another try

Time-Travel-Rephotography

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+Subproject commit 2045d895f671e72e4dca1f81327b1ce462a7d32f

Time_TravelRephotography/LICENSE

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-MIT License
-
-Copyright (c) 2020 Time-Travel-Rephotography
-
-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.

Time_TravelRephotography/LICENSE-NVIDIA

@@ -1,101 +0,0 @@
-Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-
-
-Nvidia Source Code License-NC
-
-=======================================================================
-
-1. Definitions
-
-"Licensor" means any person or entity that distributes its Work.
-
-"Software" means the original work of authorship made available under
-this License.
-
-"Work" means the Software and any additions to or derivative works of
-the Software that are made available under this License.
-
-"Nvidia Processors" means any central processing unit (CPU), graphics
-processing unit (GPU), field-programmable gate array (FPGA),
-application-specific integrated circuit (ASIC) or any combination
-thereof designed, made, sold, or provided by Nvidia or its affiliates.
-
-The terms "reproduce," "reproduction," "derivative works," and
-"distribution" have the meaning as provided under U.S. copyright law;
-provided, however, that for the purposes of this License, derivative
-works shall not include works that remain separable from, or merely
-link (or bind by name) to the interfaces of, the Work.
-
-Works, including the Software, are "made available" under this License
-by including in or with the Work either (a) a copyright notice
-referencing the applicability of this License to the Work, or (b) a
-copy of this License.
-
-2. License Grants
-
-    2.1 Copyright Grant. Subject to the terms and conditions of this
-    License, each Licensor grants to you a perpetual, worldwide,
-    non-exclusive, royalty-free, copyright license to reproduce,
-    prepare derivative works of, publicly display, publicly perform,
-    sublicense and distribute its Work and any resulting derivative
-    works in any form.
-
-3. Limitations
-
-    3.1 Redistribution. You may reproduce or distribute the Work only
-    if (a) you do so under this License, (b) you include a complete
-    copy of this License with your distribution, and (c) you retain
-    without modification any copyright, patent, trademark, or
-    attribution notices that are present in the Work.
-
-    3.2 Derivative Works. You may specify that additional or different
-    terms apply to the use, reproduction, and distribution of your
-    derivative works of the Work ("Your Terms") only if (a) Your Terms
-    provide that the use limitation in Section 3.3 applies to your
-    derivative works, and (b) you identify the specific derivative
-    works that are subject to Your Terms. Notwithstanding Your Terms,
-    this License (including the redistribution requirements in Section
-    3.1) will continue to apply to the Work itself.
-
-    3.3 Use Limitation. The Work and any derivative works thereof only
-    may be used or intended for use non-commercially. The Work or
-    derivative works thereof may be used or intended for use by Nvidia
-    or its affiliates commercially or non-commercially. As used herein,
-    "non-commercially" means for research or evaluation purposes only.
-
-    3.4 Patent Claims. If you bring or threaten to bring a patent claim
-    against any Licensor (including any claim, cross-claim or
-    counterclaim in a lawsuit) to enforce any patents that you allege
-    are infringed by any Work, then your rights under this License from
-    such Licensor (including the grants in Sections 2.1 and 2.2) will
-    terminate immediately.
-
-    3.5 Trademarks. This License does not grant any rights to use any
-    Licensor's or its affiliates' names, logos, or trademarks, except
-    as necessary to reproduce the notices described in this License.
-
-    3.6 Termination. If you violate any term of this License, then your
-    rights under this License (including the grants in Sections 2.1 and
-    2.2) will terminate immediately.
-
-4. Disclaimer of Warranty.
-
-THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-THIS LICENSE. 
-
-5. Limitation of Liability.
-
-EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
-THE POSSIBILITY OF SUCH DAMAGES.
-
-=======================================================================

Time_TravelRephotography/LICENSE-STYLEGAN2

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2019 Kim Seonghyeon
-
-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.

Time_TravelRephotography/dnnlib/__init__.py

@@ -1,11 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-from .util import EasyDict, make_cache_dir_path

Time_TravelRephotography/dnnlib/tflib/__init__.py

@@ -1,20 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-from . import autosummary
-from . import network
-from . import optimizer
-from . import tfutil
-from . import custom_ops
-
-from .tfutil import *
-from .network import Network
-
-from .optimizer import Optimizer
-
-from .custom_ops import get_plugin

Time_TravelRephotography/dnnlib/tflib/autosummary.py

@@ -1,193 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Helper for adding automatically tracked values to Tensorboard.
-
-Autosummary creates an identity op that internally keeps track of the input
-values and automatically shows up in TensorBoard. The reported value
-represents an average over input components. The average is accumulated
-constantly over time and flushed when save_summaries() is called.
-
-Notes:
-- The output tensor must be used as an input for something else in the
-  graph. Otherwise, the autosummary op will not get executed, and the average
-  value will not get accumulated.
-- It is perfectly fine to include autosummaries with the same name in
-  several places throughout the graph, even if they are executed concurrently.
-- It is ok to also pass in a python scalar or numpy array. In this case, it
-  is added to the average immediately.
-"""
-
-from collections import OrderedDict
-import numpy as np
-import tensorflow as tf
-from tensorboard import summary as summary_lib
-from tensorboard.plugins.custom_scalar import layout_pb2
-
-from . import tfutil
-from .tfutil import TfExpression
-from .tfutil import TfExpressionEx
-
-# Enable "Custom scalars" tab in TensorBoard for advanced formatting.
-# Disabled by default to reduce tfevents file size.
-enable_custom_scalars = False
-
-_dtype = tf.float64
-_vars = OrderedDict()  # name => [var, ...]
-_immediate = OrderedDict()  # name => update_op, update_value
-_finalized = False
-_merge_op = None
-
-
-def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
-    """Internal helper for creating autosummary accumulators."""
-    assert not _finalized
-    name_id = name.replace("/", "_")
-    v = tf.cast(value_expr, _dtype)
-
-    if v.shape.is_fully_defined():
-        size = np.prod(v.shape.as_list())
-        size_expr = tf.constant(size, dtype=_dtype)
-    else:
-        size = None
-        size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype))
-
-    if size == 1:
-        if v.shape.ndims != 0:
-            v = tf.reshape(v, [])
-        v = [size_expr, v, tf.square(v)]
-    else:
-        v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))]
-    v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype))
-
-    with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None):
-        var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False)  # [sum(1), sum(x), sum(x**2)]
-    update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
-
-    if name in _vars:
-        _vars[name].append(var)
-    else:
-        _vars[name] = [var]
-    return update_op
-
-
-def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx:
-    """Create a new autosummary.
-
-    Args:
-        name:     Name to use in TensorBoard
-        value:    TensorFlow expression or python value to track
-        passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.
-
-    Example use of the passthru mechanism:
-
-    n = autosummary('l2loss', loss, passthru=n)
-
-    This is a shorthand for the following code:
-
-    with tf.control_dependencies([autosummary('l2loss', loss)]):
-        n = tf.identity(n)
-    """
-    tfutil.assert_tf_initialized()
-    name_id = name.replace("/", "_")
-
-    if tfutil.is_tf_expression(value):
-        with tf.name_scope("summary_" + name_id), tf.device(value.device):
-            condition = tf.convert_to_tensor(condition, name='condition')
-            update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op)
-            with tf.control_dependencies([update_op]):
-                return tf.identity(value if passthru is None else passthru)
-
-    else:  # python scalar or numpy array
-        assert not tfutil.is_tf_expression(passthru)
-        assert not tfutil.is_tf_expression(condition)
-        if condition:
-            if name not in _immediate:
-                with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
-                    update_value = tf.placeholder(_dtype)
-                    update_op = _create_var(name, update_value)
-                    _immediate[name] = update_op, update_value
-            update_op, update_value = _immediate[name]
-            tfutil.run(update_op, {update_value: value})
-        return value if passthru is None else passthru
-
-
-def finalize_autosummaries() -> None:
-    """Create the necessary ops to include autosummaries in TensorBoard report.
-    Note: This should be done only once per graph.
-    """
-    global _finalized
-    tfutil.assert_tf_initialized()
-
-    if _finalized:
-        return None
-
-    _finalized = True
-    tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list])
-
-    # Create summary ops.
-    with tf.device(None), tf.control_dependencies(None):
-        for name, vars_list in _vars.items():
-            name_id = name.replace("/", "_")
-            with tfutil.absolute_name_scope("Autosummary/" + name_id):
-                moments = tf.add_n(vars_list)
-                moments /= moments[0]
-                with tf.control_dependencies([moments]):  # read before resetting
-                    reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list]
-                    with tf.name_scope(None), tf.control_dependencies(reset_ops):  # reset before reporting
-                        mean = moments[1]
-                        std = tf.sqrt(moments[2] - tf.square(moments[1]))
-                        tf.summary.scalar(name, mean)
-                        if enable_custom_scalars:
-                            tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std)
-                            tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std)
-
-    # Setup layout for custom scalars.
-    layout = None
-    if enable_custom_scalars:
-        cat_dict = OrderedDict()
-        for series_name in sorted(_vars.keys()):
-            p = series_name.split("/")
-            cat = p[0] if len(p) >= 2 else ""
-            chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
-            if cat not in cat_dict:
-                cat_dict[cat] = OrderedDict()
-            if chart not in cat_dict[cat]:
-                cat_dict[cat][chart] = []
-            cat_dict[cat][chart].append(series_name)
-        categories = []
-        for cat_name, chart_dict in cat_dict.items():
-            charts = []
-            for chart_name, series_names in chart_dict.items():
-                series = []
-                for series_name in series_names:
-                    series.append(layout_pb2.MarginChartContent.Series(
-                        value=series_name,
-                        lower="xCustomScalars/" + series_name + "/margin_lo",
-                        upper="xCustomScalars/" + series_name + "/margin_hi"))
-                margin = layout_pb2.MarginChartContent(series=series)
-                charts.append(layout_pb2.Chart(title=chart_name, margin=margin))
-            categories.append(layout_pb2.Category(title=cat_name, chart=charts))
-        layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))
-    return layout
-
-def save_summaries(file_writer, global_step=None):
-    """Call FileWriter.add_summary() with all summaries in the default graph,
-    automatically finalizing and merging them on the first call.
-    """
-    global _merge_op
-    tfutil.assert_tf_initialized()
-
-    if _merge_op is None:
-        layout = finalize_autosummaries()
-        if layout is not None:
-            file_writer.add_summary(layout)
-        with tf.device(None), tf.control_dependencies(None):
-            _merge_op = tf.summary.merge_all()
-
-    file_writer.add_summary(_merge_op.eval(), global_step)

