mirror of https://github.com/open-mmlab/mmcv.git
Kai Chen
GitHub
commit
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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line.
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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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SOURCEDIR = .
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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API
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====
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fileio
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--------------
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.. automodule:: mmcv.fileio
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:members:
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image
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--------------
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.. automodule:: mmcv.image
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:members:
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video
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--------------
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.. automodule:: mmcv.video
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:members:
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arraymisc
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--------------
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.. automodule:: mmcv.arraymisc
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:members:
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visualization
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--------------
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.. automodule:: mmcv.visualization
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:members:
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utils
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--------------
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.. automodule:: mmcv.utils
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:members:
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runner
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--------------
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.. automodule:: mmcv.runner
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:members:
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# -*- coding: utf-8 -*-
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#
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# Configuration file for the Sphinx documentation builder.
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#
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# This file does only contain a selection of the most common options. For a
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# full list see the documentation:
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# http://www.sphinx-doc.org/en/master/config
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# -- Path setup --------------------------------------------------------------
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
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import os
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import sys
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sys.path.insert(0, os.path.abspath('..'))
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import mmcv # noqa: E402
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# -- Project information -----------------------------------------------------
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project = 'mmcv'
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copyright = '2018, Kai Chen'
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author = 'Kai Chen'
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# The short X.Y version
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version = mmcv.__version__
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# The full version, including alpha/beta/rc tags
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release = mmcv.__version__
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# -- General configuration ---------------------------------------------------
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# If your documentation needs a minimal Sphinx version, state it here.
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#
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# needs_sphinx = '1.0'
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|
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# Add any Sphinx extension module names here, as strings. They can be
|
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
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# ones.
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extensions = [
|
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'sphinx.ext.autodoc',
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'sphinx.ext.napoleon',
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'sphinx.ext.viewcode',
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]
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autodoc_mock_imports = ['cv2', 'torch']
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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|
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# The suffix(es) of source filenames.
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# You can specify multiple suffix as a list of string:
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#
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source_suffix = ['.rst', '.md']
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source_parsers = {'.md': 'recommonmark.parser.CommonMarkParser'}
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# The master toctree document.
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master_doc = 'index'
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|
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# The language for content autogenerated by Sphinx. Refer to documentation
|
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# for a list of supported languages.
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#
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||||
# This is also used if you do content translation via gettext catalogs.
|
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# Usually you set "language" from the command line for these cases.
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language = None
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|
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# List of patterns, relative to source directory, that match files and
|
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
|
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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|
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# The name of the Pygments (syntax highlighting) style to use.
|
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pygments_style = 'sphinx'
|
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|
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# -- Options for HTML output -------------------------------------------------
|
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|
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# The theme to use for HTML and HTML Help pages. See the documentation for
|
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# a list of builtin themes.
|
||||
#
|
||||
html_theme = 'sphinx_rtd_theme'
|
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|
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# Theme options are theme-specific and customize the look and feel of a theme
|
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# further. For a list of options available for each theme, see the
|
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# documentation.
|
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#
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# html_theme_options = {}
|
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|
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# Add any paths that contain custom static files (such as style sheets) here,
|
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# relative to this directory. They are copied after the builtin static files,
|
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']
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|
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# Custom sidebar templates, must be a dictionary that maps document names
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# to template names.
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#
|
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# The default sidebars (for documents that don't match any pattern) are
|
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# defined by theme itself. Builtin themes are using these templates by
|
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# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
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# 'searchbox.html']``.
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#
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# html_sidebars = {}
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# -- Options for HTMLHelp output ---------------------------------------------
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|
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# Output file base name for HTML help builder.
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htmlhelp_basename = 'mmcvdoc'
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# -- Options for LaTeX output ------------------------------------------------
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latex_elements = {
|
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# The paper size ('letterpaper' or 'a4paper').
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#
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# 'papersize': 'letterpaper',
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# The font size ('10pt', '11pt' or '12pt').
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#
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# 'pointsize': '10pt',
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# Additional stuff for the LaTeX preamble.
