publish

.github/CODEOWNERS

@@ -0,0 +1,4 @@
+# See: https://help.github.com/en/articles/about-code-owners
+#
+# Owners will be requested for review when someone opens a pull request.
+*       @jantic @alexandrevicenzi

.gitignore

@@ -0,0 +1,120 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# DeOldify
+data
+*SymbolicLinks.sh
+*.ipynb_checkpoints*
+ColorizeTraining*[0-9]*.ipynb
+*Colorizer[0-9]*.ipynb
+lesson7-superres*.ipynb
+test.py
+result_images
+*.prof
+*.pth
+video
+/test_images 
+deoldify/.ipynb_checkpoints/*-checkpoint.py
+tmp*

.pre-commit-config.yaml

@@ -0,0 +1,7 @@
+repos:
+-   repo: https://github.com/ambv/black
+    rev: stable
+    hooks:
+    - id: black
+      args: [-S]
+      language_version: python3.6

.pylintrc

@@ -0,0 +1,579 @@
+[MASTER]
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-whitelist=
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS
+
+# Add files or directories matching the regex patterns to the blacklist. The
+# regex matches against base names, not paths.
+ignore-patterns=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook='import sys; sys.path.append("./venv/lib/python3.7/site-packages")'
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Specify a configuration file.
+#rcfile=
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
+confidence=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=print-statement,
+        parameter-unpacking,
+        unpacking-in-except,
+        old-raise-syntax,
+        backtick,
+        long-suffix,
+        old-ne-operator,
+        old-octal-literal,
+        import-star-module-level,
+        non-ascii-bytes-literal,
+        raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        locally-enabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-symbolic-message-instead,
+        apply-builtin,
+        basestring-builtin,
+        buffer-builtin,
+        cmp-builtin,
+        coerce-builtin,
+        execfile-builtin,
+        file-builtin,
+        long-builtin,
+        raw_input-builtin,
+        reduce-builtin,
+        standarderror-builtin,
+        unicode-builtin,
+        xrange-builtin,
+        coerce-method,
+        delslice-method,
+        getslice-method,
+        setslice-method,
+        no-absolute-import,
+        old-division,
+        dict-iter-method,
+        dict-view-method,
+        next-method-called,
+        metaclass-assignment,
+        indexing-exception,
+        raising-string,
+        reload-builtin,
+        oct-method,
+        hex-method,
+        nonzero-method,
+        cmp-method,
+        input-builtin,
+        round-builtin,
+        intern-builtin,
+        unichr-builtin,
+        map-builtin-not-iterating,
+        zip-builtin-not-iterating,
+        range-builtin-not-iterating,
+        filter-builtin-not-iterating,
+        using-cmp-argument,
+        eq-without-hash,
+        div-method,
+        idiv-method,
+        rdiv-method,
+        exception-message-attribute,
+        invalid-str-codec,
+        sys-max-int,
+        bad-python3-import,
+        deprecated-string-function,
+        deprecated-str-translate-call,
+        deprecated-itertools-function,
+        deprecated-types-field,
+        next-method-defined,
+        dict-items-not-iterating,
+        dict-keys-not-iterating,
+        dict-values-not-iterating,
+        deprecated-operator-function,
+        deprecated-urllib-function,
+        xreadlines-attribute,
+        deprecated-sys-function,
+        exception-escape,
+        comprehension-escape,
+        # Disabled due Black
+        bad-continuation,
+        bad-whitespace,
+        # We don't care about these
+        redundant-keyword-arg,
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+
+
+[REPORTS]
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+#msg-template=
+
+# Set the output format. Available formats are text, parseable, colorized, json
+# and msvs (visual studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit
+
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[SIMILARITIES]
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Number of spaces of indent required inside a hanging  or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=100
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# List of optional constructs for which whitespace checking is disabled. `dict-
+# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
+# `trailing-comma` allows a space between comma and closing bracket: (a, ).
+# `empty-line` allows space-only lines.
+no-space-check=trailing-comma,
+               dict-separator
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style.
+#attr-rgx=
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style.
+#class-attribute-rgx=
+
+# Naming style matching correct class names.
+class-naming-style=PascalCase
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style.
+#function-rgx=
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names=f,
+           i,
+           j,
+           k,
+           s,
+           t,
+           ex,
+           Run,
+           _
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style.
+#method-rgx=
+
+# Naming style matching correct module names.
+module-naming-style=snake_case
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style.
+variable-rgx=_?[a-z][A-Za-z0-9_]{0,30}$
+argument-rgx=_?[a-z][A-Za-z0-9_]{0,30}$
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=torch.mm,
+                  torch.diag,
+                  torch.symeig,
+                  torch.sqrt,
+                  torch.cat,
+                  cv2.cvtColor,
+                  cv2.COLOR_BGR2YUV,
+                  cv2.COLOR_YUV2BGR,
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices=1
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. Available dictionaries: en_IE (myspell), en_ZM
+# (myspell), en_GB (myspell), en_HK (myspell), en_BZ (myspell), en_PH
+# (myspell), en_ZA (myspell), en_MW (myspell), en_AU (myspell), en_CA
+# (myspell), en_JM (myspell), en_GH (myspell), en_TT (myspell), en_SG
+# (myspell), en_BW (myspell), en_US (myspell), en_NZ (myspell), en_AG
+# (myspell), en_ZW (myspell), en_NA (myspell), en_IN (myspell), en_BS
+# (myspell), en_DK (myspell), en_NG (myspell)..
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[IMPORTS]
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=optparse,tkinter.tix
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled).
+ext-import-graph=
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled).
+import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=cls
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement.
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception".
+overgeneral-exceptions=Exception

.travis.yml

@@ -0,0 +1,10 @@
+sudo: false
+language: python
+install: pip install tox
+matrix:
+  include:
+  - python: "3.6"
+    env: TOX_ENV=static
+  - python: "3.6"
+    env: TOX_ENV=format
+script: tox -e $TOX_ENV

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.formatting.provider": "yapf"
+}

ColorFIDBenchmarkArtistic.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Color FID Benchmark (HQ)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
+    "os.environ['OMP_NUM_THREADS']='1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statistics\n",
+    "from fastai import *\n",
+    "from deoldify.visualize import *\n",
+    "import cv2\n",
+    "from fid.fid_score import *\n",
+    "from fid.inception import *\n",
+    "import imageio\n",
+    "plt.style.use('dark_background')\n",
+    "torch.backends.cudnn.benchmark=True\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"torch.nn.functional\")\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message='.*?retrieve source code for container of type.*?')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  Data should come from here:  'https://datasets.figure-eight.com/figure_eight_datasets/open-images/test_challenge.zip'\n",
+    "#NOTE:  Minimum recommmended number of samples is 10K.  Source:  https://github.com/bioinf-jku/TTUR\n",
+    "\n",
+    "path = Path('data/ColorBenchmark')\n",
+    "path_hr = path/'source'\n",
+    "path_lr = path/'bandw'\n",
+    "path_results = Path('./result_images/ColorBenchmarkFID/artistic')\n",
+    "path_rendered = path_results/'rendered'\n",
+    "\n",
+    "#path = Path('data/DeOldifyColor')\n",
+    "#path_hr = path\n",
+    "#path_lr = path/'bandw'\n",
+    "#path_results = Path('./result_images/ColorBenchmark/edge')\n",
+    "#path_rendered = path_results/'rendered'\n",
+    "\n",
+    "#num_images = 2048\n",
+    "#num_images = 15000\n",
+    "num_images = 50000\n",
+    "render_factor=35\n",
+    "fid_batch_size = 4\n",
+    "eval_size=299"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def inception_model(dims:int):\n",
+    "    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n",
+    "    model = InceptionV3([block_idx])\n",
+    "    model.cuda()\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_before_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def render_images(colorizer, source_dir:Path, filtered_dir:Path, target_dir:Path, render_factor:int, num_images:int)->[(Path, Path, Path)]:\n",
+    "    results = []\n",
+    "    bandw_list = ImageList.from_folder(path_lr)\n",
+    "    bandw_list = bandw_list[:num_images]\n",
+    "\n",
+    "    if len(bandw_list.items) == 0: return results\n",
+    "\n",
+    "    results = []\n",
+    "    img_iterator = progress_bar(bandw_list.items)\n",
+    "\n",
+    "    for bandw_path in img_iterator:\n",
+    "        target_path = target_dir/bandw_path.relative_to(source_dir)\n",
+    "\n",
+    "        try:\n",
+    "            result_image = colorizer.get_transformed_image(path=bandw_path, render_factor=render_factor)\n",
+    "            result_path = Path(str(path_results) + '/' + bandw_path.parent.name + '/' + bandw_path.name)\n",
+    "            if not result_path.parent.exists():\n",
+    "                result_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "            result_image.save(result_path)\n",
+    "            results.append((result_path, bandw_path, target_path))\n",
+    "        except Exception as err:\n",
+    "            print('Failed to render image.  Skipping.  Details: {0}'.format(err))\n",
+    "    \n",
+    "    return results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_fid_score(render_results, bs:int, eval_size:int):\n",
+    "    dims = 2048\n",
+    "    cuda = True\n",
+    "    model = inception_model(dims=dims)\n",
+    "    rendered_paths = []\n",
+    "    target_paths = []\n",
+    "    \n",
+    "    for render_result in render_results:\n",
+    "        rendered_path, _, target_path = render_result\n",
+    "        rendered_paths.append(str(rendered_path))\n",
+    "        target_paths.append(str(target_path))\n",
+    "        \n",
+    "    rendered_m, rendered_s = calculate_activation_statistics(files=rendered_paths, model=model, batch_size=bs, dims=dims, cuda=cuda)\n",
+    "    target_m, target_s = calculate_activation_statistics(files=target_paths, model=model, batch_size=bs, dims=dims, cuda=cuda)\n",
+    "    fid_score = calculate_frechet_distance(rendered_m, rendered_s, target_m, target_s)\n",
+    "    del model\n",
+    "    return fid_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and whites source images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_before_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_results.parent.mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rendering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colorizer = get_image_colorizer(artistic=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "render_results = render_images(colorizer=colorizer, source_dir=path_lr, target_dir=path_hr, filtered_dir=path_results, render_factor=render_factor, num_images=num_images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Colorizaton Scoring"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fid_score = calculate_fid_score(render_results, bs=fid_batch_size, eval_size=eval_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('FID Score: ' + str(fid_score))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ColorizeTrainingArtistic.ipynb

@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Artistic Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTES:  \n",
+    "* This is \"NoGAN\" based training, described in the DeOldify readme.\n",
+    "* This model prioritizes colorful renderings.  It has higher variation in renderings at different resolutions compared to the \"stable\" model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import fastai\n",
+    "from fastai import *\n",
+    "from fastai.vision import *\n",
+    "from fastai.callbacks.tensorboard import *\n",
+    "from fastai.vision.gan import *\n",
+    "from deoldify.generators import *\n",
+    "from deoldify.critics import *\n",
+    "from deoldify.dataset import *\n",
+    "from deoldify.loss import *\n",
+    "from deoldify.save import *\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "from PIL import ImageFile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
+    "path_hr = path\n",
+    "path_lr = path/'bandw'\n",
+    "\n",
+    "proj_id = 'ArtisticModel'\n",
+    "\n",
+    "gen_name = proj_id + '_gen'\n",
+    "pre_gen_name = gen_name + '_0'\n",
+    "crit_name = proj_id + '_crit'\n",
+    "\n",
+    "name_gen = proj_id + '_image_gen'\n",
+    "path_gen = path/name_gen\n",
+    "\n",
+    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
+    "\n",
+    "nf_factor = 1.5\n",
+    "pct_start = 1e-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(bs:int, sz:int, keep_pct:float):\n",
+    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
+    "                             random_seed=None, keep_pct=keep_pct)\n",
+    "\n",
+    "def get_crit_data(classes, bs, sz):\n",
+    "    src = ImageList.from_folder(path, include=classes, recurse=True).split_by_rand_pct(0.1, seed=42)\n",
+    "    ll = src.label_from_folder(classes=classes)\n",
+    "    data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
+    "           .databunch(bs=bs).normalize(imagenet_stats))\n",
+    "    return data\n",
+    "\n",
+    "def create_training_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  \n",
+    "    \n",
+    "def save_preds(dl):\n",
+    "    i=0\n",
+    "    names = dl.dataset.items\n",
+    "    \n",
+    "    for b in dl:\n",
+    "        preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
+    "        for o in preds:\n",
+    "            o.save(path_gen/names[i].name)\n",
+    "            i += 1\n",
+    "    \n",
+    "def save_gen_images():\n",
+    "    if path_gen.exists(): shutil.rmtree(path_gen)\n",
+    "    path_gen.mkdir(exist_ok=True)\n",
+    "    data_gen = get_data(bs=bs, sz=sz, keep_pct=0.085)\n",
+    "    save_preds(data_gen.fix_dl)\n",
+    "    PIL.Image.open(path_gen.ls()[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and white training images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_training_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-train generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Most of the training takes place here in pretraining for NoGAN.  The goal here is to take the generator as far as possible with conventional training, as that is much easier to control and obtain glitch-free results compared to GAN training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 64px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=88\n",
+    "sz=64\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.callback_fns.append(partial(ImageGenTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GenPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=0.8, max_lr=slice(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start,  max_lr=slice(3e-7, 3e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 128px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=22\n",
+    "sz=128\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(1e-7,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 192px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=11\n",
+    "sz=192\n",
+    "keep_pct=0.50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeatable GAN Cycle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Best results so far have been based on repeating the cycle below a few times (about 5-8?), until diminishing returns are hit (no improvement in image quality).  Each time you repeat the cycle, you want to increment that old_checkpoint_num by 1 so that new check points don't overwrite the old.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_checkpoint_num = 0\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Generated Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_gen_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain Critic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Only need full pretraining of critic when starting from scratch.  Otherwise, just finetune!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if old_checkpoint_num == 0:\n",
+    "    bs=64\n",
+    "    sz=128\n",
+    "    learn_gen=None\n",
+    "    gc.collect()\n",
+    "    data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)\n",
+    "    data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)\n",
+    "    learn_critic = colorize_crit_learner(data=data_crit, nf=256)\n",
+    "    learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))\n",
+    "    learn_critic.fit_one_cycle(6, 1e-3)\n",
+    "    learn_critic.save(crit_old_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=16\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.fit_one_cycle(4, 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.save(crit_new_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit=None\n",
+    "learn_gen=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr=1e-5\n",
+    "sz=192\n",
+    "bs=9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_deep(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
+    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,2.0), show_img=False, switcher=switcher,\n",
+    "                                 opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\n",
+    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
+    "learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100))\n",
+    "learn.callback_fns.append(partial(GANSaveCallback, learn_gen=learn_gen, filename=gen_new_checkpoint_name, save_iters=100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Instructions:  \n",
+    "Find the checkpoint just before where glitches start to be introduced.  This is all very new so you may need to play around with just how far you go here with keep_pct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.03)\n",
+    "learn_gen.freeze_to(-1)\n",
+    "learn.fit(1,lr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ColorizeTrainingStable.ipynb

@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stable Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTES:  \n",
+    "* This is \"NoGAN\" based training, described in the DeOldify readme.\n",
+    "* This model prioritizes stable and reliable renderings.  It does particularly well on portraits and landscapes.  It's not as colorful as the artistic model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import fastai\n",
+    "from fastai import *\n",
+    "from fastai.vision import *\n",
+    "from fastai.callbacks.tensorboard import *\n",
+    "from fastai.vision.gan import *\n",
+    "from deoldify.generators import *\n",
+    "from deoldify.critics import *\n",
+    "from deoldify.dataset import *\n",
+    "from deoldify.loss import *\n",
+    "from deoldify.save import *\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "from PIL import ImageFile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
+    "path_hr = path\n",
+    "path_lr = path/'bandw'\n",
+    "\n",
+    "proj_id = 'StableModel'\n",
+    "\n",
+    "gen_name = proj_id + '_gen'\n",
+    "pre_gen_name = gen_name + '_0'\n",
+    "crit_name = proj_id + '_crit'\n",
+    "\n",
+    "name_gen = proj_id + '_image_gen'\n",
+    "path_gen = path/name_gen\n",
+    "\n",
+    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
+    "\n",
+    "nf_factor = 2\n",
+    "pct_start = 1e-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(bs:int, sz:int, keep_pct:float):\n",
+    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
+    "                             random_seed=None, keep_pct=keep_pct)\n",
+    "\n",
+    "def get_crit_data(classes, bs, sz):\n",
+    "    src = ImageList.from_folder(path, include=classes, recurse=True).split_by_rand_pct(0.1, seed=42)\n",
+    "    ll = src.label_from_folder(classes=classes)\n",
+    "    data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
+    "           .databunch(bs=bs).normalize(imagenet_stats))\n",
+    "    return data\n",
+    "\n",
+    "def create_training_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  \n",
+    "    \n",
+    "def save_preds(dl):\n",
+    "    i=0\n",
+    "    names = dl.dataset.items\n",
+    "    \n",
+    "    for b in dl:\n",
+    "        preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
+    "        for o in preds:\n",
+    "            o.save(path_gen/names[i].name)\n",
+    "            i += 1\n",
+    "    \n",
+    "def save_gen_images():\n",
+    "    if path_gen.exists(): shutil.rmtree(path_gen)\n",
+    "    path_gen.mkdir(exist_ok=True)\n",
+    "    data_gen = get_data(bs=bs, sz=sz, keep_pct=0.085)\n",
+    "    save_preds(data_gen.fix_dl)\n",
+    "    PIL.Image.open(path_gen.ls()[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and white training images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_training_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-train generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Most of the training takes place here in pretraining for NoGAN.  The goal here is to take the generator as far as possible with conventional training, as that is much easier to control and obtain glitch-free results compared to GAN training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 64px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=88\n",
+    "sz=64\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.callback_fns.append(partial(ImageGenTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GenPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=0.8, max_lr=slice(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start,  max_lr=slice(3e-7, 3e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 128px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=20\n",
+    "sz=128\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(1e-7,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 192px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192\n",
+    "keep_pct=0.50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeatable GAN Cycle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Best results so far have been based on repeating the cycle below a few times (about 5-8?), until diminishing returns are hit (no improvement in image quality).  Each time you repeat the cycle, you want to increment that old_checkpoint_num by 1 so that new check points don't overwrite the old.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_checkpoint_num = 0\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Generated Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_gen_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain Critic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Only need full pretraining of critic when starting from scratch.  Otherwise, just finetune!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if old_checkpoint_num == 0:\n",
+    "    bs=64\n",
+    "    sz=128\n",
+    "    learn_gen=None\n",
+    "    gc.collect()\n",
+    "    data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)\n",
+    "    data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)\n",
+    "    learn_critic = colorize_crit_learner(data=data_crit, nf=256)\n",
+    "    learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))\n",
+    "    learn_critic.fit_one_cycle(6, 1e-3)\n",
+    "    learn_critic.save(crit_old_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=16\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.fit_one_cycle(4, 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.save(crit_new_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit=None\n",
+    "learn_gen=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr=2e-5\n",
+    "sz=192\n",
+    "bs=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
+    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,1.5), show_img=False, switcher=switcher,\n",
+    "                                 opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\n",
+    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
+    "learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100))\n",
+    "learn.callback_fns.append(partial(GANSaveCallback, learn_gen=learn_gen, filename=gen_new_checkpoint_name, save_iters=100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Instructions:  \n",
+    "Find the checkpoint just before where glitches start to be introduced.  This is all very new so you may need to play around with just how far you go here with keep_pct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.03)\n",
+    "learn_gen.freeze_to(-1)\n",
+    "learn.fit(1,lr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ColorizeTrainingStableLargeBatch.ipynb

@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stable Model Training (Large Batch/Limited GPU Memory Support)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IMPORTANT: Training has -not- been verified by myself for this notebook ~jantic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTES:  \n",
+    "* This is \"NoGAN\" based training, described in the DeOldify readme.\n",
+    "* This model prioritizes stable and reliable renderings.  It does particularly well on portraits and landscapes.  It's not as colorful as the artistic model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICES']='0' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fastai\n",
+    "from fastai import *\n",
+    "from fastai.vision import *\n",
+    "from fastai.callbacks.tensorboard import *\n",
+    "from fastai.vision.gan import *\n",
+    "from deoldify.generators import *\n",
+    "from deoldify.critics import *\n",
+    "from deoldify.dataset import *\n",
+    "from deoldify.loss import *\n",
+    "from deoldify.save import *\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "from PIL import ImageFile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Activate Large Model Support for PyTorch\n",
+    "This will allow us to fit the model within a GPU with smaller memory capacity (e.g. GTX 1070 8Gb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Large Model Support (LMS) is a feature provided in IBM Watson Machine Learning Community Edition (WML-CE) PyTorch V1.1.0 that allows the successful training of deep learning models that would otherwise exhaust GPU memory and abort with “out-of-memory” errors. LMS manages this oversubscription of GPU memory by temporarily swapping tensors to host memory when they are not needed. One or more elements of a deep learning model can lead to GPU memory exhaustion.\n",
+    "\n",
+    "Requires the use of IBM WML-CE (Available here: https://www.ibm.com/support/knowledgecenter/en/SS5SF7_1.6.1/welcome/welcome.html)\n",
+    "\n",
+    "Further Reading on PyTorch with Large Model Support: https://www.ibm.com/support/knowledgecenter/en/SS5SF7_1.6.1/navigation/wmlce_getstarted_pytorch.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set limit of GPU used before swapping to tensors to host memory\n",
+    "max_gpu_mem = 7\n",
+    "\n",
+    "def gb_to_bytes(gb):\n",
+    "    return gb*1024*1024*1024\n",
+    "\n",
+    "# Enable PyTorch LMS\n",
+    "torch.cuda.set.enabled_lms(True)\n",
+    "# Set LMS limit\n",
+    "torch.cuda.set_limit_lms(gb_to_bytes(max_gpu_mem))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check LMS is enabled\n",
+    "torch.cuda.get_enabled_lms()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check LMS Limit has been set\n",
+    "torch.cuda.get_limit_lms()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to Training Data\n",
+    "path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
+    "path_hr = path\n",
+    "\n",
+    "# Path to Black and White images\n",
+    "path_bandw = Path('/training/DeOldify')\n",
+    "path_lr = path_bandw/'bandw'\n",
+    "\n",
+    "# Name of Model\n",
+    "proj_id = 'StableModel'\n",
+    "\n",
+    "# Name of Generator\n",
+    "gen_name = proj_id + '_gen'\n",
+    "pre_gen_name = gen_name + '_0'\n",
+    "\n",
+    "# Name of Critic\n",
+    "crit_name = proj_id + '_crit'\n",
+    "\n",
+    "# Name of Generated Images folder, located within the Black and White folder\n",
+    "name_gen = proj_id + '_image_gen'\n",
+    "path_gen = path/name_gen\n",
+    "\n",
+    "# Path to tensorboard data\n",
+    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
+    "\n",
+    "nf_factor = 2\n",
+    "pct_start = 1e-8\n",
+    "\n",
+    "# Number of workers for DataLoader\n",
+    "num_works = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(bs:int, sz:int, keep_pct:float):\n",
+    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
+    "                             random_seed=None, keep_pct=keep_pct, num_workers=num_works)\n",
+    "\n",
+    "def get_crit_data(classes, bs, sz):\n",
+    "    src = ImageList.from_folder(path, include=classes, recurse=True).split_by_rand_pct(0.1, seed=42)\n",
+    "    ll = src.label_from_folder(classes=classes)\n",
+    "    data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
+    "           .databunch(bs=bs).normalize(imagenet_stats))\n",
+    "    return data\n",
+    "\n",
+    "def create_training_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  \n",
+    "    \n",
+    "def save_preds(dl):\n",
+    "    i=0\n",
+    "    names = dl.dataset.items\n",
+    "    \n",
+    "    for b in dl:\n",
+    "        preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
+    "        for o in preds:\n",
+    "            o.save(path_gen/names[i].name)\n",
+    "            i += 1\n",
+    "    \n",
+    "def save_gen_images():\n",
+    "    if path_gen.exists(): shutil.rmtree(path_gen)\n",
+    "    path_gen.mkdir(exist_ok=True)\n",
+    "    data_gen = get_data(bs=bs, sz=sz, keep_pct=0.085)\n",
+    "    save_preds(data_gen.fix_dl)\n",
+    "    PIL.Image.open(path_gen.ls()[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and white training images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_training_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-train generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Most of the training takes place here in pretraining for NoGAN.  The goal here is to take the generator as far as possible with conventional training, as that is much easier to control and obtain glitch-free results compared to GAN training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 64px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=88 # This can be increased if using PyTorch LMS, training could be slower.\n",
+    "sz=64\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.callback_fns.append(partial(ImageGenTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GenPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=0.8, max_lr=slice(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start,  max_lr=slice(3e-7, 3e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 128px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=40 # This can be increased if using PyTorch LMS, training could be slower.\n",
+    "sz=128\n",
+    "keep_pct=1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(1e-7,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 192px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=16 # This can be increased if using PyTorch LMS, training could be slower.\n",
+    "sz=192\n",
+    "keep_pct=0.50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 256px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8 # This can be increased if using PyTorch LMS, training could be slower.\n",
+    "sz=256\n",
+    "keep_pct=0.50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(sz=sz, bs=bs, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeatable GAN Cycle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Best results so far have been based on repeating the cycle below a few times (about 5-8?), until diminishing returns are hit (no improvement in image quality).  Each time you repeat the cycle, you want to increment that old_checkpoint_num by 1 so that new check points don't overwrite the old.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_checkpoint_num = 0\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Generated Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_gen_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain Critic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Only need full pretraining of critic when starting from scratch.  Otherwise, just finetune!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if old_checkpoint_num == 0:\n",
+    "    bs=64\n",
+    "    sz=128\n",
+    "    learn_gen=None\n",
+    "    gc.collect()\n",
+    "    data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)\n",
+    "    data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)\n",
+    "    learn_critic = colorize_crit_learner(data=data_crit, nf=256)\n",
+    "    learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))\n",
+    "    learn_critic.fit_one_cycle(6, 1e-3)\n",
+    "    learn_critic.save(crit_old_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.fit_one_cycle(4, 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.save(crit_new_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit=None\n",
+    "learn_gen=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr=2e-5\n",
+    "sz=256\n",
+    "bs=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
+    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,1.5), show_img=False, switcher=switcher,\n",
+    "                                 opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\n",
+    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
+    "learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100))\n",
+    "learn.callback_fns.append(partial(GANSaveCallback, learn_gen=learn_gen, filename=gen_new_checkpoint_name, save_iters=100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Instructions:  \n",
+    "Find the checkpoint just before where glitches start to be introduced.  This is all very new so you may need to play around with just how far you go here with keep_pct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.03)\n",
+    "learn_gen.freeze_to(-1)\n",
+    "learn.fit(1,lr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ColorizeTrainingVideo.ipynb

@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Video Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTES:  \n",
+    "* It's assumed that there's a pretrained generator from the ColorizeTrainingStable notebook available at the specified path.\n",
+    "* This is \"NoGAN\" based training, described in the DeOldify readme."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import fastai\n",
+    "from fastai import *\n",
+    "from fastai.vision import *\n",
+    "from fastai.callbacks.tensorboard import *\n",
+    "from fastai.vision.gan import *\n",
+    "from deoldify.generators import *\n",
+    "from deoldify.critics import *\n",
+    "from deoldify.dataset import *\n",
+    "from deoldify.loss import *\n",
+    "from deoldify.save import *\n",
+    "from deoldify.augs import noisify \n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "from PIL import ImageFile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
+    "path_hr = path\n",
+    "path_lr = path/'bandw'\n",
+    "\n",
+    "proj_id = 'VideoModel'\n",
+    "gen_name = proj_id + '_gen'\n",
+    "pre_gen_name = gen_name + '_0'\n",
+    "crit_name = proj_id + '_crit'\n",
+    "\n",
+    "name_gen = proj_id + '_image_gen'\n",
+    "path_gen = path/name_gen\n",
+    "\n",
+    "TENSORBOARD_PATH = Path('data/tensorboard/' + proj_id)\n",
+    "\n",
+    "nf_factor = 2\n",
+    "xtra_tfms=[noisify(p=0.8)]\n",
+    "pct_start = 1e-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(bs:int, sz:int, keep_pct:float):\n",
+    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, \n",
+    "                             random_seed=None, keep_pct=keep_pct, xtra_tfms=xtra_tfms)\n",
+    "\n",
+    "def get_crit_data(classes, bs, sz):\n",
+    "    src = ImageList.from_folder(path, include=classes, recurse=True).split_by_rand_pct(0.1, seed=42)\n",
+    "    ll = src.label_from_folder(classes=classes)\n",
+    "    data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
+    "           .databunch(bs=bs).normalize(imagenet_stats))\n",
+    "    return data\n",
+    "\n",
+    "def create_training_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  \n",
+    "    \n",
+    "def save_preds(dl):\n",
+    "    i=0\n",
+    "    names = dl.dataset.items\n",
+    "    \n",
+    "    for b in dl:\n",
+    "        preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
+    "        for o in preds:\n",
+    "            o.save(path_gen/names[i].name)\n",
+    "            i += 1\n",
+    "            \n",
+    "def save_gen_images():\n",
+    "    if path_gen.exists(): shutil.rmtree(path_gen)\n",
+    "    path_gen.mkdir(exist_ok=True)\n",
+    "    data_gen = get_data(bs=bs, sz=sz, keep_pct=0.085)\n",
+    "    save_preds(data_gen.fix_dl)\n",
+    "    PIL.Image.open(path_gen.ls()[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and white training images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_training_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finetune Generator With Noise Augmented Images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### This helps the generator better deal with noisy/grainy video (which is pretty normal)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192\n",
+    "keep_pct=0.25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.callback_fns.append(partial(ImageGenTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GenPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = learn_gen.load(pre_gen_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(1, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeatable GAN Cycle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Best results so far have been based only doing a single run of the cells below (otherwise glitches are introduced that are visible in video).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_checkpoint_num = 0\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Generated Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_gen_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain Critic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=16\n",
+    "sz=192"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.callback_fns.append(partial(LearnerTensorboardWriter, base_dir=TENSORBOARD_PATH, name='CriticPre'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.fit_one_cycle(4, 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_critic.save(crit_new_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit=None\n",
+    "learn_gen=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr=5e-6\n",
+    "sz=192\n",
+    "bs=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
+    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,1.5), show_img=False, switcher=switcher,\n",
+    "                                 opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\n",
+    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
+    "learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=TENSORBOARD_PATH, name='GanLearner', visual_iters=100, stats_iters=10, loss_iters=1))\n",
+    "learn.callback_fns.append(partial(GANSaveCallback, learn_gen=learn_gen, filename=gen_new_checkpoint_name, save_iters=100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Instructions:  \n",
+    "Find the checkpoint just before where glitches start to be introduced.  So far this has been found at the point of iterating through 1.4% of the data when using learning rate of 1e-5, and at 2.2% of the data for 5e-6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.data = get_data(sz=sz, bs=bs, keep_pct=0.03)\n",
+    "learn_gen.freeze_to(-1)\n",
+    "learn.fit(1,lr)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ColorizeTrainingWandb.ipynb