Time_TravelRephotography/dnnlib/tflib/custom_ops.py

@@ -1,171 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""TensorFlow custom ops builder.
-"""
-
-import os
-import re
-import uuid
-import hashlib
-import tempfile
-import shutil
-import tensorflow as tf
-from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
-
-#----------------------------------------------------------------------------
-# Global options.
-
-cuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache')
-cuda_cache_version_tag = 'v1'
-do_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe!
-verbose = True # Print status messages to stdout.
-
-compiler_bindir_search_path = [
-    'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64',
-    'C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.23.28105/bin/Hostx64/x64',
-    'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin',
-]
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    for compiler_path in compiler_bindir_search_path:
-        if os.path.isdir(compiler_path):
-            return compiler_path
-    return None
-
-def _get_compute_cap(device):
-    caps_str = device.physical_device_desc
-    m = re.search('compute capability: (\\d+).(\\d+)', caps_str)
-    major = m.group(1)
-    minor = m.group(2)
-    return (major, minor)
-
-def _get_cuda_gpu_arch_string():
-    gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']
-    if len(gpus) == 0:
-        raise RuntimeError('No GPU devices found')
-    (major, minor) = _get_compute_cap(gpus[0])
-    return 'sm_%s%s' % (major, minor)
-
-def _run_cmd(cmd):
-    with os.popen(cmd) as pipe:
-        output = pipe.read()
-        status = pipe.close()
-    if status is not None:
-        raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output))
-
-def _prepare_nvcc_cli(opts):
-    cmd = 'nvcc ' + opts.strip()
-    cmd += ' --disable-warnings'
-    cmd += ' --include-path "%s"' % tf.sysconfig.get_include()
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')
-
-    compiler_bindir = _find_compiler_bindir()
-    if compiler_bindir is None:
-        # Require that _find_compiler_bindir succeeds on Windows.  Allow
-        # nvcc to use whatever is the default on Linux.
-        if os.name == 'nt':
-            raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__)
-    else:
-        cmd += ' --compiler-bindir "%s"' % compiler_bindir
-    cmd += ' 2>&1'
-    return cmd
-
-#----------------------------------------------------------------------------
-# Main entry point.
-
-_plugin_cache = dict()
-
-def get_plugin(cuda_file):
-    cuda_file_base = os.path.basename(cuda_file)
-    cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
-
-    # Already in cache?
-    if cuda_file in _plugin_cache:
-        return _plugin_cache[cuda_file]
-
-    # Setup plugin.
-    if verbose:
-        print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
-    try:
-        # Hash CUDA source.
-        md5 = hashlib.md5()
-        with open(cuda_file, 'rb') as f:
-            md5.update(f.read())
-        md5.update(b'\n')
-
-        # Hash headers included by the CUDA code by running it through the preprocessor.
-        if not do_not_hash_included_headers:
-            if verbose:
-                print('Preprocessing... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
-                _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
-                with open(tmp_file, 'rb') as f:
-                    bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
-                    good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
-                    for ln in f:
-                        if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
-                            ln = ln.replace(bad_file_str, good_file_str)
-                            md5.update(ln)
-                    md5.update(b'\n')
-
-        # Select compiler options.
-        compile_opts = ''
-        if os.name == 'nt':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
-        elif os.name == 'posix':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')
-            compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\''
-        else:
-            assert False # not Windows or Linux, w00t?
-        compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()
-        compile_opts += ' --use_fast_math'
-        nvcc_cmd = _prepare_nvcc_cli(compile_opts)
-
-        # Hash build configuration.
-        md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
-        md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
-        md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
-
-        # Compile if not already compiled.
-        bin_file_ext = '.dll' if os.name == 'nt' else '.so'
-        bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
-        if not os.path.isfile(bin_file):
-            if verbose:
-                print('Compiling... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
-                _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
-                os.makedirs(cuda_cache_path, exist_ok=True)
-                intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
-                shutil.copyfile(tmp_file, intermediate_file)
-                os.rename(intermediate_file, bin_file) # atomic
-
-        # Load.
-        if verbose:
-            print('Loading... ', end='', flush=True)
-        plugin = tf.load_op_library(bin_file)
-
-        # Add to cache.
-        _plugin_cache[cuda_file] = plugin
-        if verbose:
-            print('Done.', flush=True)
-        return plugin
-
-    except:
-        if verbose:
-            print('Failed!', flush=True)
-        raise
-
-#----------------------------------------------------------------------------