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#
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# 'preamble': '',
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# Latex figure (float) alignment
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#
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# 'figure_align': 'htbp',
|
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}
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|
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# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
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latex_documents = [
|
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(master_doc, 'mmcv.tex', 'mmcv Documentation', 'Kai Chen', 'manual'),
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]
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|
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# -- Options for manual page output ------------------------------------------
|
||||
|
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# One entry per manual page. List of tuples
|
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# (source start file, name, description, authors, manual section).
|
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man_pages = [(master_doc, 'mmcv', 'mmcv Documentation', [author], 1)]
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|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'mmcv', 'mmcv Documentation', author, 'mmcv',
|
||||
'One line description of project.', 'Miscellaneous'),
|
||||
]
|
||||
|
||||
# -- Options for Epub output -------------------------------------------------
|
||||
|
||||
# Bibliographic Dublin Core info.
|
||||
epub_title = project
|
||||
|
||||
# The unique identifier of the text. This can be a ISBN number
|
||||
# or the project homepage.
|
||||
#
|
||||
# epub_identifier = ''
|
||||
|
||||
# A unique identification for the text.
|
||||
#
|
||||
# epub_uid = ''
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# A list of files that should not be packed into the epub file.
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epub_exclude_files = ['search.html']
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# -- Extension configuration -------------------------------------------------
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## Image
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||||
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This module provides some image processing methods, which requires `opencv` to
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to be installed.
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### Read/Write/Show
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To read or write images files, use `imread` or `imwrite`.
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|
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```python
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import mmcv
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img = mmcv.imread('test.jpg')
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img = mmcv.imread('test.jpg', flag='grayscale')
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img_ = mmcv.imread(img) # nothing will happen, img_ = img
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mmcv.imwrite(img, 'out.jpg')
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```
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To read images from bytes
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||||
|
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```python
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with open('test.jpg', 'rb') as f:
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data = f.read()
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img = mmcv.imfrombytes(data)
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||||
```
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|
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To show an image file or a loaded image
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```python
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mmcv.imshow('tests/data/color.jpg')
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# this is equivalent to
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for i in range(10):
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img = np.random.randint(256, size=(100, 100, 3), dtype=np.uint8)
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mmcv.imshow(img, win_name='test image', wait_time=200)
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```
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|
||||
### Color space conversion
|
||||
Supported conversion methods:
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||||
- bgr2gray
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||||
- gray2bgr
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||||
- bgr2rgb
|
||||
- rgb2bgr
|
||||
- bgr2hsv
|
||||
- hsv2bgr
|
||||
|
||||
```python
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||||
img = mmcv.imread('tests/data/color.jpg')
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||||
img1 = mmcv.bgr2rgb(img)
|
||||
img2 = mmcv.rgb2gray(img1)
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||||
img3 = mmcv.bgr2hsv(img)
|
||||
```
|
||||
|
||||
### Resize
|
||||
There are three resize methods. All `imresize_*` methods have an argument `return_scale`,
|
||||
if this argument is `False`, then the return value is merely the resized image, otherwise
|
||||
is a tuple `(resized_img, scale)`.
|
||||
|
||||
```python
|
||||
# resize to a given size
|
||||
mmcv.imresize(img, (1000, 600), return_scale=True)
|
||||
|
||||
# resize to the same size of another image
|
||||
mmcv.imresize_like(img, dst_img, return_scale=False)
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||||
|
||||
# resize by a ratio
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||||
mmcv.imrescale(img, 0.5)
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||||
|
||||
# resize so that the max edge no longer than 1000, short edge no longer than 800
|
||||
# without changing the aspect ratio
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||||
mmcv.imrescale(img, (1000, 800))
|
||||
```
|
||||
|
||||
### Rotate
|
||||
To rotate an image by some angle, use `imrotate`. The center can be specified,
|
||||
which is the center of original image by default. There are two modes of rotating,
|
||||
one is to keep the image size unchanged so that some parts of the image will be
|
||||
cropped after rotating, the other is to extend the image size to fit the rotated
|
||||
image.
|
||||
|
||||
```python
|
||||
img = mmcv.imread('tests/data/color.jpg')
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||||
|
||||
# rotate the image clockwise by 30 degrees.
|
||||
img_ = mmcv.imrotate(img, 30)
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||||
|
||||
# rotate the image counterclockwise by 90 degrees.
|
||||
img_ = mmcv.imrotate(img, -90)
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||||
|
||||
# rotate the image clockwise by 30 degrees, and rescale it by 1.5x at the same time.
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||||
img_ = mmcv.imrotate(img, 30, scale=1.5)
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||||
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# rotate the image clockwise by 30 degrees, with (100, 100) as the center.
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||||
img_ = mmcv.imrotate(img, 30, center=(100, 100))
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# rotate the image clockwise by 30 degrees, and extend the image size.