@@ -0,0 +1,870 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stable Model Training with monitoring through Weights & Biases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTES:  \n",
+    "* This is \"NoGAN\" based training, described in the DeOldify readme.\n",
+    "* This model prioritizes stable and reliable renderings.  It does particularly well on portraits and landscapes.  It's not as colorful as the artistic model.\n",
+    "* Training with this notebook has been logged and monitored through [Weights & Biases](https://www.wandb.com/). Refer to [W&B Report](https://app.wandb.ai/borisd13/DeOldify/reports?view=borisd13%2FDeOldify).\n",
+    "* It is **highly** recommended to use a 11 Go GPU to run this notebook. Anything lower will require to reduce the batch size (leading to moro instability) or use of \"Large Model Support\" from IBM WML-CE (not so easy to setup). An alternative is to rent ressources online."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install W&B Callback\n",
+    "#!pip install wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import fastai\n",
+    "from fastai import *\n",
+    "from fastai.vision import *\n",
+    "from fastai.vision.gan import *\n",
+    "from deoldify.generators import *\n",
+    "from deoldify.critics import *\n",
+    "from deoldify.dataset import *\n",
+    "from deoldify.loss import *\n",
+    "from deoldify.save import *\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "from PIL import ImageFile\n",
+    "from torch.utils.data.sampler import RandomSampler, SequentialSampler\n",
+    "from tqdm import tqdm\n",
+    "import wandb\n",
+    "from wandb.fastai import WandbCallback"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up W&B: checks user can connect to W&B servers\n",
+    "# Note: set up API key the first time\n",
+    "wandb.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dataset can be downloaded from https://www.kaggle.com/c/imagenet-object-localization-challenge/data\n",
+    "path = Path('data/imagenet/ILSVRC/Data/CLS-LOC')\n",
+    "path_hr = path\n",
+    "path_lr = path/'bandw'\n",
+    "\n",
+    "proj_id = 'StableModel'\n",
+    "\n",
+    "gen_name = proj_id + '_gen'\n",
+    "pre_gen_name = gen_name + '_0'\n",
+    "crit_name = proj_id + '_crit'\n",
+    "\n",
+    "name_gen = proj_id + '_image_gen'\n",
+    "path_gen = path/name_gen\n",
+    "\n",
+    "nf_factor = 2\n",
+    "pct_start = 1e-8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Iterating through the dataset\n",
+    "\n",
+    "The dataset is very large and it would take a long time to iterate through all the samples at each epoch.\n",
+    "\n",
+    "We use custom samplers in order to limit epochs to subsets of data while still iterating slowly through the entire dataset (epoch after epoch). This let us run the validation loop more often where we log metrics as well as prediction samples on validation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reduce quantity of samples per training epoch\n",
+    "# Adapted from https://forums.fast.ai/t/epochs-of-arbitrary-length/27777/10\n",
+    "\n",
+    "@classmethod\n",
+    "def create(cls, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, path:PathOrStr='.', bs:int=64,\n",
+    "            val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None,\n",
+    "            device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, sampler=None, **dl_kwargs)->'DataBunch':\n",
+    "    \"Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()`\"\n",
+    "    datasets = cls._init_ds(train_ds, valid_ds, test_ds)\n",
+    "    val_bs = ifnone(val_bs, bs)\n",
+    "    if sampler is None: sampler = [RandomSampler] + 3*[SequentialSampler]\n",
+    "    dls = [DataLoader(d, b, sampler=sa(d), drop_last=sh, num_workers=num_workers, **dl_kwargs) for d,b,sh,sa in\n",
+    "            zip(datasets, (bs,val_bs,val_bs,val_bs), (True,False,False,False), sampler) if d is not None]\n",
+    "    return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)\n",
+    "\n",
+    "ImageDataBunch.create = create\n",
+    "ImageImageList._bunch = ImageDataBunch\n",
+    "\n",
+    "class FixedLenRandomSampler(RandomSampler):\n",
+    "    def __init__(self, data_source, epoch_size):\n",
+    "        super().__init__(data_source)\n",
+    "        self.epoch_size = epoch_size\n",
+    "        self.not_sampled = np.array([True]*len(data_source))\n",
+    "    \n",
+    "    @property\n",
+    "    def reset_state(self): self.not_sampled[:] = True\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        ns = sum(self.not_sampled)\n",
+    "        idx_last = []\n",
+    "        if ns >= len(self):\n",
+    "            idx = np.random.choice(np.where(self.not_sampled)[0], size=len(self), replace=False).tolist()\n",
+    "            if ns == len(self): self.reset_state\n",
+    "        else:\n",
+    "            idx_last = np.where(self.not_sampled)[0].tolist()\n",
+    "            self.reset_state\n",
+    "            idx = np.random.choice(np.where(self.not_sampled)[0], size=len(self)-len(idx_last), replace=False).tolist()\n",
+    "        self.not_sampled[idx] = False\n",
+    "        idx = [*idx_last, *idx]\n",
+    "        return iter(idx)\n",
+    "    \n",
+    "    def __len__(self):\n",
+    "        return self.epoch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(bs:int, sz:int, keep_pct=1.0, random_seed=None, valid_pct=0.2, epoch_size=1000):\n",
+    "    \n",
+    "    # Create samplers\n",
+    "    train_sampler = partial(FixedLenRandomSampler, epoch_size=epoch_size)\n",
+    "    samplers = [train_sampler, SequentialSampler, SequentialSampler, SequentialSampler]\n",
+    "\n",
+    "    return get_colorize_data(sz=sz, bs=bs, crappy_path=path_lr, good_path=path_hr, random_seed=random_seed,\n",
+    "                             keep_pct=keep_pct, samplers=samplers, valid_pct=valid_pct)\n",
+    "\n",
+    "# Function modified to allow use of custom samplers\n",
+    "def get_colorize_data(sz:int, bs:int, crappy_path:Path, good_path:Path, random_seed:int=None,\n",
+    "        keep_pct:float=1.0, num_workers:int=8, samplers=None, valid_pct=0.2, xtra_tfms=[])->ImageDataBunch:\n",
+    "    src = (ImageImageList.from_folder(crappy_path, convert_mode='RGB')\n",
+    "        .use_partial_data(sample_pct=keep_pct, seed=random_seed)\n",
+    "        .split_by_rand_pct(valid_pct, seed=random_seed))\n",
+    "    data = (src.label_from_func(lambda x: good_path/x.relative_to(crappy_path))\n",
+    "        .transform(get_transforms(max_zoom=1.2, max_lighting=0.5, max_warp=0.25, xtra_tfms=xtra_tfms), size=sz, tfm_y=True)\n",
+    "        .databunch(bs=bs, num_workers=num_workers, sampler=samplers, no_check=True)\n",
+    "        .normalize(imagenet_stats, do_y=True))\n",
+    "    data.c = 3\n",
+    "    return data\n",
+    "\n",
+    "# Function to limit amount of data in critic\n",
+    "def filter_data(pct=1.0):\n",
+    "    def _f(fname):\n",
+    "        if 'test' in str(fname):\n",
+    "            if np.random.random_sample() > pct:\n",
+    "                return False\n",
+    "        return True\n",
+    "    return _f\n",
+    "\n",
+    "def get_crit_data(classes, bs, sz, pct=1.0):\n",
+    "    src = ImageList.from_folder(path, include=classes, recurse=True).filter_by_func(filter_data(pct)).split_by_rand_pct(0.1)\n",
+    "    ll = src.label_from_folder(classes=classes)\n",
+    "    data = (ll.transform(get_transforms(max_zoom=2.), size=sz)\n",
+    "           .databunch(bs=bs).normalize(imagenet_stats))\n",
+    "    return data\n",
+    "\n",
+    "def create_training_images(fn,i):\n",
+    "    dest = path_lr/fn.relative_to(path_hr)\n",
+    "    dest.parent.mkdir(parents=True, exist_ok=True)\n",
+    "    img = PIL.Image.open(fn).convert('LA').convert('RGB')\n",
+    "    img.save(dest)  \n",
+    "    \n",
+    "def save_preds(dl):\n",
+    "    i=0\n",
+    "    names = dl.dataset.items    \n",
+    "    for b in tqdm(dl):\n",
+    "        preds = learn_gen.pred_batch(batch=b, reconstruct=True)\n",
+    "        for o in preds:\n",
+    "            o.save(path_gen/names[i].name)\n",
+    "            i += 1\n",
+    "    \n",
+    "def save_gen_images(keep_pct):\n",
+    "    if path_gen.exists(): shutil.rmtree(path_gen)\n",
+    "    path_gen.mkdir(exist_ok=True)\n",
+    "    data_gen = get_data(bs=bs, sz=sz, keep_pct=keep_pct)\n",
+    "    save_preds(data_gen.fix_dl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create black and white training images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only runs if the directory isn't already created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not path_lr.exists():\n",
+    "    il = ImageList.from_folder(path_hr)\n",
+    "    parallel(create_training_images, il.items)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of black & white images\n",
+    "data_size = len(list(path_lr.rglob('*.*')))\n",
+    "print('Number of black & white images:', data_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-train generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Most of the training takes place here in pretraining for NoGAN.  The goal here is to take the generator as far as possible with conventional training, as that is much easier to control and obtain glitch-free results compared to GAN training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 64px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Init logging of a new run\n",
+    "wandb.init(tags=['Pre-train Gen'])  # tags are optional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=88\n",
+    "sz=64\n",
+    "\n",
+    "# Define target number of training/validation samples as well as number of epochs\n",
+    "epoch_train_size = 100 * bs\n",
+    "epoch_valid_size = 10 * bs\n",
+    "valid_pct = epoch_valid_size / data_size\n",
+    "number_epochs = (data_size - epoch_valid_size) // epoch_train_size\n",
+    "\n",
+    "# Log hyper parameters\n",
+    "wandb.config.update({\"Step 1 - batch size\": bs, \"Step 1 - image size\": sz,\n",
+    "                     \"Step 1 - epoch size\": epoch_train_size, \"Step 1 - number epochs\": number_epochs})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, random_seed=123, valid_pct=valid_pct, epoch_size=100*bs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.callback_fns.append(partial(WandbCallback,\n",
+    "                                      input_type='images'))  # log prediction samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(number_epochs, pct_start=0.8, max_lr=slice(1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(number_epochs, pct_start=pct_start,  max_lr=slice(3e-7, 3e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 128px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=20\n",
+    "sz=128\n",
+    "\n",
+    "# Define target number of training/validation samples as well as number of epochs\n",
+    "epoch_train_size = 100 * bs\n",
+    "epoch_valid_size = 10 * bs\n",
+    "valid_pct = epoch_valid_size / data_size\n",
+    "number_epochs = (data_size - epoch_valid_size) // epoch_train_size\n",
+    "\n",
+    "# Log hyper parameters\n",
+    "wandb.config.update({\"Step 2 - batch size\": bs, \"Step 2 - image size\": sz,\n",
+    "                     \"Step 2 - epoch size\": epoch_train_size, \"Step 2 - number epochs\": number_epochs})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(bs=bs, sz=sz, random_seed=123, valid_pct=valid_pct, epoch_size=100*bs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(number_epochs, pct_start=pct_start, max_lr=slice(1e-7,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 192px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192\n",
+    "\n",
+    "# Define target number of training/validation samples as well as number of epochs\n",
+    "epoch_train_size = 100 * bs\n",
+    "epoch_valid_size = 10 * bs\n",
+    "valid_pct = epoch_valid_size / data_size\n",
+    "number_epochs = (data_size - epoch_valid_size) // epoch_train_size // 2  # Training is long - we use half of data\n",
+    "\n",
+    "# Log hyper parameters\n",
+    "wandb.config.update({\"Step 3 - batch size\": bs, \"Step 3 - image size\": sz,\n",
+    "                     \"Step 3 - epoch size\": epoch_train_size, \"Step 3 - number epochs\": number_epochs})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.data = get_data(bs=bs, sz=sz, random_seed=123, valid_pct=valid_pct, epoch_size=100*bs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.unfreeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.fit_one_cycle(number_epochs, pct_start=pct_start, max_lr=slice(5e-8,5e-5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen.save(pre_gen_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# End logging of current session run\n",
+    "# Note: this is optional and would be automatically triggered when stopping the kernel\n",
+    "wandb.join()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeatable GAN Cycle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NOTE\n",
+    "Best results so far have been based on repeating the cycle below a few times (about 5-8?), until diminishing returns are hit (no improvement in image quality).  Each time you repeat the cycle, you want to increment that old_checkpoint_num by 1 so that new check points don't overwrite the old.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_checkpoint_num = 0\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Generated Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=8\n",
+    "sz=192\n",
+    "\n",
+    "# Define target number of training/validation samples as well as number of epochs\n",
+    "epoch_train_size = 100 * bs\n",
+    "epoch_valid_size = 10 * bs\n",
+    "valid_pct = epoch_valid_size / data_size\n",
+    "number_epochs = (data_size - epoch_valid_size) // epoch_train_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = get_data(bs=bs, sz=sz, random_seed=123, valid_pct=valid_pct, epoch_size=100*bs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_gen_images(0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain Critic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Only need full pretraining of critic when starting from scratch.  Otherwise, just finetune!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if old_checkpoint_num == 0:\n",
+    "    \n",
+    "    # Init logging of a new run\n",
+    "    wandb.init(tags=['Pre-train Crit'])  # tags are optional\n",
+    "    \n",
+    "    bs=64\n",
+    "    sz=128\n",
+    "    learn_gen=None\n",
+    "    \n",
+    "    # Log hyper parameters\n",
+    "    wandb.config.update({\"Step 1 - batch size\": bs, \"Step 1 - image size\": sz})\n",
+    "\n",
+    "    gc.collect()    \n",
+    "    data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)\n",
+    "    data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)\n",
+    "    learn_crit = colorize_crit_learner(data=data_crit, nf=256)\n",
+    "    learn_crit.callback_fns.append(partial(WandbCallback))  # log prediction samples\n",
+    "    learn_crit.fit_one_cycle(6, 1e-3)\n",
+    "    learn_crit.save(crit_old_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bs=16\n",
+    "sz=192\n",
+    "\n",
+    "# Log hyper parameters\n",
+    "wandb.config.update({\"Step 2 - batch size\": bs, \"Step 2 - image size\": sz})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit.fit_one_cycle(4, 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn_crit.save(crit_new_checkpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# free up memory\n",
+    "learn_crit=None\n",
+    "learn_gen=None\n",
+    "learn=None\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set old_checkpoint_num to last iteration\n",
+    "old_checkpoint_num = 0\n",
+    "save_checkpoints = False\n",
+    "batch_per_epoch = 200\n",
+    "\n",
+    "checkpoint_num = old_checkpoint_num + 1\n",
+    "gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)   \n",
+    "\n",
+    "if False:   # need only to do it once\n",
+    "        \n",
+    "    # Generate data\n",
+    "    print('Generating data…')\n",
+    "    bs=8\n",
+    "    sz=192\n",
+    "    epoch_train_size = batch_per_epoch * bs\n",
+    "    epoch_valid_size = batch_per_epoch * bs // 10\n",
+    "    valid_pct = epoch_valid_size / data_size\n",
+    "    data_gen = get_data(bs=bs, sz=sz, epoch_size=epoch_train_size, valid_pct=valid_pct)\n",
+    "    learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)\n",
+    "    save_gen_images(0.02)\n",
+    "\n",
+    "    # Pre-train critic\n",
+    "    print('Pre-training critic…')\n",
+    "    bs=16\n",
+    "    sz=192\n",
+    "\n",
+    "    len_test = len(list((path / 'test').rglob('*.*')))\n",
+    "    len_gen = len(list((path / name_gen).rglob('*.*')))\n",
+    "    keep_test_pct = len_gen / len_test * 2\n",
+    "\n",
+    "    data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz, pct=keep_test_pct)\n",
+    "    learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_old_checkpoint_name, with_opt=False)\n",
+    "    learn_crit.fit_one_cycle(1, 1e-4)\n",
+    "    learn_crit.save(crit_new_checkpoint_name)\n",
+    "\n",
+    "# Creating GAN\n",
+    "print('Creating GAN…')\n",
+    "sz=192\n",
+    "bs=8\n",
+    "lr_GAN=2e-5\n",
+    "epoch_train_size = batch_per_epoch * bs\n",
+    "epoch_valid_size = batch_per_epoch * bs // 10\n",
+    "valid_pct = epoch_valid_size / data_size\n",
+    "len_test = len(list((path / 'test').rglob('*.*')))\n",
+    "len_gen = len(list((path / name_gen).rglob('*.*')))\n",
+    "keep_test_pct = len_gen / len_test * 2\n",
+    "\n",
+    "data_crit = get_crit_data([name_gen, 'test'], bs=bs, sz=sz, pct=keep_test_pct)\n",
+    "learn_crit = colorize_crit_learner(data=data_crit, nf=256).load(crit_new_checkpoint_name, with_opt=False)\n",
+    "data_gen = get_data(bs=bs, sz=sz, epoch_size=epoch_train_size, valid_pct=valid_pct)\n",
+    "learn_gen = gen_learner_wide(data=data_gen, gen_loss=FeatureLoss(), nf_factor=nf_factor).load(gen_old_checkpoint_name, with_opt=False)\n",
+    "switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)\n",
+    "learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.0,1.5), show_img=False, switcher=switcher,\n",
+    "                                 opt_func=partial(optim.Adam, betas=(0.,0.9)), wd=1e-3)\n",
+    "learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))\n",
+    "learn.callback_fns.append(partial(WandbCallback, input_type='images', seed=None, save_model=False))\n",
+    "learn.data = get_data(bs=bs, sz=sz, epoch_size=epoch_train_size, valid_pct=valid_pct)\n",
+    "\n",
+    "# Start logging to W&B\n",
+    "wandb.init(tags=['GAN'])\n",
+    "wandb.config.update({\"learning rate\": lr_GAN})  \n",
+    "\n",
+    "# Run the loop until satisfied with the results\n",
+    "while True:\n",
+    "\n",
+    "    # Current loop\n",
+    "    checkpoint_num = old_checkpoint_num + 1\n",
+    "    gen_old_checkpoint_name = gen_name + '_' + str(old_checkpoint_num)\n",
+    "    gen_new_checkpoint_name = gen_name + '_' + str(checkpoint_num)\n",
+    "    crit_old_checkpoint_name = crit_name + '_' + str(old_checkpoint_num)\n",
+    "    crit_new_checkpoint_name= crit_name + '_' + str(checkpoint_num)      \n",
+    "    \n",
+    "    \n",
+    "    # GAN for 10 epochs between each checkpoint\n",
+    "    try:\n",
+    "        learn.fit(1, lr_GAN)\n",
+    "    except:\n",
+    "        # Sometimes we get an error for some unknown reason during callbacks\n",
+    "        learn.callback_fns[-1](learn).on_epoch_end(old_checkpoint_num, None, [])\n",
+    "        \n",
+    "    if save_checkpoints:\n",
+    "        learn_crit.save(crit_new_checkpoint_name)\n",
+    "        learn_gen.save(gen_new_checkpoint_name)\n",
+    "    \n",
+    "    old_checkpoint_num += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# End logging of current session run\n",
+    "# Note: this is optional and would be automatically triggered when stopping the kernel\n",
+    "wandb.join()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ImageColorizerArtisticTests.ipynb

@@ -0,0 +1,3319 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deoldify.visualize import *\n",
+    "plt.style.use('dark_background')\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*?Your .*? set is empty.*?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Adjust render_factor (int) if image doesn't look quite right (max 64 on 11GB GPU).  The default here works for most photos.  \n",
+    "#It literally just is a number multiplied by 16 to get the square render resolution.  \n",
+    "#Note that this doesn't affect the resolution of the final output- the output is the same resolution as the input.\n",
+    "#Example:  render_factor=21 => color is rendered at 16x21 = 336x336 px.  \n",
+    "render_factor=35"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis = get_image_colorizer(render_factor=render_factor, artistic=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/poolparty.jpg\", render_factor=38, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1852GatekeepersWindsor.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Chief.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1850SchoolForGirls.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AtlanticCityBeach1905.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CottonMillWorkers1913.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BrooklynNavyYardHospital.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FinnishPeasant1867.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AtlanticCity1905.png\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PushingCart.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Drive1905.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IronLung.png\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FamilyWithDog.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DayAtSeaBelgium.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marilyn_woods.jpg\", render_factor=29, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/OldWomanSweden1904.jpg\", render_factor=36, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WomenTapingPlanes.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/overmiller.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BritishDispatchRider.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MuseauNacionalDosCoches.jpg\", render_factor=17, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/abe.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RossCorbettHouseCork.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HPLabelleOfficeMontreal.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/einstein_beach.jpg\", render_factor=29, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/airmen1943.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/20sWoman.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/egypt-1.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Rutherford_Hayes.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/einstein_portrait.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/pinkerton.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WaltWhitman.jpg\", render_factor=12, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dorothea-lange.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Hemmingway2.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/hemmingway.jpg\", render_factor=9, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/smoking_kid.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/teddy_rubble.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_2.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/camera_man.jpg\", render_factor=23, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/migrant_mother.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marktwain.jpg\", render_factor=10, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HelenKeller.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Evelyn_Nesbit.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Eddie-Adams.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/soldier_kids.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsYosemite.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/unnamed.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/workers_canyon.jpg\", render_factor=48, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CottonMill.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/JudyGarland.jpeg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/kids_pit.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/last_samurai.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsWhiteChurch.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
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+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marilyn_portrait.jpg\", render_factor=30, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/Unidentified1855.jpg\", render_factor=32, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/royal_family.jpg\", render_factor=33, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/redwood_lumberjacks.jpg\", render_factor=47, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/poverty.jpg\", render_factor=26, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/paperboy.jpg\", render_factor=40, compare=True)"
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+   "outputs": [],
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+    "vis.plot_transformed_image(\"test_images/NativeAmericans.jpg\", render_factor=22, compare=True)"
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+   "outputs": [],
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+    "vis.plot_transformed_image(\"test_images/helmut_newton-.jpg\", render_factor=43, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/Greece1911.jpg\", render_factor=26, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/FatMenClub.jpg\", render_factor=35, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/EgyptColosus.jpg\", render_factor=35, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/dustbowl_people.jpg\", render_factor=24, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/dustbowl_5.jpg\", render_factor=18, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/dustbowl_1.jpg\", render_factor=15, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/DriveThroughGiantTree.jpg\", render_factor=39, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/covered-wagons-traveling.jpg\", render_factor=18, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/civil-war_2.jpg\", render_factor=12, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/civil_war_4.jpg\", render_factor=15, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/civil_war_3.jpg\", render_factor=46, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/civil_war.jpg\", render_factor=45, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/BritishSlum.jpg\", render_factor=45, compare=True)"
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/bicycles.jpg\", render_factor=33, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/brooklyn_girls_1940s.jpg\", render_factor=35, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/40sCouple.jpg\", render_factor=20, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/1946Wedding.jpg\", render_factor=30, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/Dolores1920s.jpg\", render_factor=35, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/TitanicGym.jpg\", render_factor=31, compare=True)"
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/FrenchVillage1950s.jpg\", render_factor=38, compare=True)"
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+   "metadata": {},
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+   "source": [
+    "vis.plot_transformed_image(\"test_images/ClassDivide1930sBrittain.jpg\", render_factor=30, compare=True)"
+   ]
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+   "metadata": {},
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+   "source": [
+    "vis.plot_transformed_image(\"test_images/1870sSphinx.jpg\", render_factor=15, compare=True)"
+   ]
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/1890Surfer.png\", render_factor=30, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/TV1930s.jpg\", render_factor=30, compare=True)"
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+    "vis.plot_transformed_image(\"test_images/1864UnionSoldier.jpg\", render_factor=13, compare=True)"
+   ]
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+    "vis.plot_transformed_image(\"test_images/1890sMedStudents.jpg\", render_factor=23, compare=True)"
+   ]
+  },
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+   "metadata": {},
+   "outputs": [],
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+    "vis.plot_transformed_image(\"test_images/BellyLaughWWI.jpg\", render_factor=13, compare=True)"
+   ]
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/PiggyBackRide.jpg\", render_factor=20, compare=True)"
+   ]
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+    "vis.plot_transformed_image(\"test_images/HealingTree.jpg\", render_factor=13, compare=True)"
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/ManPile.jpg\", render_factor=30, compare=True)"
+   ]
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+   "metadata": {},
+   "outputs": [],
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+    "vis.plot_transformed_image(\"test_images/1910Bike.jpg\", render_factor=20, compare=True)"
+   ]
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+   "cell_type": "code",
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+   "metadata": {},
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+   "source": [
+    "vis.plot_transformed_image(\"test_images/FreeportIL.jpg\", render_factor=36, compare=True)"
+   ]
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+   "cell_type": "code",
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DutchBabyCoupleEllis.jpg\", render_factor=25, compare=True)"
+   ]
+  },
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/InuitWoman1903.png\", render_factor=33, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
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+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sDancing.jpg\", render_factor=16, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AirmanDad.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910Racket.png\", render_factor=34, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1880Paris.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Deadwood1860s.jpg\", render_factor=38, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1860sSamauris.jpg\", render_factor=34, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonUnderground1860.jpg\", render_factor=40, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
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+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Mid1800sSisters.jpg\", render_factor=22, compare=True)"
+   ]
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1860Girls.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SanFran1851.jpg\", render_factor=22, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Kabuki1870s.png\", render_factor=25, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Mormons1870s.jpg\", render_factor=47, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EgyptianWomenLate1800s.jpg\", render_factor=7, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PicadillyLate1800s.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SutroBaths1880s.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1880sBrooklynBridge.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ChinaOpiumc1880.jpg\", render_factor=43, compare=True)"
+   ]
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Locomotive1880s.jpg\", render_factor=10, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ViennaBoys1880s.png\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/VictorianDragQueen1880s.png\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Sami1880s.jpg\", render_factor=39, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Ballet1890Russia.jpg\", render_factor=32, compare=True)"
+   ]
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+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Rottindean1890s.png\", render_factor=22, compare=True)"
+   ]
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sPingPong.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1937.png\", render_factor=36, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Harlem1932.jpg\", render_factor=27, compare=True)"
+   ]
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/OregonTrail1870s.jpg\", render_factor=25, compare=True)"
+   ]
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+   "metadata": {},
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+    "vis.plot_transformed_image(\"test_images/1899NycBlizzard.jpg\", render_factor=20, compare=True)"
+   ]
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Edinburgh1920s.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
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+    "vis.plot_transformed_image(\"test_images/1890sShoeShopOhio.jpg\", render_factor=46, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sTouristsEgypt.png\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1938Reading.jpg\", render_factor=27, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1850Geography.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1901Electrophone.jpg\", render_factor=7, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Texas1938Woman.png\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MaioreWoman1895NZ.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WestVirginiaHouse.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sGuadalope.jpg\", render_factor=33, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1909Chicago.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sFarmKid.jpg\", render_factor=12, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLate1800s.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sDaytonaBeach.png\", render_factor=24, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930sGeorgia.jpg\", render_factor=17, compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NorwegianBride1920s.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Depression.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1888Slum.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LivingRoom1920Sweden.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1896NewsBoyGirl.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PetDucks1927.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1899SodaFountain.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TimesSquare1955.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PuppyGify.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890CliffHouseSF.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1908FamilyPhoto.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sSaloon.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890BostonHospital.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1870Girl.jpg\", render_factor=9, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AustriaHungaryWomen1890s.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Shack.jpg\",render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Apsaroke1908.png\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1948CarsGrandma.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PlanesManhattan1931.jpg\", render_factor=11, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WorriedKid1940sNyc.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sFamilyPhoto.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CatWash1931.jpg\", render_factor=34, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940sBeerRiver.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/VictorianLivingRoom.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1897BlindmansBluff.jpg\", render_factor=23, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1874Mexico.png\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MadisonSquare1900.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1867MusicianConstantinople.jpg\", render_factor=11, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1925Girl.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1907Cowboys.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWIIPeeps.jpg\", render_factor=26, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BabyBigBoots.jpg\", render_factor=17, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1895BikeMaidens.jpg\", render_factor=8, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IrishLate1800s.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LibraryOfCongress1910.jpg\", render_factor=33, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1875Olds.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SenecaNative1908.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWIHospital.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1892WaterLillies.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreekImmigrants1905.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FatMensShop.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/KidCage1930s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FarmWomen1895.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewZealand1860s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/JerseyShore1905.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonKidsEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NYStreetClean1906.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Boston1937.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Cork1905.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BoxedBedEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZoologischerGarten1898.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EmpireState1930.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Agamemnon1919.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AppalachianLoggers1901.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWISikhs.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MementoMori1865.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RepBrennanRadio1922.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Late1800sNative.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GasPrices1939.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1933RockefellerCenter.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Scotland1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920CobblersShopLondon.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1909ParisFirstFemaleTaxisDriver.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HoovervilleSeattle1932.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ElephantLondon1934.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Jane_Addams.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsAdobe.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CricketLondon1930.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Donegal1907Yarn.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsChurch.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BreadDelivery1920sIreland.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BritishTeaBombay1890s.png\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CafeParis1928.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BigManTavern1908NYC.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Cars1890sIreland.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GalwayIreland1902.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HomeIreland1924.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HydeParkLondon1920s.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1929LondonOverFleetSt.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AccordianKid1900Paris.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsBuildings.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AthleticClubParis1913.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BombedLibraryLondon1940.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Boston1937.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BoulevardDuTemple1838.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BumperCarsParis1930.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CafeTerrace1925Paris.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CoalDeliveryParis1915.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CorkKids1910.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DeepSeaDiver1915.png\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EastEndLondonStreetKids1901.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FreightTrainTeens1934.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HarrodsLondon1920.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HerbSeller1899Paris.jpg\", render_factor=17, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CalcuttaPoliceman1920.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ElectricScooter1915.jpeg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreatGrandparentsIrelandEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HalloweenEarly1900s.jpg\", render_factor=11, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IceManLondon1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LeBonMarcheParis1875.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LittleAirplane1934.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RoyalUniversityMedStudent1900Ireland.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LewisTomalinLondon1895.png\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SunHelmetsLondon1933.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Killarney1910.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonSheep1920s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PostOfficeVermont1914.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ServantsBessboroughHouse1908Ireland.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WaterfordIreland1909.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Lisbon1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1918WartimeClothesManufacture.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonHeatWave1935.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonsSmallestShop1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MetropolitanDistrictRailway1869London.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NativeWoman1926.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PaddysMarketCork1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PaddysMarketCork1900s.jpg\", render_factor=i, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1920Cart.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLadies1910.jpg\", render_factor=38, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLadies1930s.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Sphinx.jpeg\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TheatreGroupBombay1875.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WorldsFair1900Paris.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1850Coach.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1900EastEndBlacksmith.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1930sCheetah.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonFireBrigadeMember1926.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonGarbageTruck1910.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonRailwayWork1931.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonStreets1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MuffinManlLondon1910.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NativeCouple1912.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewspaperCivilWar1863.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PaddingtonStationLondon1907.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1899StreetDig.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1926.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisWomenFurs1920s.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PeddlerParis1899.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SchoolKidsConnemaraIreland1901.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SecondHandClothesLondonLate1800s.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SoapBoxRacerParis1920s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SoccerMotorcycles1923London.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WalkingLibraryLondon1930.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonStreetDoctor1877.png\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/jacksonville.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZebraCarriageLondon1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/StreetGramaphonePlayerLondon1920s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/YaleBranchBarnardsExpress.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SynagogueInterior.PNG\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ArmisticeDay1918.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FlyingMachinesParis1909.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreatAunt1920.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewBrunswick1915.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ShoeMakerLate1800s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SpottedBull1908.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TouristsGermany1904.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TunisianStudents1914.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Yorktown1862.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonFashion1911.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939GypsyKids.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936OpiumShanghai.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1923HollandTunnel.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939YakimaWAGirl.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GoldenGateConstruction.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PostCivilWarAncestors.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939SewingBike.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930MaineSchoolBus.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1913NewYorkConstruction.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1945HiroshimaChild.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1941GeorgiaFarmhouse.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1934UmbriaItaly.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sLadiesTeaParty.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1919WWIAviationOxygenMask.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900NJThanksgiving.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940Connecticut.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1911ThanksgivingMaskers.jpg\", render_factor=36, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910ThanksgivingMaskersII.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936PetToad.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1908RookeriesLondon.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sChineseImmigrants.jpg\", render_factor=36, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1897VancouverAmberlamps.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1929VictorianCosplayLondon.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1959ParisFriends.png\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1925GypsyCampMaryland.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1941PoolTableGeorgia.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900ParkDog.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1886Hoop.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1950sLondonPoliceChild.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1886ProspectPark.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930sRooftopPoland.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1919RevereBeach.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936ParisCafe.jpg\", render_factor=47, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1902FrenchYellowBellies.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940PAFamily.jpg\", render_factor=34, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910Finland.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZebraCarriageLondon1900.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1904ChineseMan.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CrystalPalaceLondon1854.PNG\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James1.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James2.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James3.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James4.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James5.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James6.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "navigate_num": "#000000",
+    "navigate_text": "#333333",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700",
+    "sidebar_border": "#EEEEEE",
+    "wrapper_background": "#FFFFFF"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "67px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ImageColorizerColab.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'> Artistic Colorizer </font>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "663IVxfrpIAb"
+   },
+   "source": [
+    "#◢ DeOldify - Colorize your own photos!\n",
+    "\n",
+    "####**Credits:**\n",
+    "\n",
+    "Special thanks to:\n",
+    "\n",
+    "Matt Robinson and María Benavente for pioneering the DeOldify image colab notebook.  \n",
+    "\n",
+    "Dana Kelley for doing things, breaking stuff & having an opinion on everything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZjPqTBNoohK9"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "#◢ Verify Correct Runtime Settings\n",
+    "\n",
+    "**<font color='#FF000'> IMPORTANT </font>**\n",
+    "\n",
+    "In the \"Runtime\" menu for the notebook window, select \"Change runtime type.\" Ensure that the following are selected:\n",
+    "* Runtime Type = Python 3\n",
+    "* Hardware Accelerator = GPU \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "gaEJBGDlptEo"
+   },
+   "source": [
+    "#◢ Git clone and install DeOldify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-T-svuHytJ-8"
+   },
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/jantic/DeOldify.git DeOldify "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd DeOldify"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BDFjbNxaadNK"
+   },
+   "source": [
+    "#◢ Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "00_GcC_trpdE"
+   },
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "if not torch.cuda.is_available():\n",
+    "    print('GPU not available.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Lsx7xCXNSVt6"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements-colab.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MsJa69CMwj3l"
+   },
+   "outputs": [],
+   "source": [
+    "import fastai\n",
+    "from deoldify.visualize import *\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*?Your .*? set is empty.*?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir 'models'\n",
+    "!wget https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth -O ./models/ColorizeArtistic_gen.pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tzHVnegp21hC"
+   },
+   "outputs": [],
+   "source": [
+    "colorizer = get_image_colorizer(artistic=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BDFjbNxaadNJ"
+   },
+   "source": [
+    "#◢ Instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### source_url\n",
+    "Type in a url to a direct link of an image.  Usually that means they'll end in .png, .jpg, etc. NOTE: If you want to use your own image, upload it first to a site like Imgur. \n",
+    "\n",
+    "### render_factor\n",
+    "The default value of 35 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which the color portion of the image is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality images in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality images, but the colors may get slightly washed out. \n",
+    "\n",
+    "### watermarked\n",
+    "Selected by default, this places a watermark icon of a palette at the bottom left corner of the image.  This is intended to be a standard way to convey to others viewing the image that it is colorized by AI. We want to help promote this as a standard, especially as the technology continues to improve and the distinction between real and fake becomes harder to discern. This palette watermark practice was initiated and lead by the company MyHeritage in the MyHeritage In Color feature (which uses a newer version of DeOldify than what you're using here).\n",
+    "\n",
+    "#### How to Download a Copy\n",
+    "Simply right click on the displayed image and click \"Save image as...\"!\n",
+    "\n",
+    "## Pro Tips\n",
+    "\n",
+    "You can evaluate how well the image is rendered at each render_factor by using the code at the bottom (that cell under \"See how well render_factor values perform on a frame here\"). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "sUQrbSYipiJn"
+   },
+   "source": [
+    "#◢ Colorize!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_url = '' #@param {type:\"string\"}\n",
+    "render_factor = 35  #@param {type: \"slider\", min: 7, max: 40}\n",
+    "watermarked = True #@param {type:\"boolean\"}\n",
+    "\n",
+    "if source_url is not None and source_url !='':\n",
+    "    image_path = colorizer.plot_transformed_image_from_url(url=source_url, render_factor=render_factor, compare=True, watermarked=watermarked)\n",
+    "    show_image_in_notebook(image_path)\n",
+    "else:\n",
+    "    print('Provide an image url and try again.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See how well render_factor values perform on the image here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10,40,2):\n",
+    "    colorizer.plot_transformed_image('test_images/image.png', render_factor=i, display_render_factor=True, figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "X7Ycv_Y9xAHp"
+   },
+   "source": [
+    "---\n",
+    "#⚙ Recommended image sources \n",
+    "* [/r/TheWayWeWere](https://www.reddit.com/r/TheWayWeWere/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "ImageColorizerColab.ipynb",
+   "provenance": [],
+   "toc_visible": true,
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ImageColorizerColabStable.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColabStable.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'> Stable Colorizer </font>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "663IVxfrpIAb"
+   },
+   "source": [
+    "#◢ DeOldify - Colorize your own photos!\n",
+    "\n",
+    "####**Credits:**\n",
+    "\n",
+    "Special thanks to:\n",
+    "\n",
+    "Matt Robinson and María Benavente for pioneering the DeOldify image colab notebook.  \n",
+    "\n",
+    "Dana Kelley for doing things, breaking stuff & having an opinion on everything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZjPqTBNoohK9"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "#◢ Verify Correct Runtime Settings\n",
+    "\n",
+    "**<font color='#FF000'> IMPORTANT </font>**\n",
+    "\n",
+    "In the \"Runtime\" menu for the notebook window, select \"Change runtime type.\" Ensure that the following are selected:\n",
+    "* Runtime Type = Python 3\n",
+    "* Hardware Accelerator = GPU \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "gaEJBGDlptEo"
+   },
+   "source": [
+    "#◢ Git clone and install DeOldify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-T-svuHytJ-8"
+   },
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/jantic/DeOldify.git DeOldify "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd DeOldify"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BDFjbNxaadNK"
+   },
+   "source": [
+    "#◢ Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "00_GcC_trpdE"
+   },
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "if not torch.cuda.is_available():\n",
+    "    print('GPU not available.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Lsx7xCXNSVt6"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements-colab.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MsJa69CMwj3l"
+   },
+   "outputs": [],
+   "source": [
+    "import fastai\n",
+    "from deoldify.visualize import *\n",
+    "\n",
+    "torch.backends.cudnn.benchmark = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir 'models'\n",
+    "!wget https://www.dropbox.com/s/usf7uifrctqw9rl/ColorizeStable_gen.pth?dl=0 -O ./models/ColorizeStable_gen.pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tzHVnegp21hC"
+   },
+   "outputs": [],
+   "source": [
+    "colorizer = get_image_colorizer(artistic=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BDFjbNxaadNJ"
+   },
+   "source": [
+    "#◢ Instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### source_url\n",
+    "Type in a url to a direct link of an image.  Usually that means they'll end in .png, .jpg, etc. NOTE: If you want to use your own image, upload it first to a site like Imgur. \n",
+    "\n",
+    "### render_factor\n",
+    "The default value of 35 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which the color portion of the image is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality images in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality images, but the colors may get slightly washed out. \n",
+    "\n",
+    "### watermarked\n",
+    "Selected by default, this places a watermark icon of a palette at the bottom left corner of the image.  This is intended to be a standard way to convey to others viewing the image that it is colorized by AI. We want to help promote this as a standard, especially as the technology continues to improve and the distinction between real and fake becomes harder to discern. This palette watermark practice was initiated and lead by the company MyHeritage in the MyHeritage In Color feature (which uses a newer version of DeOldify than what you're using here).\n",
+    "\n",
+    "#### How to Download a Copy\n",
+    "Simply right click on the displayed image and click \"Save image as...\"!\n",
+    "\n",
+    "## Pro Tips\n",
+    "\n",
+    "You can evaluate how well the image is rendered at each render_factor by using the code at the bottom (that cell under \"See how well render_factor values perform on a frame here\"). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "sUQrbSYipiJn"
+   },
+   "source": [
+    "#◢ Colorize!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_url = '' #@param {type:\"string\"}\n",
+    "render_factor = 35  #@param {type: \"slider\", min: 7, max: 40}\n",
+    "watermarked = True #@param {type:\"boolean\"}\n",
+    "\n",
+    "if source_url is not None and source_url !='':\n",
+    "    image_path = colorizer.plot_transformed_image_from_url(url=source_url, render_factor=render_factor, compare=True, watermarked=watermarked)\n",
+    "    show_image_in_notebook(image_path)\n",
+    "else:\n",
+    "    print('Provide an image url and try again.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See how well render_factor values perform on the image here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10,40,2):\n",
+    "    colorizer.plot_transformed_image('test_images/image.png', render_factor=i, display_render_factor=True, figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "X7Ycv_Y9xAHp"
+   },
+   "source": [
+    "---\n",
+    "#⚙ Recommended image sources \n",
+    "* [/r/TheWayWeWere](https://www.reddit.com/r/TheWayWeWere/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "ImageColorizerColabStable.ipynb",
+   "provenance": [],
+   "toc_visible": true,
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