Time_TravelRephotography/dnnlib/tflib/network.py

@@ -1,592 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Helper for managing networks."""
-
-import types
-import inspect
-import re
-import uuid
-import sys
-import numpy as np
-import tensorflow as tf
-
-from collections import OrderedDict
-from typing import Any, List, Tuple, Union
-
-from . import tfutil
-from .. import util
-
-from .tfutil import TfExpression, TfExpressionEx
-
-_import_handlers = []  # Custom import handlers for dealing with legacy data in pickle import.
-_import_module_src = dict()  # Source code for temporary modules created during pickle import.
-
-
-def import_handler(handler_func):
-    """Function decorator for declaring custom import handlers."""
-    _import_handlers.append(handler_func)
-    return handler_func
-
-
-class Network:
-    """Generic network abstraction.
-
-    Acts as a convenience wrapper for a parameterized network construction
-    function, providing several utility methods and convenient access to
-    the inputs/outputs/weights.
-
-    Network objects can be safely pickled and unpickled for long-term
-    archival purposes. The pickling works reliably as long as the underlying
-    network construction function is defined in a standalone Python module
-    that has no side effects or application-specific imports.
-
-    Args:
-        name: Network name. Used to select TensorFlow name and variable scopes.
-        func_name: Fully qualified name of the underlying network construction function, or a top-level function object.
-        static_kwargs: Keyword arguments to be passed in to the network construction function.
-
-    Attributes:
-        name: User-specified name, defaults to build func name if None.
-        scope: Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.
-        static_kwargs: Arguments passed to the user-supplied build func.
-        components: Container for sub-networks. Passed to the build func, and retained between calls.
-        num_inputs: Number of input tensors.
-        num_outputs: Number of output tensors.
-        input_shapes: Input tensor shapes (NC or NCHW), including minibatch dimension.
-        output_shapes: Output tensor shapes (NC or NCHW), including minibatch dimension.
-        input_shape: Short-hand for input_shapes[0].
-        output_shape: Short-hand for output_shapes[0].
-        input_templates: Input placeholders in the template graph.
-        output_templates: Output tensors in the template graph.
-        input_names: Name string for each input.
-        output_names: Name string for each output.
-        own_vars: Variables defined by this network (local_name => var), excluding sub-networks.
-        vars: All variables (local_name => var).
-        trainables: All trainable variables (local_name => var).
-        var_global_to_local: Mapping from variable global names to local names.
-    """
-
-    def __init__(self, name: str = None, func_name: Any = None, **static_kwargs):
-        tfutil.assert_tf_initialized()
-        assert isinstance(name, str) or name is None
-        assert func_name is not None
-        assert isinstance(func_name, str) or util.is_top_level_function(func_name)
-        assert util.is_pickleable(static_kwargs)
-
-        self._init_fields()
-        self.name = name
-        self.static_kwargs = util.EasyDict(static_kwargs)
-
-        # Locate the user-specified network build function.
-        if util.is_top_level_function(func_name):
-            func_name = util.get_top_level_function_name(func_name)
-        module, self._build_func_name = util.get_module_from_obj_name(func_name)
-        self._build_func = util.get_obj_from_module(module, self._build_func_name)
-        assert callable(self._build_func)
-
-        # Dig up source code for the module containing the build function.
-        self._build_module_src = _import_module_src.get(module, None)
-        if self._build_module_src is None:
-            self._build_module_src = inspect.getsource(module)
-
-        # Init TensorFlow graph.
-        self._init_graph()
-        self.reset_own_vars()
-
-    def _init_fields(self) -> None:
-        self.name = None
-        self.scope = None
-        self.static_kwargs = util.EasyDict()
-        self.components = util.EasyDict()
-        self.num_inputs = 0
-        self.num_outputs = 0
-        self.input_shapes = [[]]
-        self.output_shapes = [[]]
-        self.input_shape = []
-        self.output_shape = []
-        self.input_templates = []
-        self.output_templates = []
-        self.input_names = []
-        self.output_names = []
-        self.own_vars = OrderedDict()
-        self.vars = OrderedDict()
-        self.trainables = OrderedDict()
-        self.var_global_to_local = OrderedDict()
-
-        self._build_func = None  # User-supplied build function that constructs the network.
-        self._build_func_name = None  # Name of the build function.
-        self._build_module_src = None  # Full source code of the module containing the build function.
-        self._run_cache = dict()  # Cached graph data for Network.run().
-
-    def _init_graph(self) -> None:
-        # Collect inputs.
-        self.input_names = []
-
-        for param in inspect.signature(self._build_func).parameters.values():
-            if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
-                self.input_names.append(param.name)
-
-        self.num_inputs = len(self.input_names)
-        assert self.num_inputs >= 1
-
-        # Choose name and scope.
-        if self.name is None:
-            self.name = self._build_func_name
-        assert re.match("^[A-Za-z0-9_.\\-]*$", self.name)
-        with tf.name_scope(None):
-            self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True)
-
-        # Finalize build func kwargs.
-        build_kwargs = dict(self.static_kwargs)
-        build_kwargs["is_template_graph"] = True
-        build_kwargs["components"] = self.components
-
-        # Build template graph.
-        with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope):  # ignore surrounding scopes
-            assert tf.get_variable_scope().name == self.scope
-            assert tf.get_default_graph().get_name_scope() == self.scope
-            with tf.control_dependencies(None):  # ignore surrounding control dependencies
-                self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
-                out_expr = self._build_func(*self.input_templates, **build_kwargs)
-
-        # Collect outputs.
-        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
-        self.num_outputs = len(self.output_templates)
-        assert self.num_outputs >= 1
-        assert all(tfutil.is_tf_expression(t) for t in self.output_templates)
-
-        # Perform sanity checks.
-        if any(t.shape.ndims is None for t in self.input_templates):
-            raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.")
-        if any(t.shape.ndims is None for t in self.output_templates):
-            raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.")
-        if any(not isinstance(comp, Network) for comp in self.components.values()):
-            raise ValueError("Components of a Network must be Networks themselves.")
-        if len(self.components) != len(set(comp.name for comp in self.components.values())):
-            raise ValueError("Components of a Network must have unique names.")
-
-        # List inputs and outputs.
-        self.input_shapes = [t.shape.as_list() for t in self.input_templates]
-        self.output_shapes = [t.shape.as_list() for t in self.output_templates]
-        self.input_shape = self.input_shapes[0]
-        self.output_shape = self.output_shapes[0]
-        self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates]
-
-        # List variables.
-        self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/"))
-        self.vars = OrderedDict(self.own_vars)
-        self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items())
-        self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable)
-        self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items())
-
-    def reset_own_vars(self) -> None:
-        """Re-initialize all variables of this network, excluding sub-networks."""
-        tfutil.run([var.initializer for var in self.own_vars.values()])
-
-    def reset_vars(self) -> None:
-        """Re-initialize all variables of this network, including sub-networks."""
-        tfutil.run([var.initializer for var in self.vars.values()])
-
-    def reset_trainables(self) -> None:
-        """Re-initialize all trainable variables of this network, including sub-networks."""
-        tfutil.run([var.initializer for var in self.trainables.values()])
-
-    def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]:
-        """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s)."""
-        assert len(in_expr) == self.num_inputs
-        assert not all(expr is None for expr in in_expr)
-
-        # Finalize build func kwargs.
-        build_kwargs = dict(self.static_kwargs)
-        build_kwargs.update(dynamic_kwargs)
-        build_kwargs["is_template_graph"] = False
-        build_kwargs["components"] = self.components
-
-        # Build TensorFlow graph to evaluate the network.
-        with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name):
-            assert tf.get_variable_scope().name == self.scope
-            valid_inputs = [expr for expr in in_expr if expr is not None]
-            final_inputs = []
-            for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes):
-                if expr is not None:
-                    expr = tf.identity(expr, name=name)
-                else:
-                    expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name)
-                final_inputs.append(expr)
-            out_expr = self._build_func(*final_inputs, **build_kwargs)
-
-        # Propagate input shapes back to the user-specified expressions.
-        for expr, final in zip(in_expr, final_inputs):
-            if isinstance(expr, tf.Tensor):
-                expr.set_shape(final.shape)
-
-        # Express outputs in the desired format.
-        assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        if return_as_list:
-            out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
-        return out_expr
-
-    def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str:
-        """Get the local name of a given variable, without any surrounding name scopes."""
-        assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str)
-        global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name
-        return self.var_global_to_local[global_name]
-
-    def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression:
-        """Find variable by local or global name."""
-        assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str)
-        return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name
-
-    def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray:
-        """Get the value of a given variable as NumPy array.
-        Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible."""
-        return self.find_var(var_or_local_name).eval()
-
-    def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None:
-        """Set the value of a given variable based on the given NumPy array.
-        Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible."""
-        tfutil.set_vars({self.find_var(var_or_local_name): new_value})
-
-    def __getstate__(self) -> dict:
-        """Pickle export."""
-        state = dict()
-        state["version"]            = 4
-        state["name"]               = self.name
-        state["static_kwargs"]      = dict(self.static_kwargs)
-        state["components"]         = dict(self.components)
-        state["build_module_src"]   = self._build_module_src
-        state["build_func_name"]    = self._build_func_name
-        state["variables"]          = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values()))))
-        return state
-
-    def __setstate__(self, state: dict) -> None:
-        """Pickle import."""
-        # pylint: disable=attribute-defined-outside-init
-        tfutil.assert_tf_initialized()
-        self._init_fields()
-
-        # Execute custom import handlers.
-        for handler in _import_handlers:
-            state = handler(state)
-
-        # Set basic fields.
-        assert state["version"] in [2, 3, 4]
-        self.name = state["name"]
-        self.static_kwargs = util.EasyDict(state["static_kwargs"])
-        self.components = util.EasyDict(state.get("components", {}))
-        self._build_module_src = state["build_module_src"]
-        self._build_func_name = state["build_func_name"]
-
-        # Create temporary module from the imported source code.
-        module_name = "_tflib_network_import_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _import_module_src[module] = self._build_module_src
-        exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used
-
-        # Locate network build function in the temporary module.
-        self._build_func = util.get_obj_from_module(module, self._build_func_name)
-        assert callable(self._build_func)
-
-        # Init TensorFlow graph.
-        self._init_graph()
-        self.reset_own_vars()
-        tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]})
-
-    def clone(self, name: str = None, **new_static_kwargs) -> "Network":
-        """Create a clone of this network with its own copy of the variables."""
-        # pylint: disable=protected-access
-        net = object.__new__(Network)
-        net._init_fields()
-        net.name = name if name is not None else self.name
-        net.static_kwargs = util.EasyDict(self.static_kwargs)
-        net.static_kwargs.update(new_static_kwargs)
-        net._build_module_src = self._build_module_src
-        net._build_func_name = self._build_func_name
-        net._build_func = self._build_func
-        net._init_graph()
-        net.copy_vars_from(self)
-        return net
-
-    def copy_own_vars_from(self, src_net: "Network") -> None:
-        """Copy the values of all variables from the given network, excluding sub-networks."""
-        names = [name for name in self.own_vars.keys() if name in src_net.own_vars]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def copy_vars_from(self, src_net: "Network") -> None:
-        """Copy the values of all variables from the given network, including sub-networks."""
-        names = [name for name in self.vars.keys() if name in src_net.vars]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def copy_trainables_from(self, src_net: "Network") -> None:
-        """Copy the values of all trainable variables from the given network, including sub-networks."""
-        names = [name for name in self.trainables.keys() if name in src_net.trainables]
-        tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names}))
-
-    def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network":
-        """Create new network with the given parameters, and copy all variables from this network."""
-        if new_name is None:
-            new_name = self.name
-        static_kwargs = dict(self.static_kwargs)
-        static_kwargs.update(new_static_kwargs)
-        net = Network(name=new_name, func_name=new_func_name, **static_kwargs)
-        net.copy_vars_from(self)
-        return net
-
-    def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation:
-        """Construct a TensorFlow op that updates the variables of this network
-        to be slightly closer to those of the given network."""
-        with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"):
-            ops = []
-            for name, var in self.vars.items():
-                if name in src_net.vars:
-                    cur_beta = beta if name in self.trainables else beta_nontrainable
-                    new_value = tfutil.lerp(src_net.vars[name], var, cur_beta)
-                    ops.append(var.assign(new_value))
-            return tf.group(*ops)
-
-    def run(self,
-            *in_arrays: Tuple[Union[np.ndarray, None], ...],
-            input_transform: dict = None,
-            output_transform: dict = None,
-            return_as_list: bool = False,
-            print_progress: bool = False,
-            minibatch_size: int = None,
-            num_gpus: int = 1,
-            assume_frozen: bool = False,
-            **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]:
-        """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
-
-        Args:
-            input_transform:    A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network.
-                                The dict must contain a 'func' field that points to a top-level function. The function is called with the input
-                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
-            output_transform:   A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network.
-                                The dict must contain a 'func' field that points to a top-level function. The function is called with the output
-                                TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs.
-            return_as_list:     True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
-            print_progress:     Print progress to the console? Useful for very large input arrays.
-            minibatch_size:     Maximum minibatch size to use, None = disable batching.
-            num_gpus:           Number of GPUs to use.
-            assume_frozen:      Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls.
-            dynamic_kwargs:     Additional keyword arguments to be passed into the network build function.
-        """
-        assert len(in_arrays) == self.num_inputs
-        assert not all(arr is None for arr in in_arrays)
-        assert input_transform is None or util.is_top_level_function(input_transform["func"])
-        assert output_transform is None or util.is_top_level_function(output_transform["func"])
-        output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs)
-        num_items = in_arrays[0].shape[0]
-        if minibatch_size is None:
-            minibatch_size = num_items
-
-        # Construct unique hash key from all arguments that affect the TensorFlow graph.
-        key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs)
-        def unwind_key(obj):
-            if isinstance(obj, dict):
-                return [(key, unwind_key(value)) for key, value in sorted(obj.items())]
-            if callable(obj):
-                return util.get_top_level_function_name(obj)
-            return obj
-        key = repr(unwind_key(key))
-
-        # Build graph.
-        if key not in self._run_cache:
-            with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None):
-                with tf.device("/cpu:0"):
-                    in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
-                    in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))
-
-                out_split = []
-                for gpu in range(num_gpus):
-                    with tf.device("/gpu:%d" % gpu):
-                        net_gpu = self.clone() if assume_frozen else self
-                        in_gpu = in_split[gpu]
-
-                        if input_transform is not None:
-                            in_kwargs = dict(input_transform)
-                            in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs)
-                            in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu)
-
-                        assert len(in_gpu) == self.num_inputs
-                        out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs)
-
-                        if output_transform is not None:
-                            out_kwargs = dict(output_transform)
-                            out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs)
-                            out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu)
-
-                        assert len(out_gpu) == self.num_outputs
-                        out_split.append(out_gpu)
-
-                with tf.device("/cpu:0"):
-                    out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
-                    self._run_cache[key] = in_expr, out_expr
-
-        # Run minibatches.
-        in_expr, out_expr = self._run_cache[key]
-        out_arrays = [np.empty([num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr]
-
-        for mb_begin in range(0, num_items, minibatch_size):
-            if print_progress:
-                print("\r%d / %d" % (mb_begin, num_items), end="")
-
-            mb_end = min(mb_begin + minibatch_size, num_items)
-            mb_num = mb_end - mb_begin
-            mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)]
-            mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in)))
-
-            for dst, src in zip(out_arrays, mb_out):
-                dst[mb_begin: mb_end] = src
-
-        # Done.
-        if print_progress:
-            print("\r%d / %d" % (num_items, num_items))
-
-        if not return_as_list:
-            out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
-        return out_arrays
-
-    def list_ops(self) -> List[TfExpression]:
-        include_prefix = self.scope + "/"
-        exclude_prefix = include_prefix + "_"
-        ops = tf.get_default_graph().get_operations()
-        ops = [op for op in ops if op.name.startswith(include_prefix)]
-        ops = [op for op in ops if not op.name.startswith(exclude_prefix)]
-        return ops
-
-    def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]:
-        """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to
-        individual layers of the network. Mainly intended to be used for reporting."""
-        layers = []
-
-        def recurse(scope, parent_ops, parent_vars, level):
-            # Ignore specific patterns.
-            if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]):
-                return
-
-            # Filter ops and vars by scope.
-            global_prefix = scope + "/"
-            local_prefix = global_prefix[len(self.scope) + 1:]
-            cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]]
-            cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]]
-            if not cur_ops and not cur_vars:
-                return
-
-            # Filter out all ops related to variables.
-            for var in [op for op in cur_ops if op.type.startswith("Variable")]:
-                var_prefix = var.name + "/"
-                cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)]
-
-            # Scope does not contain ops as immediate children => recurse deeper.
-            contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops)
-            if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1:
-                visited = set()
-                for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]:
-                    token = rel_name.split("/")[0]
-                    if token not in visited:
-                        recurse(global_prefix + token, cur_ops, cur_vars, level + 1)
-                        visited.add(token)
-                return
-
-            # Report layer.
-            layer_name = scope[len(self.scope) + 1:]
-            layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1]
-            layer_trainables = [var for _name, var in cur_vars if var.trainable]
-            layers.append((layer_name, layer_output, layer_trainables))
-
-        recurse(self.scope, self.list_ops(), list(self.vars.items()), 0)
-        return layers
-
-    def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None:
-        """Print a summary table of the network structure."""
-        rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]]
-        rows += [["---"] * 4]
-        total_params = 0
-
-        for layer_name, layer_output, layer_trainables in self.list_layers():
-            num_params = sum(int(np.prod(var.shape.as_list())) for var in layer_trainables)
-            weights = [var for var in layer_trainables if var.name.endswith("/weight:0")]
-            weights.sort(key=lambda x: len(x.name))
-            if len(weights) == 0 and len(layer_trainables) == 1:
-                weights = layer_trainables
-            total_params += num_params
-
-            if not hide_layers_with_no_params or num_params != 0:
-                num_params_str = str(num_params) if num_params > 0 else "-"
-                output_shape_str = str(layer_output.shape)
-                weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-"
-                rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]]
-
-        rows += [["---"] * 4]
-        rows += [["Total", str(total_params), "", ""]]
-
-        widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-        print()
-        for row in rows:
-            print("  ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths)))
-        print()
-
-    def setup_weight_histograms(self, title: str = None) -> None:
-        """Construct summary ops to include histograms of all trainable parameters in TensorBoard."""
-        if title is None:
-            title = self.name
-
-        with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
-            for local_name, var in self.trainables.items():
-                if "/" in local_name:
-                    p = local_name.split("/")
-                    name = title + "_" + p[-1] + "/" + "_".join(p[:-1])
-                else:
-                    name = title + "_toplevel/" + local_name
-
-                tf.summary.histogram(name, var)
-
-#----------------------------------------------------------------------------
-# Backwards-compatible emulation of legacy output transformation in Network.run().
-
-_print_legacy_warning = True
-
-def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
-    global _print_legacy_warning
-    legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"]
-    if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs):
-        return output_transform, dynamic_kwargs
-
-    if _print_legacy_warning:
-        _print_legacy_warning = False
-        print()
-        print("WARNING: Old-style output transformations in Network.run() are deprecated.")
-        print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'")
-        print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.")
-        print()
-    assert output_transform is None
-
-    new_kwargs = dict(dynamic_kwargs)
-    new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs}
-    new_transform["func"] = _legacy_output_transform_func
-    return new_transform, new_kwargs
-
-def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None):
-    if out_mul != 1.0:
-        expr = [x * out_mul for x in expr]
-
-    if out_add != 0.0:
-        expr = [x + out_add for x in expr]
-
-    if out_shrink > 1:
-        ksize = [1, 1, out_shrink, out_shrink]
-        expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr]
-
-    if out_dtype is not None:
-        if tf.as_dtype(out_dtype).is_integer:
-            expr = [tf.round(x) for x in expr]
-        expr = [tf.saturate_cast(x, out_dtype) for x in expr]
-    return expr