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img_ = mmcv.imrotate(img, 30, auto_bound=True)
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```
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|
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### Flip
|
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To flip an image, use `imflip`.
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||||
|
||||
```python
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img = mmcv.imread('tests/data/color.jpg')
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# flip the image horizontally
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mmcv.imflip(img)
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# flip the image vertically
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mmcv.imflip(img, direction='vertical')
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||||
```
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### Crop
|
||||
`imcrop` can crop the image with one or some regions, represented as (x1, y1, x2, y2).
|
||||
|
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```python
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import mmcv
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import numpy as np
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img = mmcv.read_img('tests/data/color.jpg')
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# crop the region (10, 10, 100, 120)
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bboxes = np.array([10, 10, 100, 120])
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patch = mmcv.crop_img(img, bboxes)
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# crop two regions (10, 10, 100, 120) and (0, 0, 50, 50)
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bboxes = np.array([[10, 10, 100, 120], [0, 0, 50, 50]])
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patches = mmcv.crop_img(img, bboxes)
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# crop two regions, and rescale the patches by 1.2x
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patches = mmcv.crop_img(img, bboxes, scale_ratio=1.2)
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```
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|
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### Padding
|
||||
There are two methods `impad` and `impad_to_multiple` to pad an image to the
|
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specific size with given values.
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```python
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img = mmcv.read_img('tests/data/color.jpg')
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# pad the image to (1000, 1200) with all zeros
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img_ = mmcv.pad_img(img, (1000, 1200), pad_val=0)
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# pad the image to (1000, 1200) with different values for three channels.
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img_ = mmcv.pad_img(img, (1000, 1200), pad_val=[100, 50, 200])
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# pad an image so that each edge is a multiple of some value.
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img_ = mmcv.impad_to_multiple(img, 32)
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```
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Welcome to mmcv's documentation!
|
||||
==================================
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|
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.. toctree::
|
||||
:maxdepth: 2
|
||||
|
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intro.md
|
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io.md
|
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image.md
|
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video.md
|
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visualization.md
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utils.md
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runner.md
|
||||
api.rst
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|
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|
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|
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Indices and tables
|
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==================
|
||||
|
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* :ref:`genindex`
|
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* :ref:`search`
|
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|
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@ -0,0 +1,33 @@
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## Introduction
|
||||
|
||||
`mmcv` is a foundational python library for computer vision research and supports many
|
||||
research projects in MMLAB, such as [mmdetection](https://github.com/open-mmlab/mmdetection).
|
||||
|
||||
It provides the following functionalities.
|
||||
|
||||
- Universal IO APIs
|
||||
- Image processing
|
||||
- Video processing
|
||||
- Image and annotation visualization
|
||||
- Useful utilities (progress bar, timer, ...)
|
||||
- PyTorch runner with hooking machanism
|
||||
- Various CNN architectures
|
||||
|
||||
See the [documentation](http://mmcv.readthedocs.io/en/latest) for more features and usage.
|
||||
|
||||
|
||||
### Installation
|
||||
|
||||
Try and start with
|
||||
|
||||
```shell
|
||||
pip install mmcv
|
||||
```
|
||||
|
||||
If you want to install from source
|
||||
|
||||
```shell
|
||||
git clone https://github.com/open-mmlab/mmcv.git
|
||||
cd mmcv
|
||||
pip install . # (add "-e" if you want to develop or modify the codes)
|
||||
```
|
||||
|
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|
|||
## File IO
|
||||
|
||||
This module provides two universal API to load and dump files of different formats.
|
||||
|
||||
### Load and dump data
|
||||
`mmcv` provides a universal api for loading and dumping data, currently
|
||||
supported formats are json, yaml and pickle.
|
||||
|
||||
```python
|
||||
import mmcv
|
||||
|
||||
# load data from a file
|
||||
data = mmcv.load('test.json')
|
||||
data = mmcv.load('test.yaml')
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||||
data = mmcv.load('test.pkl')
|
||||
# load data from a file-like object
|
||||
with open('test.json', 'r') as f:
|
||||
data = mmcv.load(f)
|
||||
|
||||
# dump data to a string
|
||||
json_str = mmcv.dump(data, 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, format='yaml')
|
||||
```
|
||||
|
||||
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
|
||||
from six.moves import cPickle as 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
|
||||
```
|
||||
|
||||
Then 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 5 lines.
|
||||
```
|
||||
1 cat
|
||||
2 dog cow
|
||||
3 panda
|
||||
```
|
||||
|
||||
Then use `dict_from_file` to load the list from a.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'}
|
||||
```
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=.
|
||||
set BUILDDIR=_build
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.http://sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
|
||||
|
||||
:end
|
||||
popd
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
## Runner
|
||||
|
||||
The runner module aims to help users to start training with less code, while stays
|
||||
flexible and configurable.