ImageColorizerStableTests.ipynb

@@ -0,0 +1,3334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deoldify.visualize import *\n",
+    "plt.style.use('dark_background')\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*?Your .*? set is empty.*?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Adjust render_factor (int) if image doesn't look quite right (max 64 on 11GB GPU).  The default here works for most photos.  \n",
+    "#It literally just is a number multiplied by 16 to get the square render resolution.  \n",
+    "#Note that this doesn't affect the resolution of the final output- the output is the same resolution as the input.\n",
+    "#Example:  render_factor=21 => color is rendered at 16x21 = 336x336 px.  \n",
+    "render_factor=35"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis = get_image_colorizer(render_factor=render_factor, artistic=False)\n",
+    "#vis = get_video_colorizer(render_factor=render_factor).vis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/poolparty.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1852GatekeepersWindsor.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Chief.jpg\", render_factor=10, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1850SchoolForGirls.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AtlanticCityBeach1905.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CottonMillWorkers1913.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BrooklynNavyYardHospital.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FinnishPeasant1867.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AtlanticCity1905.png\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PushingCart.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Drive1905.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IronLung.png\", render_factor=26, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FamilyWithDog.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DayAtSeaBelgium.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marilyn_woods.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/OldWomanSweden1904.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WomenTapingPlanes.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/overmiller.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BritishDispatchRider.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MuseauNacionalDosCoches.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/abe.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RossCorbettHouseCork.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HPLabelleOfficeMontreal.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/einstein_beach.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/airmen1943.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/20sWoman.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/egypt-1.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Rutherford_Hayes.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/einstein_portrait.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/pinkerton.jpg\", render_factor=7, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WaltWhitman.jpg\", render_factor=9, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dorothea-lange.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Hemmingway2.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/hemmingway.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/smoking_kid.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/teddy_rubble.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_2.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/camera_man.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/migrant_mother.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marktwain.jpg\", render_factor=14, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HelenKeller.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Evelyn_Nesbit.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Eddie-Adams.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/soldier_kids.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsYosemite.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/unnamed.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/workers_canyon.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CottonMill.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/JudyGarland.jpeg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/kids_pit.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/last_samurai.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsWhiteChurch.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/opium.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dorothea_lange_2.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/rgs.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/wh-auden.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/w-b-yeats.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/marilyn_portrait.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/wilson-slaverevivalmeeting.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ww1_trench.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/women-bikers.png\", render_factor=23, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Unidentified1855.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/skycrapper_lunch.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/sioux.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/school_kids.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/royal_family.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/redwood_lumberjacks.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/poverty.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/paperboy.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NativeAmericans.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/helmut_newton-.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Greece1911.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FatMenClub.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EgyptColosus.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/egypt-2.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_sd.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_people.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_5.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/dustbowl_1.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DriveThroughGiantTree.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/covered-wagons-traveling.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/civil-war_2.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/civil_war_4.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/civil_war_3.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/civil_war.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BritishSlum.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/bicycles.jpg\", render_factor=27, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/brooklyn_girls_1940s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/40sCouple.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1946Wedding.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Dolores1920s.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TitanicGym.jpg\", render_factor=26, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FrenchVillage1950s.jpg\", render_factor=41, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FrenchVillage1950s.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ClassDivide1930sBrittain.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1870sSphinx.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890Surfer.png\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TV1930s.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1864UnionSoldier.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sMedStudents.jpg\", render_factor=18, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BellyLaughWWI.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PiggyBackRide.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HealingTree.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ManPile.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910Bike.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FreeportIL.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DutchBabyCoupleEllis.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/InuitWoman1903.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sDancing.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AirmanDad.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910Racket.png\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1880Paris.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Deadwood1860s.jpg\", render_factor=13, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1860sSamauris.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonUnderground1860.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Mid1800sSisters.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1860Girls.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SanFran1851.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Kabuki1870s.png\", render_factor=8, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Mormons1870s.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EgyptianWomenLate1800s.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PicadillyLate1800s.jpg\", render_factor=26, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SutroBaths1880s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1880sBrooklynBridge.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ChinaOpiumc1880.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Locomotive1880s.jpg\", render_factor=9, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ViennaBoys1880s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/VictorianDragQueen1880s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Sami1880s.jpg\", render_factor=44, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ArkansasCowboys1880s.jpg\", render_factor=22, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Ballet1890Russia.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Rottindean1890s.png\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sPingPong.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1937.png\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Harlem1932.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/OregonTrail1870s.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EasterNyc1911.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1899NycBlizzard.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Edinburgh1920s.jpg\", render_factor=17, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sShoeShopOhio.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sTouristsEgypt.png\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1938Reading.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1850Geography.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1901Electrophone.jpg\", render_factor=10, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(8, 47):\n",
+    "    vis.plot_transformed_image(\"test_images/1901Electrophone.jpg\", render_factor=i, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Texas1938Woman.png\", render_factor=38, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MaioreWoman1895NZ.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WestVirginiaHouse.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sGuadalope.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1909Chicago.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sFarmKid.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLate1800s.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sDaytonaBeach.png\", render_factor=23, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930sGeorgia.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NorwegianBride1920s.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Depression.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1888Slum.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LivingRoom1920Sweden.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1896NewsBoyGirl.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PetDucks1927.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1899SodaFountain.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TimesSquare1955.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PuppyGify.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890CliffHouseSF.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1908FamilyPhoto.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sSaloon.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890BostonHospital.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1870Girl.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AustriaHungaryWomen1890s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Shack.jpg\",render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Apsaroke1908.png\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1948CarsGrandma.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PlanesManhattan1931.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WorriedKid1940sNyc.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920sFamilyPhoto.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CatWash1931.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940sBeerRiver.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/VictorianLivingRoom.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1897BlindmansBluff.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1874Mexico.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MadisonSquare1900.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1867MusicianConstantinople.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1925Girl.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1907Cowboys.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWIIPeeps.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BabyBigBoots.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1895BikeMaidens.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IrishLate1800s.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LibraryOfCongress1910.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1875Olds.jpg\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SenecaNative1908.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWIHospital.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1892WaterLillies.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreekImmigrants1905.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FatMensShop.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/KidCage1930s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FarmWomen1895.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewZealand1860s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/JerseyShore1905.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonKidsEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NYStreetClean1906.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Boston1937.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Cork1905.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BoxedBedEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZoologischerGarten1898.jpg\", compare=True)"
+   ]
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EmpireState1930.jpg\", compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Agamemnon1919.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AppalachianLoggers1901.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WWISikhs.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MementoMori1865.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RepBrennanRadio1922.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Late1800sNative.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GasPrices1939.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1933RockefellerCenter.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Scotland1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1920CobblersShopLondon.jpg\", compare=True)"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1909ParisFirstFemaleTaxisDriver.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HoovervilleSeattle1932.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ElephantLondon1934.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Jane_Addams.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsAdobe.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CricketLondon1930.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Donegal1907Yarn.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsChurch.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BreadDelivery1920sIreland.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BritishTeaBombay1890s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CafeParis1928.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BigManTavern1908NYC.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Cars1890sIreland.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GalwayIreland1902.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HomeIreland1924.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HydeParkLondon1920s.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1929LondonOverFleetSt.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AccordianKid1900Paris.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AnselAdamsBuildings.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/AthleticClubParis1913.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BombedLibraryLondon1940.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Boston1937.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BoulevardDuTemple1838.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/BumperCarsParis1930.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CafeTerrace1925Paris.jpg\", render_factor=24, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CoalDeliveryParis1915.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CorkKids1910.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/DeepSeaDiver1915.png\", render_factor=16, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/EastEndLondonStreetKids1901.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FreightTrainTeens1934.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HarrodsLondon1920.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HerbSeller1899Paris.jpg\", render_factor=17, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CalcuttaPoliceman1920.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ElectricScooter1915.jpeg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreatGrandparentsIrelandEarly1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/HalloweenEarly1900s.jpg\", render_factor=11, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/IceManLondon1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LeBonMarcheParis1875.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LittleAirplane1934.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/RoyalUniversityMedStudent1900Ireland.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LewisTomalinLondon1895.png\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SunHelmetsLondon1933.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Killarney1910.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonSheep1920s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PostOfficeVermont1914.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ServantsBessboroughHouse1908Ireland.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WaterfordIreland1909.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Lisbon1919.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1918WartimeClothesManufacture.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonHeatWave1935.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonsSmallestShop1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MetropolitanDistrictRailway1869London.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NativeWoman1926.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PaddysMarketCork1900s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1920Cart.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLadies1910.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisLadies1930s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Sphinx.jpeg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TheatreGroupBombay1875.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WorldsFair1900Paris.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1850Coach.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1900EastEndBlacksmith.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/London1930sCheetah.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonFireBrigadeMember1926.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonGarbageTruck1910.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonRailwayWork1931.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonStreets1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/MuffinManlLondon1910.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NativeCouple1912.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewspaperCivilWar1863.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PaddingtonStationLondon1907.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1899StreetDig.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Paris1926.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ParisWomenFurs1920s.jpg\", render_factor=21, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PeddlerParis1899.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SchoolKidsConnemaraIreland1901.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SecondHandClothesLondonLate1800s.jpg\", render_factor=33, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SoapBoxRacerParis1920s.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SoccerMotorcycles1923London.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/WalkingLibraryLondon1930.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonStreetDoctor1877.png\", render_factor=38, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/jacksonville.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZebraCarriageLondon1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/StreetGramaphonePlayerLondon1920s.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/YaleBranchBarnardsExpress.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SynagogueInterior.PNG\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ArmisticeDay1918.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/FlyingMachinesParis1909.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GreatAunt1920.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/NewBrunswick1915.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ShoeMakerLate1800s.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/SpottedBull1908.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TouristsGermany1904.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/TunisianStudents1914.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/Yorktown1862.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/LondonFashion1911.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939GypsyKids.jpg\", render_factor=37, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936OpiumShanghai.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1923HollandTunnel.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939YakimaWAGirl.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/GoldenGateConstruction.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/PostCivilWarAncestors.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1939SewingBike.png\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930MaineSchoolBus.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1913NewYorkConstruction.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1945HiroshimaChild.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1941GeorgiaFarmhouse.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1934UmbriaItaly.jpg\", render_factor=21) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900sLadiesTeaParty.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1919WWIAviationOxygenMask.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900NJThanksgiving.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940Connecticut.jpg\", render_factor=43, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940Connecticut.jpg\", render_factor=i, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1911ThanksgivingMaskers.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910ThanksgivingMaskersII.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936PetToad.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1908RookeriesLondon.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1890sChineseImmigrants.jpg\", render_factor=25, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1897VancouverAmberlamps.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1929VictorianCosplayLondon.jpg\", render_factor=35, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1959ParisFriends.png\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1925GypsyCampMaryland.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1941PoolTableGeorgia.jpg\", render_factor=45, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1900ParkDog.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1886Hoop.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1950sLondonPoliceChild.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1886ProspectPark.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1930sRooftopPoland.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1919RevereBeach.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1936ParisCafe.jpg\", render_factor=46, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1902FrenchYellowBellies.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1940PAFamily.jpg\", render_factor=42, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1910Finland.jpg\", render_factor=40, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/ZebraCarriageLondon1900.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/1904ChineseMan.jpg\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/CrystalPalaceLondon1854.PNG\", compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James1.jpg\", render_factor=15, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James2.jpg\", render_factor=20, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James3.jpg\", render_factor=19, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James4.jpg\", render_factor=30, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James5.jpg\", render_factor=32, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis.plot_transformed_image(\"test_images/James6.jpg\", render_factor=28, compare=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "navigate_num": "#000000",
+    "navigate_text": "#333333",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700",
+    "sidebar_border": "#EEEEEE",
+    "wrapper_background": "#FFFFFF"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "67px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Jason Antic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

@@ -0,0 +1,3 @@
+include README.md
+include LICENSE
+include requirements.txt

README.md

@@ -1,12 +1,538 @@
----
-title: Deoldify
-emoji: 🌍
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# DeOldify
+
+**Quick Start**: The easiest way to colorize images using open source DeOldify
+(for free!) is here: [DeOldify Image Colorization on DeepAI](https://deepai.org/machine-learning-model/colorizer)
+
+**Desktop**: Want to run open source DeOldify for photos on Windows desktop?
+ColorfulSoft made such a thing here and it really works-  <https://github.com/ColorfulSoft/DeOldify.NET>.
+No GPU required!
+
+The **most advanced** version of DeOldify image colorization is available here,
+exclusively.  Try a few images for free! [MyHeritage In Color](https://www.myheritage.com/incolor)
+
+**Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces)
+with [Gradio](https://github.com/gradio-app/gradio).
+See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/PaddlePaddle/deoldify)
+
+----------------------------
+
+Image (artistic) [![Colab for images](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColab.ipynb)
+| Video [![Colab for video](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jantic/DeOldify/blob/master/VideoColorizerColab.ipynb)
+
+Having trouble with the default image colorizer, aka "artistic"?  Try the
+"stable" one below.  It generally won't produce colors that are as interesting as
+"artistic", but the glitches are noticeably reduced.
+
+Image (stable) [![Colab for stable model](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColabStable.ipynb)
+
+Instructions on how to use the Colabs above have been kindly provided in video
+tutorial form by Old Ireland in Colour's John Breslin.  It's great! Click video
+image below to watch.
+
+[![DeOldify Tutorial](http://img.youtube.com/vi/VaEl0faDw38/0.jpg)](http://www.youtube.com/watch?v=VaEl0faDw38)
+
+Get more updates on [Twitter
+![Twitter logo](resource_images/twitter.svg)](https://twitter.com/DeOldify).
+
+## Table of Contents
+
+- [About DeOldify](#about-deoldify)
+- [Example Videos](#example-videos)
+- [Example Images](#example-images)
+- [Stuff That Should Probably Be In A Paper](#stuff-that-should-probably-be-in-a-paper)
+  - [How to Achieve Stable Video](#how-to-achieve-stable-video)
+  - [What is NoGAN?](#what-is-nogan)
+- [Why Three Models?](#why-three-models)
+- [Technical Details](#the-technical-details)
+- [Going Forward](#this-project-going-forward)
+- [Getting Started Yourself](#getting-started-yourself)
+  - [Easiest Approach](#easiest-approach)
+  - [Your Own Machine](#your-own-machine-not-as-easy)
+- [Pretrained Weights](#pretrained-weights)
+
+## About DeOldify
+
+Simply put, the mission of this project is to colorize and restore old images and
+film footage. We'll get into the details in a bit, but first let's see some
+pretty pictures and videos!
+
+### New and Exciting Stuff in DeOldify
+
+- Glitches and artifacts are almost entirely eliminated
+- Better skin (less zombies)
+- More highly detailed and photorealistic renders
+- Much less "blue bias"
+- **Video** - it actually looks good!  
+- **NoGAN** - a new and weird but highly effective way to do GAN training for
+  image to image.
+
+## Example Videos
+
+**Note:**  Click images to watch
+
+### Facebook F8 Demo
+
+[![DeOldify Facebook F8 Movie Colorization Demo](http://img.youtube.com/vi/l3UXXid04Ys/0.jpg)](http://www.youtube.com/watch?v=l3UXXid04Ys)
+
+### Silent Movie Examples
+
+[![DeOldify Silent Movie Examples](http://img.youtube.com/vi/EXn-n2iqEjI/0.jpg)](http://www.youtube.com/watch?v=EXn-n2iqEjI)
+
+## Example Images
+
+"Migrant Mother" by Dorothea Lange (1936)
+
+![Migrant Mother](https://i.imgur.com/Bt0vnke.jpg)
+
+Woman relaxing in her livingroom in Sweden (1920)
+
+![Sweden Living Room](https://i.imgur.com/158d0oU.jpg)
+
+"Toffs and Toughs" by Jimmy Sime (1937)
+
+![Class Divide](https://i.imgur.com/VYuav4I.jpg)
+
+Thanksgiving Maskers (1911)
+
+![Thanksgiving Maskers](https://i.imgur.com/n8qVJ5c.jpg)
+
+Glen Echo Madame Careta Gypsy Camp in Maryland (1925)
+
+![Gypsy Camp](https://i.imgur.com/1oYrJRI.jpg)
+
+"Mr. and Mrs. Lemuel Smith and their younger children in their farm house,
+Carroll County, Georgia." (1941)
+
+![Georgia Farmhouse](https://i.imgur.com/I2j8ynm.jpg)
+
+"Building the Golden Gate Bridge" (est 1937)
+
+![Golden Gate Bridge](https://i.imgur.com/6SbFjfq.jpg)
+
+> **Note:**  What you might be wondering is while this render looks cool, are the
+> colors accurate? The original photo certainly makes it look like the towers of
+> the bridge could be white. We looked into this and it turns out the answer is
+> no - the towers were already covered in red primer by this time. So that's
+> something to keep in mind- historical accuracy remains a huge challenge!
+
+"Terrasse de café, Paris" (1925)
+
+![Cafe Paris](https://i.imgur.com/WprQwP5.jpg)
+
+Norwegian Bride (est late 1890s)
+
+![Norwegian Bride](https://i.imgur.com/MmtvrZm.jpg)
+
+Zitkála-Šá (Lakota: Red Bird), also known as Gertrude Simmons Bonnin (1898)
+
+![Native Woman](https://i.imgur.com/zIGM043.jpg)
+
+Chinese Opium Smokers (1880)
+
+![Opium Real](https://i.imgur.com/lVGq8Vq.jpg)
+
+## Stuff That Should Probably Be In A Paper
+
+### How to Achieve Stable Video
+
+NoGAN training is crucial to getting the kind of stable and colorful images seen
+in this iteration of DeOldify. NoGAN training combines the benefits of GAN
+training (wonderful colorization) while eliminating the nasty side effects
+(like flickering objects in video). Believe it or not, video is rendered using
+isolated image generation without any sort of temporal modeling tacked on. The
+process performs 30-60 minutes of the GAN portion of "NoGAN" training, using 1%
+to 3% of imagenet data once.  Then, as with still image colorization, we
+"DeOldify" individual frames before rebuilding the video.
+
+In addition to improved video stability, there is an interesting thing going on
+here worth mentioning. It turns out the models I run, even different ones and
+with different training structures, keep arriving at more or less the same
+solution.  That's even the case for the colorization of things you may think
+would be arbitrary and unknowable, like the color of clothing, cars, and even
+special effects (as seen in "Metropolis").
+
+![Metropolis Special FX](https://thumbs.gfycat.com/HeavyLoneBlowfish-size_restricted.gif)
+
+My best guess is that the models are learning some interesting rules about how to
+colorize based on subtle cues present in the black and white images that I
+certainly wouldn't expect to exist.  This result leads to nicely deterministic and
+consistent results, and that means you don't have track model colorization
+decisions because they're not arbitrary.  Additionally, they seem remarkably
+robust so that even in moving scenes the renders are very consistent.
+
+![Moving Scene Example](https://thumbs.gfycat.com/FamiliarJubilantAsp-size_restricted.gif)
+
+Other ways to stabilize video add up as well. First, generally speaking rendering
+at a higher resolution (higher render_factor) will increase stability of
+colorization decisions.  This stands to reason because the model has higher
+fidelity image information to work with and will have a greater chance of making
+the "right" decision consistently.  Closely related to this is the use of
+resnet101 instead of resnet34 as the backbone of the generator- objects are
+detected more consistently and correctly with this. This is especially important
+for getting good, consistent skin rendering.  It can be particularly visually
+jarring if you wind up with "zombie hands", for example.
+
+![Zombie Hand Example](https://thumbs.gfycat.com/ThriftyInferiorIsabellinewheatear-size_restricted.gif)
+
+Additionally, gaussian noise augmentation during training appears to help but at
+this point the conclusions as to just how much are bit more tenuous (I just
+haven't formally measured this yet).  This is loosely based on work done in style
+transfer video, described here:
+ <https://medium.com/element-ai-research-lab/stabilizing-neural-style-transfer-for-video-62675e203e42>.
+
+Special thanks go to Rani Horev for his contributions in implementing this noise
+augmentation.
+
+### What is NoGAN?
+
+This is a new type of GAN training that I've developed to solve some key problems
+in the previous DeOldify model. It provides the benefits of GAN training while
+spending minimal time doing direct GAN training.  Instead, most of the training
+time is spent pretraining the generator and critic separately with more
+straight-forward, fast and reliable conventional methods.  A key insight here is
+that those more "conventional" methods generally get you most of the results you
+need, and that GANs can be used to close the gap on realism. During the very
+short amount of actual GAN training the generator not only gets the full
+realistic colorization capabilities that used to take days of progressively
+resized GAN training, but it also doesn't accrue nearly as much of the artifacts
+and other ugly baggage of GANs. In fact, you can pretty much eliminate glitches
+and artifacts almost entirely depending on your approach. As far as I know this
+is a new technique. And it's incredibly effective.
+
+#### Original DeOldify Model
+
+![Before Flicker](https://thumbs.gfycat.com/CoordinatedVeneratedHogget-size_restricted.gif)
+
+#### NoGAN-Based DeOldify Model
+
+![After Flicker](https://thumbs.gfycat.com/OilyBlackArctichare-size_restricted.gif)
+
+The steps are as follows: First train the generator in a conventional way by
+itself with just the feature loss. Next, generate images from that, and train
+the critic on distinguishing between those outputs and real images as a basic
+binary classifier. Finally, train the generator and critic together in a GAN
+setting (starting right at the target size of 192px in this case).  Now for
+the weird part:  All the useful GAN training here only takes place within a very
+small window of time.  There's an inflection point where it appears the critic
+has transferred everything it can that is useful to the generator. Past this
+point, image quality oscillates between the best that you can get at the
+inflection point, or bad in a predictable way (orangish skin, overly red lips,
+etc).  There appears to be no productive training after the inflection point.
+And this point lies within training on just 1% to 3% of the Imagenet Data!
+That amounts to about 30-60 minutes of training at 192px.
+
+The hard part is finding this inflection point.  So far, I've accomplished this
+by making a whole bunch of model save checkpoints (every 0.1% of data iterated
+on) and then just looking for the point where images look great before they go
+totally bonkers with orange skin (always the first thing to go). Additionally,
+generator rendering starts immediately getting glitchy and inconsistent at this
+point, which is no good particularly for video. What I'd really like to figure
+out is what the tell-tale sign of the inflection point is that can be easily
+automated as an early stopping point.  Unfortunately, nothing definitive is
+jumping out at me yet.  For one, it's happening in the middle of training loss
+decreasing- not when it flattens out, which would seem more reasonable on the surface.
+
+Another key thing about NoGAN training is you can repeat pretraining the critic
+on generated images after the initial GAN training, then repeat the GAN training
+itself in the same fashion.  This is how I was able to get extra colorful results
+with the "artistic" model.  But this does come at a cost currently- the output of
+the generator becomes increasingly inconsistent and you have to experiment with
+render resolution (render_factor) to get the best result.  But the renders are
+still glitch free and way more consistent than I was ever able to achieve with
+the original DeOldify model. You can do about five of these repeat cycles, give
+or take, before you get diminishing returns, as far as I can tell.
+
+Keep in mind- I haven't been entirely rigorous in figuring out what all is going
+on in NoGAN- I'll save that for a paper. That means there's a good chance I'm
+wrong about something.  But I think it's definitely worth putting out there now
+because I'm finding it very useful- it's solving basically much of my remaining
+problems I had in DeOldify.
+
+This builds upon a technique developed in collaboration with Jeremy Howard and
+Sylvain Gugger for Fast.AI's Lesson 7 in version 3 of Practical Deep Learning
+for Coders Part I. The particular lesson notebook can be found here:
+  <https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson7-superres-gan.ipynb>
+
+## Why Three Models?
+
+There are now three models to choose from in DeOldify. Each of these has key
+strengths and weaknesses, and so have different use cases.  Video is for video
+of course.  But stable and artistic are both for images, and sometimes one will
+do images better than the other.
+
+More details:
+
+- **Artistic** - This model achieves the highest quality results in image
+coloration, in terms of interesting details and vibrance. The most notable
+drawback however is that it's a bit of a pain to fiddle around with to get the
+best results (you have to adjust the rendering resolution or render_factor to
+achieve this).  Additionally, the model does not do as well as stable in a few
+key common scenarios- nature scenes and portraits.  The model uses a resnet34
+backbone on a UNet with an emphasis on depth of layers on the decoder side.
+This model was trained with 5 critic pretrain/GAN cycle repeats via NoGAN, in
+addition to the initial generator/critic pretrain/GAN NoGAN training, at 192px.
+This adds up to a total of 32% of Imagenet data trained once (12.5 hours of
+direct GAN training).
+
+- **Stable** - This model achieves the best results with landscapes and
+portraits.  Notably, it produces less "zombies"- where faces or limbs stay gray
+rather than being colored in properly.  It generally has less weird
+miscolorations than artistic, but it's also less colorful in general.  This
+model uses a resnet101 backbone on a UNet with an emphasis on width of layers on
+the decoder side.  This model was trained with 3 critic pretrain/GAN cycle
+repeats via NoGAN, in addition to the initial generator/critic pretrain/GAN
+NoGAN training, at 192px.  This adds up to a total of 7% of Imagenet data
+trained once (3 hours of direct GAN training).
+
+- **Video** - This model is optimized for smooth, consistent and flicker-free
+video.  This would definitely be the least colorful of the three models, but
+it's honestly not too far off from "stable". The model is the same as "stable"
+in terms of architecture, but differs in training.  It's trained for a mere 2.2% 
+of Imagenet data once at 192px, using only the initial generator/critic 
+pretrain/GAN NoGAN training (1 hour of direct GAN training).
+
+Because the training of the artistic and stable models was done before the
+"inflection point" of NoGAN training described in "What is NoGAN???" was
+discovered, I believe this amount of training on them can be knocked down
+considerably. As far as I can tell, the models were stopped at "good points"
+that were well beyond where productive training was taking place.  I'll be
+looking into this in the future.
+
+Ideally, eventually these three models will be consolidated into one that has all
+these good desirable unified.  I think there's a path there, but it's going to
+require more work!  So for now, the most practical solution appears to be to
+maintain multiple models.
+
+## The Technical Details
+
+This is a deep learning based model.  More specifically, what I've done is
+combined the following approaches:
+
+### [Self-Attention Generative Adversarial Network](https://arxiv.org/abs/1805.08318)
+
+Except the generator is a **pretrained U-Net**, and I've just modified it to
+have the spectral normalization and self-attention.  It's a pretty
+straightforward translation.
+
+### [Two Time-Scale Update Rule](https://arxiv.org/abs/1706.08500)
+
+This is also very straightforward – it's just one to one generator/critic
+iterations and higher critic learning rate.
+This is modified to incorporate a "threshold" critic loss that makes sure that
+the critic is "caught up" before moving on to generator training.
+This is particularly useful for the "NoGAN" method described below.
+
+### NoGAN
+
+There's no paper here! This is a new type of GAN training that I've developed to
+solve some key problems in the previous DeOldify model.
+The gist is that you get the benefits of GAN training while spending minimal time
+doing direct GAN training.
+More details are in the [What is NoGAN?](#what-is-nogan) section (it's a doozy).
+
+### Generator Loss
+
+Loss during NoGAN learning is two parts:  One is a basic Perceptual Loss (or
+Feature Loss) based on VGG16 – this just biases the generator model to replicate
+the input image.
+The second is the loss score from the critic.  For the curious – Perceptual Loss
+isn't sufficient by itself to produce good results.
+It tends to just encourage a bunch of brown/green/blue – you know, cheating to
+the test, basically, which neural networks are really good at doing!
+Key thing to realize here is that GANs essentially are learning the loss function
+for you – which is really one big step closer to toward the ideal that we're
+shooting for in machine learning.
+And of course you generally get much better results when you get the machine to
+learn something you were previously hand coding.
+That's certainly the case here.
+
+**Of note:**  There's no longer any "Progressive Growing of GANs" type training
+going on here.  It's just not needed in lieu of the superior results obtained
+by the "NoGAN" technique described above.
+
+The beauty of this model is that it should be generally useful for all sorts of
+image modification, and it should do it quite well.
+What you're seeing above are the results of the colorization model, but that's
+just one component in a pipeline that I'm developing with the exact same approach.
+
+## This Project, Going Forward
+
+So that's the gist of this project – I'm looking to make old photos and film
+look reeeeaaally good with GANs, and more importantly, make the project *useful*.
+In the meantime though this is going to be my baby and I'll be actively updating
+and improving the code over the foreseeable future.
+I'll try to make this as user-friendly as possible, but I'm sure there's going
+to be hiccups along the way.
+
+Oh and I swear I'll document the code properly...eventually.  Admittedly I'm
+*one of those* people who believes in "self documenting code" (LOL).
+
+## Getting Started Yourself
+
+### Easiest Approach
+
+The easiest way to get started is to go straight to the Colab notebooks:
+
+Image [![Colab for images](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColab.ipynb)
+| Video [![Colab for video](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jantic/DeOldify/blob/master/VideoColorizerColab.ipynb)
+
+Special thanks to Matt Robinson and María Benavente for their image Colab notebook
+contributions, and Robert Bell for the video Colab notebook work!
+
+### Your Own Machine (not as easy)
+
+#### Hardware and Operating System Requirements
+
+- **(Training Only) BEEFY Graphics card**.  I'd really like to have more memory
+  than the 11 GB in my GeForce 1080TI (11GB).  You'll have a tough time with less.
+  The Generators and Critic are ridiculously large.  
+- **(Colorization Alone) A decent graphics card**. Approximately 4GB+ memory
+  video cards should be sufficient.
+- **Linux**.  I'm using Ubuntu 18.04, and I know 16.04 works fine too.  **Windows
+  is not supported and any issues brought up related to this will not be investigated.**
+
+#### Easy Install
+
+You should now be able to do a simple install with Anaconda. Here are the steps:
+
+Open the command line and navigate to the root folder you wish to install.  Then
+type the following commands
+
+```console
+git clone https://github.com/jantic/DeOldify.git DeOldify
+cd DeOldify
+conda env create -f environment.yml
+```
+
+Then start running with these commands:
+
+```console
+source activate deoldify
+jupyter lab
+```
+
+From there you can start running the notebooks in Jupyter Lab, via the url they
+provide you in the console.
+
+> **Note:** You can also now do "conda activate deoldify" if you have the latest
+version of conda and in fact that's now recommended. But a lot of people don't
+have that yet so I'm not going to make it the default instruction here yet.
+
+**Alternative Install:** User daddyparodz has kindly created an installer script
+for Ubuntu, and in particular Ubuntu on WSL, that may make things easier:
+  <https://github.com/daddyparodz/AutoDeOldifyLocal>
+
+#### Note on test_images Folder
+
+The images in the `test_images` folder have been removed because they were using
+Git LFS and that costs a lot of money when GitHub actually charges for bandwidth
+on a popular open source project (they had a billing bug for while that was
+recently fixed).  The notebooks that use them (the image test ones) still point
+to images in that directory that I (Jason) have personally and I'd like to keep
+it that way because, after all, I'm by far the primary and most active developer.
+But they won't work for you.  Still, those notebooks are a convenient template
+for making your own tests if you're so inclined.
+
+#### Typical training
+
+The notebook `ColorizeTrainingWandb` has been created to log and monitor results
+through [Weights & Biases](https://www.wandb.com/). You can find a description of
+typical training by consulting [W&B Report](https://app.wandb.ai/borisd13/DeOldify/reports?view=borisd13%2FDeOldify).
+
+## Pretrained Weights
+
+To start right away on your own machine with your own images or videos without
+training the models yourself, you'll need to download the "Completed Generator
+Weights" listed below and drop them in the /models/ folder.
+
+The colorization inference notebooks should be able to guide you from here. The
+notebooks to use are named ImageColorizerArtistic.ipynb,
+ImageColorizerStable.ipynb, and VideoColorizer.ipynb.
+
+### Completed Generator Weights
+
+- [Artistic](https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth)
+- [Stable](https://www.dropbox.com/s/usf7uifrctqw9rl/ColorizeStable_gen.pth?dl=0)
+- [Video](https://data.deepai.org/deoldify/ColorizeVideo_gen.pth)
+
+### Completed Critic Weights
+
+- [Artistic](https://www.dropbox.com/s/1qd663zbk6ntzuy/ColorizeArtistic_crit.pth?dl=0)
+- [Stable](https://www.dropbox.com/s/wlqu6w88qwzcvfn/ColorizeStable_crit.pth?dl=0)
+- [Video](https://www.dropbox.com/s/oyl6qmwpdvpm95d/ColorizeVideo_crit.pth?dl=0)
+
+### Pretrain Only Generator Weights
+
+- [Artistic](https://www.dropbox.com/s/lbuv6911aivm9zi/ColorizeArtistic_PretrainOnly_gen.pth?dl=0)
+- [Stable](https://www.dropbox.com/s/6ita1pwyqjsmx4p/ColorizeStable_PretrainOnly_gen.pth?dl=0)
+- [Video](https://www.dropbox.com/s/tl4uzkwwapz68ca/ColorizeVideo_PretrainOnly_gen.pth?dl=0)
+
+### Pretrain Only Critic Weights
+
+- [Artistic](https://www.dropbox.com/s/6td494kcjqfmh26/ColorizeArtistic_PretrainOnly_crit.pth?dl=0)
+- [Stable](https://www.dropbox.com/s/houkmrdivbia7z8/ColorizeStable_PretrainOnly_crit.pth?dl=0)
+- [Video](https://www.dropbox.com/s/80wpz16x7yudblh/ColorizeVideo_PretrainOnly_crit.pth?dl=0)
+
+## Want the Old DeOldify?
+
+We suspect some of you are going to want access to the original DeOldify model
+for various reasons.  We have that archived here:  <https://github.com/dana-kelley/DeOldify>
+
+## Want More?
+
+Follow [#DeOldify](https://twitter.com/search?q=%23Deoldify) on Twitter.
+
+## License
+
+All code in this repository is under the MIT license as specified by the LICENSE
+file.
+
+The model weights listed in this readme under the "Pretrained Weights" section
+are trained by ourselves and are released under the MIT license.
+
+## A Statement on Open Source Support
+
+We believe that open source has done a lot of good for the world.  After all,
+DeOldify simply wouldn't exist without it. But we also believe that there needs
+to be boundaries on just how much is reasonable to be expected from an open
+source project maintained by just two developers.
+
+Our stance is that we're providing the code and documentation on research that
+we believe is beneficial to the world.  What we have provided are novel takes
+on colorization, GANs, and video that are hopefully somewhat friendly for
+developers and researchers to learn from and adopt. This is the culmination of
+well over a year of continuous work, free for you. What wasn't free was
+shouldered by us, the developers.  We left our jobs, bought expensive GPUs, and
+had huge electric bills as a result of dedicating ourselves to this.
+
+What we haven't provided here is a ready to use free "product" or "app", and we
+don't ever intend on providing that.  It's going to remain a Linux based project
+without Windows support, coded in Python, and requiring people to have some extra
+technical background to be comfortable using it.  Others have stepped in with
+their own apps made with DeOldify, some paid and some free, which is what we want!
+We're instead focusing on what we believe we can do best- making better
+commercial models that people will pay for.
+Does that mean you're not getting the very best for free?  Of course. We simply
+don't believe that we're obligated to provide that, nor is it feasible! We
+compete on research and sell that.  Not a GUI or web service that wraps said
+research- that part isn't something we're going to be great at anyways. We're not
+about to shoot ourselves in the foot by giving away our actual competitive
+advantage for free, quite frankly.
+
+We're also not willing to go down the rabbit hole of providing endless, open
+ended and personalized support on this open source project.  Our position is
+this:  If you have the proper background and resources, the project provides
+more than enough to get you started. We know this because we've seen plenty of
+people using it and making money off of their own projects with it.
+
+Thus, if you have an issue come up and it happens to be an actual bug that
+having it be fixed will benefit users generally, then great- that's something
+we'll be happy to look into.
+
+In contrast, if you're asking about something that really amounts to asking for
+personalized and time consuming support that won't benefit anybody else, we're
+not going to help. It's simply not in our interest to do that. We have bills to
+pay, after all. And if you're asking for help on something that can already be
+derived from the documentation or code?  That's simply annoying, and we're not
+going to pretend to be ok with that.