Time_TravelRephotography/dnnlib/tflib/ops/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-# empty

Time_TravelRephotography/dnnlib/tflib/ops/fused_bias_act.cu

@@ -1,190 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-//
-// This work is made available under the Nvidia Source Code License-NC.
-// To view a copy of this license, visit
-// https://nvlabs.github.io/stylegan2/license.html
-
-#define EIGEN_USE_GPU
-#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include <stdio.h>
-
-using namespace tensorflow;
-using namespace tensorflow::shape_inference;
-
-#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false)
-
-//------------------------------------------------------------------------
-// CUDA kernel.
-
-template <class T>
-struct FusedBiasActKernelParams
-{
-    const T*    x;      // [sizeX]
-    const T*    b;      // [sizeB] or NULL
-    const T*    ref;    // [sizeX] or NULL
-    T*          y;      // [sizeX]
-
-    int         grad;
-    int         axis;
-    int         act;
-    float       alpha;
-    float       gain;
-
-    int         sizeX;
-    int         sizeB;
-    int         stepB;
-    int         loopX;
-};
-
-template <class T>
-static __global__ void FusedBiasActKernel(const FusedBiasActKernelParams<T> p)
-{
-    const float expRange        = 80.0f;
-    const float halfExpRange    = 40.0f;
-    const float seluScale       = 1.0507009873554804934193349852946f;
-    const float seluAlpha       = 1.6732632423543772848170429916717f;
-
-    // Loop over elements.
-    int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
-    for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
-    {
-        // Load and apply bias.
-        float x = (float)p.x[xi];
-        if (p.b)
-            x += (float)p.b[(xi / p.stepB) % p.sizeB];
-        float ref = (p.ref) ? (float)p.ref[xi] : 0.0f;
-        if (p.gain != 0.0f & p.act != 9)
-            ref /= p.gain;
-
-        // Evaluate activation func.
-        float y;
-        switch (p.act * 10 + p.grad)
-        {
-            // linear
-            default:
-            case 10: y = x; break;
-            case 11: y = x; break;
-            case 12: y = 0.0f; break;
-
-            // relu
-            case 20: y = (x > 0.0f) ? x : 0.0f; break;
-            case 21: y = (ref > 0.0f) ? x : 0.0f; break;
-            case 22: y = 0.0f; break;
-
-            // lrelu
-            case 30: y = (x > 0.0f) ? x : x * p.alpha; break;
-            case 31: y = (ref > 0.0f) ? x : x * p.alpha; break;
-            case 32: y = 0.0f; break;
-
-            // tanh
-            case 40: { float c = expf(x); float d = 1.0f / c; y = (x < -expRange) ? -1.0f : (x > expRange) ? 1.0f : (c - d) / (c + d); } break;
-            case 41: y = x * (1.0f - ref * ref); break;
-            case 42: y = x * (1.0f - ref * ref) * (-2.0f * ref); break;
-
-            // sigmoid
-            case 50: y = (x < -expRange) ? 0.0f : 1.0f / (expf(-x) + 1.0f); break;
-            case 51: y = x * ref * (1.0f - ref); break;
-            case 52: y = x * ref * (1.0f - ref) * (1.0f - 2.0f * ref); break;
-
-            // elu
-            case 60: y = (x >= 0.0f) ? x : expf(x) - 1.0f; break;
-            case 61: y = (ref >= 0.0f) ? x : x * (ref + 1.0f); break;
-            case 62: y = (ref >= 0.0f) ? 0.0f : x * (ref + 1.0f); break;
-
-            // selu
-            case 70: y = (x >= 0.0f) ? seluScale * x : (seluScale * seluAlpha) * (expf(x) - 1.0f); break;
-            case 71: y = (ref >= 0.0f) ? x * seluScale : x * (ref + seluScale * seluAlpha); break;
-            case 72: y = (ref >= 0.0f) ? 0.0f : x * (ref + seluScale * seluAlpha); break;
-
-            // softplus
-            case 80: y = (x > expRange) ? x : logf(expf(x) + 1.0f); break;
-            case 81: y = x * (1.0f - expf(-ref)); break;
-            case 82: { float c = expf(-ref); y = x * c * (1.0f - c); } break;
-
-            // swish
-            case 90: y = (x < -expRange) ? 0.0f : x / (expf(-x) + 1.0f); break;
-            case 91: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? x : x * c * (ref + d) / (d * d); } break;
-            case 92: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? 0.0f : x * c * (ref * (2.0f - d) + 2.0f * d) / (d * d * d); } break;
-        }
-
-        // Apply gain and store.
-        p.y[xi] = (T)(y * p.gain);
-    }
-}
-
-//------------------------------------------------------------------------
-// TensorFlow op.
-
-template <class T>
-struct FusedBiasActOp : public OpKernel
-{
-    FusedBiasActKernelParams<T> m_attribs;
-
-    FusedBiasActOp(OpKernelConstruction* ctx) : OpKernel(ctx)
-    {
-        memset(&m_attribs, 0, sizeof(m_attribs));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("grad", &m_attribs.grad));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &m_attribs.axis));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("act", &m_attribs.act));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("alpha", &m_attribs.alpha));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("gain", &m_attribs.gain));
-        OP_REQUIRES(ctx, m_attribs.grad >= 0, errors::InvalidArgument("grad must be non-negative"));
-        OP_REQUIRES(ctx, m_attribs.axis >= 0, errors::InvalidArgument("axis must be non-negative"));
-        OP_REQUIRES(ctx, m_attribs.act >= 0, errors::InvalidArgument("act must be non-negative"));
-    }
-
-    void Compute(OpKernelContext* ctx)
-    {
-        FusedBiasActKernelParams<T> p = m_attribs;
-        cudaStream_t stream = ctx->eigen_device<Eigen::GpuDevice>().stream();
-
-        const Tensor& x     = ctx->input(0); // [...]
-        const Tensor& b     = ctx->input(1); // [sizeB] or [0]
-        const Tensor& ref   = ctx->input(2); // x.shape or [0]
-        p.x = x.flat<T>().data();
-        p.b = (b.NumElements()) ? b.flat<T>().data() : NULL;
-        p.ref = (ref.NumElements()) ? ref.flat<T>().data() : NULL;
-        OP_REQUIRES(ctx, b.NumElements() == 0 || m_attribs.axis < x.dims(), errors::InvalidArgument("axis out of bounds"));
-        OP_REQUIRES(ctx, b.dims() == 1, errors::InvalidArgument("b must have rank 1"));
-        OP_REQUIRES(ctx, b.NumElements() == 0 || b.NumElements() == x.dim_size(m_attribs.axis), errors::InvalidArgument("b has wrong number of elements"));
-        OP_REQUIRES(ctx, ref.NumElements() == ((p.grad == 0) ? 0 : x.NumElements()), errors::InvalidArgument("ref has wrong number of elements"));
-        OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("x is too large"));
-
-        p.sizeX = (int)x.NumElements();
-        p.sizeB = (int)b.NumElements();
-        p.stepB = 1;
-        for (int i = m_attribs.axis + 1; i < x.dims(); i++)
-            p.stepB *= (int)x.dim_size(i);
-
-        Tensor* y = NULL; // x.shape
-        OP_REQUIRES_OK(ctx, ctx->allocate_output(0, x.shape(), &y));
-        p.y = y->flat<T>().data();
-
-        p.loopX = 4;
-        int blockSize = 4 * 32;
-        int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
-        void* args[] = {&p};
-        OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel((void*)FusedBiasActKernel<T>, gridSize, blockSize, args, 0, stream));
-    }
-};
-
-REGISTER_OP("FusedBiasAct")
-    .Input      ("x: T")
-    .Input      ("b: T")
-    .Input      ("ref: T")
-    .Output     ("y: T")
-    .Attr       ("T: {float, half}")
-    .Attr       ("grad: int = 0")
-    .Attr       ("axis: int = 1")
-    .Attr       ("act: int = 0")
-    .Attr       ("alpha: float = 0.0")
-    .Attr       ("gain: float = 1.0");
-REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint<float>("T"), FusedBiasActOp<float>);
-REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint<Eigen::half>("T"), FusedBiasActOp<Eigen::half>);
-
-//------------------------------------------------------------------------