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
## Utils
|
||||
|
||||
### Config
|
||||
|
||||
`Config` class is used for manipulating config and config files. It supports
|
||||
loading configs from multiple file formats including **python**, **json** and **yaml**.
|
||||
It provides dict-like apis to get and set values.
|
||||
|
||||
Here is an example of the config file `test.py`.
|
||||
|
||||
```python
|
||||
a = 1
|
||||
b = {'b1': [0, 1, 2], 'b2': None}
|
||||
c = (1, 2)
|
||||
d = 'string'
|
||||
```
|
||||
|
||||
To load and use configs
|
||||
|
||||
```python
|
||||
cfg = Config.fromfile('test.py')
|
||||
assert cfg.a == 1
|
||||
assert cfg.b.b1 == [0, 1, 2]
|
||||
cfg.c = None
|
||||
assert cfg.c == None
|
||||
```
|
||||
|
||||
### ProgressBar
|
||||
|
||||
If you want to apply a method to a list of items and track the progress, `track_progress`
|
||||
is a good choice. It will display a progress bar to tell the progress and ETA.
|
||||
|
||||
```python
|
||||
import mmcv
|
||||
|
||||
def func(item):
|
||||
# do something
|
||||
pass
|
||||
|
||||
tasks = [item_1, item_2, ..., item_n]
|
||||
|
||||
mmcv.track_progress(func, tasks)
|
||||
```
|
||||
|
||||
The output is like the following.
|
||||
|
||||
|
||||
There is another method `track_parallel_progress`, which wraps multiprocessing and
|
||||
progress visualization.
|
||||
|
||||
```python
|
||||
mmcv.track_parallel_progress(func, tasks, 8) # 8 workers
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
### Timer
|
||||
|
||||
It is convinient to compute the runtime of a code block with `Timer`.
|
||||
|
||||
```python
|
||||
import time
|
||||
|
||||
with mmcv.Timer():
|
||||
# simulate some code block
|
||||
time.sleep(1)
|
||||
```
|
||||
|
||||
or try with `since_start()` and `since_last_check()`. This former can
|
||||
return the runtime since the timer starts and the latter will return the time
|
||||
since the last time checked.
|
||||
|
||||
```python
|
||||
timer = mmcv.Timer()
|
||||
# code block 1 here
|
||||
print(timer.since_start())
|
||||
# code block 2 here
|
||||
print(timer.since_last_check())
|
||||
print(timer.since_start())
|
||||
```
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
## Video
|
||||
|
||||
This module provides the following functionalities.
|
||||
|
||||
- A `VideoReader` class with friendly apis to read and convert videos.
|
||||
- Some methods for editing (cut, concat, resize) videos.
|
||||
- Optical flow read/write.
|
||||
|
||||
|
||||
The `VideoReader` class provides sequence like apis to access video frames.
|
||||
It will internally cache the frames which have been visited.
|
||||
|
||||
```python
|
||||
video = mmcv.VideoReader('test.mp4')
|
||||
|
||||
# obtain basic information
|
||||
print(len(video))
|
||||
print(video.width, video.height, video.resolution, video.fps)
|
||||
|
||||
# iterate over all frames
|
||||
for frame in video:
|
||||
print(frame.shape)
|
||||
|
||||
# read the next frame
|
||||
img = video.read()
|
||||
|
||||
# read a frame by index
|
||||
img = video[100]
|
||||
|
||||
# read some frames
|
||||
img = video[5:10]
|
||||
```
|
||||
|
||||
To convert a video to images or generate a video from a image directory.
|
||||
|
||||
```python
|
||||
# split a video into frames and save to a folder
|
||||
video = mmcv.VideoReader('test.mp4')
|
||||
video.cvt2frames('out_dir')
|
||||
|
||||
# generate video from frames
|
||||
mmcv.frames2video('out_dir', 'test.avi')
|
||||
```
|
||||
|
||||
There are also some methods for editing videos, which wraps the commands of ffmpeg.