VideoColorizer.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deoldify.visualize import *\n",
+    "plt.style.use('dark_background')\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*?Your .*? set is empty.*?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colorizer = get_video_colorizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Instructions\n",
+    "\n",
+    "### source_url\n",
+    "Type in a url hosting a video from YouTube, Imgur, Twitter, Reddit, Vimeo, etc.  Many sources work!  GIFs also work.  Full list here: https://ytdl-org.github.io/youtube-dl/supportedsites.html NOTE: If you want to use your own video, you can set source_url to None and just upload the file to video/source/ in Jupyter.  Just make sure that the file_name parameter matches the file you uploaded.\n",
+    "\n",
+    "\n",
+    "### file_name\n",
+    "Name this whatever sensible file name you want (minus extension)! It should actually exist in video/source if source_url=None\n",
+    "\n",
+    "\n",
+    "### render_factor\n",
+    "The default value of 21 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which the color portion of the video is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality film in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality videos and inconsistencies (flashy render) will generally be reduced, but the colors may get slightly washed out. \n",
+    "\n",
+    "\n",
+    "### file_name_ext\n",
+    "There's no reason to changes this.\n",
+    "\n",
+    "\n",
+    "### result_path\n",
+    "Ditto- don't change.\n",
+    "\n",
+    "\n",
+    "### How to Download a Copy\n",
+    "Simply shift+right click on the displayed video and click \"Save video as...\"!\n",
+    "\n",
+    "\n",
+    "## Pro Tips\n",
+    "1. If a video takes a long time to render and you're wondering how well the frames will actually be colorized, you can preview how well the frames will be rendered at each render_factor by using the code at the bottom. Just stop the video rendering by hitting the stop button on the cell, then run that bottom cell under \"See how well render_factor values perform on a frame here\". It's not perfect and you may still need to experiment a bit especially when it comes to figuring out how to reduce frame inconsistency.  But it'll go a long way in narrowing down what actually works.\n",
+    "\n",
+    "\n",
+    "## Troubleshooting\n",
+    "The video player may wind up not showing up, in which case- make sure to wait for the Jupyter cell to complete processing first (the play button will stop spinning).  Then follow these alternative download instructions\n",
+    "\n",
+    "1. In the menu to the left, click Home icon.\n",
+    "2. By default, rendered video will be in /video/result/\n",
+    "\n",
+    "If a video you downloaded doesn't play, it's probably because the cell didn't complete processing and the video is in a half-finished state.\n",
+    "If you get a 'CUDA out of memory' error, you probably have the render_factor too high.  The max is 44 on 11GB video cards."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Colorize!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NOTE:  Max is 44 with 11GB video cards.  21 is a good default\n",
+    "render_factor=21\n",
+    "#NOTE:  Make source_url None to just read from file at ./video/source/[file_name] directly without modification\n",
+    "source_url='https://twitter.com/silentmoviegifs/status/1116751583386034176'\n",
+    "file_name = 'DogShy1926'\n",
+    "file_name_ext = file_name + '.mp4'\n",
+    "result_path = None\n",
+    "\n",
+    "if source_url is not None:\n",
+    "    result_path = colorizer.colorize_from_url(source_url, file_name_ext, render_factor=render_factor)\n",
+    "else:\n",
+    "    result_path = colorizer.colorize_from_file_name(file_name_ext, render_factor=render_factor)\n",
+    "\n",
+    "show_video_in_notebook(result_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See how well render_factor values perform on a frame here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10,45,2):\n",
+    "    colorizer.vis.plot_transformed_image('video/bwframes/' + file_name + '/00001.jpg', render_factor=i, display_render_factor=True, figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "navigate_num": "#000000",
+    "navigate_text": "#333333",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700",
+    "sidebar_border": "#EEEEEE",
+    "wrapper_background": "#FFFFFF"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "67px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

VideoColorizerColab.ipynb

@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/jantic/DeOldify/blob/master/VideoColorizerColab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'> Video Colorizer </font>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "663IVxfrpIAb"
+   },
+   "source": [
+    "#◢ DeOldify - Colorize your own videos!\n",
+    "\n",
+    "\n",
+    "_FYI: This notebook is intended as a tool to colorize gifs and short videos, if you are trying to convert longer video you may hit the limit on processing space. Running the Jupyter notebook on your own machine is recommended (and faster) for larger video sizes._\n",
+    "\n",
+    "####**Credits:**\n",
+    "\n",
+    "Big special thanks to:\n",
+    "\n",
+    "Robert Bell for all his work on the video Colab notebook, and paving the way to video in DeOldify!\n",
+    "\n",
+    "Dana Kelley for doing things, breaking stuff & having an opinion on everything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZjPqTBNoohK9"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "#◢ Verify Correct Runtime Settings\n",
+    "\n",
+    "**<font color='#FF000'> IMPORTANT </font>**\n",
+    "\n",
+    "In the \"Runtime\" menu for the notebook window, select \"Change runtime type.\" Ensure that the following are selected:\n",
+    "* Runtime Type = Python 3\n",
+    "* Hardware Accelerator = GPU \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "gaEJBGDlptEo"
+   },
+   "source": [
+    "#◢ Git clone and install DeOldify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-T-svuHytJ-8"
+   },
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/jantic/DeOldify.git DeOldify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd DeOldify"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BDFjbNxaadNJ"
+   },
+   "source": [
+    "#◢ Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "00_GcC_trpdE"
+   },
+   "outputs": [],
+   "source": [
+    "#NOTE:  This must be the first call in order to work properly!\n",
+    "from deoldify import device\n",
+    "from deoldify.device_id import DeviceId\n",
+    "#choices:  CPU, GPU0...GPU7\n",
+    "device.set(device=DeviceId.GPU0)\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "if not torch.cuda.is_available():\n",
+    "    print('GPU not available.')\n",
+    "\n",
+    "from os import path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Lsx7xCXNSVt6"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements-colab.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MsJa69CMwj3l"
+   },
+   "outputs": [],
+   "source": [
+    "import fastai\n",
+    "from deoldify.visualize import *\n",
+    "from pathlib import Path\n",
+    "torch.backends.cudnn.benchmark=True\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*?Your .*? set is empty.*?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir 'models'\n",
+    "!wget https://data.deepai.org/deoldify/ColorizeVideo_gen.pth -O ./models/ColorizeVideo_gen.pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tzHVnegp21hC"
+   },
+   "outputs": [],
+   "source": [
+    "colorizer = get_video_colorizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#◢ Instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### source_url\n",
+    "Type in a url hosting a video from YouTube, Imgur, Twitter, Reddit, Vimeo, etc.  Many sources work!  GIFs also work.  Full list here: https://ytdl-org.github.io/youtube-dl/supportedsites.html NOTE: If you want to use your own video, upload it first to a site like YouTube. \n",
+    "\n",
+    "### render_factor\n",
+    "The default value of 21 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which the color portion of the video is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality film in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality videos and inconsistencies (flashy render) will generally be reduced, but the colors may get slightly washed out.\n",
+    "\n",
+    "### watermarked\n",
+    "Selected by default, this places a watermark icon of a palette at the bottom left corner of the image.  This is intended to be a standard way to convey to others viewing the image that it is colorized by AI. We want to help promote this as a standard, especially as the technology continues to improve and the distinction between real and fake becomes harder to discern. This palette watermark practice was initiated and lead by the company MyHeritage in the MyHeritage In Color feature (which uses a newer version of DeOldify than what you're using here).\n",
+    "\n",
+    "### How to Download a Copy\n",
+    "Simply right click on the displayed video and click \"Save video as...\"!\n",
+    "\n",
+    "## Pro Tips\n",
+    "1. If a video takes a long time to render and you're wondering how well the frames will actually be colorized, you can preview how well the frames will be rendered at each render_factor by using the code at the bottom. Just stop the video rendering by hitting the stop button on the cell, then run that bottom cell under \"See how well render_factor values perform on a frame here\". It's not perfect and you may still need to experiment a bit especially when it comes to figuring out how to reduce frame inconsistency.  But it'll go a long way in narrowing down what actually works.\n",
+    "2. If videos are taking way too much time for your liking, running the Jupyter notebook VideoColorizer.ipynb on your own machine (with DeOldify installed) will generally be much faster (as long as you have the hardware for it).   \n",
+    "3. Longer videos (running multiple minutes) are going to have a rough time on Colabs. You'll be much better off using a local install of DeOldify instead in this case.\n",
+    "\n",
+    "## Troubleshooting\n",
+    "The video player may wind up not showing up, in which case- make sure to wait for the Jupyter cell to complete processing first (the play button will stop spinning).  Then follow these alternative download instructions\n",
+    "\n",
+    "1. In the menu to the left, click Files\n",
+    "2. If you don't see the 'DeOldify' folder, click \"Refresh\"\n",
+    "3. By default, rendered video will be in /DeOldify/video/result/\n",
+    "\n",
+    "If a video you downloaded doesn't play, it's probably because the cell didn't complete processing and the video is in a half-finished state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "sUQrbSYipiJn"
+   },
+   "source": [
+    "#◢ Colorize!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_url = '' #@param {type:\"string\"}\n",
+    "render_factor = 21  #@param {type: \"slider\", min: 5, max: 40}\n",
+    "watermarked = True #@param {type:\"boolean\"}\n",
+    "\n",
+    "if source_url is not None and source_url !='':\n",
+    "    video_path = colorizer.colorize_from_url(source_url, 'video.mp4', render_factor, watermarked=watermarked)\n",
+    "    show_video_in_notebook(video_path)\n",
+    "else:\n",
+    "    print('Provide a video url and try again.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## See how well render_factor values perform on a frame here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(10,40,2):\n",
+    "    colorizer.vis.plot_transformed_image('video/bwframes/video/00001.jpg', render_factor=i, display_render_factor=True, figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "X7Ycv_Y9xAHp"
+   },
+   "source": [
+    "---\n",
+    "#⚙ Recommended video and gif sources \n",
+    "* [/r/Nickelodeons/](https://www.reddit.com/r/Nickelodeons/)\n",
+    "* [r/silentmoviegifs](https://www.reddit.com/r/silentmoviegifs/)\n",
+    "* https://twitter.com/silentmoviegifs "
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "VideoColorizerColab.ipynb",
+   "provenance": [],
+   "toc_visible": true,
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

app.py

@@ -0,0 +1,29 @@
+import os
+import gradio as gr
+from deoldify import device
+from deoldify.device_id import DeviceId
+from deoldify.visualize import *
+import tempfile
+
+if not os.path.exists("./models/ColorizeArtistic_gen.pth"):
+    os.system(
+        'wget "https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth" -O "./models/ColorizeArtistic_gen.pth"'
+    )
+
+device.set(device=DeviceId.GPU0)
+
+colorizer = get_image_colorizer(artistic=True)
+
+
+def colorize(image):
+    tmp_folder = tempfile.TemporaryDirectory()
+    return colorizer.plot_transformed_image_from_url(
+        url=image,
+        path=f"{tmp_folder.name}/input.png",
+        render_factor=35,
+        compare=True,
+        results_dir=f"{tmp_folder.name}/output.png")
+
+
+gr.Interface(colorize, ["text"],
+             ["image"]).queue(default_enabled=True).launch(show_api=True)

demo.py

@@ -0,0 +1,21 @@
+#NOTE:  This must be the first call in order to work properly!
+from deoldify import device
+from deoldify.device_id import DeviceId
+from deoldify.visualize import *
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, required=True, help="the image path")
+parser.add_argument("-o", "--output", type=str, required=True, help="the output path")
+parser.add_argument("--artistic", action="store_true", default=True, help="enable artistic mode")
+parser.add_argument("--compare", action="store_true", default=True, help="enable compare mode")
+parser.add_argument("-r", "--render_factor", type=int, default=35, help="max is 45 for 11GB video cards. default is 35")
+args = parser.parse_args()
+
+device.set(device=DeviceId.GPU0)
+
+colorizer = get_image_colorizer(artistic=args.artistic)
+
+result_path = colorizer.plot_transformed_image(path=args.input, render_factor=args.render_factor, compare=args.compare, results_dir=args.output)
+
+print(result_path)

deoldify/__init__.py

@@ -0,0 +1,8 @@
+import sys
+import logging
+logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
+logging.getLogger().setLevel(logging.INFO)
+
+from deoldify._device import _Device
+
+device = _Device()

deoldify/_device.py

@@ -0,0 +1,30 @@
+import os
+from enum import Enum
+from .device_id import DeviceId
+
+#NOTE:  This must be called first before any torch imports in order to work properly!
+
+class DeviceException(Exception):
+    pass
+
+class _Device:
+    def __init__(self):
+        self.set(DeviceId.CPU)
+
+    def is_gpu(self):
+        ''' Returns `True` if the current device is GPU, `False` otherwise. '''
+        return self.current() is not DeviceId.CPU
+  
+    def current(self):
+        return self._current_device
+
+    def set(self, device:DeviceId):     
+        if device == DeviceId.CPU:
+            os.environ['CUDA_VISIBLE_DEVICES']=''
+        else:
+            os.environ['CUDA_VISIBLE_DEVICES']=str(device.value)
+            import torch
+            torch.backends.cudnn.benchmark=False
+        
+        self._current_device = device    
+        return device

deoldify/augs.py

@@ -0,0 +1,29 @@
+import random
+
+from fastai.vision.image import TfmPixel
+
+# Contributed by Rani Horev. Thank you!
+def _noisify(
+    x, pct_pixels_min: float = 0.001, pct_pixels_max: float = 0.4, noise_range: int = 30
+):
+    if noise_range > 255 or noise_range < 0:
+        raise Exception("noise_range must be between 0 and 255, inclusively.")
+
+    h, w = x.shape[1:]
+    img_size = h * w
+    mult = 10000.0
+    pct_pixels = (
+        random.randrange(int(pct_pixels_min * mult), int(pct_pixels_max * mult)) / mult
+    )
+    noise_count = int(img_size * pct_pixels)
+
+    for ii in range(noise_count):
+        yy = random.randrange(h)
+        xx = random.randrange(w)
+        noise = random.randrange(-noise_range, noise_range) / 255.0
+        x[:, yy, xx].add_(noise)
+
+    return x
+
+
+noisify = TfmPixel(_noisify)

deoldify/critics.py

@@ -0,0 +1,47 @@
+from fastai.basic_train import Learner
+from fastai.core import *
+from fastai.layers import NormType, conv_layer
+from fastai.torch_core import *
+from fastai.vision import *
+from fastai.vision.data import ImageDataBunch
+from fastai.vision.gan import AdaptiveLoss, accuracy_thresh_expand
+
+_conv_args = dict(leaky=0.2, norm_type=NormType.Spectral)
+
+
+def _conv(ni: int, nf: int, ks: int = 3, stride: int = 1, **kwargs):
+    return conv_layer(ni, nf, ks=ks, stride=stride, **_conv_args, **kwargs)
+
+
+def custom_gan_critic(
+    n_channels: int = 3, nf: int = 256, n_blocks: int = 3, p: int = 0.15
+):
+    "Critic to train a `GAN`."
+    layers = [_conv(n_channels, nf, ks=4, stride=2), nn.Dropout2d(p / 2)]
+    for i in range(n_blocks):
+        layers += [
+            _conv(nf, nf, ks=3, stride=1),
+            nn.Dropout2d(p),
+            _conv(nf, nf * 2, ks=4, stride=2, self_attention=(i == 0)),
+        ]
+        nf *= 2
+    layers += [
+        _conv(nf, nf, ks=3, stride=1),
+        _conv(nf, 1, ks=4, bias=False, padding=0, use_activ=False),
+        Flatten(),
+    ]
+    return nn.Sequential(*layers)
+
+
+def colorize_crit_learner(
+    data: ImageDataBunch,
+    loss_critic=AdaptiveLoss(nn.BCEWithLogitsLoss()),
+    nf: int = 256,
+) -> Learner:
+    return Learner(
+        data,
+        custom_gan_critic(nf=nf),
+        metrics=accuracy_thresh_expand,
+        loss_func=loss_critic,
+        wd=1e-3,
+    )

deoldify/dataset.py

@@ -0,0 +1,46 @@
+from fastai import *
+from fastai.core import *
+from fastai.vision.transform import get_transforms
+from fastai.vision.data import ImageImageList, ImageDataBunch, imagenet_stats
+
+
+def get_colorize_data(
+    sz: int,
+    bs: int,
+    crappy_path: Path,
+    good_path: Path,
+    random_seed: int = None,
+    keep_pct: float = 1.0,
+    num_workers: int = 8,
+    stats: tuple = imagenet_stats,
+    xtra_tfms=[],
+) -> ImageDataBunch:
+    
+    src = (
+        ImageImageList.from_folder(crappy_path, convert_mode='RGB')
+        .use_partial_data(sample_pct=keep_pct, seed=random_seed)
+        .split_by_rand_pct(0.1, seed=random_seed)
+    )
+
+    data = (
+        src.label_from_func(lambda x: good_path / x.relative_to(crappy_path))
+        .transform(
+            get_transforms(
+                max_zoom=1.2, max_lighting=0.5, max_warp=0.25, xtra_tfms=xtra_tfms
+            ),
+            size=sz,
+            tfm_y=True,
+        )
+        .databunch(bs=bs, num_workers=num_workers, no_check=True)
+        .normalize(stats, do_y=True)
+    )
+
+    data.c = 3
+    return data
+
+
+def get_dummy_databunch() -> ImageDataBunch:
+    path = Path('./dummy/')
+    return get_colorize_data(
+        sz=1, bs=1, crappy_path=path, good_path=path, keep_pct=0.001
+    )

deoldify/device_id.py

@@ -0,0 +1,12 @@
+from enum import IntEnum
+
+class DeviceId(IntEnum):
+    GPU0 = 0,
+    GPU1 = 1,
+    GPU2 = 2,
+    GPU3 = 3,
+    GPU4 = 4,
+    GPU5 = 5,
+    GPU6 = 6,
+    GPU7 = 7,
+    CPU = 99

deoldify/filters.py

@@ -0,0 +1,124 @@
+from fastai.basic_data import DatasetType
+from fastai.basic_train import Learner
+from abc import ABC, abstractmethod
+from fastai.core import *
+from fastai.vision import *
+from fastai.vision.image import *
+from fastai.vision.data import *
+from fastai import *
+import cv2
+from PIL import Image as PilImage
+from deoldify import device as device_settings
+import logging
+
+
+class IFilter(ABC):
+    @abstractmethod
+    def filter(
+        self, orig_image: PilImage, filtered_image: PilImage, render_factor: int
+    ) -> PilImage:
+        pass
+
+
+class BaseFilter(IFilter):
+    def __init__(self, learn: Learner, stats: tuple = imagenet_stats):
+        super().__init__()
+        self.learn = learn
+        
+        if not device_settings.is_gpu():
+            self.learn.model = self.learn.model.cpu()
+        
+        self.device = next(self.learn.model.parameters()).device
+        self.norm, self.denorm = normalize_funcs(*stats)
+
+    def _transform(self, image: PilImage) -> PilImage:
+        return image
+
+    def _scale_to_square(self, orig: PilImage, targ: int) -> PilImage:
+        # a simple stretch to fit a square really makes a big difference in rendering quality/consistency.
+        # I've tried padding to the square as well (reflect, symetric, constant, etc).  Not as good!
+        targ_sz = (targ, targ)
+        return orig.resize(targ_sz, resample=PIL.Image.BILINEAR)
+
+    def _get_model_ready_image(self, orig: PilImage, sz: int) -> PilImage:
+        result = self._scale_to_square(orig, sz)
+        result = self._transform(result)
+        return result
+
+    def _model_process(self, orig: PilImage, sz: int) -> PilImage:
+        model_image = self._get_model_ready_image(orig, sz)
+        x = pil2tensor(model_image, np.float32)
+        x = x.to(self.device)
+        x.div_(255)
+        x, y = self.norm((x, x), do_x=True)
+        
+        try:
+            result = self.learn.pred_batch(
+                ds_type=DatasetType.Valid, batch=(x[None], y[None]), reconstruct=True
+            )
+        except RuntimeError as rerr:
+            if 'memory' not in str(rerr):
+                raise rerr
+            logging.warn('Warning: render_factor was set too high, and out of memory error resulted. Returning original image.')
+            return model_image
+            
+        out = result[0]
+        out = self.denorm(out.px, do_x=False)
+        out = image2np(out * 255).astype(np.uint8)
+        return PilImage.fromarray(out)
+
+    def _unsquare(self, image: PilImage, orig: PilImage) -> PilImage:
+        targ_sz = orig.size
+        image = image.resize(targ_sz, resample=PIL.Image.BILINEAR)
+        return image
+
+
+class ColorizerFilter(BaseFilter):
+    def __init__(self, learn: Learner, stats: tuple = imagenet_stats):
+        super().__init__(learn=learn, stats=stats)
+        self.render_base = 16
+
+    def filter(
+        self, orig_image: PilImage, filtered_image: PilImage, render_factor: int, post_process: bool = True) -> PilImage:
+        render_sz = render_factor * self.render_base
+        model_image = self._model_process(orig=filtered_image, sz=render_sz)
+        raw_color = self._unsquare(model_image, orig_image)
+
+        if post_process:
+            return self._post_process(raw_color, orig_image)
+        else:
+            return raw_color
+
+    def _transform(self, image: PilImage) -> PilImage:
+        return image.convert('LA').convert('RGB')
+
+    # This takes advantage of the fact that human eyes are much less sensitive to
+    # imperfections in chrominance compared to luminance.  This means we can
+    # save a lot on memory and processing in the model, yet get a great high
+    # resolution result at the end.  This is primarily intended just for
+    # inference
+    def _post_process(self, raw_color: PilImage, orig: PilImage) -> PilImage:
+        color_np = np.asarray(raw_color)
+        orig_np = np.asarray(orig)
+        color_yuv = cv2.cvtColor(color_np, cv2.COLOR_BGR2YUV)
+        # do a black and white transform first to get better luminance values
+        orig_yuv = cv2.cvtColor(orig_np, cv2.COLOR_BGR2YUV)
+        hires = np.copy(orig_yuv)
+        hires[:, :, 1:3] = color_yuv[:, :, 1:3]
+        final = cv2.cvtColor(hires, cv2.COLOR_YUV2BGR)
+        final = PilImage.fromarray(final)
+        return final
+
+
+class MasterFilter(BaseFilter):
+    def __init__(self, filters: List[IFilter], render_factor: int):
+        self.filters = filters
+        self.render_factor = render_factor
+
+    def filter(
+        self, orig_image: PilImage, filtered_image: PilImage, render_factor: int = None, post_process: bool = True) -> PilImage:
+        render_factor = self.render_factor if render_factor is None else render_factor
+        for filter in self.filters:
+            filtered_image = filter.filter(orig_image, filtered_image, render_factor, post_process)
+
+        return filtered_image