Time_TravelRephotography/dnnlib/tflib/ops/fused_bias_act.py

@@ -1,198 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Custom TensorFlow ops for efficient bias and activation."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-from ...util import EasyDict
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   EasyDict(func=lambda x, **_:        x,                          def_alpha=None, def_gain=1.0,           cuda_idx=1, ref='y', zero_2nd_grad=True),
-    'relu':     EasyDict(func=lambda x, **_:        tf.nn.relu(x),              def_alpha=None, def_gain=np.sqrt(2),    cuda_idx=2, ref='y', zero_2nd_grad=True),
-    'lrelu':    EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', zero_2nd_grad=True),
-    'tanh':     EasyDict(func=lambda x, **_:        tf.nn.tanh(x),              def_alpha=None, def_gain=1.0,           cuda_idx=4, ref='y', zero_2nd_grad=False),
-    'sigmoid':  EasyDict(func=lambda x, **_:        tf.nn.sigmoid(x),           def_alpha=None, def_gain=1.0,           cuda_idx=5, ref='y', zero_2nd_grad=False),
-    'elu':      EasyDict(func=lambda x, **_:        tf.nn.elu(x),               def_alpha=None, def_gain=1.0,           cuda_idx=6, ref='y', zero_2nd_grad=False),
-    'selu':     EasyDict(func=lambda x, **_:        tf.nn.selu(x),              def_alpha=None, def_gain=1.0,           cuda_idx=7, ref='y', zero_2nd_grad=False),
-    'softplus': EasyDict(func=lambda x, **_:        tf.nn.softplus(x),          def_alpha=None, def_gain=1.0,           cuda_idx=8, ref='y', zero_2nd_grad=False),
-    'swish':    EasyDict(func=lambda x, **_:        tf.nn.sigmoid(x) * x,       def_alpha=None, def_gain=np.sqrt(2),    cuda_idx=9, ref='x', zero_2nd_grad=False),
-}
-
-#----------------------------------------------------------------------------
-
-def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, impl='cuda'):
-    r"""Fused bias and activation function.
-
-    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
-    and scales the result by `gain`. Each of the steps is optional. In most cases,
-    the fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can have any shape, but if `b` is defined, the
-                dimension corresponding to `axis`, as well as the rank, must be known.
-        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                as `x`. The shape must be known, and it must match the dimension of `x`
-                corresponding to `axis`.
-        axis:   The dimension in `x` corresponding to the elements of `b`.
-                The value of `axis` is ignored if `b` is not specified.
-        act:    Name of the activation function to evaluate, or `"linear"` to disable.
-                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
-                See `activation_funcs` for a full list. `None` is not allowed.
-        alpha:  Shape parameter for the activation function, or `None` to use the default.
-        gain:   Scaling factor for the output tensor, or `None` to use default.
-                See `activation_funcs` for the default scaling of each activation function.
-                If unsure, consider specifying `1.0`.
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _fused_bias_act_ref,
-        'cuda': _fused_bias_act_cuda,
-    }
-    return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
-
-#----------------------------------------------------------------------------
-
-def _fused_bias_act_ref(x, b, axis, act, alpha, gain):
-    """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops."""
-
-    # Validate arguments.
-    x = tf.convert_to_tensor(x)
-    b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype)
-    act_spec = activation_funcs[act]
-    assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
-    assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
-    if alpha is None:
-        alpha = act_spec.def_alpha
-    if gain is None:
-        gain = act_spec.def_gain
-
-    # Add bias.
-    if b.shape[0] != 0:
-        x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)])
-
-    # Evaluate activation function.
-    x = act_spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    if gain != 1:
-        x *= gain
-    return x
-
-#----------------------------------------------------------------------------
-
-def _fused_bias_act_cuda(x, b, axis, act, alpha, gain):
-    """Fast CUDA implementation of `fused_bias_act()` using custom ops."""
-
-    # Validate arguments.
-    x = tf.convert_to_tensor(x)
-    empty_tensor = tf.constant([], dtype=x.dtype)
-    b = tf.convert_to_tensor(b) if b is not None else empty_tensor
-    act_spec = activation_funcs[act]
-    assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis])
-    assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
-    if alpha is None:
-        alpha = act_spec.def_alpha
-    if gain is None:
-        gain = act_spec.def_gain
-
-    # Special cases.
-    if act == 'linear' and b is None and gain == 1.0:
-        return x
-    if act_spec.cuda_idx is None:
-        return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
-
-    # CUDA kernel.
-    cuda_kernel = _get_plugin().fused_bias_act
-    cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, alpha=alpha, gain=gain)
-
-    # Forward pass: y = func(x, b).
-    def func_y(x, b):
-        y = cuda_kernel(x=x, b=b, ref=empty_tensor, grad=0, **cuda_kwargs)
-        y.set_shape(x.shape)
-        return y
-
-    # Backward pass: dx, db = grad(dy, x, y)
-    def grad_dx(dy, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs)
-        dx.set_shape(x.shape)
-        return dx
-    def grad_db(dx):
-        if b.shape[0] == 0:
-            return empty_tensor
-        db = dx
-        if axis < x.shape.rank - 1:
-            db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank)))
-        if axis > 0:
-            db = tf.reduce_sum(db, list(range(axis)))
-        db.set_shape(b.shape)
-        return db
-
-    # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y)
-    def grad2_d_dy(d_dx, d_db, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs)
-        d_dy.set_shape(x.shape)
-        return d_dy
-    def grad2_d_x(d_dx, d_db, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs)
-        d_x.set_shape(x.shape)
-        return d_x
-
-    # Fast version for piecewise-linear activation funcs.
-    @tf.custom_gradient
-    def func_zero_2nd_grad(x, b):
-        y = func_y(x, b)
-        @tf.custom_gradient
-        def grad(dy):
-            dx = grad_dx(dy, x, y)
-            db = grad_db(dx)
-            def grad2(d_dx, d_db):
-                d_dy = grad2_d_dy(d_dx, d_db, x, y)
-                return d_dy
-            return (dx, db), grad2
-        return y, grad
-
-    # Slow version for general activation funcs.
-    @tf.custom_gradient
-    def func_nonzero_2nd_grad(x, b):
-        y = func_y(x, b)
-        def grad_wrap(dy):
-            @tf.custom_gradient
-            def grad_impl(dy, x):
-                dx = grad_dx(dy, x, y)
-                db = grad_db(dx)
-                def grad2(d_dx, d_db):
-                    d_dy = grad2_d_dy(d_dx, d_db, x, y)
-                    d_x = grad2_d_x(d_dx, d_db, x, y)
-                    return d_dy, d_x
-                return (dx, db), grad2
-            return grad_impl(dy, x)
-        return y, grad_wrap
-
-    # Which version to use?
-    if act_spec.zero_2nd_grad:
-        return func_zero_2nd_grad(x, b)
-    return func_nonzero_2nd_grad(x, b)
-
-#----------------------------------------------------------------------------