|
||||
|
||||
```python
|
||||
# cut a video clip
|
||||
mmcv.cut_video('test.mp4', 'clip1.mp4', start=3, end=10, vcodec='h264')
|
||||
|
||||
# join a list of video clips
|
||||
mmcv.concat_video(['clip1.mp4', 'clip2.mp4'], 'joined.mp4', log_level='quiet')
|
||||
|
||||
# resize a video with the specified size
|
||||
mmcv.resize_video('test.mp4', 'resized1.mp4', (360, 240))
|
||||
|
||||
# resize a video with a scaling ratio of 2
|
||||
mmcv.resize_video('test.mp4', 'resized2.mp4', ratio=2)
|
||||
```
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
## Visualization
|
||||
|
||||
`mmcv` can show images and annotations (currently supported types include bounding boxes).
|
||||
|
||||
```python
|
||||
# show an image file
|
||||
mmcv.imshow('a.jpg')
|
||||
|
||||
# show a loaded image
|
||||
img = np.random.rand(100, 100, 3)
|
||||
mmcv.imshow(img)
|
||||
|
||||
# show image with bounding boxes
|
||||
img = np.random.rand(100, 100, 3)
|
||||
bboxes = np.array([[0, 0, 50, 50], [20, 20, 60, 60]])
|
||||
mmcv.imshow_bboxes(img, bboxes)
|
||||
```
|
||||
|
||||
`mmcv` can also visualize special images such as optical flows.
|
||||
|
||||
```python
|
||||
flow = mmcv.flowread('test.flo')
|
||||
mmcv.flowshow(flow)
|
||||
```
|
||||
|
|
@ -109,7 +109,7 @@ def bbox_scaling(bboxes, scale, clip_shape=None):
|
|||
return scaled_bboxes
|
||||
|
||||
|
||||
def imcrop(img, bboxes, scale_ratio=1.0, pad_fill=None):
|
||||
def imcrop(img, bboxes, scale=1.0, pad_fill=None):
|
||||
"""Crop image patches.
|
||||
|
||||
3 steps: scale the bboxes -> clip bboxes -> crop and pad.
|
||||
|
|
@ -117,7 +117,7 @@ def imcrop(img, bboxes, scale_ratio=1.0, pad_fill=None):
|
|||
Args:
|
||||
img (ndarray): Image to be cropped.
|
||||
bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes.
|
||||
scale_ratio (float, optional): Scale ratio of bboxes, the default value
|
||||
scale (float, optional): Scale ratio of bboxes, the default value
|
||||
1.0 means no padding.
|
||||
pad_fill (number or list): Value to be filled for padding, None for
|
||||
no padding.
|
||||
|
|
@ -132,7 +132,7 @@ def imcrop(img, bboxes, scale_ratio=1.0, pad_fill=None):
|
|||
assert len(pad_fill) == chn
|
||||
|
||||
_bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes
|
||||
scaled_bboxes = bbox_scaling(_bboxes, scale_ratio).astype(np.int32)
|
||||
scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32)
|
||||
clipped_bbox = bbox_clip(scaled_bboxes, img.shape)
|
||||
|
||||
patches = []
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ from .hooks import (Hook, CheckpointHook, ClosureHook, LrUpdaterHook,
|
|||
from .checkpoint import (load_state_dict, load_checkpoint, weights_to_cpu,
|
||||
save_checkpoint)
|
||||
from .parallel import parallel_test, worker_func
|
||||
from .priority import Priority, get_priority
|
||||
from .utils import (get_host_info, get_dist_info, master_only, get_time_str,
|
||||
obj_from_dict)
|
||||
|
||||
|
|
@ -15,6 +16,7 @@ __all__ = [
|
|||
'LrUpdaterHook', 'OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook',
|
||||
'LoggerHook', 'TextLoggerHook', 'PaviLoggerHook', 'TensorboardLoggerHook',
|
||||
'load_state_dict', 'load_checkpoint', 'weights_to_cpu', 'save_checkpoint',
|
||||
'parallel_test', 'worker_func', 'get_host_info', 'get_dist_info',
|
||||
'master_only', 'get_time_str', 'obj_from_dict'
|
||||
'parallel_test', 'worker_func', 'Priority', 'get_priority',
|
||||
'get_host_info', 'get_dist_info', 'master_only', 'get_time_str',
|
||||
'obj_from_dict'
|
||||
]
|
||||
|
|
|
|||
|
|
@ -4,7 +4,14 @@ from ..hook import Hook
|
|||
|
||||
|
||||
class LoggerHook(Hook):
|
||||
"""Base class for logger hooks."""