deoldify/generators.py

@@ -0,0 +1,155 @@
+from fastai.basic_data import DataBunch
+from fastai.basic_train import Learner
+from fastai.layers import NormType
+from fastai.torch_core import SplitFuncOrIdxList, apply_init, to_device
+from fastai.vision import *
+from fastai.vision.learner import cnn_config, create_body
+from torch import nn
+from .unet import DynamicUnetWide, DynamicUnetDeep
+from .dataset import *
+
+# Weights are implicitly read from ./models/ folder
+def gen_inference_wide(
+    root_folder: Path, weights_name: str, nf_factor: int = 2, arch=models.resnet101) -> Learner:
+    data = get_dummy_databunch()
+    learn = gen_learner_wide(
+        data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch
+    )
+    learn.path = root_folder
+    learn.load(weights_name)
+    learn.model.eval()
+    return learn
+
+
+def gen_learner_wide(
+    data: ImageDataBunch, gen_loss, arch=models.resnet101, nf_factor: int = 2
+) -> Learner:
+    return unet_learner_wide(
+        data,
+        arch=arch,
+        wd=1e-3,
+        blur=True,
+        norm_type=NormType.Spectral,
+        self_attention=True,
+        y_range=(-3.0, 3.0),
+        loss_func=gen_loss,
+        nf_factor=nf_factor,
+    )
+
+
+# The code below is meant to be merged into fastaiv1 ideally
+def unet_learner_wide(
+    data: DataBunch,
+    arch: Callable,
+    pretrained: bool = True,
+    blur_final: bool = True,
+    norm_type: Optional[NormType] = NormType,
+    split_on: Optional[SplitFuncOrIdxList] = None,
+    blur: bool = False,
+    self_attention: bool = False,
+    y_range: Optional[Tuple[float, float]] = None,
+    last_cross: bool = True,
+    bottle: bool = False,
+    nf_factor: int = 1,
+    **kwargs: Any
+) -> Learner:
+    "Build Unet learner from `data` and `arch`."
+    meta = cnn_config(arch)
+    body = create_body(arch, pretrained)
+    model = to_device(
+        DynamicUnetWide(
+            body,
+            n_classes=data.c,
+            blur=blur,
+            blur_final=blur_final,
+            self_attention=self_attention,
+            y_range=y_range,
+            norm_type=norm_type,
+            last_cross=last_cross,
+            bottle=bottle,
+            nf_factor=nf_factor,
+        ),
+        data.device,
+    )
+    learn = Learner(data, model, **kwargs)
+    learn.split(ifnone(split_on, meta['split']))
+    if pretrained:
+        learn.freeze()
+    apply_init(model[2], nn.init.kaiming_normal_)
+    return learn
+
+
+# ----------------------------------------------------------------------
+
+# Weights are implicitly read from ./models/ folder
+def gen_inference_deep(
+    root_folder: Path, weights_name: str, arch=models.resnet34, nf_factor: float = 1.5) -> Learner:
+    data = get_dummy_databunch()
+    learn = gen_learner_deep(
+        data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor
+    )
+    learn.path = root_folder
+    learn.load(weights_name)
+    learn.model.eval()
+    return learn
+
+
+def gen_learner_deep(
+    data: ImageDataBunch, gen_loss, arch=models.resnet34, nf_factor: float = 1.5
+) -> Learner:
+    return unet_learner_deep(
+        data,
+        arch,
+        wd=1e-3,
+        blur=True,
+        norm_type=NormType.Spectral,
+        self_attention=True,
+        y_range=(-3.0, 3.0),
+        loss_func=gen_loss,
+        nf_factor=nf_factor,
+    )
+
+
+# The code below is meant to be merged into fastaiv1 ideally
+def unet_learner_deep(
+    data: DataBunch,
+    arch: Callable,
+    pretrained: bool = True,
+    blur_final: bool = True,
+    norm_type: Optional[NormType] = NormType,
+    split_on: Optional[SplitFuncOrIdxList] = None,
+    blur: bool = False,
+    self_attention: bool = False,
+    y_range: Optional[Tuple[float, float]] = None,
+    last_cross: bool = True,
+    bottle: bool = False,
+    nf_factor: float = 1.5,
+    **kwargs: Any
+) -> Learner:
+    "Build Unet learner from `data` and `arch`."
+    meta = cnn_config(arch)
+    body = create_body(arch, pretrained)
+    model = to_device(
+        DynamicUnetDeep(
+            body,
+            n_classes=data.c,
+            blur=blur,
+            blur_final=blur_final,
+            self_attention=self_attention,
+            y_range=y_range,
+            norm_type=norm_type,
+            last_cross=last_cross,
+            bottle=bottle,
+            nf_factor=nf_factor,
+        ),
+        data.device,
+    )
+    learn = Learner(data, model, **kwargs)
+    learn.split(ifnone(split_on, meta['split']))
+    if pretrained:
+        learn.freeze()
+    apply_init(model[2], nn.init.kaiming_normal_)
+    return learn
+
+
+# -----------------------------

deoldify/layers.py

@@ -0,0 +1,46 @@
+from fastai.layers import *
+from fastai.torch_core import *
+
+
+# The code below is meant to be merged into fastaiv1 ideally
+
+
+def custom_conv_layer(
+    ni: int,
+    nf: int,
+    ks: int = 3,
+    stride: int = 1,
+    padding: int = None,
+    bias: bool = None,
+    is_1d: bool = False,
+    norm_type: Optional[NormType] = NormType.Batch,
+    use_activ: bool = True,
+    leaky: float = None,
+    transpose: bool = False,
+    init: Callable = nn.init.kaiming_normal_,
+    self_attention: bool = False,
+    extra_bn: bool = False,
+):
+    "Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers."
+    if padding is None:
+        padding = (ks - 1) // 2 if not transpose else 0
+    bn = norm_type in (NormType.Batch, NormType.BatchZero) or extra_bn == True
+    if bias is None:
+        bias = not bn
+    conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d
+    conv = init_default(
+        conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding),
+        init,
+    )
+    if norm_type == NormType.Weight:
+        conv = weight_norm(conv)
+    elif norm_type == NormType.Spectral:
+        conv = spectral_norm(conv)
+    layers = [conv]
+    if use_activ:
+        layers.append(relu(True, leaky=leaky))
+    if bn:
+        layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf))
+    if self_attention:
+        layers.append(SelfAttention(nf))
+    return nn.Sequential(*layers)

deoldify/loss.py

@@ -0,0 +1,136 @@
+from fastai import *
+from fastai.core import *
+from fastai.torch_core import *
+from fastai.callbacks import hook_outputs
+import torchvision.models as models
+
+
+class FeatureLoss(nn.Module):
+    def __init__(self, layer_wgts=[20, 70, 10]):
+        super().__init__()
+
+        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
+        requires_grad(self.m_feat, False)
+        blocks = [
+            i - 1
+            for i, o in enumerate(children(self.m_feat))
+            if isinstance(o, nn.MaxPool2d)
+        ]
+        layer_ids = blocks[2:5]
+        self.loss_features = [self.m_feat[i] for i in layer_ids]
+        self.hooks = hook_outputs(self.loss_features, detach=False)
+        self.wgts = layer_wgts
+        self.metric_names = ['pixel'] + [f'feat_{i}' for i in range(len(layer_ids))]
+        self.base_loss = F.l1_loss
+
+    def _make_features(self, x, clone=False):
+        self.m_feat(x)
+        return [(o.clone() if clone else o) for o in self.hooks.stored]
+
+    def forward(self, input, target):
+        out_feat = self._make_features(target, clone=True)
+        in_feat = self._make_features(input)
+        self.feat_losses = [self.base_loss(input, target)]
+        self.feat_losses += [
+            self.base_loss(f_in, f_out) * w
+            for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)
+        ]
+
+        self.metrics = dict(zip(self.metric_names, self.feat_losses))
+        return sum(self.feat_losses)
+
+    def __del__(self):
+        self.hooks.remove()
+
+
+# Refactored code, originally from https://github.com/VinceMarron/style_transfer
+class WassFeatureLoss(nn.Module):
+    def __init__(self, layer_wgts=[5, 15, 2], wass_wgts=[3.0, 0.7, 0.01]):
+        super().__init__()
+        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
+        requires_grad(self.m_feat, False)
+        blocks = [
+            i - 1
+            for i, o in enumerate(children(self.m_feat))
+            if isinstance(o, nn.MaxPool2d)
+        ]
+        layer_ids = blocks[2:5]
+        self.loss_features = [self.m_feat[i] for i in layer_ids]
+        self.hooks = hook_outputs(self.loss_features, detach=False)
+        self.wgts = layer_wgts
+        self.wass_wgts = wass_wgts
+        self.metric_names = (
+            ['pixel']
+            + [f'feat_{i}' for i in range(len(layer_ids))]
+            + [f'wass_{i}' for i in range(len(layer_ids))]
+        )
+        self.base_loss = F.l1_loss
+
+    def _make_features(self, x, clone=False):
+        self.m_feat(x)
+        return [(o.clone() if clone else o) for o in self.hooks.stored]
+
+    def _calc_2_moments(self, tensor):
+        chans = tensor.shape[1]
+        tensor = tensor.view(1, chans, -1)
+        n = tensor.shape[2]
+        mu = tensor.mean(2)
+        tensor = (tensor - mu[:, :, None]).squeeze(0)
+        # Prevents nasty bug that happens very occassionally- divide by zero.  Why such things happen?
+        if n == 0:
+            return None, None
+        cov = torch.mm(tensor, tensor.t()) / float(n)
+        return mu, cov
+
+    def _get_style_vals(self, tensor):
+        mean, cov = self._calc_2_moments(tensor)
+        if mean is None:
+            return None, None, None
+        eigvals, eigvects = torch.symeig(cov, eigenvectors=True)
+        eigroot_mat = torch.diag(torch.sqrt(eigvals.clamp(min=0)))
+        root_cov = torch.mm(torch.mm(eigvects, eigroot_mat), eigvects.t())
+        tr_cov = eigvals.clamp(min=0).sum()
+        return mean, tr_cov, root_cov
+
+    def _calc_l2wass_dist(
+        self, mean_stl, tr_cov_stl, root_cov_stl, mean_synth, cov_synth
+    ):
+        tr_cov_synth = torch.symeig(cov_synth, eigenvectors=True)[0].clamp(min=0).sum()
+        mean_diff_squared = (mean_stl - mean_synth).pow(2).sum()
+        cov_prod = torch.mm(torch.mm(root_cov_stl, cov_synth), root_cov_stl)
+        var_overlap = torch.sqrt(
+            torch.symeig(cov_prod, eigenvectors=True)[0].clamp(min=0) + 1e-8
+        ).sum()
+        dist = mean_diff_squared + tr_cov_stl + tr_cov_synth - 2 * var_overlap
+        return dist
+
+    def _single_wass_loss(self, pred, targ):
+        mean_test, tr_cov_test, root_cov_test = targ
+        mean_synth, cov_synth = self._calc_2_moments(pred)
+        loss = self._calc_l2wass_dist(
+            mean_test, tr_cov_test, root_cov_test, mean_synth, cov_synth
+        )
+        return loss
+
+    def forward(self, input, target):
+        out_feat = self._make_features(target, clone=True)
+        in_feat = self._make_features(input)
+        self.feat_losses = [self.base_loss(input, target)]
+        self.feat_losses += [
+            self.base_loss(f_in, f_out) * w
+            for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)
+        ]
+
+        styles = [self._get_style_vals(i) for i in out_feat]
+
+        if styles[0][0] is not None:
+            self.feat_losses += [
+                self._single_wass_loss(f_pred, f_targ) * w
+                for f_pred, f_targ, w in zip(in_feat, styles, self.wass_wgts)
+            ]
+
+        self.metrics = dict(zip(self.metric_names, self.feat_losses))
+        return sum(self.feat_losses)
+
+    def __del__(self):
+        self.hooks.remove()

deoldify/save.py

@@ -0,0 +1,29 @@
+from fastai.basic_train import Learner, LearnerCallback
+from fastai.vision.gan import GANLearner
+
+
+class GANSaveCallback(LearnerCallback):
+    """A `LearnerCallback` that saves history of metrics while training `learn` into CSV `filename`."""
+
+    def __init__(
+        self,
+        learn: GANLearner,
+        learn_gen: Learner,
+        filename: str,
+        save_iters: int = 1000,
+    ):
+        super().__init__(learn)
+        self.learn_gen = learn_gen
+        self.filename = filename
+        self.save_iters = save_iters
+
+    def on_batch_end(self, iteration: int, epoch: int, **kwargs) -> None:
+        if iteration == 0:
+            return
+
+        if iteration % self.save_iters == 0:
+            self._save_gen_learner(iteration=iteration, epoch=epoch)
+
+    def _save_gen_learner(self, iteration: int, epoch: int):
+        filename = '{}_{}_{}'.format(self.filename, epoch, iteration)
+        self.learn_gen.save(filename)

deoldify/unet.py

@@ -0,0 +1,285 @@
+from fastai.layers import *
+from .layers import *
+from fastai.torch_core import *
+from fastai.callbacks.hooks import *
+from fastai.vision import *
+
+
+# The code below is meant to be merged into fastaiv1 ideally
+
+__all__ = ['DynamicUnetDeep', 'DynamicUnetWide']
+
+
+def _get_sfs_idxs(sizes: Sizes) -> List[int]:
+    "Get the indexes of the layers where the size of the activation changes."
+    feature_szs = [size[-1] for size in sizes]
+    sfs_idxs = list(
+        np.where(np.array(feature_szs[:-1]) != np.array(feature_szs[1:]))[0]
+    )
+    if feature_szs[0] != feature_szs[1]:
+        sfs_idxs = [0] + sfs_idxs
+    return sfs_idxs
+
+
+class CustomPixelShuffle_ICNR(nn.Module):
+    "Upsample by `scale` from `ni` filters to `nf` (default `ni`), using `nn.PixelShuffle`, `icnr` init, and `weight_norm`."
+
+    def __init__(
+        self,
+        ni: int,
+        nf: int = None,
+        scale: int = 2,
+        blur: bool = False,
+        leaky: float = None,
+        **kwargs
+    ):
+        super().__init__()
+        nf = ifnone(nf, ni)
+        self.conv = custom_conv_layer(
+            ni, nf * (scale ** 2), ks=1, use_activ=False, **kwargs
+        )
+        icnr(self.conv[0].weight)
+        self.shuf = nn.PixelShuffle(scale)
+        # Blurring over (h*w) kernel
+        # "Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts"
+        # - https://arxiv.org/abs/1806.02658
+        self.pad = nn.ReplicationPad2d((1, 0, 1, 0))
+        self.blur = nn.AvgPool2d(2, stride=1)
+        self.relu = relu(True, leaky=leaky)
+
+    def forward(self, x):
+        x = self.shuf(self.relu(self.conv(x)))
+        return self.blur(self.pad(x)) if self.blur else x
+
+
+class UnetBlockDeep(nn.Module):
+    "A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."
+
+    def __init__(
+        self,
+        up_in_c: int,
+        x_in_c: int,
+        hook: Hook,
+        final_div: bool = True,
+        blur: bool = False,
+        leaky: float = None,
+        self_attention: bool = False,
+        nf_factor: float = 1.0,
+        **kwargs
+    ):
+        super().__init__()
+        self.hook = hook
+        self.shuf = CustomPixelShuffle_ICNR(
+            up_in_c, up_in_c // 2, blur=blur, leaky=leaky, **kwargs
+        )
+        self.bn = batchnorm_2d(x_in_c)
+        ni = up_in_c // 2 + x_in_c
+        nf = int((ni if final_div else ni // 2) * nf_factor)
+        self.conv1 = custom_conv_layer(ni, nf, leaky=leaky, **kwargs)
+        self.conv2 = custom_conv_layer(
+            nf, nf, leaky=leaky, self_attention=self_attention, **kwargs
+        )
+        self.relu = relu(leaky=leaky)
+
+    def forward(self, up_in: Tensor) -> Tensor:
+        s = self.hook.stored
+        up_out = self.shuf(up_in)
+        ssh = s.shape[-2:]
+        if ssh != up_out.shape[-2:]:
+            up_out = F.interpolate(up_out, s.shape[-2:], mode='nearest')
+        cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))
+        return self.conv2(self.conv1(cat_x))
+
+
+class DynamicUnetDeep(SequentialEx):
+    "Create a U-Net from a given architecture."
+
+    def __init__(
+        self,
+        encoder: nn.Module,
+        n_classes: int,
+        blur: bool = False,
+        blur_final=True,
+        self_attention: bool = False,
+        y_range: Optional[Tuple[float, float]] = None,
+        last_cross: bool = True,
+        bottle: bool = False,
+        norm_type: Optional[NormType] = NormType.Batch,
+        nf_factor: float = 1.0,
+        **kwargs
+    ):
+        extra_bn = norm_type == NormType.Spectral
+        imsize = (256, 256)
+        sfs_szs = model_sizes(encoder, size=imsize)
+        sfs_idxs = list(reversed(_get_sfs_idxs(sfs_szs)))
+        self.sfs = hook_outputs([encoder[i] for i in sfs_idxs], detach=False)
+        x = dummy_eval(encoder, imsize).detach()
+
+        ni = sfs_szs[-1][1]
+        middle_conv = nn.Sequential(
+            custom_conv_layer(
+                ni, ni * 2, norm_type=norm_type, extra_bn=extra_bn, **kwargs
+            ),
+            custom_conv_layer(
+                ni * 2, ni, norm_type=norm_type, extra_bn=extra_bn, **kwargs
+            ),
+        ).eval()
+        x = middle_conv(x)
+        layers = [encoder, batchnorm_2d(ni), nn.ReLU(), middle_conv]
+
+        for i, idx in enumerate(sfs_idxs):
+            not_final = i != len(sfs_idxs) - 1
+            up_in_c, x_in_c = int(x.shape[1]), int(sfs_szs[idx][1])
+            do_blur = blur and (not_final or blur_final)
+            sa = self_attention and (i == len(sfs_idxs) - 3)
+            unet_block = UnetBlockDeep(
+                up_in_c,
+                x_in_c,
+                self.sfs[i],
+                final_div=not_final,
+                blur=blur,
+                self_attention=sa,
+                norm_type=norm_type,
+                extra_bn=extra_bn,
+                nf_factor=nf_factor,
+                **kwargs
+            ).eval()
+            layers.append(unet_block)
+            x = unet_block(x)
+
+        ni = x.shape[1]
+        if imsize != sfs_szs[0][-2:]:
+            layers.append(PixelShuffle_ICNR(ni, **kwargs))
+        if last_cross:
+            layers.append(MergeLayer(dense=True))
+            ni += in_channels(encoder)
+            layers.append(res_block(ni, bottle=bottle, norm_type=norm_type, **kwargs))
+        layers += [
+            custom_conv_layer(ni, n_classes, ks=1, use_activ=False, norm_type=norm_type)
+        ]
+        if y_range is not None:
+            layers.append(SigmoidRange(*y_range))
+        super().__init__(*layers)
+
+    def __del__(self):
+        if hasattr(self, "sfs"):
+            self.sfs.remove()
+
+
+# ------------------------------------------------------
+class UnetBlockWide(nn.Module):
+    "A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."
+
+    def __init__(
+        self,
+        up_in_c: int,
+        x_in_c: int,
+        n_out: int,
+        hook: Hook,
+        final_div: bool = True,
+        blur: bool = False,
+        leaky: float = None,
+        self_attention: bool = False,
+        **kwargs
+    ):
+        super().__init__()
+        self.hook = hook
+        up_out = x_out = n_out // 2
+        self.shuf = CustomPixelShuffle_ICNR(
+            up_in_c, up_out, blur=blur, leaky=leaky, **kwargs
+        )
+        self.bn = batchnorm_2d(x_in_c)
+        ni = up_out + x_in_c
+        self.conv = custom_conv_layer(
+            ni, x_out, leaky=leaky, self_attention=self_attention, **kwargs
+        )
+        self.relu = relu(leaky=leaky)
+
+    def forward(self, up_in: Tensor) -> Tensor:
+        s = self.hook.stored
+        up_out = self.shuf(up_in)
+        ssh = s.shape[-2:]
+        if ssh != up_out.shape[-2:]:
+            up_out = F.interpolate(up_out, s.shape[-2:], mode='nearest')
+        cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))
+        return self.conv(cat_x)
+
+
+class DynamicUnetWide(SequentialEx):
+    "Create a U-Net from a given architecture."
+
+    def __init__(
+        self,
+        encoder: nn.Module,
+        n_classes: int,
+        blur: bool = False,
+        blur_final=True,
+        self_attention: bool = False,
+        y_range: Optional[Tuple[float, float]] = None,
+        last_cross: bool = True,
+        bottle: bool = False,
+        norm_type: Optional[NormType] = NormType.Batch,
+        nf_factor: int = 1,
+        **kwargs
+    ):
+
+        nf = 512 * nf_factor
+        extra_bn = norm_type == NormType.Spectral
+        imsize = (256, 256)
+        sfs_szs = model_sizes(encoder, size=imsize)
+        sfs_idxs = list(reversed(_get_sfs_idxs(sfs_szs)))
+        self.sfs = hook_outputs([encoder[i] for i in sfs_idxs], detach=False)
+        x = dummy_eval(encoder, imsize).detach()
+
+        ni = sfs_szs[-1][1]
+        middle_conv = nn.Sequential(
+            custom_conv_layer(
+                ni, ni * 2, norm_type=norm_type, extra_bn=extra_bn, **kwargs
+            ),
+            custom_conv_layer(
+                ni * 2, ni, norm_type=norm_type, extra_bn=extra_bn, **kwargs
+            ),
+        ).eval()
+        x = middle_conv(x)
+        layers = [encoder, batchnorm_2d(ni), nn.ReLU(), middle_conv]
+
+        for i, idx in enumerate(sfs_idxs):
+            not_final = i != len(sfs_idxs) - 1
+            up_in_c, x_in_c = int(x.shape[1]), int(sfs_szs[idx][1])
+            do_blur = blur and (not_final or blur_final)
+            sa = self_attention and (i == len(sfs_idxs) - 3)
+
+            n_out = nf if not_final else nf // 2
+
+            unet_block = UnetBlockWide(
+                up_in_c,
+                x_in_c,
+                n_out,
+                self.sfs[i],
+                final_div=not_final,
+                blur=blur,
+                self_attention=sa,
+                norm_type=norm_type,
+                extra_bn=extra_bn,
+                **kwargs
+            ).eval()
+            layers.append(unet_block)
+            x = unet_block(x)
+
+        ni = x.shape[1]
+        if imsize != sfs_szs[0][-2:]:
+            layers.append(PixelShuffle_ICNR(ni, **kwargs))
+        if last_cross:
+            layers.append(MergeLayer(dense=True))
+            ni += in_channels(encoder)
+            layers.append(res_block(ni, bottle=bottle, norm_type=norm_type, **kwargs))
+        layers += [
+            custom_conv_layer(ni, n_classes, ks=1, use_activ=False, norm_type=norm_type)
+        ]
+        if y_range is not None:
+            layers.append(SigmoidRange(*y_range))
+        super().__init__(*layers)
+
+    def __del__(self):
+        if hasattr(self, "sfs"):
+            self.sfs.remove()