Time_TravelRephotography/dnnlib/tflib/ops/upfirdn_2d.cu

@@ -1,328 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-//
-// This work is made available under the Nvidia Source Code License-NC.
-// To view a copy of this license, visit
-// https://nvlabs.github.io/stylegan2/license.html
-
-#define EIGEN_USE_GPU
-#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
-#include "tensorflow/core/framework/op.h"
-#include "tensorflow/core/framework/op_kernel.h"
-#include "tensorflow/core/framework/shape_inference.h"
-#include <stdio.h>
-
-using namespace tensorflow;
-using namespace tensorflow::shape_inference;
-
-//------------------------------------------------------------------------
-// Helpers.
-
-#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false)
-
-static __host__ __device__ __forceinline__ int floorDiv(int a, int b)
-{
-    int c = a / b;
-    if (c * b > a)
-        c--;
-    return c;
-}
-
-//------------------------------------------------------------------------
-// CUDA kernel params.
-
-template <class T>
-struct UpFirDn2DKernelParams
-{
-    const T*    x;          // [majorDim, inH, inW, minorDim]
-    const T*    k;          // [kernelH, kernelW]
-    T*          y;          // [majorDim, outH, outW, minorDim]
-
-    int         upx;
-    int         upy;
-    int         downx;
-    int         downy;
-    int         padx0;
-    int         padx1;
-    int         pady0;
-    int         pady1;
-
-    int         majorDim;
-    int         inH;
-    int         inW;
-    int         minorDim;
-    int         kernelH;
-    int         kernelW;
-    int         outH;
-    int         outW;
-    int         loopMajor;
-    int         loopX;
-};
-
-//------------------------------------------------------------------------
-// General CUDA implementation for large filter kernels.
-
-template <class T>
-static __global__ void UpFirDn2DKernel_large(const UpFirDn2DKernelParams<T> p)
-{
-    // Calculate thread index.
-    int minorIdx = blockIdx.x * blockDim.x + threadIdx.x;
-    int outY = minorIdx / p.minorDim;
-    minorIdx -= outY * p.minorDim;
-    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
-    int majorIdxBase = blockIdx.z * p.loopMajor;
-    if (outXBase >= p.outW || outY >= p.outH || majorIdxBase >= p.majorDim)
-        return;
-
-    // Setup Y receptive field.
-    int midY = outY * p.downy + p.upy - 1 - p.pady0;
-    int inY = min(max(floorDiv(midY, p.upy), 0), p.inH);
-    int h = min(max(floorDiv(midY + p.kernelH, p.upy), 0), p.inH) - inY;
-    int kernelY = midY + p.kernelH - (inY + 1) * p.upy;
-
-    // Loop over majorDim and outX.
-    for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor && majorIdx < p.majorDim; loopMajor++, majorIdx++)
-    for (int loopX = 0, outX = outXBase; loopX < p.loopX && outX < p.outW; loopX++, outX += blockDim.y)
-    {
-        // Setup X receptive field.
-        int midX = outX * p.downx + p.upx - 1 - p.padx0;
-        int inX = min(max(floorDiv(midX, p.upx), 0), p.inW);
-        int w = min(max(floorDiv(midX + p.kernelW, p.upx), 0), p.inW) - inX;
-        int kernelX = midX + p.kernelW - (inX + 1) * p.upx;
-
-        // Initialize pointers.
-        const T* xp = &p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx];
-        const T* kp = &p.k[kernelY * p.kernelW + kernelX];
-        int xpx = p.minorDim;
-        int kpx = -p.upx;
-        int xpy = p.inW * p.minorDim;
-        int kpy = -p.upy * p.kernelW;
-
-        // Inner loop.
-        float v = 0.0f;
-        for (int y = 0; y < h; y++)
-        {
-            for (int x = 0; x < w; x++)
-            {
-                v += (float)(*xp) * (float)(*kp);
-                xp += xpx;
-                kp += kpx;
-            }
-            xp += xpy - w * xpx;
-            kp += kpy - w * kpx;
-        }
-
-        // Store result.
-        p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v;
-    }
-}
-
-//------------------------------------------------------------------------
-// Specialized CUDA implementation for small filter kernels.
-
-template <class T, int upx, int upy, int downx, int downy, int kernelW, int kernelH, int tileOutW, int tileOutH>
-static __global__ void UpFirDn2DKernel_small(const UpFirDn2DKernelParams<T> p)
-{
-    //assert(kernelW % upx == 0);
-    //assert(kernelH % upy == 0);
-    const int tileInW = ((tileOutW - 1) * downx + kernelW - 1) / upx + 1;
-    const int tileInH = ((tileOutH - 1) * downy + kernelH - 1) / upy + 1;
-    __shared__ volatile float sk[kernelH][kernelW];
-    __shared__ volatile float sx[tileInH][tileInW];
-
-    // Calculate tile index.
-    int minorIdx = blockIdx.x;
-    int tileOutY = minorIdx / p.minorDim;
-    minorIdx -= tileOutY * p.minorDim;
-    tileOutY *= tileOutH;
-    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
-    int majorIdxBase = blockIdx.z * p.loopMajor;
-    if (tileOutXBase >= p.outW | tileOutY >= p.outH | majorIdxBase >= p.majorDim)
-        return;
-
-    // Load filter kernel (flipped).
-    for (int tapIdx = threadIdx.x; tapIdx < kernelH * kernelW; tapIdx += blockDim.x)
-    {
-        int ky = tapIdx / kernelW;
-        int kx = tapIdx - ky * kernelW;
-        float v = 0.0f;
-        if (kx < p.kernelW & ky < p.kernelH)
-            v = (float)p.k[(p.kernelH - 1 - ky) * p.kernelW + (p.kernelW - 1 - kx)];
-        sk[ky][kx] = v;
-    }
-
-    // Loop over majorDim and outX.
-    for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor & majorIdx < p.majorDim; loopMajor++, majorIdx++)
-    for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outW; loopX++, tileOutX += tileOutW)
-    {
-        // Load input pixels.
-        int tileMidX = tileOutX * downx + upx - 1 - p.padx0;
-        int tileMidY = tileOutY * downy + upy - 1 - p.pady0;
-        int tileInX = floorDiv(tileMidX, upx);
-        int tileInY = floorDiv(tileMidY, upy);
-        __syncthreads();
-        for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW; inIdx += blockDim.x)
-        {
-            int relInY = inIdx / tileInW;
-            int relInX = inIdx - relInY * tileInW;
-            int inX = relInX + tileInX;
-            int inY = relInY + tileInY;
-            float v = 0.0f;
-            if (inX >= 0 & inY >= 0 & inX < p.inW & inY < p.inH)
-                v = (float)p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx];
-            sx[relInY][relInX] = v;
-        }
-
-        // Loop over output pixels.
-        __syncthreads();
-        for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW; outIdx += blockDim.x)
-        {
-            int relOutY = outIdx / tileOutW;
-            int relOutX = outIdx - relOutY * tileOutW;
-            int outX = relOutX + tileOutX;
-            int outY = relOutY + tileOutY;
-
-            // Setup receptive field.
-            int midX = tileMidX + relOutX * downx;
-            int midY = tileMidY + relOutY * downy;
-            int inX = floorDiv(midX, upx);
-            int inY = floorDiv(midY, upy);
-            int relInX = inX - tileInX;
-            int relInY = inY - tileInY;
-            int kernelX = (inX + 1) * upx - midX - 1; // flipped
-            int kernelY = (inY + 1) * upy - midY - 1; // flipped
-
-            // Inner loop.
-            float v = 0.0f;
-            #pragma unroll
-            for (int y = 0; y < kernelH / upy; y++)
-                #pragma unroll
-                for (int x = 0; x < kernelW / upx; x++)
-                    v += sx[relInY + y][relInX + x] * sk[kernelY + y * upy][kernelX + x * upx];
-
-            // Store result.
-            if (outX < p.outW & outY < p.outH)
-                p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v;
-        }
-    }
-}
-
-//------------------------------------------------------------------------
-// TensorFlow op.
-
-template <class T>
-struct UpFirDn2DOp : public OpKernel
-{
-    UpFirDn2DKernelParams<T> m_attribs;
-
-    UpFirDn2DOp(OpKernelConstruction* ctx) : OpKernel(ctx)
-    {
-        memset(&m_attribs, 0, sizeof(m_attribs));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("upx", &m_attribs.upx));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("upy", &m_attribs.upy));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("downx", &m_attribs.downx));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("downy", &m_attribs.downy));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("padx0", &m_attribs.padx0));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("padx1", &m_attribs.padx1));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("pady0", &m_attribs.pady0));
-        OP_REQUIRES_OK(ctx, ctx->GetAttr("pady1", &m_attribs.pady1));
-        OP_REQUIRES(ctx, m_attribs.upx >= 1 && m_attribs.upy >= 1, errors::InvalidArgument("upx and upy must be at least 1x1"));
-        OP_REQUIRES(ctx, m_attribs.downx >= 1 && m_attribs.downy >= 1, errors::InvalidArgument("downx and downy must be at least 1x1"));
-    }
-
-    void Compute(OpKernelContext* ctx)
-    {
-        UpFirDn2DKernelParams<T> p = m_attribs;
-        cudaStream_t stream = ctx->eigen_device<Eigen::GpuDevice>().stream();
-
-        const Tensor& x = ctx->input(0); // [majorDim, inH, inW, minorDim]
-        const Tensor& k = ctx->input(1); // [kernelH, kernelW]
-        p.x = x.flat<T>().data();
-        p.k = k.flat<T>().data();
-        OP_REQUIRES(ctx, x.dims() == 4, errors::InvalidArgument("input must have rank 4"));
-        OP_REQUIRES(ctx, k.dims() == 2, errors::InvalidArgument("kernel must have rank 2"));
-        OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("input too large"));
-        OP_REQUIRES(ctx, k.NumElements() <= kint32max, errors::InvalidArgument("kernel too large"));
-
-        p.majorDim  = (int)x.dim_size(0);
-        p.inH       = (int)x.dim_size(1);
-        p.inW       = (int)x.dim_size(2);
-        p.minorDim  = (int)x.dim_size(3);
-        p.kernelH   = (int)k.dim_size(0);
-        p.kernelW   = (int)k.dim_size(1);
-        OP_REQUIRES(ctx, p.kernelW >= 1 && p.kernelH >= 1, errors::InvalidArgument("kernel must be at least 1x1"));
-
-        p.outW = (p.inW * p.upx + p.padx0 + p.padx1 - p.kernelW + p.downx) / p.downx;
-        p.outH = (p.inH * p.upy + p.pady0 + p.pady1 - p.kernelH + p.downy) / p.downy;
-        OP_REQUIRES(ctx, p.outW >= 1 && p.outH >= 1, errors::InvalidArgument("output must be at least 1x1"));
-
-        Tensor* y = NULL; // [majorDim, outH, outW, minorDim]
-        TensorShape ys;
-        ys.AddDim(p.majorDim);
-        ys.AddDim(p.outH);
-        ys.AddDim(p.outW);
-        ys.AddDim(p.minorDim);
-        OP_REQUIRES_OK(ctx, ctx->allocate_output(0, ys, &y));
-        p.y = y->flat<T>().data();
-        OP_REQUIRES(ctx, y->NumElements() <= kint32max, errors::InvalidArgument("output too large"));
-
-        // Choose CUDA kernel to use.
-        void* cudaKernel = (void*)UpFirDn2DKernel_large<T>;
-        int tileOutW = -1;
-        int tileOutH = -1;
-        if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 7 && p.kernelH <= 7) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 1,1, 7,7, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 1,1, 6,6, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 5 && p.kernelH <= 5) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 1,1, 5,5, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 1,1, 4,4, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 3 && p.kernelH <= 3) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 1,1, 3,3, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 8) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 2,2, 1,1, 8,8, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 2,2, 1,1, 6,6, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 2,2, 1,1, 4,4, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 2 && p.kernelH <= 2) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 2,2, 1,1, 2,2, 64,16>; tileOutW = 64; tileOutH = 16; }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 8 && p.kernelH <= 8) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 2,2, 8,8, 32,8>;  tileOutW = 32; tileOutH = 8;  }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 6 && p.kernelH <= 6) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 2,2, 6,6, 32,8>;  tileOutW = 32; tileOutH = 8;  }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 4 && p.kernelH <= 4) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 2,2, 4,4, 32,8>;  tileOutW = 32; tileOutH = 8;  }
-        if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 2 && p.kernelH <= 2) { cudaKernel = (void*)UpFirDn2DKernel_small<T, 1,1, 2,2, 2,2, 32,8>;  tileOutW = 32; tileOutH = 8;  }
-
-        // Choose launch params.
-        dim3 blockSize;
-        dim3 gridSize;
-        if (tileOutW > 0 && tileOutH > 0) // small
-        {
-            p.loopMajor = (p.majorDim - 1) / 16384 + 1;
-            p.loopX = 1;
-            blockSize = dim3(32 * 8, 1, 1);
-            gridSize = dim3(((p.outH - 1) / tileOutH + 1) * p.minorDim, (p.outW - 1) / (p.loopX * tileOutW) + 1, (p.majorDim - 1) / p.loopMajor + 1);
-        }
-        else // large
-        {
-            p.loopMajor = (p.majorDim - 1) / 16384 + 1;
-            p.loopX = 4;
-            blockSize = dim3(4, 32, 1);
-            gridSize = dim3((p.outH * p.minorDim - 1) / blockSize.x + 1, (p.outW - 1) / (p.loopX * blockSize.y) + 1, (p.majorDim - 1) / p.loopMajor + 1);
-        }
-
-        // Launch CUDA kernel.
-        void* args[] = {&p};
-        OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel(cudaKernel, gridSize, blockSize, args, 0, stream));
-    }
-};
-
-REGISTER_OP("UpFirDn2D")
-    .Input      ("x: T")
-    .Input      ("k: T")
-    .Output     ("y: T")
-    .Attr       ("T: {float, half}")
-    .Attr       ("upx: int = 1")
-    .Attr       ("upy: int = 1")
-    .Attr       ("downx: int = 1")
-    .Attr       ("downy: int = 1")
-    .Attr       ("padx0: int = 0")
-    .Attr       ("padx1: int = 0")
-    .Attr       ("pady0: int = 0")
-    .Attr       ("pady1: int = 0");
-REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint<float>("T"), UpFirDn2DOp<float>);
-REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint<Eigen::half>("T"), UpFirDn2DOp<Eigen::half>);
-
-//------------------------------------------------------------------------