|
||||
"""Base class for logger hooks.
|
||||
|
||||
Args:
|
||||
interval (int): Logging interval (every k iterations).
|
||||
ignore_last (bool): Ignore the log of last iterations in each epoch
|
||||
if less than `interval`.
|
||||
reset_flag (bool): Whether to clear the output buffer after logging.
|
||||
"""
|
||||
|
||||
__metaclass__ = ABCMeta
|
||||
|
||||
|
|
|
|||
|
|
@ -145,12 +145,11 @@ class PaviLoggerHook(LoggerHook):
|
|||
instance_id=None,
|
||||
config_file=None,
|
||||
interval=10,
|
||||
reset_meter=True,
|
||||
ignore_last=True):
|
||||
ignore_last=True,
|
||||
reset_flag=True):
|
||||
self.pavi = PaviClient(url, username, password, instance_id)
|
||||
self.config_file = config_file
|
||||
super(PaviLoggerHook, self).__init__(interval, reset_meter,
|
||||
ignore_last)
|
||||
super(PaviLoggerHook, self).__init__(interval, ignore_last, reset_flag)
|
||||
|
||||
def before_run(self, runner):
|
||||
super(PaviLoggerHook, self).before_run(runner)
|
||||
|
|
|
|||
|
|
@ -4,13 +4,10 @@ from ...utils import master_only
|
|||
|
||||
class TensorboardLoggerHook(LoggerHook):
|
||||
|
||||
def __init__(self,
|
||||
log_dir,
|
||||
interval=10,
|
||||
reset_meter=True,
|
||||
ignore_last=True):
|
||||
super(TensorboardLoggerHook, self).__init__(interval, reset_meter,
|
||||
ignore_last)
|
||||
def __init__(self, log_dir, interval=10, ignore_last=True,
|
||||
reset_flag=True):
|
||||
super(TensorboardLoggerHook, self).__init__(interval, ignore_last,
|
||||
reset_flag)
|
||||
self.log_dir = log_dir
|
||||
|
||||
@master_only
|
||||
|
|
|
|||
|
|
@ -0,0 +1,53 @@
|
|||
from enum import Enum
|
||||
|
||||
|
||||
class Priority(Enum):
|
||||
"""Hook priority levels.
|
||||
|
||||
+------------+------------+
|
||||
| Level | Value |
|
||||
+============+============+
|
||||
| HIGHEST | 0 |
|
||||
+------------+------------+
|
||||
| VERY_HIGH | 10 |
|
||||
+------------+------------+
|
||||
| HIGH | 30 |
|
||||
+------------+------------+
|
||||
| NORMAL | 50 |
|
||||
+------------+------------+
|
||||
| LOW | 70 |
|
||||
+------------+------------+
|
||||
| VERY_LOW | 90 |
|
||||
+------------+------------+
|
||||
| LOWEST | 100 |
|
||||
+------------+------------+
|
||||
"""
|
||||
|
||||
HIGHEST = 0
|
||||
VERY_HIGH = 10
|
||||
HIGH = 30
|
||||
NORMAL = 50
|
||||
LOW = 70
|
||||
VERY_LOW = 90
|
||||
LOWEST = 100
|
||||
|
||||
|
||||
def get_priority(priority):
|
||||
"""Get priority value.
|
||||
|
||||
Args:
|
||||
priority (int or str or :obj:`Priority`): Priority.
|
||||
|
||||
Returns:
|
||||
int: The priority value.
|
||||
"""
|
||||
if isinstance(priority, int):
|
||||
if priority < 0 or priority > 100:
|
||||
raise ValueError('priority must be between 0 and 100')
|
||||
return priority
|
||||
elif isinstance(priority, Priority):
|
||||
return priority.value
|
||||
elif isinstance(priority, str):
|
||||
return Priority[priority.upper()].value
|
||||
else:
|
||||
raise TypeError('priority must be an integer or Priority enum value')
|
||||
|
|
@ -10,11 +10,24 @@ from .log_buffer import LogBuffer
|
|||
from .hooks import (Hook, LrUpdaterHook, CheckpointHook, IterTimerHook,
|
||||
OptimizerHook, lr_updater)
|
||||
from .checkpoint import load_checkpoint, save_checkpoint
|
||||
from .priority import get_priority
|
||||
from .utils import get_dist_info, get_host_info, get_time_str, obj_from_dict
|
||||
|
||||
|
||||
class Runner(object):
|
||||
"""A training helper for PyTorch."""