deoldify/visualize.py

@@ -0,0 +1,487 @@
+from fastai.core import *
+from fastai.vision import *
+from matplotlib.axes import Axes
+from .filters import IFilter, MasterFilter, ColorizerFilter
+from .generators import gen_inference_deep, gen_inference_wide
+from PIL import Image
+import ffmpeg
+import yt_dlp as youtube_dl
+import gc
+import requests
+from io import BytesIO
+import base64
+from IPython import display as ipythondisplay
+from IPython.display import HTML
+from IPython.display import Image as ipythonimage
+import cv2
+import logging
+
+# adapted from https://www.pyimagesearch.com/2016/04/25/watermarking-images-with-opencv-and-python/
+def get_watermarked(pil_image: Image) -> Image:
+    try:
+        image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+        (h, w) = image.shape[:2]
+        image = np.dstack([image, np.ones((h, w), dtype="uint8") * 255])
+        pct = 0.05
+        full_watermark = cv2.imread(
+            'C:/Users/galquja/Desktop/Tools/tools/DeOldify/resource_images/watermark.png', cv2.IMREAD_UNCHANGED
+        )
+        (fwH, fwW) = full_watermark.shape[:2]
+        wH = int(pct * h)
+        wW = int((pct * h / fwH) * fwW)
+        watermark = cv2.resize(full_watermark, (wH, wW), interpolation=cv2.INTER_AREA)
+        overlay = np.zeros((h, w, 4), dtype="uint8")
+        (wH, wW) = watermark.shape[:2]
+        overlay[h - wH - 10 : h - 10, 10 : 10 + wW] = watermark
+        # blend the two images together using transparent overlays
+        output = image.copy()
+        cv2.addWeighted(overlay, 0.5, output, 1.0, 0, output)
+        rgb_image = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        final_image = Image.fromarray(rgb_image)
+        return final_image
+    except:
+        # Don't want this to crash everything, so let's just not watermark the image for now.
+        return pil_image
+
+
+class ModelImageVisualizer:
+    def __init__(self, filter: IFilter, results_dir: str = None):
+        self.filter = filter
+        self.results_dir = None if results_dir is None else Path(results_dir)
+        # self.results_dir.mkdir(parents=True, exist_ok=True)
+
+    def _clean_mem(self):
+        torch.cuda.empty_cache()
+        # gc.collect()
+
+    def _open_pil_image(self, path: Path) -> Image:
+        return PIL.Image.open(path).convert('RGB')
+
+    def _get_image_from_url(self, url: str) -> Image:
+        response = requests.get(url, timeout=30, headers={'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'})
+        img = PIL.Image.open(BytesIO(response.content)).convert('RGB')
+        return img
+
+    def plot_transformed_image_from_url(
+        self,
+        url: str,
+        path: str = 'test_images/image.png',
+        results_dir:Path = None,
+        figsize: Tuple[int, int] = (20, 20),
+        render_factor: int = None,
+        
+        display_render_factor: bool = False,
+        compare: bool = False,
+        post_process: bool = True,
+        watermarked: bool = True,
+    ) -> Path:
+        img = self._get_image_from_url(url)
+        img.save(path)
+        return self.plot_transformed_image(
+            path=path,
+            results_dir=results_dir,
+            figsize=figsize,
+            render_factor=render_factor,
+            display_render_factor=display_render_factor,
+            compare=compare,
+            post_process = post_process,
+            watermarked=watermarked,
+        )
+
+    def plot_transformed_image(
+        self,
+        path: str,
+        results_dir:Path = None,
+        figsize: Tuple[int, int] = (20, 20),
+        render_factor: int = None,
+        display_render_factor: bool = False,
+        compare: bool = False,
+        post_process: bool = True,
+        watermarked: bool = True,
+    ) -> Path:
+        path = Path(path)
+        if results_dir is None:
+            results_dir = Path(self.results_dir)
+        result = self.get_transformed_image(
+            path, render_factor, post_process=post_process,watermarked=watermarked
+        )
+        orig = self._open_pil_image(path)
+        if compare:
+            self._plot_comparison(
+                figsize, render_factor, display_render_factor, orig, result
+            )
+        else:
+            self._plot_solo(figsize, render_factor, display_render_factor, result)
+
+        orig.close()
+        result_path = self._save_result_image(path, result, results_dir=results_dir)
+        result.close()
+        return result_path
+
+    def _plot_comparison(
+        self,
+        figsize: Tuple[int, int],
+        render_factor: int,
+        display_render_factor: bool,
+        orig: Image,
+        result: Image,
+    ):
+        fig, axes = plt.subplots(1, 2, figsize=figsize)
+        self._plot_image(
+            orig,
+            axes=axes[0],
+            figsize=figsize,
+            render_factor=render_factor,
+            display_render_factor=False,
+        )
+        self._plot_image(
+            result,
+            axes=axes[1],
+            figsize=figsize,
+            render_factor=render_factor,
+            display_render_factor=display_render_factor,
+        )
+
+    def _plot_solo(
+        self,
+        figsize: Tuple[int, int],
+        render_factor: int,
+        display_render_factor: bool,
+        result: Image,
+    ):
+        fig, axes = plt.subplots(1, 1, figsize=figsize)
+        self._plot_image(
+            result,
+            axes=axes,
+            figsize=figsize,
+            render_factor=render_factor,
+            display_render_factor=display_render_factor,
+        )
+
+    def _save_result_image(self, source_path: Path, image: Image, results_dir = None) -> Path:
+        if results_dir is None:
+            results_dir = Path(self.results_dir)
+        result_path = results_dir
+        image.save(result_path)
+        return result_path
+
+    def get_transformed_image(
+        self, path: Path, render_factor: int = None, post_process: bool = True,
+        watermarked: bool = True,
+    ) -> Image:
+        self._clean_mem()
+        orig_image = self._open_pil_image(path)
+        filtered_image = self.filter.filter(
+            orig_image, orig_image, render_factor=render_factor,post_process=post_process
+        )
+
+        if watermarked:
+            return get_watermarked(filtered_image)
+
+        return filtered_image
+
+    def _plot_image(
+        self,
+        image: Image,
+        render_factor: int,
+        axes: Axes = None,
+        figsize=(20, 20),
+        display_render_factor = False,
+    ):
+        if axes is None:
+            _, axes = plt.subplots(figsize=figsize)
+        axes.imshow(np.asarray(image) / 255)
+        axes.axis('off')
+        if render_factor is not None and display_render_factor:
+            plt.text(
+                10,
+                10,
+                'render_factor: ' + str(render_factor),
+                color='white',
+                backgroundcolor='black',
+            )
+
+    def _get_num_rows_columns(self, num_images: int, max_columns: int) -> Tuple[int, int]:
+        columns = min(num_images, max_columns)
+        rows = num_images // columns
+        rows = rows if rows * columns == num_images else rows + 1
+        return rows, columns
+
+
+class VideoColorizer:
+    def __init__(self, vis: ModelImageVisualizer):
+        self.vis = vis
+        workfolder = Path('./video')
+        self.source_folder = workfolder / "source"
+        self.bwframes_root = workfolder / "bwframes"
+        self.audio_root = workfolder / "audio"
+        self.colorframes_root = workfolder / "colorframes"
+        self.result_folder = workfolder / "result"
+
+    def _purge_images(self, dir):
+        for f in os.listdir(dir):
+            if re.search('.*?\.jpg', f):
+                os.remove(os.path.join(dir, f))
+
+    def _get_ffmpeg_probe(self, path:Path):
+        try:
+            probe = ffmpeg.probe(str(path))
+            return probe
+        except ffmpeg.Error as e:
+            logging.error("ffmpeg error: {0}".format(e), exc_info=True)
+            logging.error('stdout:' + e.stdout.decode('UTF-8'))
+            logging.error('stderr:' + e.stderr.decode('UTF-8'))
+            raise e
+        except Exception as e:
+            logging.error('Failed to instantiate ffmpeg.probe.  Details: {0}'.format(e), exc_info=True)   
+            raise e
+
+    def _get_fps(self, source_path: Path) -> str:
+        probe = self._get_ffmpeg_probe(source_path)
+        stream_data = next(
+            (stream for stream in probe['streams'] if stream['codec_type'] == 'video'),
+            None,
+        )
+        return stream_data['avg_frame_rate']
+
+    def _download_video_from_url(self, source_url, source_path: Path):
+        if source_path.exists():
+            source_path.unlink()
+
+        ydl_opts = {
+            'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4',
+            'outtmpl': str(source_path),
+            'retries': 30,
+            'fragment-retries': 30
+        }
+        with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([source_url])
+
+    def _extract_raw_frames(self, source_path: Path):
+        bwframes_folder = self.bwframes_root / (source_path.stem)
+        bwframe_path_template = str(bwframes_folder / '%5d.jpg')
+        bwframes_folder.mkdir(parents=True, exist_ok=True)
+        self._purge_images(bwframes_folder)
+
+        process = (
+            ffmpeg
+                .input(str(source_path))
+                .output(str(bwframe_path_template), format='image2', vcodec='mjpeg', **{'q:v':'0'})
+                .global_args('-hide_banner')
+                .global_args('-nostats')
+                .global_args('-loglevel', 'error')
+        )
+
+        try:
+            process.run()
+        except ffmpeg.Error as e:
+            logging.error("ffmpeg error: {0}".format(e), exc_info=True)
+            logging.error('stdout:' + e.stdout.decode('UTF-8'))
+            logging.error('stderr:' + e.stderr.decode('UTF-8'))
+            raise e
+        except Exception as e:
+            logging.error('Errror while extracting raw frames from source video.  Details: {0}'.format(e), exc_info=True)   
+            raise e
+
+    def _colorize_raw_frames(
+        self, source_path: Path, render_factor: int = None, post_process: bool = True,
+        watermarked: bool = True,
+    ):
+        colorframes_folder = self.colorframes_root / (source_path.stem)
+        colorframes_folder.mkdir(parents=True, exist_ok=True)
+        self._purge_images(colorframes_folder)
+        bwframes_folder = self.bwframes_root / (source_path.stem)
+
+        for img in progress_bar(os.listdir(str(bwframes_folder))):
+            img_path = bwframes_folder / img
+
+            if os.path.isfile(str(img_path)):
+                color_image = self.vis.get_transformed_image(
+                    str(img_path), render_factor=render_factor, post_process=post_process,watermarked=watermarked
+                )
+                color_image.save(str(colorframes_folder / img))
+
+    def _build_video(self, source_path: Path) -> Path:
+        colorized_path = self.result_folder / (
+            source_path.name.replace('.mp4', '_no_audio.mp4')
+        )
+        colorframes_folder = self.colorframes_root / (source_path.stem)
+        colorframes_path_template = str(colorframes_folder / '%5d.jpg')
+        colorized_path.parent.mkdir(parents=True, exist_ok=True)
+        if colorized_path.exists():
+            colorized_path.unlink()
+        fps = self._get_fps(source_path)
+
+        process = (
+            ffmpeg 
+                .input(str(colorframes_path_template), format='image2', vcodec='mjpeg', framerate=fps) 
+                .output(str(colorized_path), crf=17, vcodec='libx264')
+                .global_args('-hide_banner')
+                .global_args('-nostats')
+                .global_args('-loglevel', 'error')
+        )
+
+        try:
+            process.run()
+        except ffmpeg.Error as e:
+            logging.error("ffmpeg error: {0}".format(e), exc_info=True)
+            logging.error('stdout:' + e.stdout.decode('UTF-8'))
+            logging.error('stderr:' + e.stderr.decode('UTF-8'))
+            raise e
+        except Exception as e:
+            logging.error('Errror while building output video.  Details: {0}'.format(e), exc_info=True)   
+            raise e
+
+        result_path = self.result_folder / source_path.name
+        if result_path.exists():
+            result_path.unlink()
+        # making copy of non-audio version in case adding back audio doesn't apply or fails.
+        shutil.copyfile(str(colorized_path), str(result_path))
+
+        # adding back sound here
+        audio_file = Path(str(source_path).replace('.mp4', '.aac'))
+        if audio_file.exists():
+            audio_file.unlink()
+
+        os.system(
+            'ffmpeg -y -i "'
+            + str(source_path)
+            + '" -vn -acodec copy "'
+            + str(audio_file)
+            + '"'
+            + ' -hide_banner'
+            + ' -nostats'
+            + ' -loglevel error'
+        )
+
+        if audio_file.exists():
+            os.system(
+                'ffmpeg -y -i "'
+                + str(colorized_path)
+                + '" -i "'
+                + str(audio_file)
+                + '" -shortest -c:v copy -c:a aac -b:a 256k "'
+                + str(result_path)
+                + '"'
+                + ' -hide_banner'
+                + ' -nostats'
+                + ' -loglevel error'
+            )
+        logging.info('Video created here: ' + str(result_path))
+        return result_path
+
+    def colorize_from_url(
+        self,
+        source_url,
+        file_name: str,
+        render_factor: int = None,
+        post_process: bool = True,
+        watermarked: bool = True,
+
+    ) -> Path:
+        source_path = self.source_folder / file_name
+        self._download_video_from_url(source_url, source_path)
+        return self._colorize_from_path(
+            source_path, render_factor=render_factor, post_process=post_process,watermarked=watermarked
+        )
+
+    def colorize_from_file_name(
+        self, file_name: str, render_factor: int = None,  watermarked: bool = True, post_process: bool = True,
+    ) -> Path:
+        source_path = self.source_folder / file_name
+        return self._colorize_from_path(
+            source_path, render_factor=render_factor,  post_process=post_process,watermarked=watermarked
+        )
+
+    def _colorize_from_path(
+        self, source_path: Path, render_factor: int = None,  watermarked: bool = True, post_process: bool = True
+    ) -> Path:
+        if not source_path.exists():
+            raise Exception(
+                'Video at path specfied, ' + str(source_path) + ' could not be found.'
+            )
+        self._extract_raw_frames(source_path)
+        self._colorize_raw_frames(
+            source_path, render_factor=render_factor,post_process=post_process,watermarked=watermarked
+        )
+        return self._build_video(source_path)
+
+
+def get_video_colorizer(render_factor: int = 21) -> VideoColorizer:
+    return get_stable_video_colorizer(render_factor=render_factor)
+
+
+def get_artistic_video_colorizer(
+    root_folder: Path = Path('C:/Users/galquja/Desktop/Tools/tools/DeOldify'),
+    weights_name: str = 'ColorizeArtistic_gen',
+    results_dir='result_images',
+    render_factor: int = 35
+) -> VideoColorizer:
+    learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
+    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
+    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
+    return VideoColorizer(vis)
+
+
+def get_stable_video_colorizer(
+    root_folder: Path = Path('C:/Users/galquja/Desktop/Tools/tools/DeOldify'),
+    weights_name: str = 'ColorizeVideo_gen',
+    results_dir='result_images',
+    render_factor: int = 21
+) -> VideoColorizer:
+    learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
+    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
+    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
+    return VideoColorizer(vis)
+
+
+def get_image_colorizer(
+    root_folder: Path = Path('C:/Users/galquja/Desktop/Tools/tools/DeOldify'), render_factor: int = 35, artistic: bool = True
+) -> ModelImageVisualizer:
+    if artistic:
+        return get_artistic_image_colorizer(root_folder=root_folder, render_factor=render_factor)
+    else:
+        return get_stable_image_colorizer(root_folder=root_folder, render_factor=render_factor)
+
+
+def get_stable_image_colorizer(
+    root_folder: Path = Path('C:/Users/galquja/Desktop/Tools/tools/DeOldify'),
+    weights_name: str = 'ColorizeStable_gen',
+    results_dir='result_images',
+    render_factor: int = 35
+) -> ModelImageVisualizer:
+    learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
+    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
+    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
+    return vis
+
+
+def get_artistic_image_colorizer(
+    root_folder: Path = Path('C:/Users/galquja/Desktop/Tools/tools/DeOldify'),
+    weights_name: str = 'ColorizeArtistic_gen',
+    results_dir='result_images',
+    render_factor: int = 35
+) -> ModelImageVisualizer:
+    learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
+    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
+    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
+    return vis
+
+
+def show_image_in_notebook(image_path: Path):
+    ipythondisplay.display(ipythonimage(str(image_path)))
+
+
+def show_video_in_notebook(video_path: Path):
+    video = io.open(video_path, 'r+b').read()
+    encoded = base64.b64encode(video)
+    ipythondisplay.display(
+        HTML(
+            data='''<video alt="test" autoplay 
+                loop controls style="height: 400px;">
+                <source src="data:video/mp4;base64,{0}" type="video/mp4" />
+             </video>'''.format(
+                encoded.decode('ascii')
+            )
+        )
+    )

environment.yml

@@ -0,0 +1,22 @@
+name: deoldify
+channels:
+- fastai
+- conda-forge
+- defaults
+dependencies:
+- pip
+- fastai=1.0.60
+- python=3.10
+- pytorch::pytorch=1.11.0
+- pytorch::torchvision
+- pytorch::torchaudio
+- tensorboardX
+- jupyterlab
+- pillow>=9.0.0
+- ipywidgets
+- ffmpeg
+- pip:
+  - ffmpeg-python
+  - opencv-python>=3.3.0.10
+  - wandb
+  - yt-dlp

fastai/LICENSE

@@ -0,0 +1,60 @@
+Apache License, Version 2.0 Apache License Version 2.0, January 2004 http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
+
+"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
+
+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
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+
+"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+
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+
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+
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+
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+
+5. Submission of Contributions.
+
+Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+6. Trademarks.
+
+This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty.
+
+Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability.
+
+In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability.
+
+While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+

fastai/__init__.py

@@ -0,0 +1,2 @@
+from .version import __version__
+

fastai/basic_data.py

@@ -0,0 +1,279 @@
+"`fastai.data` loads and manages datasets with `DataBunch`"
+from .torch_core import *
+from torch.utils.data.dataloader import default_collate
+
+DatasetType = Enum('DatasetType', 'Train Valid Test Single Fix')
+__all__ = ['DataBunch', 'DeviceDataLoader', 'DatasetType', 'load_data']
+
+old_dl_init = torch.utils.data.DataLoader.__init__
+
+def intercept_args(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
+                 num_workers=0, collate_fn=default_collate, pin_memory=True, drop_last=False,
+                 timeout=0, worker_init_fn=None):
+    self.init_kwargs = {'batch_size':batch_size, 'shuffle':shuffle, 'sampler':sampler, 'batch_sampler':batch_sampler,
+                        'num_workers':num_workers, 'collate_fn':collate_fn, 'pin_memory':pin_memory,
+                        'drop_last': drop_last, 'timeout':timeout, 'worker_init_fn':worker_init_fn}
+    old_dl_init(self, dataset, **self.init_kwargs)
+
+torch.utils.data.DataLoader.__init__ = intercept_args
+
+def DataLoader___getattr__(dl, k:str)->Any: return getattr(dl.dataset, k)
+DataLoader.__getattr__ = DataLoader___getattr__
+
+def DataLoader___setstate__(dl, data:Any): dl.__dict__.update(data)
+DataLoader.__setstate__ = DataLoader___setstate__
+
+@dataclass
+class DeviceDataLoader():
+    "Bind a `DataLoader` to a `torch.device`."
+    dl: DataLoader
+    device: torch.device
+    tfms: List[Callable]=None
+    collate_fn: Callable=data_collate
+    def __post_init__(self):
+        self.dl.collate_fn=self.collate_fn
+        self.tfms = listify(self.tfms)
+
+    def __len__(self)->int: return len(self.dl)
+    def __getattr__(self,k:str)->Any: return getattr(self.dl, k)
+    def __setstate__(self,data:Any): self.__dict__.update(data)
+
+    @property
+    def batch_size(self):   return self.dl.batch_size
+    @batch_size.setter
+    def batch_size(self,v):
+        new_kwargs = {**self.dl.init_kwargs, 'batch_size':v, 'collate_fn':self.collate_fn}
+        self.dl = self.dl.__class__(self.dl.dataset, **new_kwargs)
+        if hasattr(self.dl.dataset, 'bs'): self.dl.dataset.bs = v
+
+    @property
+    def num_workers(self):   return self.dl.num_workers
+    @num_workers.setter
+    def num_workers(self,v): self.dl.num_workers = v
+
+    def add_tfm(self,tfm:Callable)->None:
+        "Add `tfm` to `self.tfms`."
+        self.tfms.append(tfm)
+    def remove_tfm(self,tfm:Callable)->None:
+        "Remove `tfm` from `self.tfms`."
+        if tfm in self.tfms: self.tfms.remove(tfm)
+
+    def new(self, **kwargs):
+        "Create a new copy of `self` with `kwargs` replacing current values."
+        new_kwargs = {**self.dl.init_kwargs, **kwargs}
+        return DeviceDataLoader(self.dl.__class__(self.dl.dataset, **new_kwargs), self.device, self.tfms,
+                                self.collate_fn)
+
+    def proc_batch(self,b:Tensor)->Tensor:
+        "Process batch `b` of `TensorImage`."
+        b = to_device(b, self.device)
+        for f in listify(self.tfms): b = f(b)
+        return b
+
+    def __iter__(self):
+        "Process and returns items from `DataLoader`."
+        for b in self.dl: yield self.proc_batch(b)
+
+    @classmethod
+    def create(cls, dataset:Dataset, bs:int=64, shuffle:bool=False, device:torch.device=defaults.device,
+               tfms:Collection[Callable]=tfms, num_workers:int=defaults.cpus, collate_fn:Callable=data_collate, **kwargs:Any):
+        "Create DeviceDataLoader from `dataset` with `bs` and `shuffle`: process using `num_workers`."
+        return cls(DataLoader(dataset, batch_size=bs, shuffle=shuffle, num_workers=num_workers, **kwargs),
+                   device=device, tfms=tfms, collate_fn=collate_fn)
+
+class DataBunch():
+    "Bind `train_dl`,`valid_dl` and `test_dl` in a data object."
+
+    def __init__(self, train_dl:DataLoader, valid_dl:DataLoader, fix_dl:DataLoader=None, test_dl:Optional[DataLoader]=None,
+                 device:torch.device=None, dl_tfms:Optional[Collection[Callable]]=None, path:PathOrStr='.',
+                 collate_fn:Callable=data_collate, no_check:bool=False):
+        self.dl_tfms = listify(dl_tfms)
+        self.device = defaults.device if device is None else device
+        assert not isinstance(train_dl,DeviceDataLoader)
+        def _create_dl(dl, **kwargs):
+            if dl is None: return None
+            return DeviceDataLoader(dl, self.device, self.dl_tfms, collate_fn, **kwargs)
+        self.train_dl,self.valid_dl,self.fix_dl,self.test_dl = map(_create_dl, [train_dl,valid_dl,fix_dl,test_dl])
+        if fix_dl is None: self.fix_dl = self.train_dl.new(shuffle=False, drop_last=False)
+        self.single_dl = _create_dl(DataLoader(valid_dl.dataset, batch_size=1, num_workers=0))
+        self.path = Path(path)
+        if not no_check: self.sanity_check()
+
+    def __repr__(self)->str:
+        return f'{self.__class__.__name__};\n\nTrain: {self.train_ds};\n\nValid: {self.valid_ds};\n\nTest: {self.test_ds}'
+
+    @staticmethod
+    def _init_ds(train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None):
+        # train_ds, but without training tfms
+        fix_ds = valid_ds.new(train_ds.x, train_ds.y) if hasattr(valid_ds,'new') else train_ds
+        return [o for o in (train_ds,valid_ds,fix_ds,test_ds) if o is not None]
+
+    @classmethod
+    def create(cls, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, path:PathOrStr='.', bs:int=64,
+               val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None,
+               device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, **dl_kwargs)->'DataBunch':
+        "Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()`"
+        datasets = cls._init_ds(train_ds, valid_ds, test_ds)
+        val_bs = ifnone(val_bs, bs)
+        dls = [DataLoader(d, b, shuffle=s, drop_last=s, num_workers=num_workers, **dl_kwargs) for d,b,s in
+               zip(datasets, (bs,val_bs,val_bs,val_bs), (True,False,False,False)) if d is not None]
+        return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)
+
+    def __getattr__(self,k:int)->Any: return getattr(self.train_dl, k)
+    def __setstate__(self,data:Any): self.__dict__.update(data)
+
+    def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
+        "Returns appropriate `Dataset` for validation, training, or test (`ds_type`)."
+        #TODO: refactor
+        return (self.train_dl if ds_type == DatasetType.Train else
+                self.test_dl if ds_type == DatasetType.Test else
+                self.valid_dl if ds_type == DatasetType.Valid else
+                self.single_dl if ds_type == DatasetType.Single else
+                self.fix_dl)
+
+    @property
+    def dls(self)->List[DeviceDataLoader]:
+        "Returns a list of all DeviceDataLoaders. If you need a specific DeviceDataLoader, access via the relevant property (`train_dl`, `valid_dl`, etc) as the index of DLs in this list is not guaranteed to remain constant."
+        res = [self.train_dl, self.fix_dl, self.single_dl]
+        # Preserve the original ordering of Train, Valid, Fix, Single, Test Data Loaders
+        # (Unknown/not verified as of 1.0.47 whether there are other methods explicitly using DLs their list index)
+        if self.valid_dl: res.insert(1, self.valid_dl)
+        return res if not self.test_dl else res + [self.test_dl]
+
+    def add_tfm(self,tfm:Callable)->None:
+        for dl in self.dls: dl.add_tfm(tfm)
+
+    def remove_tfm(self,tfm:Callable)->None:
+        for dl in self.dls: dl.remove_tfm(tfm)
+
+    def save(self, file:PathLikeOrBinaryStream= 'data_save.pkl')->None:
+        "Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer)"
+        if not getattr(self, 'label_list', False):
+            warn("Serializing the `DataBunch` only works when you created it using the data block API.")
+            return
+        try_save(self.label_list, self.path, file)
+
+    def add_test(self, items:Iterator, label:Any=None, tfms=None, tfm_y=None)->None:
+        "Add the `items` as a test set. Pass along `label` otherwise label them with `EmptyLabel`."
+        self.label_list.add_test(items, label=label, tfms=tfms, tfm_y=tfm_y)
+        vdl = self.valid_dl
+        dl = DataLoader(self.label_list.test, vdl.batch_size, shuffle=False, drop_last=False, num_workers=vdl.num_workers)
+        self.test_dl = DeviceDataLoader(dl, vdl.device, vdl.tfms, vdl.collate_fn)
+
+    def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
+        "Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
+        dl = self.dl(ds_type)
+        w = self.num_workers
+        self.num_workers = 0
+        try:     x,y = next(iter(dl))
+        finally: self.num_workers = w
+        if detach: x,y = to_detach(x,cpu=cpu),to_detach(y,cpu=cpu)
+        norm = getattr(self,'norm',False)
+        if denorm and norm:
+            x = self.denorm(x)
+            if norm.keywords.get('do_y',False): y = self.denorm(y, do_x=True)
+        return x,y
+
+    def one_item(self, item, detach:bool=False, denorm:bool=False, cpu:bool=False):
+        "Get `item` into a batch. Optionally `detach` and `denorm`."
+        ds = self.single_ds
+        with ds.set_item(item):
+            return self.one_batch(ds_type=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu)
+
+    def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
+        "Show a batch of data in `ds_type` on a few `rows`."
+        x,y = self.one_batch(ds_type, True, True)
+        if reverse: x,y = x.flip(0),y.flip(0)
+        n_items = rows **2 if self.train_ds.x._square_show else rows
+        if self.dl(ds_type).batch_size < n_items: n_items = self.dl(ds_type).batch_size
+        xs = [self.train_ds.x.reconstruct(grab_idx(x, i)) for i in range(n_items)]
+        #TODO: get rid of has_arg if possible
+        if has_arg(self.train_ds.y.reconstruct, 'x'):
+            ys = [self.train_ds.y.reconstruct(grab_idx(y, i), x=x) for i,x in enumerate(xs)]
+        else : ys = [self.train_ds.y.reconstruct(grab_idx(y, i)) for i in range(n_items)]
+        self.train_ds.x.show_xys(xs, ys, **kwargs)
+ 
+    def export(self, file:PathLikeOrBinaryStream='export.pkl'):
+        "Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer)"
+        xtra = dict(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {}
+        try_save(self.valid_ds.get_state(**xtra), self.path, file)
+
+    def _grab_dataset(self, dl:DataLoader):
+        ds = dl.dl.dataset
+        while hasattr(ds, 'dataset'): ds = ds.dataset
+        return ds
+
+    @property
+    def train_ds(self)->Dataset: return self._grab_dataset(self.train_dl)
+    @property
+    def valid_ds(self)->Dataset: return self._grab_dataset(self.valid_dl)
+    @property
+    def single_ds(self)->Dataset: return self._grab_dataset(self.single_dl)
+    @property
+    def loss_func(self)->OptLossFunc:
+        return getattr(self.train_ds.y, 'loss_func', F.nll_loss) if hasattr(self.train_ds, 'y') else F.nll_loss
+
+    @property
+    def test_ds(self)->Dataset:
+        return self._grab_dataset(self.test_dl) if self.test_dl is not None else None
+
+    @property
+    def empty_val(self)->bool:
+        if not hasattr(self, 'valid_dl') or self.valid_dl is None:            return True
+        if hasattr(self.valid_ds, 'items') and len(self.valid_ds.items) == 0: return True
+        return (len(self.valid_ds) == 0)
+
+    @property
+    def is_empty(self)->bool:
+        return not ((self.train_dl and len(self.train_ds.items) != 0) or 
+                    (self.valid_dl and len(self.valid_ds.items) != 0) or 
+                    (self.test_dl  and len(self.test_ds.items)  != 0))
+    
+    @property
+    def batch_size(self):   return self.train_dl.batch_size
+    @batch_size.setter
+    def batch_size(self,v):
+        self.train_dl.batch_size,self.valid_dl.batch_size = v,v
+        if self.test_dl is not None: self.test_dl.batch_size = v
+
+    def sanity_check(self):
+        "Check the underlying data in the training set can be properly loaded."
+        final_message = "You can deactivate this warning by passing `no_check=True`."
+        if not hasattr(self.train_ds, 'items') or len(self.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): return
+        if len(self.train_dl) == 0:
+            warn(f"""Your training dataloader is empty, you have only {len(self.train_dl.dataset)} items in your training set.
+                 Your batch size is {self.train_dl.batch_size}, you should lower it.""")
+            print(final_message)
+            return
+        idx = next(iter(self.train_dl.batch_sampler))
+        samples,fails = [],[]
+        for i in idx:
+            try:    samples.append(self.train_dl.dataset[i])
+            except: fails.append(i)
+        if len(fails) > 0:
+            warn_msg = "There seems to be something wrong with your dataset, for example, in the first batch can't access"
+            if len(fails) == len(idx):
+                warn_msg += f" any element of self.train_ds.\nTried: {show_some(idx)}"
+            else:
+                warn_msg += f" these elements in self.train_ds: {show_some(fails)}"
+            warn(warn_msg)
+            print(final_message)
+            return
+        try: batch = self.collate_fn(samples)
+        except:
+            message = "It's not possible to collate samples of your dataset together in a batch."
+            try:
+                shapes = [[o[i].data.shape for o in samples] for i in range(2)]
+                message += f'\nShapes of the inputs/targets:\n{shapes}'
+            except: pass
+            warn(message)
+            print(final_message)
+
+def load_data(path:PathOrStr, file:PathLikeOrBinaryStream='data_save.pkl', bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus,
+              dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate,
+              no_check:bool=False, **kwargs)->DataBunch:
+    "Load a saved `DataBunch` from `path/file`. `file` can be file-like (file or buffer)"
+    source = Path(path)/file if is_pathlike(file) else file
+    ll = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)
+    return ll.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, dl_tfms=dl_tfms, device=device,
+                        collate_fn=collate_fn, no_check=no_check, **kwargs)