Time_TravelRephotography/dnnlib/tflib/ops/upfirdn_2d.py

@@ -1,366 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Custom TensorFlow ops for efficient resampling of 2D images."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-#----------------------------------------------------------------------------
-
-def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
-    r"""Pad, upsample, FIR filter, and downsample a batch of 2D images.
-
-    Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`
-    and performs the following operations for each image, batched across
-    `majorDim` and `minorDim`:
-
-    1. Pad the image with zeros by the specified number of pixels on each side
-       (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value
-       corresponds to cropping the image.
-
-    2. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).
-
-    3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the
-       image so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by throwing away pixels (`downx`, `downy`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:      Input tensor of the shape `[majorDim, inH, inW, minorDim]`.
-        k:      2D FIR filter of the shape `[firH, firW]`.
-        upx:    Integer upsampling factor along the X-axis (default: 1).
-        upy:    Integer upsampling factor along the Y-axis (default: 1).
-        downx:  Integer downsampling factor along the X-axis (default: 1).
-        downy:  Integer downsampling factor along the Y-axis (default: 1).
-        padx0:  Number of pixels to pad on the left side (default: 0).
-        padx1:  Number of pixels to pad on the right side (default: 0).
-        pady0:  Number of pixels to pad on the top side (default: 0).
-        pady1:  Number of pixels to pad on the bottom side (default: 0).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _upfirdn_2d_ref,
-        'cuda': _upfirdn_2d_cuda,
-    }
-    return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    assert x.shape.rank == 4
-    inH = x.shape[1].value
-    inW = x.shape[2].value
-    minorDim = _shape(x, 3)
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    # Upsample (insert zeros).
-    x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim])
-    x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]])
-    x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim])
-
-    # Pad (crop if negative).
-    x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]])
-    x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :]
-
-    # Convolve with filter.
-    x = tf.transpose(x, [0, 3, 1, 2])
-    x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1])
-    w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype)
-    x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW')
-    x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1])
-    x = tf.transpose(x, [0, 2, 3, 1])
-
-    # Downsample (throw away pixels).
-    return x[:, ::downy, ::downx, :]
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Fast CUDA implementation of `upfirdn_2d()` using custom ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    majorDim, inH, inW, minorDim = x.shape.as_list()
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1
-    outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1
-    assert outW >= 1 and outH >= 1
-
-    kc = tf.constant(k, dtype=x.dtype)
-    gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype)
-    gpadx0 = kernelW - padx0 - 1
-    gpady0 = kernelH - pady0 - 1
-    gpadx1 = inW * upx - outW * downx + padx0 - upx + 1
-    gpady1 = inH * upy - outH * downy + pady0 - upy + 1
-
-    @tf.custom_gradient
-    def func(x):
-        y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-        y.set_shape([majorDim, outH, outW, minorDim])
-        @tf.custom_gradient
-        def grad(dy):
-            dx = _get_plugin().up_fir_dn2d(x=dy, k=gkc, upx=downx, upy=downy, downx=upx, downy=upy, padx0=gpadx0, padx1=gpadx1, pady0=gpady0, pady1=gpady1)
-            dx.set_shape([majorDim, inH, inW, minorDim])
-            return dx, func
-        return y, grad
-    return func(x)
-
-#----------------------------------------------------------------------------
-
-def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Filter a batch of 2D images with the given FIR filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and filters each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    k = _setup_kernel(k) * gain
-    p = k.shape[0] - 1
-    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Upsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and upsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the upsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * (gain * (factor ** 2))
-    p = k.shape[0] - factor
-    return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Downsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and downsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the downsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * gain
-    p = k.shape[0] - factor
-    return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    assert w.shape.rank == 4
-    convH = w.shape[0].value
-    convW = w.shape[1].value
-    inC = _shape(w, 2)
-    outC = _shape(w, 3)
-    assert convW == convH
-
-    # Setup filter kernel.
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * (gain * (factor ** 2))
-    p = (k.shape[0] - factor) - (convW - 1)
-
-    # Determine data dimensions.
-    if data_format == 'NCHW':
-        stride = [1, 1, factor, factor]
-        output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW]
-        num_groups = _shape(x, 1) // inC
-    else:
-        stride = [1, factor, factor, 1]
-        output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC]
-        num_groups = _shape(x, 3) // inC
-
-    # Transpose weights.
-    w = tf.reshape(w, [convH, convW, inC, num_groups, -1])
-    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
-    w = tf.reshape(w, [convH, convW, -1, num_groups * inC])
-
-    # Execute.
-    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
-    return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
-    r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    w = tf.convert_to_tensor(w)
-    convH, convW, _inC, _outC = w.shape.as_list()
-    assert convW == convH
-    if k is None:
-        k = [1] * factor
-    k = _setup_kernel(k) * gain
-    p = (k.shape[0] - factor) + (convW - 1)
-    if data_format == 'NCHW':
-        s = [1, 1, factor, factor]
-    else:
-        s = [1, factor, factor, 1]
-    x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
-    return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _shape(tf_expr, dim_idx):
-    if tf_expr.shape.rank is not None:
-        dim = tf_expr.shape[dim_idx].value
-        if dim is not None:
-            return dim
-    return tf.shape(tf_expr)[dim_idx]
-
-def _setup_kernel(k):
-    k = np.asarray(k, dtype=np.float32)
-    if k.ndim == 1:
-        k = np.outer(k, k)
-    k /= np.sum(k)
-    assert k.ndim == 2
-    assert k.shape[0] == k.shape[1]
-    return k
-
-def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'):
-    assert data_format in ['NCHW', 'NHWC']
-    assert x.shape.rank == 4
-    y = x
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1])
-    y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)])
-    return y
-
-#----------------------------------------------------------------------------

