isinstance(file, str):
file_format = file.split('.')[-1]
elif file is None:
raise ValueError(
'file_format must be specified since file is None')
if file_format not in file_handlers:
raise TypeError(f'Unsupported format: {file_format}')
f: FileLikeObject
handler = file_handlers[file_format]
if file is None:
return handler.dump_to_str(obj, **kwargs)
elif isinstance(file, str):
file_client = FileClient.infer_client(file_client_args, file)
if handler.str_like:
with StringIO() as f:
handler.dump_to_fileobj(obj, f, **kwargs)
file_client.put_text(f.getvalue(), file)
else:
with BytesIO() as f:
handler.dump_to_fileobj(obj, f, **kwargs)
file_client.put(f.getvalue(), file)
elif hasattr(file, 'write'):
handler.dump_to_fileobj(obj, file, **kwargs)
else:
raise TypeError('"file" must be a filename str or a file-object')
def _register_handler(handler: BaseFileHandler,
file_formats: Union[str, List[str]]) -> None:
"""Register a handler for some file extensions.
Args:
handler (:obj:`BaseFileHandler`): Handler to be registered.
file_formats (str or list[str]): File formats to be handled by this
handler.
"""
if not isinstance(handler, BaseFileHandler):
raise TypeError(
f'handler must be a child of BaseFileHandler, not {type(handler)}')
if isinstance(file_formats, str):
file_formats = [file_formats]
if not is_list_of(file_formats, str):
raise TypeError('file_formats must be a str or a list of str')
for ext in file_formats:
file_handlers[ext] = handler
def register_handler(file_formats: Union[str, list], **kwargs) -> Callable:
def wrap(cls):
_register_handler(cls(**kwargs), file_formats)
return cls
return wrap
mmcv/fileio/parse.py deleted 100644 → 0
View file @ 0b4285d9
# Copyright (c) OpenMMLab. All rights reserved.
from io import StringIO
from pathlib import Path
from typing import Dict, List, Optional, Union
from .file_client import FileClient
def list_from_file(filename: Union[str, Path],
prefix: str = '',
offset: int = 0,
max_num: int = 0,
encoding: str = 'utf-8',
file_client_args: Optional[Dict] = None) -> List:
"""Load a text file and parse the content as a list of strings.
Note:
In v1.3.16 and later, ``list_from_file`` supports loading a text file
which can be storaged in different backends and parsing the content as
a list for strings.
Args:
filename (str): Filename.
prefix (str): The prefix to be inserted to the beginning of each item.
offset (int): The offset of lines.
max_num (int): The maximum number of lines to be read,
zeros and negatives mean no limitation.
encoding (str): Encoding used to open the file. Default utf-8.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
Examples:
>>> list_from_file('/path/of/your/file') # disk
['hello', 'world']
>>> list_from_file('s3://path/of/your/file') # ceph or petrel
['hello', 'world']
Returns:
list[str]: A list of strings.
"""
cnt = 0
item_list = []
file_client = FileClient.infer_client(file_client_args, filename)
with StringIO(file_client.get_text(filename, encoding)) as f:
for _ in range(offset):
f.readline()
for line in f:
if 0 < max_num <= cnt:
break
item_list.append(prefix + line.rstrip('\n\r'))
cnt += 1
return item_list
def dict_from_file(filename: Union[str, Path],
key_type: type = str,
encoding: str = 'utf-8',
file_client_args: Optional[Dict] = None) -> Dict:
"""Load a text file and parse the content as a dict.
Each line of the text file will be two or more columns split by
whitespaces or tabs. The first column will be parsed as dict keys, and
the following columns will be parsed as dict values.
Note:
In v1.3.16 and later, ``dict_from_file`` supports loading a text file
which can be storaged in different backends and parsing the content as
a dict.
Args:
filename(str): Filename.
key_type(type): Type of the dict keys. str is user by default and
type conversion will be performed if specified.
encoding (str): Encoding used to open the file. Default utf-8.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
Examples:
>>> dict_from_file('/path/of/your/file') # disk
{'key1': 'value1', 'key2': 'value2'}
>>> dict_from_file('s3://path/of/your/file') # ceph or petrel
{'key1': 'value1', 'key2': 'value2'}
Returns:
dict: The parsed contents.
"""
mapping = {}
file_client = FileClient.infer_client(file_client_args, filename)
with StringIO(file_client.get_text(filename, encoding)) as f:
for line in f:
items = line.rstrip('\n').split()
assert len(items) >= 2
key = key_type(items[0])
val = items[1:] if len(items) > 2 else items[1]
mapping[key] = val
return mapping
mmcv/image/io.py
View file @ 0fe1c647
......@@ -8,8 +8,8 @@ import cv2
import numpy as np
from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
IMREAD_UNCHANGED)
from mmengine.fileio import FileClient
from mmcv.fileio import FileClient
from mmcv.utils import is_filepath, is_str
try:
......@@ -167,7 +167,7 @@ def imread(img_or_path,
If backend is None, the global imread_backend specified by
``mmcv.use_backend()`` will be used. Default: None.
file_client_args (dict | None): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Default: None.
Returns:
......@@ -283,7 +283,7 @@ def imwrite(img,
auto_mkdir (bool): If the parent folder of `file_path` does not exist,
whether to create it automatically. It will be deprecated.
file_client_args (dict | None): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Default: None.
Returns:
......
mmcv/runner/checkpoint.py
View file @ 0fe1c647
......@@ -12,14 +12,15 @@ from importlib import import_module
from tempfile import TemporaryDirectory
from typing import Callable, Dict, List, Optional, Tuple, Union
import mmengine
import torch
import torch.nn as nn
import torchvision
from mmengine.fileio import FileClient
from mmengine.fileio import load as load_file
from torch.optim import Optimizer
import mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import digit_version, load_url, mkdir_or_exist
from .dist_utils import get_dist_info
......@@ -136,7 +137,7 @@ def get_torchvision_models():
# 'resnet50' or 'ResNet50_Weights.IMAGENET1K_V1' in the config.
json_path = osp.join(mmcv.__path__[0],
'model_zoo/torchvision_0.12.json')
model_urls = mmcv.load(json_path)
model_urls = mmengine.load(json_path)
for cls_name, cls in torchvision.models.__dict__.items():
# The name of torchvision model weights classes ends with
# `_Weights` such as `ResNet18_Weights`. However, some model weight
......@@ -409,8 +410,8 @@ def load_from_ceph(filename: str,
'petrel'. Default: 'petrel'.
.. warning::
:class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
:class:`mmengine.fileio.file_client.CephBackend` will be deprecated,
please use :class:`mmengine.fileio.file_client.PetrelBackend` instead.
Returns:
dict or OrderedDict: The loaded checkpoint.
......@@ -751,7 +752,7 @@ def save_checkpoint(model: torch.nn.Module,
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Default: None.
`New in version 1.3.16.`
"""
......
mmcv/runner/hooks/checkpoint.py
View file @ 0fe1c647
......@@ -3,7 +3,8 @@ import os.path as osp
import warnings
from typing import Optional
from mmcv.fileio import FileClient
from mmengine.fileio import FileClient
from ..dist_utils import allreduce_params, master_only
from .hook import HOOKS, Hook
......@@ -35,7 +36,7 @@ class CheckpointHook(Hook):
sync_buffer (bool, optional): Whether to synchronize buffers in
different gpus. Default: False.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Default: None.
`New in version 1.3.16.`
......
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