fastai/basic_train.py

@@ -0,0 +1,629 @@
+"Provides basic training and validation with `Learner`"
+from .torch_core import *
+from .basic_data import *
+from .callback import *
+from .data_block import *
+from .utils.ipython import gpu_mem_restore
+import inspect
+from fastprogress.fastprogress import format_time, IN_NOTEBOOK
+from time import time
+from fastai.sixel import plot_sixel
+
+__all__ = ['Learner', 'LearnerCallback', 'Recorder', 'RecordOnCPU', 'fit', 'loss_batch', 'train_epoch', 'validate',
+           'get_preds', 'load_learner']
+
+defaults.lr = slice(3e-3)
+defaults.wd = 1e-2
+defaults.extra_callbacks    = None
+defaults.extra_callback_fns = None
+
+def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
+               cb_handler:Optional[CallbackHandler]=None, count:[int]=[1], batch_multiplier:int=1)->Tuple[Union[Tensor,int,float,str]]:
+    "Calculate loss and metrics for a batch, call out to callbacks as necessary."
+    cb_handler = ifnone(cb_handler, CallbackHandler())
+    if not is_listy(xb): xb = [xb]
+    if not is_listy(yb): yb = [yb]
+    out = model(*xb)
+
+    if not loss_func: return to_detach(out), yb[0].detach()
+    out = cb_handler.on_loss_begin(out)
+    loss = loss_func(out, *yb)/batch_multiplier
+    count[0]-=1
+
+    if opt is not None:
+        loss,skip_bwd = cb_handler.on_backward_begin(loss)
+        if not skip_bwd:                     loss.backward()
+        if count[0] == 0:
+            if not cb_handler.on_backward_end(): opt.step()
+            if not cb_handler.on_step_end():     opt.zero_grad()
+            count[0] = batch_multiplier
+
+    return loss.detach().cpu()
+
+def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,
+              activ:nn.Module=None, loss_func:OptLossFunc=None, n_batch:Optional[int]=None) -> List[Tensor]:
+    "Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batches `n_batch`."
+    res = [torch.cat(o).cpu() for o in
+           zip(*validate(model, dl, cb_handler=cb_handler, pbar=pbar, average=False, n_batch=n_batch))]
+    if loss_func is not None:
+        with NoneReduceOnCPU(loss_func) as lf: res.append(lf(res[0], res[1]))
+    if activ is not None: res[0] = activ(res[0])
+    return res
+
+def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
+             pbar:Optional[PBar]=None, average=True, n_batch:Optional[int]=None)->Iterator[Tuple[Union[Tensor,int],...]]:
+    "Calculate `loss_func` of `model` on `dl` in evaluation mode."
+    model.eval()
+    with torch.no_grad():
+        val_losses,nums = [],[]
+        if cb_handler: cb_handler.set_dl(dl)
+        for xb,yb in progress_bar(dl, parent=pbar, leave=(pbar is not None)):
+            if cb_handler: xb, yb = cb_handler.on_batch_begin(xb, yb, train=False)
+            val_loss = loss_batch(model, xb, yb, loss_func, cb_handler=cb_handler)
+            val_losses.append(val_loss)
+            if not is_listy(yb): yb = [yb]
+            nums.append(first_el(yb).shape[0])
+            if cb_handler and cb_handler.on_batch_end(val_losses[-1]): break
+            if n_batch and (len(nums)>=n_batch): break
+        nums = np.array(nums, dtype=np.float32)
+        if average: return (to_np(torch.stack(val_losses)) * nums).sum() / nums.sum()
+        else:       return val_losses
+
+def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
+    "Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`."
+    model.train()
+    for xb,yb in dl:
+        loss = loss_func(model(xb), yb)
+        loss.backward()
+        opt.step()
+        opt.zero_grad()
+
+@dataclass
+class BasicLearner():
+    model:nn.Module
+    loss_func:LossFunction
+    opt:optim.Optimizer
+    data:DataBunch
+
+def fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None, batch_multiplier:int=1)->None:
+    "Fit the `model` on `data` and learn using `loss_func` and `opt`."
+    assert len(learn.data.train_dl) != 0, f"""Your training dataloader is empty, can't train a model.
+        Use a smaller batch size (batch size={learn.data.train_dl.batch_size} for {len(learn.data.train_dl.dataset)} elements)."""
+    cb_handler = CallbackHandler(callbacks, metrics)
+    pbar = master_bar(range(epochs))
+    cb_handler.on_train_begin(epochs, pbar=pbar, metrics=metrics)
+
+    exception=False
+    try:
+        for epoch in pbar:
+            learn.model.train()
+            cb_handler.set_dl(learn.data.train_dl)
+            cb_handler.on_epoch_begin()
+            count = [batch_multiplier]
+            for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
+                xb, yb = cb_handler.on_batch_begin(xb, yb)
+                loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler, count=count, batch_multiplier=batch_multiplier)
+                if cb_handler.on_batch_end(loss): break
+
+            if not cb_handler.skip_validate and not learn.data.empty_val:
+                val_loss = validate(learn.model, learn.data.valid_dl, loss_func=learn.loss_func,
+                                       cb_handler=cb_handler, pbar=pbar)
+            else: val_loss=None
+            if cb_handler.on_epoch_end(val_loss): break
+    except Exception as e:
+        exception = e
+        raise
+    finally: cb_handler.on_train_end(exception)
+
+loss_func_name2activ = {'cross_entropy_loss': F.softmax, 'nll_loss': torch.exp, 'poisson_nll_loss': torch.exp,
+    'kl_div_loss': torch.exp, 'bce_with_logits_loss': torch.sigmoid, 'cross_entropy': F.softmax,
+    'kl_div': torch.exp, 'binary_cross_entropy_with_logits': torch.sigmoid,
+}
+
+def _loss_func_name2activ(name:str, axis:int=-1):
+    res = loss_func_name2activ[name]
+    if res == F.softmax: res = partial(F.softmax, dim=axis)
+    return res
+
+def _loss_func2activ(loss_func):
+    if getattr(loss_func,'keywords',None):
+        if not loss_func.keywords.get('log_input', True): return
+    axis = getattr(loss_func, 'axis', -1)
+    # flattened loss
+    loss_func = getattr(loss_func, 'func', loss_func)
+    # could have a partial inside flattened loss! Duplicate on purpose.
+    loss_func = getattr(loss_func, 'func', loss_func)
+    cls_name = camel2snake(loss_func.__class__.__name__)
+    if cls_name == 'mix_up_loss':
+        loss_func = loss_func.crit
+        cls_name = camel2snake(loss_func.__class__.__name__)
+    if cls_name in loss_func_name2activ:
+        if cls_name == 'poisson_nll_loss' and (not getattr(loss_func, 'log_input', True)): return
+        return _loss_func_name2activ(cls_name, axis)
+    if getattr(loss_func,'__name__','') in loss_func_name2activ:
+        return _loss_func_name2activ(loss_func.__name__, axis)
+    return noop
+
+@dataclass
+class Learner():
+    "Trainer for `model` using `data` to minimize `loss_func` with optimizer `opt_func`."
+    data:DataBunch
+    model:nn.Module
+    opt_func:Callable=AdamW
+    loss_func:Callable=None
+    metrics:Collection[Callable]=None
+    true_wd:bool=True
+    bn_wd:bool=True
+    wd:Floats=defaults.wd
+    train_bn:bool=True
+    path:str = None
+    model_dir:PathOrStr = 'models'
+    callback_fns:Collection[Callable]=None
+    callbacks:Collection[Callback]=field(default_factory=list)
+    layer_groups:Collection[nn.Module]=None
+    add_time:bool=True
+    silent:bool=None
+    def __post_init__(self)->None:
+        "Setup path,metrics, callbacks and ensure model directory exists."
+        self.path = Path(ifnone(self.path, self.data.path))
+        self.model = self.model.to(self.data.device)
+        self.loss_func = self.loss_func or self.data.loss_func
+        self.metrics=listify(self.metrics)
+        if not self.layer_groups: self.layer_groups = [nn.Sequential(*flatten_model(self.model))]
+        self.callbacks = listify(self.callbacks)
+        if self.silent is None: self.silent = defaults.silent
+        self.callback_fns = [partial(Recorder, add_time=self.add_time, silent=self.silent)] + listify(self.callback_fns)
+
+    def init(self, init): apply_init(self.model, init)
+
+    def _test_writeable_path(self):
+        path = self.path/self.model_dir
+        try:
+            path.mkdir(parents=True, exist_ok=True)
+            tmp_file = get_tmp_file(path)
+        except OSError as e:
+            raise Exception(f"{e}\nCan't write to '{path}', set `learn.model_dir` attribute in Learner to a full libpath path that is writable") from None
+        os.remove(tmp_file)
+
+    def lr_range(self, lr:Union[float,slice])->np.ndarray:
+        "Build differential learning rates from `lr`."
+        if not isinstance(lr,slice): return lr
+        if lr.start: res = even_mults(lr.start, lr.stop, len(self.layer_groups))
+        else: res = [lr.stop/10]*(len(self.layer_groups)-1) + [lr.stop]
+        return np.array(res)
+
+    def fit(self, epochs:int, lr:Union[Floats,slice]=defaults.lr,
+            wd:Floats=None, callbacks:Collection[Callback]=None, batch_multiplier:int=1)->None:
+        "Fit the model on this learner with `lr` learning rate, `wd` weight decay for `epochs` with `callbacks`."
+        lr = self.lr_range(lr)
+        if wd is None: wd = self.wd
+        if not getattr(self, 'opt', False): self.create_opt(lr, wd)
+        else: self.opt.lr,self.opt.wd = lr,wd
+        callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
+        if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
+        fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks, batch_multiplier=batch_multiplier)
+
+    def create_opt(self, lr:Floats, wd:Floats=0.)->None:
+        "Create optimizer with `lr` learning rate and `wd` weight decay."
+        self.opt = OptimWrapper.create(self.opt_func, lr, self.layer_groups, wd=wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
+
+    def split(self, split_on:SplitFuncOrIdxList)->None:
+        "Split the model at `split_on`."
+        if isinstance(split_on,Callable): split_on = split_on(self.model)
+        self.layer_groups = split_model(self.model, split_on)
+        return self
+
+    def freeze_to(self, n:int)->None:
+        "Freeze layers up to layer group `n`."
+        for g in self.layer_groups[:n]:
+            for l in g:
+                if not self.train_bn or not isinstance(l, bn_types): requires_grad(l, False)
+        for g in self.layer_groups[n:]: requires_grad(g, True)
+        self.create_opt(defaults.lr)
+
+    def freeze(self)->None:
+        "Freeze up to last layer group."
+        assert(len(self.layer_groups)>1)
+        self.freeze_to(-1)
+
+    def unfreeze(self):
+        "Unfreeze entire model."
+        self.freeze_to(0)
+
+    def export(self, file:PathLikeOrBinaryStream='export.pkl', destroy=False):
+        "Export the state of the `Learner` in `self.path/file`. `file` can be file-like (file or buffer)"
+        if rank_distrib(): return # don't save if slave proc
+        args = ['opt_func', 'loss_func', 'metrics', 'true_wd', 'bn_wd', 'wd', 'train_bn', 'model_dir', 'callback_fns']
+        state = {a:getattr(self,a) for a in args}
+        state['cb_state'] = {cb.__class__:cb.get_state() for cb in self.callbacks}
+        #layer_groups -> need to find a way
+        #TO SEE: do we save model structure and weights separately?
+        with ModelOnCPU(self.model) as m:
+            state['model'] = m
+            xtra = dict(normalize=self.data.norm.keywords) if getattr(self.data, 'norm', False) else {}
+            state['data'] = self.data.valid_ds.get_state(**xtra)
+            state['cls'] = self.__class__
+            try_save(state, self.path, file)
+        if destroy: self.destroy()
+
+    def save(self, file:PathLikeOrBinaryStream=None, return_path:bool=False, with_opt:bool=True):
+        "Save model and optimizer state (if `with_opt`) with `file` to `self.model_dir`. `file` can be file-like (file or buffer)"
+        if is_pathlike(file): self._test_writeable_path()
+        if rank_distrib(): return # don't save if slave proc
+        target = self.path/self.model_dir/f'{file}.pth' if is_pathlike(file) else file
+        if not hasattr(self, 'opt'): with_opt=False
+        if not with_opt: state = get_model(self.model).state_dict()
+        else: state = {'model': get_model(self.model).state_dict(), 'opt':self.opt.state_dict()}
+        torch.save(state, target)
+        if return_path: return target
+
+    def dl(self, ds_type:DatasetType=DatasetType.Valid):
+        "Return DataLoader for DatasetType `ds_type`."
+        return self.data.dl(ds_type)
+
+    def load(self, file:PathLikeOrBinaryStream=None, device:torch.device=None, strict:bool=True,
+             with_opt:bool=None, purge:bool=True, remove_module:bool=False):
+        "Load model and optimizer state (if `with_opt`) `file` from `self.model_dir` using `device`. `file` can be file-like (file or buffer)"
+        if purge: self.purge(clear_opt=ifnone(with_opt, False))
+        if device is None: device = self.data.device
+        elif isinstance(device, int): device = torch.device('cuda', device)
+        source = self.path/self.model_dir/f'{file}.pth' if is_pathlike(file) else file
+        state = torch.load(source, map_location=device)
+        if set(state.keys()) == {'model', 'opt'}:
+            model_state = state['model']
+            if remove_module: model_state = remove_module_load(model_state)
+            get_model(self.model).load_state_dict(model_state, strict=strict)
+            if ifnone(with_opt,True):
+                if not hasattr(self, 'opt'): self.create_opt(defaults.lr, self.wd)
+                try:    self.opt.load_state_dict(state['opt'])
+                except: pass
+        else:
+            if with_opt: warn("Saved filed doesn't contain an optimizer state.")
+            if remove_module: state = remove_module_load(state)
+            get_model(self.model).load_state_dict(state, strict=strict)
+        del state
+        gc.collect()
+        return self
+
+    def destroy(self):
+        "Free the Learner internals, leaving just an empty shell that consumes no memory"
+
+        class ZombieLearner(Learner):
+            msg = "this object has been destroyed"
+            def __getattr__(self, item):    print(ZombieLearner.msg); return None
+            def destroyed(*args, **kwargs): print(ZombieLearner.msg)
+
+        attrs = [k for k in self.__dict__.keys() if not k.startswith("__")]
+        for a in attrs: delattr(self, a)
+        # the instance methods can still be called, but will just give a message
+        methods = [k for k in dir(self) if not k.startswith("__") and inspect.isroutine(getattr(self, k))]
+        for m in methods: setattr(self, m, ZombieLearner.destroyed)
+        self.__class__ = ZombieLearner
+        gc.collect()
+        print("this Learner object self-destroyed - it still exists, but no longer usable")
+
+    def purge(self, clear_opt:bool=True):
+        "Purge the `Learner` of all cached attributes to release some GPU memory."
+        self._test_writeable_path()
+        attrs_all = [k for k in self.__dict__.keys() if not k.startswith("__")]
+        attrs_pkl = ['bn_wd', 'callback_fns', 'layer_groups', 'loss_func', 'metrics', 'model',
+                     'model_dir', 'opt_func', 'path', 'train_bn', 'true_wd', 'wd']
+        # +callbacks: get pickled too, but not directly
+        attrs_keep = ['data', 'recorder']
+        attrs_del = list(set(attrs_all) - set(attrs_keep))
+        state = {a:getattr(self, a) for a in attrs_pkl}
+        state['cb_state'] = {cb.__class__:cb.get_state() for cb in self.callbacks}
+        if hasattr(self, 'opt'): state['opt'] = self.opt.get_state()
+
+        tmp_file = get_tmp_file(self.path/self.model_dir)
+        torch.save(state, open(tmp_file, 'wb'))
+        for a in attrs_del: delattr(self, a)
+        gc.collect()
+        state = torch.load(tmp_file)
+        os.remove(tmp_file)
+
+        for a in attrs_pkl: setattr(self, a, state[a])
+        cb_state = state.pop('cb_state')
+        self.callbacks = [load_callback(c,s, self) for c,s in cb_state.items()]
+        if not clear_opt and 'opt' in state:
+            try: self.opt = OptimWrapper.load_with_state_and_layer_group(state['opt'], self.layer_groups)
+            except: warn("Wasn't able to properly load the optimizer state again.")
+        del state
+        gc.collect()
+        return self
+
+    def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None,
+                  pbar:Optional[PBar]=None) -> List[Tensor]:
+        "Return predictions and targets on `ds_type` dataset."
+        lf = self.loss_func if with_loss else None
+        return get_preds(self.model, self.dl(ds_type), cb_handler=CallbackHandler(self.callbacks),
+                         activ=_loss_func2activ(self.loss_func), loss_func=lf, n_batch=n_batch, pbar=pbar)
+
+    def pred_batch(self, ds_type:DatasetType=DatasetType.Valid, batch:Tuple=None, reconstruct:bool=False, with_dropout:bool=False) -> List[Tensor]:
+        with torch.no_grad():
+            training = self.model.training
+            self.model.train(False)
+            "Return output of the model on one batch from `ds_type` dataset."
+            if batch is not None: xb,yb = batch
+            else: xb,yb = self.data.one_batch(ds_type, detach=False, denorm=False)
+            cb_handler = CallbackHandler(self.callbacks)
+            #xb,yb = cb_handler.on_batch_begin(xb,yb, train=False)
+            if not with_dropout: 
+                preds = loss_batch(self.model.eval(), xb, yb, cb_handler=cb_handler)
+            else: 
+                preds = loss_batch(self.model.eval().apply(self.apply_dropout), xb, yb, cb_handler=cb_handler)
+            res = _loss_func2activ(self.loss_func)(preds[0])
+            self.model.train(training)
+            if not reconstruct: return res
+            res = res.detach().cpu()
+            ds = self.dl(ds_type).dataset
+            norm = getattr(self.data, 'norm', False)
+            if norm and norm.keywords.get('do_y',False):
+                res = self.data.denorm(res, do_x=True)
+            return [ds.reconstruct(o) for o in res]
+
+    def backward(self, item):
+        "Pass `item` through the model and computes the gradient. Useful if `backward_hooks` are attached."
+        xb,yb = self.data.one_item(item)
+        loss = loss_batch(self.model.eval(), xb, yb, self.loss_func, opt=FakeOptimizer(),
+                          cb_handler=CallbackHandler(self.callbacks))
+        return loss
+
+    def predict(self, item:ItemBase, return_x:bool=False, batch_first:bool=True, with_dropout:bool=False, **kwargs):
+        "Return predicted class, label and probabilities for `item`."
+        batch = self.data.one_item(item)
+        res = self.pred_batch(batch=batch, with_dropout=with_dropout)
+        raw_pred,x = grab_idx(res,0,batch_first=batch_first),batch[0]
+        norm = getattr(self.data,'norm',False)
+        if norm:
+            x = self.data.denorm(x)
+            if norm.keywords.get('do_y',False): raw_pred = self.data.denorm(raw_pred)
+        ds = self.data.single_ds
+        pred = ds.y.analyze_pred(raw_pred, **kwargs)
+        x = ds.x.reconstruct(grab_idx(x, 0))
+        y = ds.y.reconstruct(pred, x) if has_arg(ds.y.reconstruct, 'x') else ds.y.reconstruct(pred)
+        return (x, y, pred, raw_pred) if return_x else (y, pred, raw_pred)
+
+    def validate(self, dl=None, callbacks=None, metrics=None):
+        "Validate on `dl` with potential `callbacks` and `metrics`."
+        dl = ifnone(dl, self.data.valid_dl)
+        metrics = ifnone(metrics, self.metrics)
+        cb_handler = CallbackHandler(self.callbacks + ifnone(callbacks, []), metrics)
+        cb_handler.on_epoch_begin()
+        val_metrics = validate(self.model, dl, self.loss_func, cb_handler)
+        cb_handler.on_epoch_end(val_metrics)
+        return cb_handler.state_dict['last_metrics']
+
+    def show_results(self, ds_type=DatasetType.Valid, rows:int=5, **kwargs):
+        "Show `rows` result of predictions on `ds_type` dataset."
+        #TODO: get read of has_arg x and split_kwargs_by_func if possible
+        #TODO: simplify this and refactor with pred_batch(...reconstruct=True)
+        n_items = rows ** 2 if self.data.train_ds.x._square_show_res else rows
+        if self.dl(ds_type).batch_size < n_items: n_items = self.dl(ds_type).batch_size
+        ds = self.dl(ds_type).dataset
+        self.callbacks.append(RecordOnCPU())
+        preds = self.pred_batch(ds_type)
+        *self.callbacks,rec_cpu = self.callbacks
+        x,y = rec_cpu.input,rec_cpu.target
+        norm = getattr(self.data,'norm',False)
+        if norm:
+            x = self.data.denorm(x)
+            if norm.keywords.get('do_y',False):
+                y     = self.data.denorm(y, do_x=True)
+                preds = self.data.denorm(preds, do_x=True)
+        analyze_kwargs,kwargs = split_kwargs_by_func(kwargs, ds.y.analyze_pred)
+        preds = [ds.y.analyze_pred(grab_idx(preds, i), **analyze_kwargs) for i in range(n_items)]
+        xs = [ds.x.reconstruct(grab_idx(x, i)) for i in range(n_items)]
+        if has_arg(ds.y.reconstruct, 'x'):
+            ys = [ds.y.reconstruct(grab_idx(y, i), x=x) for i,x in enumerate(xs)]
+            zs = [ds.y.reconstruct(z, x=x) for z,x in zip(preds,xs)]
+        else :
+            ys = [ds.y.reconstruct(grab_idx(y, i)) for i in range(n_items)]
+            zs = [ds.y.reconstruct(z) for z in preds]
+        ds.x.show_xyzs(xs, ys, zs, **kwargs)
+
+    def apply_dropout(self, m):
+        "If a module contains 'dropout' in it's name, it will be switched to .train() mode."
+        if 'dropout' in m.__class__.__name__.lower(): m.train()
+
+    def predict_with_mc_dropout(self, item:ItemBase, with_dropout:bool=True, n_times=10, **kwargs):
+        "Make predictions with dropout turned on for n_times (default 10)."
+        return [self.predict(item, with_dropout=with_dropout) for _ in range(n_times)]
+
+class RecordOnCPU(Callback):
+    "Store the `input` and `target` going through the model on the CPU."
+    def on_batch_begin(self, last_input,last_target,**kwargs):
+        self.input,self.target = to_cpu(last_input),to_cpu(last_target)
+
+class LearnerCallback(Callback):
+    "Base class for creating callbacks for a `Learner`."
+    def __init__(self, learn):
+        self._learn = weakref.ref(learn)
+        self.exclude,self.not_min = ['_learn'],[]
+        setattr(self.learn, self.cb_name, self)
+
+    def __getattr__(self,k): return getattr(self.learn, k)
+    def __setstate__(self,data:Any): self.__dict__.update(data)
+
+    @property
+    def learn(self) -> Learner: return self._learn()
+    @learn.setter
+    def learn(self, learn: Learner) -> None: self._learn = weakref.ref(learn)
+
+    @property
+    def cb_name(self): return camel2snake(self.__class__.__name__)
+
+class Recorder(LearnerCallback):
+    "A `LearnerCallback` that records epoch, loss, opt and metric data during training."
+    _order=-10
+    def __init__(self, learn:Learner, add_time:bool=True, silent:bool=False):
+        super().__init__(learn)
+        self.opt = self.learn.opt
+        self.train_dl = self.learn.data.train_dl
+        self.no_val,self.silent,self.add_time = False,silent,add_time
+
+    def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
+        "Initialize recording status at beginning of training."
+        self.pbar = pbar
+        self.names = ['epoch', 'train_loss'] if self.no_val else ['epoch', 'train_loss', 'valid_loss']
+        self.metrics_names = metrics_names
+        if hasattr(self, '_added_met_names'): self.metrics_names += self._added_met_names
+        self.names += self.metrics_names
+        if self.add_time: self.names.append('time')
+        if not self.silent: self.pbar.write(self.names, table=True)
+        self.losses,self.val_losses,self.lrs,self.moms,self.metrics,self.nb_batches = [],[],[],[],[],[]
+
+    def on_epoch_begin(self, **kwargs:Any)->None:
+        if self.add_time: self.start_epoch = time()
+
+    def on_batch_begin(self, train, **kwargs:Any)->None:
+        "Record learning rate and momentum at beginning of batch."
+        if train:
+            self.lrs.append(self.opt.lr)
+            self.moms.append(self.opt.mom)
+
+    def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
+        "Record the loss before any other callback has a chance to modify it."
+        self.losses.append(smooth_loss)
+        if self.pbar is not None and hasattr(self.pbar,'child'):
+            self.pbar.child.comment = f'{smooth_loss:.4f}'
+
+    def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
+                     last_metrics=MetricsList, **kwargs:Any)->bool:
+        "Save epoch info: num_batch, smooth_loss, metrics."
+        self.nb_batches.append(num_batch)
+        if last_metrics is not None: self.val_losses.append(last_metrics[0])
+        else: last_metrics = [] if self.no_val else [None]
+        if len(last_metrics) > 1: self.metrics.append(last_metrics[1:])
+        self.format_stats([epoch, smooth_loss] + last_metrics)
+
+    def format_stats(self, stats:TensorOrNumList)->None:
+        "Format stats before printing."
+        str_stats = []
+        for name,stat in zip(self.names,stats):
+            str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
+        if self.add_time: str_stats.append(format_time(time() - self.start_epoch))
+        if not self.silent: self.pbar.write(str_stats, table=True)
+
+    def add_metric_names(self, names):
+        "Add `names` to the inner metric names."
+        if hasattr(self, '_added_met_names'): self._added_met_names += names
+        else:                                 self._added_met_names  = names
+
+    def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
+        "Plot learning rate, `show_moms` to include momentum."
+        lrs = self._split_list(self.lrs, skip_start, skip_end)
+        iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
+        if show_moms:
+            moms = self._split_list(self.moms, skip_start, skip_end)
+            fig, axs = plt.subplots(1,2, figsize=(12,4))
+            axs[0].plot(iterations, lrs)
+            axs[0].set_xlabel('Iterations')
+            axs[0].set_ylabel('Learning Rate')
+            axs[1].plot(iterations, moms)
+            axs[1].set_xlabel('Iterations')
+            axs[1].set_ylabel('Momentum')
+        else:
+            fig, ax = plt.subplots()
+            ax.plot(iterations, lrs)
+            ax.set_xlabel('Iterations')
+            ax.set_ylabel('Learning Rate')
+        if ifnone(return_fig, defaults.return_fig): return fig
+        if not IN_NOTEBOOK: plot_sixel(fig)
+
+    @staticmethod
+    def smoothen_by_spline(xs, ys, **kwargs):
+        xs = np.arange(len(ys))
+        spl = scipy.interpolate.UnivariateSpline(xs, ys, **kwargs)
+        ys = spl(xs)
+        return ys
+
+    def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
+             **kwargs)->Optional[plt.Figure]:
+        "Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
+        lrs = self._split_list(self.lrs, skip_start, skip_end)
+        losses = self._split_list(self.losses, skip_start, skip_end)
+        losses = [x.item() for x in losses]
+        if 'k' in kwargs: losses = self.smoothen_by_spline(lrs, losses, **kwargs)
+        fig, ax = plt.subplots(1,1)
+        ax.plot(lrs, losses)
+        ax.set_ylabel("Loss")
+        ax.set_xlabel("Learning Rate")
+        ax.set_xscale('log')
+        ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0e'))
+        if suggestion:
+            try: mg = (np.gradient(np.array(losses))).argmin()
+            except:
+                print("Failed to compute the gradients, there might not be enough points.")
+                return
+            print(f"Min numerical gradient: {lrs[mg]:.2E}")
+            ax.plot(lrs[mg],losses[mg],markersize=10,marker='o',color='red')
+            self.min_grad_lr = lrs[mg]
+            ml = np.argmin(losses)
+            print(f"Min loss divided by 10: {lrs[ml]/10:.2E}")
+        if ifnone(return_fig, defaults.return_fig): return fig
+        if not IN_NOTEBOOK: plot_sixel(fig)
+
+    def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
+        "Plot training and validation losses."
+        fig, ax = plt.subplots(1,1)
+        losses = self._split_list(self.losses, skip_start, skip_end)
+        iterations = self._split_list(range_of(self.losses), skip_start, skip_end)
+        ax.plot(iterations, losses, label='Train')
+        val_iter = self._split_list_val(np.cumsum(self.nb_batches), skip_start, skip_end)
+        val_losses = self._split_list_val(self.val_losses, skip_start, skip_end)
+        ax.plot(val_iter, val_losses, label='Validation')
+        ax.set_ylabel('Loss')
+        ax.set_xlabel('Batches processed')
+        ax.legend()
+        if ifnone(return_fig, defaults.return_fig): return fig
+        if not IN_NOTEBOOK: plot_sixel(fig)
+
+    def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
+        "Plot metrics collected during training."
+        assert len(self.metrics) != 0, "There are no metrics to plot."
+        fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
+        val_iter = self._split_list_val(np.cumsum(self.nb_batches), skip_start, skip_end)
+        axes = axes.flatten() if len(self.metrics[0]) != 1 else [axes]
+        for i, ax in enumerate(axes):
+            values = [met[i] for met in self.metrics]
+            values = self._split_list_val(values, skip_start, skip_end)
+            ax.plot(val_iter, values)
+            ax.set_ylabel(str(self.metrics_names[i]))
+            ax.set_xlabel('Batches processed')             
+        if ifnone(return_fig, defaults.return_fig): return fig
+        if not IN_NOTEBOOK: plot_sixel(fig)
+
+    def _split_list(self, vals:Collection[float], skip_start:int, skip_end:int):
+        return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:]
+
+    def _split_list_val(self, vals:Collection[float], skip_start:int, skip_end:int):
+        val_iter = np.cumsum(self.nb_batches)
+        start_val = (val_iter - skip_start >= 0).nonzero()[0].min()
+        end_val = (val_iter[-1] - val_iter - skip_end >= 0).nonzero()[0].max()+1
+        return vals[start_val:end_val] if skip_end > 0 else vals[start_val:]
+
+class FakeOptimizer():
+    def step(self): pass
+    def zero_grad(self): pass
+
+def load_callback(class_func, state, learn:Learner):
+    init_kwargs, others = split_kwargs_by_func(state, class_func.__init__)
+    res = class_func(learn, **init_kwargs) if issubclass(class_func, LearnerCallback) else class_func(**init_kwargs)
+    for k,v in others.items(): setattr(res, k, v)
+    return res
+
+def load_learner(path:PathOrStr, file:PathLikeOrBinaryStream='export.pkl', test:ItemList=None, **db_kwargs):
+    "Load a `Learner` object saved with `export_state` in `path/file` with empty data, optionally add `test` and load on `cpu`. `file` can be file-like (file or buffer)"
+    source = Path(path)/file if is_pathlike(file) else file
+    state = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)
+    model = state.pop('model')
+    src = LabelLists.load_state(path, state.pop('data'))
+    if test is not None: src.add_test(test)
+    data = src.databunch(**db_kwargs)
+    cb_state = state.pop('cb_state')
+    clas_func = state.pop('cls')
+    res = clas_func(data, model, **state)
+    res.callback_fns = state['callback_fns'] #to avoid duplicates
+    res.callbacks = [load_callback(c,s, res) for c,s in cb_state.items()]
+    return res

fastai/basics.py

@@ -0,0 +1,29 @@
+from .basic_train import *
+from .callback import *
+from .core import *
+from .basic_data import *
+from .data_block import *
+from .layers import *
+from .metrics import *
+from .torch_core import *
+from .train import *
+from .datasets import *
+from .version import *
+from . import callbacks
+
+"""
+from . import core,torch_core,basic_data,basic_train,callback,data_block,layers,metrics,train,datasets,callbacks
+
+__all__  = [o for o in dir(core) if not o.startswith('_')]
+__all__ += [o for o in dir(torch_core) if not o.startswith('_')]
+__all__ += [*basic_train.__all__, *callback.__all__, 'core', 'torch_core', 'callbacks',
+           *basic_data.__all__, *data_block.__all__, *layers.__all__, *metrics.__all__,
+           *train.__all__, *datasets.__all__, '__version__']
+"""
+
+try: from .gen_doc.nbdoc import doc
+except: pass  # Optional if jupyter is present
+    #__all__.append('doc')
+
+__all__ = [o for o in dir(sys.modules[__name__]) if not o.startswith('_')] + ['__version__']
+