|
||||
"""A training helper for PyTorch.
|
||||
|
||||
Args:
|
||||
model (:obj:`torch.nn.Module`): The model to be run.
|
||||
batch_processor (callable): A callable method that process a data
|
||||
batch. The interface of this method should be
|
||||
`batch_processor(model, data, train_mode) -> dict`
|
||||
optimizer (dict or :obj:`torch.optim.Optimizer`): If it is a dict,
|
||||
runner will construct an optimizer according to it.
|
||||
work_dir (str, optional): The working directory to save checkpoints
|
||||
and logs.
|
||||
log_level (int): Logging level.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
model,
|
||||
|
|
@ -154,8 +167,8 @@ class Runner(object):
|
|||
logging.basicConfig(
|
||||
format='%(asctime)s - %(levelname)s - %(message)s', level=level)
|
||||
logger = logging.getLogger(__name__)
|
||||
if log_dir:
|
||||
filename = '{}_{}.log'.format(get_time_str(), self.rank)
|
||||
if log_dir and self.rank == 0:
|
||||
filename = '{}.log'.format(get_time_str())
|
||||
log_file = osp.join(log_dir, filename)
|
||||
self._add_file_handler(logger, log_file, level=level)
|
||||
return logger
|
||||
|
|
@ -171,17 +184,18 @@ class Runner(object):
|
|||
'lr is not applicable because optimizer does not exist.')
|
||||
return [group['lr'] for group in self.optimizer.param_groups]
|
||||
|
||||
def register_hook(self, hook, priority=50):
|
||||
def register_hook(self, hook, priority='NORMAL'):
|
||||
"""Register a hook into the hook list.
|
||||
|
||||
Args:
|
||||
hook (:obj:`Hook`): The hook to be registered.
|
||||
priority (int): Hook priority. Lower value means higher priority.
|
||||
priority (int or str or :obj:`Priority`): Hook priority.
|
||||
Lower value means higher priority.
|
||||
"""
|
||||
assert isinstance(hook, Hook)
|
||||
assert isinstance(priority, int) and priority >= 0 and priority <= 100
|
||||
if hasattr(hook, 'priority'):
|
||||
raise ValueError('"priority" is a reserved attribute for hooks')
|
||||
priority = get_priority(priority)
|
||||
hook.priority = priority
|
||||
# insert the hook to a sorted list
|
||||
inserted = False
|
||||
|
|
@ -292,6 +306,17 @@ class Runner(object):
|
|||
self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter)
|
||||
|
||||
def run(self, data_loaders, workflow, max_epochs, **kwargs):
|
||||
"""Start running.
|
||||
|
||||
Args:
|
||||
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
|
||||
and validation.
|
||||
workflow (list[tuple]): A list of (phase, epochs) to specify the
|
||||
running order and epochs. E.g, [('train', 2), ('val', 1)] means
|
||||
running 2 epochs for training and 1 epoch for validation,
|
||||
iteratively.
|
||||
max_epochs (int): Total training epochs.
|
||||
"""
|
||||
assert isinstance(data_loaders, list)
|
||||
assert mmcv.is_list_of(workflow, tuple)
|
||||
assert len(data_loaders) == len(workflow)
|
||||
|
|
@ -346,7 +371,7 @@ class Runner(object):
|
|||
for info in log_config['hooks']:
|
||||
logger_hook = obj_from_dict(
|
||||
info, hooks, default_args=dict(interval=log_interval))
|
||||
self.register_hook(logger_hook, priority=60)
|
||||
self.register_hook(logger_hook, priority='VERY_LOW')
|
||||
|
||||
def register_training_hooks(self,
|
||||
lr_config,
|
||||
|
|
@ -356,11 +381,12 @@ class Runner(object):
|
|||
"""Register default hooks for training.
|
||||
|
||||
Default hooks include:
|
||||
|
||||
- LrUpdaterHook
|
||||
- OptimizerStepperHook
|
||||
- CheckpointSaverHook
|
||||
- IterTimerHook
|
||||
- LoggerHook
|
||||
- LoggerHook(s)
|
||||
"""
|
||||
if optimizer_config is None:
|
||||
optimizer_config = {}
|
||||
|
|
|
|||
Loading…
Reference in New Issue