fastai/callback.py

@@ -0,0 +1,396 @@
+"Callbacks provides extensibility to the `basic_train` loop. See `train` for examples of custom callbacks."
+from .basic_data import *
+from .torch_core import *
+import torch.distributed as dist
+
+__all__ = ['AverageMetric', 'Callback', 'CallbackHandler', 'OptimWrapper', 'SmoothenValue', 'Scheduler', 'annealing_cos', 'CallbackList',
+           'annealing_exp', 'annealing_linear', 'annealing_no', 'annealing_poly']
+
+class OptimWrapper():
+    "Basic wrapper around `opt` to simplify hyper-parameters changes."
+    def __init__(self, opt:optim.Optimizer, wd:Floats=0., true_wd:bool=False, bn_wd:bool=True):
+        assert not isinstance(opt, OptimWrapper)
+        self.opt,self.true_wd,self.bn_wd = opt,true_wd,bn_wd
+        self.opt_keys = list(self.opt.param_groups[0].keys())
+        self.opt_keys.remove('params')
+        self.read_defaults()
+        self.wd = wd
+
+    @classmethod
+    def create(cls, opt_func:Union[type,Callable], lr:Union[float,Tuple,List], layer_groups:ModuleList, wd:Floats=0., 
+               true_wd:bool=False, bn_wd:bool=True)->optim.Optimizer:
+        "Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
+        split_params = split_no_wd_params(layer_groups)
+        opt = opt_func([{'params': p, 'lr':0} for p in split_params])
+        opt = cls(opt, wd=wd, true_wd=true_wd, bn_wd=bn_wd)
+        opt.lr,opt.opt_func = listify(lr, layer_groups),opt_func
+        return opt
+
+    def new(self, layer_groups:Collection[nn.Module], split_no_wd:bool=True):
+        "Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
+        opt_func = getattr(self, 'opt_func', self.opt.__class__)
+        res = self.create(opt_func, self.lr, layer_groups, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
+        res.mom,res.beta = self.mom,self.beta
+        return res
+
+    def new_with_params(self, param_groups:Collection[Collection[nn.Parameter]]):
+        "Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
+        opt_func = getattr(self, 'opt_func', self.opt.__class__)
+        opt = opt_func([{'params': p, 'lr':0} for p in param_groups])
+        opt = self.__class__(opt, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
+        opt.lr,opt.opt_func,opt.mom,opt.beta = self.lr,opt_func,self.mom,self.beta
+        return opt
+
+    def __repr__(self)->str:
+        return f'OptimWrapper over {repr(self.opt)}.\nTrue weight decay: {self.true_wd}'
+
+    #Pytorch optimizer methods
+    def step(self)->None:
+        "Set weight decay and step optimizer."
+        # weight decay outside of optimizer step (AdamW)
+        if self.true_wd:
+            for lr,wd,pg1,pg2 in zip(self._lr,self._wd,self.opt.param_groups[::2],self.opt.param_groups[1::2]):
+                for p in pg1['params']: p.data.mul_(1 - wd*lr)
+                if self.bn_wd:
+                    for p in pg2['params']: p.data.mul_(1 - wd*lr)
+            self.set_val('weight_decay', listify(0, self._wd))
+        self.opt.step()
+
+    def zero_grad(self)->None:
+        "Clear optimizer gradients."
+        self.opt.zero_grad()
+
+    #Passthrough to the inner opt.
+    def __getattr__(self, k:str)->Any: return getattr(self.opt, k, None)
+    def __setstate__(self,data:Any): self.__dict__.update(data)
+
+    def clear(self):
+        "Reset the state of the inner optimizer."
+        sd = self.state_dict()
+        sd['state'] = {}
+        self.load_state_dict(sd)
+
+    @property
+    def n_params(self): return sum([len(pg['params']) for pg in self.opt.param_groups])
+
+    #Hyperparameters as properties
+    @property
+    def lr(self)->float: return self._lr[-1]
+    @lr.setter
+    def lr(self, val:float)->None:
+        self._lr = self.set_val('lr', listify(val, self._lr))
+
+    @property
+    def mom(self)->float:return self._mom[-1]
+    @mom.setter
+    def mom(self, val:float)->None:
+        if 'momentum' in self.opt_keys: self.set_val('momentum', listify(val, self._mom))
+        elif 'betas' in self.opt_keys:  self.set_val('betas', (listify(val, self._mom), self._beta))
+        self._mom = listify(val, self._mom)
+
+    @property
+    def beta(self)->float: return None if self._beta is None else self._beta[-1]
+    @beta.setter
+    def beta(self, val:float)->None:
+        "Set beta (or alpha as makes sense for given optimizer)."
+        if val is None: return
+        if 'betas' in self.opt_keys:    self.set_val('betas', (self._mom, listify(val, self._beta)))
+        elif 'alpha' in self.opt_keys:  self.set_val('alpha', listify(val, self._beta))
+        self._beta = listify(val, self._beta)
+
+    @property
+    def wd(self)->float: return self._wd[-1]
+    @wd.setter
+    def wd(self, val:float)->None:
+        "Set weight decay."
+        if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
+        self._wd = listify(val, self._wd)
+
+    #Helper functions
+    def read_defaults(self)->None:
+        "Read the values inside the optimizer for the hyper-parameters."
+        self._beta = None
+        if 'lr' in self.opt_keys: self._lr = self.read_val('lr')
+        if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum')
+        if 'alpha' in self.opt_keys: self._beta = self.read_val('alpha')
+        if 'betas' in self.opt_keys: self._mom,self._beta = self.read_val('betas')
+        if 'weight_decay' in self.opt_keys: self._wd = self.read_val('weight_decay')
+        reserved_names = ['params', 'lr', 'momentum', 'alpha', 'betas', 'weight_decay']
+        stat_names = [n for n in self.opt_keys if n not in reserved_names]
+        self._stats = {n:self.read_val(n) for n in stat_names}
+
+    def get_stat(self, name:str)->float: 
+        if name in ['lr', 'mom', 'beta', 'wd']: return getattr(self, name)
+        else: return self._stats[name][-1]
+    def set_stat(self, name:str, value:Union[float, Collection[float]])->None:
+        if name in ['lr', 'mom', 'beta', 'wd']: setattr(self, name, value)
+        else:
+            val = listify(value, self._stats[name])
+            self.set_val(name, val)
+            self._stats[name] = val
+
+    def set_val(self, key:str, val:Any, bn_groups:bool=True)->Any:
+        "Set `val` inside the optimizer dictionary at `key`."
+        if is_tuple(val): val = [(v1,v2) for v1,v2 in zip(*val)]
+        for v,pg1,pg2 in zip(val,self.opt.param_groups[::2],self.opt.param_groups[1::2]):
+            pg1[key] = v
+            if bn_groups: pg2[key] = v
+        return val
+
+    def read_val(self, key:str) -> Union[List[float],Tuple[List[float],List[float]]]:
+        "Read a hyperparameter `key` in the optimizer dictionary."
+        val = [pg[key] for pg in self.opt.param_groups[::2]]
+        if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for o in val]
+        return val
+    
+    def get_state(self):
+        "Return the inner state minus the layer groups."
+        return {'opt_state':self.opt.state_dict(), 'lr':self._lr, 'wd':self._wd, 'beta':self._beta, 'mom':self._mom,
+                'opt_func':self.opt_func, 'true_wd':self.true_wd, 'bn_wd':self.bn_wd}
+
+    @classmethod
+    def load_with_state_and_layer_group(cls, state:dict, layer_groups:Collection[nn.Module]):
+        res = cls.create(state['opt_func'], state['lr'], layer_groups, wd=state['wd'], true_wd=state['true_wd'], 
+                     bn_wd=state['bn_wd'])
+        res._mom,res._beta = state['mom'],state['beta']
+        res.load_state_dict(state['opt_state'])
+        return res
+
+class Callback():
+    "Base class for callbacks that want to record values, dynamically change learner params, etc."
+    _order=0
+    def on_train_begin(self, **kwargs:Any)->None:
+        "To initialize constants in the callback."
+        pass
+    def on_epoch_begin(self, **kwargs:Any)->None:
+        "At the beginning of each epoch."
+        pass
+    def on_batch_begin(self, **kwargs:Any)->None:
+        "Set HP before the output and loss are computed."
+        pass
+    def on_loss_begin(self, **kwargs:Any)->None:
+        "Called after forward pass but before loss has been computed."
+        pass
+    def on_backward_begin(self, **kwargs:Any)->None:
+        "Called after the forward pass and the loss has been computed, but before backprop."
+        pass
+    def on_backward_end(self, **kwargs:Any)->None:
+        "Called after backprop but before optimizer step. Useful for true weight decay in AdamW."
+        pass
+    def on_step_end(self, **kwargs:Any)->None:
+        "Called after the step of the optimizer but before the gradients are zeroed."
+        pass
+    def on_batch_end(self, **kwargs:Any)->None:
+        "Called at the end of the batch."
+        pass
+    def on_epoch_end(self, **kwargs:Any)->None:
+        "Called at the end of an epoch."
+        pass
+    def on_train_end(self, **kwargs:Any)->None:
+        "Useful for cleaning up things and saving files/models."
+        pass
+    def jump_to_epoch(self, epoch)->None:
+        "To resume training at `epoch` directly."
+        pass
+
+    def get_state(self, minimal:bool=True):
+        "Return the inner state of the `Callback`, `minimal` or not."
+        to_remove = ['exclude', 'not_min'] + getattr(self, 'exclude', []).copy()
+        if minimal: to_remove += getattr(self, 'not_min', []).copy()
+        return {k:v for k,v in self.__dict__.items() if k not in to_remove}
+
+    def  __repr__(self):
+        attrs = func_args(self.__init__)
+        to_remove = getattr(self, 'exclude', [])
+        list_repr = [self.__class__.__name__] + [f'{k}: {getattr(self, k)}' for k in attrs if k != 'self' and k not in to_remove]
+        return '\n'.join(list_repr)
+
+class SmoothenValue():
+    "Create a smooth moving average for a value (loss, etc) using `beta`."
+    def __init__(self, beta:float):
+        self.beta,self.n,self.mov_avg = beta,0,0
+
+    def add_value(self, val:float)->None:
+        "Add `val` to calculate updated smoothed value."
+        self.n += 1
+        self.mov_avg = self.beta * self.mov_avg + (1 - self.beta) * val
+        self.smooth = self.mov_avg / (1 - self.beta ** self.n)
+
+CallbackList = Collection[Callback]
+
+def _get_init_state(): return {'epoch':0, 'iteration':0, 'num_batch':0, 'skip_validate': False}
+
+@dataclass
+class CallbackHandler():
+    "Manage all of the registered `callbacks` and `metrics`, smoothing loss by momentum `beta`."
+    callbacks:CallbackList=None
+    metrics:CallbackList=None
+    beta:float=0.98
+
+    def __post_init__(self)->None:
+        "Initialize smoother and learning stats."
+        self.callbacks = ifnone(self.callbacks, [])
+        self.metrics = ifnone(self.metrics, [])
+        self.metrics = [(met if isinstance(met, Callback) else AverageMetric(met)) for met in self.metrics]
+        self.callbacks = sorted(self.callbacks, key=lambda o: getattr(o, '_order', 0))
+        self.smoothener = SmoothenValue(self.beta)
+        self.state_dict:Dict[str,Union[int,float,Tensor]]=_get_init_state()
+
+    def _call_and_update(self, cb, cb_name, **kwargs)->None:
+        "Call `cb_name` on `cb` and update the inner state."
+        new = ifnone(getattr(cb, f'on_{cb_name}')(**self.state_dict, **kwargs), dict())
+        for k,v in new.items():
+            if k not in self.state_dict:
+                raise Exception(f"{k} isn't a valid key in the state of the callbacks.")
+            else: self.state_dict[k] = v
+    
+    def __call__(self, cb_name, call_mets=True, **kwargs)->None:
+        "Call through to all of the `CallbakHandler` functions."
+        if call_mets: 
+            for met in self.metrics: self._call_and_update(met, cb_name, **kwargs)
+        for cb in self.callbacks: self._call_and_update(cb, cb_name, **kwargs)
+
+    def set_dl(self, dl:DataLoader):
+        "Set the current `dl` used."
+        if hasattr(self, 'cb_dl'): self.callbacks.remove(self.cb_dl)
+        if isinstance(dl.dataset, Callback):
+            self.callbacks.append(dl.dataset)
+            self.cb_dl = dl.dataset
+
+    def on_train_begin(self, epochs:int, pbar:PBar, metrics:MetricFuncList)->None:
+        "About to start learning."
+        self.state_dict = _get_init_state()
+        self.state_dict.update(dict(n_epochs=epochs, pbar=pbar, metrics=metrics))
+        names = [(met.name if hasattr(met, 'name') else camel2snake(met.__class__.__name__)) for met in self.metrics]
+        self('train_begin', metrics_names=names)
+        if self.state_dict['epoch'] != 0:
+            self.state_dict['pbar'].first_bar.total -= self.state_dict['epoch']
+            for cb in self.callbacks: cb.jump_to_epoch(self.state_dict['epoch'])
+
+    def on_epoch_begin(self)->None:
+        "Handle new epoch."
+        self.state_dict['num_batch'],self.state_dict['stop_training'] = 0,False
+        self('epoch_begin')
+
+    def on_batch_begin(self, xb:Tensor, yb:Tensor, train:bool=True)->Tuple[Any,Any]:
+        "Handle new batch `xb`,`yb` in `train` or validation."
+        self.state_dict.update(dict(last_input=xb, last_target=yb, train=train, 
+            stop_epoch=False, skip_step=False, skip_zero=False, skip_bwd=False))
+        self('batch_begin', mets = not self.state_dict['train'])
+        return self.state_dict['last_input'], self.state_dict['last_target']
+
+    def on_loss_begin(self, out:Tensor)->Any:
+        "Handle start of loss calculation with model output `out`."
+        self.state_dict['last_output'] = out
+        self('loss_begin', call_mets=False)
+        return self.state_dict['last_output']
+
+    def on_backward_begin(self, loss:Tensor)->Tuple[Any,Any]:
+        "Handle gradient calculation on `loss`."
+        self.smoothener.add_value(loss.detach().cpu())
+        self.state_dict['last_loss'], self.state_dict['smooth_loss'] = loss, self.smoothener.smooth
+        self('backward_begin', call_mets=False)
+        return self.state_dict['last_loss'], self.state_dict['skip_bwd']
+
+    def on_backward_end(self)->Any:
+        "Handle end of gradient calculation."
+        self('backward_end', call_mets=False)
+        return self.state_dict['skip_step']
+
+    def on_step_end(self)->Any:
+        "Handle end of optimization step."
+        self('step_end', call_mets=False)
+        return self.state_dict['skip_zero']
+
+    def on_batch_end(self, loss:Tensor)->Any:
+        "Handle end of processing one batch with `loss`."
+        self.state_dict['last_loss'] = loss
+        self('batch_end', call_mets = not self.state_dict['train'])
+        if self.state_dict['train']:
+            self.state_dict['iteration'] += 1
+            self.state_dict['num_batch'] += 1
+        return self.state_dict['stop_epoch']
+
+    def on_epoch_end(self, val_loss:Tensor)->bool:
+        "Epoch is done, process `val_loss`."
+        self.state_dict['last_metrics'] = [val_loss] if val_loss is not None else [None]
+        self('epoch_end', call_mets = val_loss is not None)
+        self.state_dict['epoch'] += 1
+        return self.state_dict['stop_training']
+
+    def on_train_end(self, exception:Union[bool,Exception])->None:
+        "Handle end of training, `exception` is an `Exception` or False if no exceptions during training."
+        self('train_end', exception=exception)
+        
+    @property
+    def skip_validate(self): return self.state_dict['skip_validate']
+
+class AverageMetric(Callback):
+    "Wrap a `func` in a callback for metrics computation."
+    def __init__(self, func):
+        # If func has a __name__ use this one else it should be a partial
+        name = func.__name__ if hasattr(func, '__name__') else func.func.__name__
+        self.func, self.name = func, name
+        self.world = num_distrib()
+
+    def on_epoch_begin(self, **kwargs):
+        "Set the inner value to 0."
+        self.val, self.count = 0.,0
+
+    def on_batch_end(self, last_output, last_target, **kwargs):
+        "Update metric computation with `last_output` and `last_target`."
+        if not is_listy(last_target): last_target=[last_target]
+        self.count += first_el(last_target).size(0)
+        val = self.func(last_output, *last_target)
+        if self.world:
+            val = val.clone()
+            dist.all_reduce(val, op=dist.ReduceOp.SUM)
+            val /= self.world
+        self.val += first_el(last_target).size(0) * val.detach().cpu()
+
+    def on_epoch_end(self, last_metrics, **kwargs):
+        "Set the final result in `last_metrics`."
+        return add_metrics(last_metrics, self.val/self.count)
+
+def annealing_no(start:Number, end:Number, pct:float)->Number:
+    "No annealing, always return `start`."
+    return start
+def annealing_linear(start:Number, end:Number, pct:float)->Number:
+    "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
+    return start + pct * (end-start)
+def annealing_exp(start:Number, end:Number, pct:float)->Number:
+    "Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0."
+    return start * (end/start) ** pct
+def annealing_cos(start:Number, end:Number, pct:float)->Number:
+    "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
+    cos_out = np.cos(np.pi * pct) + 1
+    return end + (start-end)/2 * cos_out
+
+def do_annealing_poly(start:Number, end:Number, pct:float, degree:Number)->Number:
+    "Helper function for `anneal_poly`."
+    return end + (start-end) * (1-pct)**degree
+def annealing_poly(degree:Number)->Number:
+    "Anneal polynomically from `start` to `end` as pct goes from 0.0 to 1.0."
+    return functools.partial(do_annealing_poly, degree=degree)
+
+class Scheduler():
+    "Used to \"step\" from start,end (`vals`) over `n_iter` iterations on a schedule defined by `func`"
+    def __init__(self, vals:StartOptEnd, n_iter:int, func:Optional[AnnealFunc]=None):
+        self.start,self.end = (vals[0],vals[1]) if is_tuple(vals) else (vals,0)
+        self.n_iter = max(1,n_iter)
+        if func is None: self.func = annealing_linear if is_tuple(vals) else annealing_no
+        else:          self.func = func
+        self.n = 0
+        
+    def restart(self): self.n = 0
+
+    def step(self)->Number:
+        "Return next value along annealed schedule."
+        self.n += 1
+        return self.func(self.start, self.end, self.n/self.n_iter)
+
+    @property
+    def is_done(self)->bool:
+        "Return `True` if schedule completed."
+        return self.n >= self.n_iter
+

fastai/callbacks/__init__.py

@@ -0,0 +1,11 @@
+from .lr_finder import *
+from .one_cycle import *
+from .fp16 import *
+from .general_sched import *
+from .hooks import *
+from .mixup import *
+from .rnn import *
+from .tracker import *
+from .csv_logger import *
+from .loss_metrics import *
+from .oversampling import *

fastai/callbacks/csv_logger.py

@@ -0,0 +1,43 @@
+"A `Callback` that saves tracked metrics into a persistent file."
+#Contribution from devforfu: https://nbviewer.jupyter.org/gist/devforfu/ea0b3fcfe194dad323c3762492b05cae
+from ..torch_core import *
+from ..basic_data import DataBunch
+from ..callback import *
+from ..basic_train import Learner, LearnerCallback
+from time import time
+from fastprogress.fastprogress import format_time
+
+__all__ = ['CSVLogger']
+
+class CSVLogger(LearnerCallback):
+    "A `LearnerCallback` that saves history of metrics while training `learn` into CSV `filename`."
+    def __init__(self, learn:Learner, filename: str = 'history', append: bool = False): 
+        super().__init__(learn)
+        self.filename,self.path,self.append = filename,self.learn.path/f'{filename}.csv',append
+        self.add_time = True
+
+    def read_logged_file(self):  
+        "Read the content of saved file"
+        return pd.read_csv(self.path)
+
+    def on_train_begin(self, **kwargs: Any) -> None:
+        "Prepare file with metric names."
+        self.path.parent.mkdir(parents=True, exist_ok=True)      
+        self.file = self.path.open('a') if self.append else self.path.open('w')
+        self.file.write(','.join(self.learn.recorder.names[:(None if self.add_time else -1)]) + '\n')
+    
+    def on_epoch_begin(self, **kwargs:Any)->None:
+        if self.add_time: self.start_epoch = time()
+        
+    def on_epoch_end(self, epoch: int, smooth_loss: Tensor, last_metrics: MetricsList, **kwargs: Any) -> bool:
+        "Add a line with `epoch` number, `smooth_loss` and `last_metrics`."
+        last_metrics = ifnone(last_metrics, [])
+        stats = [str(stat) if isinstance(stat, int) else '#na#' if stat is None else f'{stat:.6f}'
+                 for name, stat in zip(self.learn.recorder.names, [epoch, smooth_loss] + last_metrics)]
+        if self.add_time: stats.append(format_time(time() - self.start_epoch))
+        str_stats = ','.join(stats)
+        self.file.write(str_stats + '\n')
+
+    def on_train_end(self, **kwargs: Any) -> None:  
+        "Close the file."
+        self.file.close()

fastai/callbacks/fp16.py

@@ -0,0 +1,122 @@
+"Callback support for half precision (fp16) training. Increases training speed."
+from ..torch_core import *
+from ..callback import *
+from ..basic_train import *
+from torch._utils import _unflatten_dense_tensors
+from torch.nn.utils import parameters_to_vector
+
+__all__ = ['MixedPrecision']
+
+def get_master(layer_groups:ModuleList, flat_master:bool=False) -> Tuple[List[List[Tensor]], List[List[Tensor]]]:
+    "Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
+    split_params = split_no_wd_params(layer_groups)
+    model_params = [[param for param in pg if param.requires_grad] for pg in split_params]
+    if flat_master:
+        master_params = []
+        for lg in model_params:
+            if len(lg) !=0 :
+                mp = parameters_to_vector([param.data.float() for param in lg])
+                mp = torch.nn.Parameter(mp, requires_grad=True)
+                if mp.grad is None: mp.grad = mp.new(*mp.size())
+                master_params.append([mp])
+            else: master_params.append([])
+        return model_params, master_params
+    else:
+        master_params = [[param.clone().float().detach() for param in lg] for lg in model_params]
+        for mp in master_params:
+            for param in mp: param.requires_grad = True
+        return model_params, master_params
+
+def model_g2master_g(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
+    "Copy the `model_params` gradients to `master_params` for the optimizer step."
+    if flat_master:
+        for model_group,master_group in zip(model_params,master_params):
+            if len(master_group) != 0:
+                if master_group[0].grad is None: master_group[0].grad = master_group[0].data.new(*master_group[0].data.size())
+                master_group[0].grad.data.copy_(parameters_to_vector([p.grad.data.float() for p in model_group]))
+    else:
+        for model_group,master_group in zip(model_params,master_params):
+            for model, master in zip(model_group, master_group):
+                if model.grad is not None:
+                    if master.grad is None: master.grad = master.data.new(*master.data.size())
+                    master.grad.data.copy_(model.grad.data)
+                else: master.grad = None
+
+def master2model(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
+    "Copy `master_params` to `model_params`."
+    if flat_master:
+        for model_group,master_group in zip(model_params,master_params):
+            if len(model_group) != 0:
+                for model, master in zip(model_group, _unflatten_dense_tensors(master_group[0].data, model_group)):
+                    model.data.copy_(master)
+    else:
+        for model_group,master_group in zip(model_params,master_params):
+            for model, master in zip(model_group, master_group): model.data.copy_(master.data)
+
+def grad_overflow(param_group):
+    for group in param_group:
+        for p in group:
+            if p.grad is not None:
+                s = float(p.grad.data.float().sum())
+                if s == float('inf') or s == float('-inf') or s != s: return True
+    return False
+
+class MixedPrecision(LearnerCallback):
+    _order = 999 #Need to run after things that could call on_backward_begin and change the loss
+    "Callback that handles mixed-precision training."
+    def __init__(self, learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,
+                 flat_master:bool=False, max_scale:float=2**24):
+        super().__init__(learn)
+        self.flat_master,self.dynamic,self.max_noskip,self.clip,self.max_scale = flat_master,dynamic,max_noskip,clip,max_scale
+        self.loss_scale = ifnone(loss_scale, 2**16 if dynamic else 512)
+        self.not_min += ['model_params', 'master_params']
+        assert torch.backends.cudnn.enabled, "Mixed precision training requires cudnn."
+        self.opt = None
+
+    def on_train_begin(self, **kwargs:Any)->None:
+        "Prepare the master model."
+        #Get a copy of the model params in FP32
+        self.model_params, self.master_params = get_master(self.learn.layer_groups, self.flat_master)
+        #Changes the optimizer so that the optimization step is done in FP32.
+        new_opt = self.learn.opt.new_with_params(self.master_params)
+        if self.opt is not None:
+            self.opt.lr,self.opt.wd = self.learn.opt.lr,self.learn.opt.wd
+            new_opt.load_state_dict(self.opt)
+        self.learn.opt.opt = new_opt.opt
+        self.noskip = 0
+
+    def on_loss_begin(self, last_output:Tensor, **kwargs:Any) -> Tensor:
+        "Convert half precision output to FP32 to avoid reduction overflow."
+        return {'last_output': to_float(last_output)}
+
+    def on_backward_begin(self, last_loss:Rank0Tensor, **kwargs:Any) -> Rank0Tensor:
+        "Scale gradients up by `self.loss_scale` to prevent underflow."
+        #To avoid gradient underflow, we scale the gradients
+        ret_loss = last_loss * self.loss_scale
+        return {'last_loss': ret_loss}
+
+    def on_backward_end(self, **kwargs:Any)->None:
+        "Convert the gradients back to FP32 and divide them by the scale."
+        if self.dynamic and grad_overflow(self.model_params) and self.loss_scale > 1:
+            self.loss_scale /= 2
+            self.noskip = 0
+            #The step will be skipped since we don't update the master grads so they are all None or zero
+        else:
+            model_g2master_g(self.model_params, self.master_params, self.flat_master)
+            for group in self.master_params:
+                for param in group:
+                    if param.grad is not None: param.grad.div_(self.loss_scale)
+            if self.clip is not None:
+                for group in self.master_params: nn.utils.clip_grad_norm_(group, self.clip)
+            if not self.dynamic: return
+            self.noskip += 1
+            if self.noskip >= self.max_noskip and self.loss_scale < self.max_scale:
+                self.loss_scale *= 2
+                self.noskip = 0
+
+    def on_step_end(self, **kwargs:Any)->None:
+        "Update the params from master to model and zero grad."
+        #Zeros the gradients of the model since the optimizer is disconnected.
+        self.learn.model.zero_grad()
+        #Update the params from master to model.
+        master2model(self.model_params, self.master_params, self.flat_master)

fastai/callbacks/general_sched.py

@@ -0,0 +1,46 @@
+from ..core import *
+from ..callback import *
+from ..basic_train import Learner, LearnerCallback
+
+__all__ = ['GeneralScheduler', 'TrainingPhase']
+
+@dataclass
+class TrainingPhase():
+    "Schedule hyper-parameters for a phase of `length` iterations."
+    length:int
+    
+    def __post_init__(self): self.scheds = dict()
+    def schedule_hp(self, name, vals, anneal=None):
+        "Adds a schedule for `name` between `vals` using `anneal`."
+        self.scheds[name] = Scheduler(vals, self.length, anneal)
+        return self
+
+class GeneralScheduler(LearnerCallback):
+    "Schedule multiple `TrainingPhase` for a `Learner`."
+    def __init__(self, learn:Learner, phases:Collection[TrainingPhase], start_epoch:int=None):
+        super().__init__(learn)
+        self.phases,self.start_epoch = phases,start_epoch
+
+    def on_train_begin(self, epoch:int, **kwargs:Any)->None:
+        "Initialize the schedulers for training."
+        res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
+        self.start_epoch = ifnone(self.start_epoch, epoch)
+        self.scheds = [p.scheds for p in self.phases]
+        self.opt = self.learn.opt
+        for k,v in self.scheds[0].items(): 
+            v.restart()
+            self.opt.set_stat(k, v.start)
+        self.idx_s = 0
+        return res
+    
+    def jump_to_epoch(self, epoch:int)->None:
+        for _ in range(len(self.learn.data.train_dl) * epoch):
+            self.on_batch_end(True)
+
+    def on_batch_end(self, train, **kwargs:Any)->None:
+        "Take a step in lr,mom sched, start next stepper when the current one is complete."
+        if train:
+            if self.idx_s >= len(self.scheds): return {'stop_training': True, 'stop_epoch': True}
+            sched = self.scheds[self.idx_s]
+            for k,v in sched.items(): self.opt.set_stat(k, v.step())
+            if list(sched.values())[0].is_done: self.idx_s += 1

fastai/callbacks/hooks.py

@@ -0,0 +1,200 @@
+"Hooks provide extensibility at the model level."
+from ..torch_core import *
+from ..callback import *
+from ..basic_train import *
+from ..basic_data import *
+
+__all__ = ['ActivationStats', 'Hook', 'HookCallback', 'Hooks', 'hook_output', 'hook_outputs',
+           'model_sizes', 'num_features_model', 'model_summary', 'dummy_eval', 'dummy_batch']
+
+class Hook():
+    "Create a hook on `m` with `hook_func`."
+    def __init__(self, m:nn.Module, hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
+        self.hook_func,self.detach,self.stored = hook_func,detach,None
+        f = m.register_forward_hook if is_forward else m.register_backward_hook
+        self.hook = f(self.hook_fn)
+        self.removed = False
+
+    def hook_fn(self, module:nn.Module, input:Tensors, output:Tensors):
+        "Applies `hook_func` to `module`, `input`, `output`."
+        if self.detach:
+            input  = (o.detach() for o in input ) if is_listy(input ) else input.detach()
+            output = (o.detach() for o in output) if is_listy(output) else output.detach()
+        self.stored = self.hook_func(module, input, output)
+
+    def remove(self):
+        "Remove the hook from the model."
+        if not self.removed:
+            self.hook.remove()
+            self.removed=True
+
+    def __enter__(self, *args): return self
+    def __exit__(self, *args): self.remove()
+
+class Hooks():
+    "Create several hooks on the modules in `ms` with `hook_func`."
+    def __init__(self, ms:Collection[nn.Module], hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
+        self.hooks = [Hook(m, hook_func, is_forward, detach) for m in ms]
+
+    def __getitem__(self,i:int)->Hook: return self.hooks[i]
+    def __len__(self)->int: return len(self.hooks)
+    def __iter__(self): return iter(self.hooks)
+    @property
+    def stored(self): return [o.stored for o in self]
+
+    def remove(self):
+        "Remove the hooks from the model."
+        for h in self.hooks: h.remove()
+
+    def __enter__(self, *args): return self
+    def __exit__ (self, *args): self.remove()
+
+def _hook_inner(m,i,o): return o if isinstance(o,Tensor) else o if is_listy(o) else list(o)
+
+def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
+    "Return a `Hook` that stores activations of `module` in `self.stored`"
+    return Hook(module, _hook_inner, detach=detach, is_forward=not grad)
+
+def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
+    "Return `Hooks` that store activations of all `modules` in `self.stored`"
+    return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad)
+
+class HookCallback(LearnerCallback):
+    "Callback that can be used to register hooks on `modules`. Implement the corresponding function in `self.hook`."
+    def __init__(self, learn:Learner, modules:Sequence[nn.Module]=None, do_remove:bool=True):
+        super().__init__(learn)
+        self.modules,self.do_remove = modules,do_remove
+
+    def on_train_begin(self, **kwargs):
+        "Register the `Hooks` on `self.modules`."
+        if not self.modules:
+            self.modules = [m for m in flatten_model(self.learn.model)
+                            if hasattr(m, 'weight')]
+        self.hooks = Hooks(self.modules, self.hook)
+
+    def on_train_end(self, **kwargs):
+        "Remove the `Hooks`."
+        if self.do_remove: self.remove()
+
+    def remove(self): 
+        if getattr(self, 'hooks', None): self.hooks.remove()
+    def __del__(self): self.remove()
+
+class ActivationStats(HookCallback):
+    "Callback that record the mean and std of activations."
+
+    def on_train_begin(self, **kwargs):
+        "Initialize stats."
+        super().on_train_begin(**kwargs)
+        self.stats = []
+
+    def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Tuple[Rank0Tensor,Rank0Tensor]:
+        "Take the mean and std of `o`."
+        return o.mean().item(),o.std().item()
+    def on_batch_end(self, train, **kwargs):
+        "Take the stored results and puts it in `self.stats`"
+        if train: self.stats.append(self.hooks.stored)
+    def on_train_end(self, **kwargs):
+        "Polish the final result."
+        super().on_train_end(**kwargs)
+        self.stats = tensor(self.stats).permute(2,1,0)
+
+def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
+    "Create a dummy batch to go through `m` with `size`."
+    ch_in = in_channels(m)
+    return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.)
+
+def dummy_eval(m:nn.Module, size:tuple=(64,64)):
+    "Pass a `dummy_batch` in evaluation mode in `m` with `size`."
+    m.eval()
+    return m(dummy_batch(m, size))
+    #return m.eval()(dummy_batch(m, size))
+
+def model_sizes(m:nn.Module, size:tuple=(64,64))->Tuple[Sizes,Tensor,Hooks]:
+    "Pass a dummy input through the model `m` to get the various sizes of activations."
+    with hook_outputs(m) as hooks:
+        x = dummy_eval(m, size)
+        return [o.stored.shape for o in hooks]
+
+def num_features_model(m:nn.Module)->int:
+    "Return the number of output features for `model`."
+    sz = 64
+    while True:
+        try: return model_sizes(m, size=(sz,sz))[-1][1]
+        except Exception as e:
+            sz *= 2
+            if sz > 2048: raise
+
+def total_params(m:nn.Module)->int:
+    params, trainable = 0, False
+    if hasattr(m, "weight") and hasattr(m.weight, "size"):
+         params += m.weight.numel()
+         trainable = m.weight.requires_grad
+    if hasattr(m, "bias") and hasattr(m.bias, "size"): params += m.bias.numel()
+    return params, trainable
+
+def hook_params(modules:Collection[nn.Module])->Hooks:
+    return Hooks(modules, lambda m, i, o: total_params(m))
+
+def params_size(m: Union[nn.Module,Learner], size: tuple = (3, 64, 64))->Tuple[Sizes, Tensor, Hooks]:
+    "Pass a dummy input through the model to get the various sizes. Returns (res,x,hooks) if `full`"
+    if isinstance(m, Learner):
+        if m.data.is_empty:
+            raise Exception("This is an empty `Learner` and `Learner.summary` requires some data to pass through the model.")
+        ds_type = DatasetType.Train if m.data.train_dl else (DatasetType.Valid if m.data.valid_dl else DatasetType.Test)
+        x = m.data.one_batch(ds_type=ds_type, detach=False, denorm=False)[0]
+        x = [o[:1] for o in x]  if is_listy(x) else x[:1]
+        m = m.model
+    elif isinstance(m, nn.Module): x = next(m.parameters()).new(1, *size)
+    else: raise TypeError('You should either pass in a Learner or nn.Module')
+    with hook_outputs(flatten_model(m)) as hook_o:
+        with hook_params(flatten_model(m))as hook_p:
+            x = m.eval()(*x) if is_listy(x) else m.eval()(x)
+            output_size = [((o.stored.shape[1:]) if o.stored is not None else None) for o in hook_o]
+            params = [(o.stored if o.stored is not None else (None,None)) for o in hook_p]
+    params, trainables = map(list,zip(*params))
+    return output_size, params, trainables
+
+def get_layer_name(layer:nn.Module)->str:
+    return str(layer.__class__).split(".")[-1].split("'")[0]
+
+def layers_info(m:Collection[nn.Module]) -> Collection[namedtuple]:
+    func = lambda m:list(map(get_layer_name, flatten_model(m)))
+    layers_names = func(m.model) if isinstance(m, Learner) else func(m)
+    layers_sizes, layers_params, layers_trainable = params_size(m)
+    layer_info = namedtuple('Layer_Information', ['Layer', 'OutputSize', 'Params', 'Trainable'])
+    return list(map(layer_info, layers_names, layers_sizes, layers_params, layers_trainable))
+
+def model_summary(m:Learner, n:int=70):
+    "Print a summary of `m` using a output text width of `n` chars"
+    info = layers_info(m)
+    header = ["Layer (type)", "Output Shape", "Param #", "Trainable"]
+    res = m.model.__class__.__name__ + "\n"
+    res += "=" * n + "\n"
+    res += f"{header[0]:<20} {header[1]:<20} {header[2]:<10} {header[3]:<10}\n"
+    res += "=" * n + "\n"
+    total_params = 0
+    total_trainable_params = 0
+    for layer, size, params, trainable in info:
+        if size is None: continue
+        total_params += int(params)
+        total_trainable_params += int(params) * trainable
+        size, trainable = str(list(size)), str(trainable)
+        res += f"{layer:<20} {size:<20} {int(params):<10,} {trainable:<10}\n"
+        res += "_" * n + "\n"
+    res += f"\nTotal params: {total_params:,}\n"
+    res += f"Total trainable params: {total_trainable_params:,}\n"
+    res += f"Total non-trainable params: {total_params - total_trainable_params:,}\n"
+           
+    res += f"Optimized with {str(m.opt_func)[25:-1].replace('>', '')}\n"
+    if m.true_wd: res += f"Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n"
+    if "wd" in str(m.opt_func) or "weight_decay" in str(m.opt_func): res += f"\x1b[1;31m Specifying weight decay in the optimizer has no effect, Learner will overwrite \x1b[0m \n"
+    if "lr" in str(m.opt_func) or "learning_rate" in str(m.opt_func): res += f"\x1b[1;31m Specifying lr in the optimizer has no effect, pass it to fit or the defaults.lr will apply \x1b[0m \n" 
+    res += f"Loss function : {m.loss_func.__class__.__name__}\n"
+    res += "=" * n + "\n"
+    res += "Callbacks functions applied \n"
+    res += "\n".join([f"    {cbs.__class__.__name__}" for cbs in m.callbacks])
+
+    return PrettyString(res)
+
+Learner.summary = model_summary

fastai/callbacks/loss_metrics.py

@@ -0,0 +1,34 @@
+from ..torch_core import *
+from ..callback import *
+from ..basic_train import Learner, LearnerCallback
+
+__all__ = ['LossMetrics']
+
+class LossMetrics(LearnerCallback):
+    "Add `loss_func.metrics` to metrics named by `loss_func.metric_names`"
+    _order = -20 #Needs to run before the recorder
+
+    def on_train_begin(self, **kwargs):
+        "Add the metrics names to the `Recorder`."
+        self.names = ifnone(self.learn.loss_func.metric_names, [])
+        if not self.names: warn('LossMetrics requested but no loss_func.metric_names provided')
+        self.learn.recorder.add_metric_names(self.names)
+
+    def on_epoch_begin(self, **kwargs):
+        "Initialize the metrics for this epoch."
+        self.metrics = {name:0. for name in self.names}
+        self.nums = 0
+
+    def on_batch_end(self, last_target, train, **kwargs):
+        "Update the metrics if not `train`"
+        if train: return
+        bs = last_target.size(0)
+        for name in self.names:
+            self.metrics[name] += bs * self.learn.loss_func.metrics[name].detach().cpu()
+        self.nums += bs
+
+    def on_epoch_end(self, last_metrics, **kwargs):
+        "Finish the computation and sends the result to the Recorder."
+        if not self.nums: return
+        metrics = [self.metrics[name]/self.nums for name in self.names]
+        return {'last_metrics': last_metrics+metrics}