Time_TravelRephotography/dnnlib/tflib/optimizer.py

@@ -1,338 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Helper wrapper for a Tensorflow optimizer."""
-
-import numpy as np
-import tensorflow as tf
-
-from collections import OrderedDict
-from typing import List, Union
-
-from . import autosummary
-from . import tfutil
-from .. import util
-
-from .tfutil import TfExpression, TfExpressionEx
-
-try:
-    # TensorFlow 1.13
-    from tensorflow.python.ops import nccl_ops
-except:
-    # Older TensorFlow versions
-    import tensorflow.contrib.nccl as nccl_ops
-
-class Optimizer:
-    """A Wrapper for tf.train.Optimizer.
-
-    Automatically takes care of:
-    - Gradient averaging for multi-GPU training.
-    - Gradient accumulation for arbitrarily large minibatches.
-    - Dynamic loss scaling and typecasts for FP16 training.
-    - Ignoring corrupted gradients that contain NaNs/Infs.
-    - Reporting statistics.
-    - Well-chosen default settings.
-    """
-
-    def __init__(self,
-        name:                   str             = "Train",                  # Name string that will appear in TensorFlow graph.
-        tf_optimizer:           str             = "tf.train.AdamOptimizer", # Underlying optimizer class.
-        learning_rate:          TfExpressionEx  = 0.001,                    # Learning rate. Can vary over time.
-        minibatch_multiplier:   TfExpressionEx  = None,                     # Treat N consecutive minibatches as one by accumulating gradients.
-        share:                  "Optimizer"     = None,                     # Share internal state with a previously created optimizer?
-        use_loss_scaling:       bool            = False,                    # Enable dynamic loss scaling for robust mixed-precision training?
-        loss_scaling_init:      float           = 64.0,                     # Log2 of initial loss scaling factor.
-        loss_scaling_inc:       float           = 0.0005,                   # Log2 of per-minibatch loss scaling increment when there is no overflow.
-        loss_scaling_dec:       float           = 1.0,                      # Log2 of per-minibatch loss scaling decrement when there is an overflow.
-        report_mem_usage:       bool            = False,                    # Report fine-grained memory usage statistics in TensorBoard?
-        **kwargs):
-
-        # Public fields.
-        self.name                   = name
-        self.learning_rate          = learning_rate
-        self.minibatch_multiplier   = minibatch_multiplier
-        self.id                     = self.name.replace("/", ".")
-        self.scope                  = tf.get_default_graph().unique_name(self.id)
-        self.optimizer_class        = util.get_obj_by_name(tf_optimizer)
-        self.optimizer_kwargs       = dict(kwargs)
-        self.use_loss_scaling       = use_loss_scaling
-        self.loss_scaling_init      = loss_scaling_init
-        self.loss_scaling_inc       = loss_scaling_inc
-        self.loss_scaling_dec       = loss_scaling_dec
-
-        # Private fields.
-        self._updates_applied       = False
-        self._devices               = OrderedDict() # device_name => EasyDict()
-        self._shared_optimizers     = OrderedDict() # device_name => optimizer_class
-        self._gradient_shapes       = None          # [shape, ...]
-        self._report_mem_usage      = report_mem_usage
-
-        # Validate arguments.
-        assert callable(self.optimizer_class)
-
-        # Share internal state if requested.
-        if share is not None:
-            assert isinstance(share, Optimizer)
-            assert self.optimizer_class is share.optimizer_class
-            assert self.learning_rate is share.learning_rate
-            assert self.optimizer_kwargs == share.optimizer_kwargs
-            self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access
-
-    def _get_device(self, device_name: str):
-        """Get internal state for the given TensorFlow device."""
-        tfutil.assert_tf_initialized()
-        if device_name in self._devices:
-            return self._devices[device_name]
-
-        # Initialize fields.
-        device = util.EasyDict()
-        device.name             = device_name
-        device.optimizer        = None          # Underlying optimizer:     optimizer_class
-        device.loss_scaling_var = None          # Log2 of loss scaling:     tf.Variable
-        device.grad_raw         = OrderedDict() # Raw gradients:            var => [grad, ...]
-        device.grad_clean       = OrderedDict() # Clean gradients:          var => grad
-        device.grad_acc_vars    = OrderedDict() # Accumulation sums:        var => tf.Variable
-        device.grad_acc_count   = None          # Accumulation counter:     tf.Variable
-        device.grad_acc         = OrderedDict() # Accumulated gradients:    var => grad
-
-        # Setup TensorFlow objects.
-        with tfutil.absolute_name_scope(self.scope + "/Devices"), tf.device(device_name), tf.control_dependencies(None):
-            if device_name not in self._shared_optimizers:
-                optimizer_name = self.scope.replace("/", "_") + "_opt%d" % len(self._shared_optimizers)
-                self._shared_optimizers[device_name] = self.optimizer_class(name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
-            device.optimizer = self._shared_optimizers[device_name]
-            if self.use_loss_scaling:
-                device.loss_scaling_var = tf.Variable(np.float32(self.loss_scaling_init), trainable=False, name="loss_scaling_var")
-
-        # Register device.
-        self._devices[device_name] = device
-        return device
-
-    def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dict]) -> None:
-        """Register the gradients of the given loss function with respect to the given variables.
-        Intended to be called once per GPU."""
-        tfutil.assert_tf_initialized()
-        assert not self._updates_applied
-        device = self._get_device(loss.device)
-
-        # Validate trainables.
-        if isinstance(trainable_vars, dict):
-            trainable_vars = list(trainable_vars.values())  # allow passing in Network.trainables as vars
-        assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1
-        assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss])
-        assert all(var.device == device.name for var in trainable_vars)
-
-        # Validate shapes.
-        if self._gradient_shapes is None:
-            self._gradient_shapes = [var.shape.as_list() for var in trainable_vars]
-        assert len(trainable_vars) == len(self._gradient_shapes)
-        assert all(var.shape.as_list() == var_shape for var, var_shape in zip(trainable_vars, self._gradient_shapes))
-
-        # Report memory usage if requested.
-        deps = []
-        if self._report_mem_usage:
-            self._report_mem_usage = False
-            try:
-                with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]):
-                    deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30))
-            except tf.errors.NotFoundError:
-                pass
-
-        # Compute gradients.
-        with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps):
-            loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
-            gate = tf.train.Optimizer.GATE_NONE  # disable gating to reduce memory usage
-            grad_list = device.optimizer.compute_gradients(loss=loss, var_list=trainable_vars, gate_gradients=gate)
-
-        # Register gradients.
-        for grad, var in grad_list:
-            if var not in device.grad_raw:
-                device.grad_raw[var] = []
-            device.grad_raw[var].append(grad)
-
-    def apply_updates(self, allow_no_op: bool = False) -> tf.Operation:
-        """Construct training op to update the registered variables based on their gradients."""
-        tfutil.assert_tf_initialized()
-        assert not self._updates_applied
-        self._updates_applied = True
-        all_ops = []
-
-        # Check for no-op.
-        if allow_no_op and len(self._devices) == 0:
-            with tfutil.absolute_name_scope(self.scope):
-                return tf.no_op(name='TrainingOp')
-
-        # Clean up gradients.
-        for device_idx, device in enumerate(self._devices.values()):
-            with tfutil.absolute_name_scope(self.scope + "/Clean%d" % device_idx), tf.device(device.name):
-                for var, grad in device.grad_raw.items():
-
-                    # Filter out disconnected gradients and convert to float32.
-                    grad = [g for g in grad if g is not None]
-                    grad = [tf.cast(g, tf.float32) for g in grad]
-
-                    # Sum within the device.
-                    if len(grad) == 0:
-                        grad = tf.zeros(var.shape)  # No gradients => zero.
-                    elif len(grad) == 1:
-                        grad = grad[0]              # Single gradient => use as is.
-                    else:
-                        grad = tf.add_n(grad)       # Multiple gradients => sum.
-
-                    # Scale as needed.
-                    scale = 1.0 / len(device.grad_raw[var]) / len(self._devices)
-                    scale = tf.constant(scale, dtype=tf.float32, name="scale")
-                    if self.minibatch_multiplier is not None:
-                        scale /= tf.cast(self.minibatch_multiplier, tf.float32)
-                    scale = self.undo_loss_scaling(scale)
-                    device.grad_clean[var] = grad * scale
-
-        # Sum gradients across devices.
-        if len(self._devices) > 1:
-            with tfutil.absolute_name_scope(self.scope + "/Broadcast"), tf.device(None):
-                for all_vars in zip(*[device.grad_clean.keys() for device in self._devices.values()]):
-                    if len(all_vars) > 0 and all(dim > 0 for dim in all_vars[0].shape.as_list()): # NCCL does not support zero-sized tensors.
-                        all_grads = [device.grad_clean[var] for device, var in zip(self._devices.values(), all_vars)]
-                        all_grads = nccl_ops.all_sum(all_grads)
-                        for device, var, grad in zip(self._devices.values(), all_vars, all_grads):
-                            device.grad_clean[var] = grad
-
-        # Apply updates separately on each device.
-        for device_idx, device in enumerate(self._devices.values()):
-            with tfutil.absolute_name_scope(self.scope + "/Apply%d" % device_idx), tf.device(device.name):
-                # pylint: disable=cell-var-from-loop
-
-                # Accumulate gradients over time.
-                if self.minibatch_multiplier is None:
-                    acc_ok = tf.constant(True, name='acc_ok')
-                    device.grad_acc = OrderedDict(device.grad_clean)
-                else:
-                    # Create variables.
-                    with tf.control_dependencies(None):
-                        for var in device.grad_clean.keys():
-                            device.grad_acc_vars[var] = tf.Variable(tf.zeros(var.shape), trainable=False, name="grad_acc_var")
-                        device.grad_acc_count = tf.Variable(tf.zeros([]), trainable=False, name="grad_acc_count")
-
-                    # Track counter.
-                    count_cur = device.grad_acc_count + 1.0
-                    count_inc_op = lambda: tf.assign(device.grad_acc_count, count_cur)
-                    count_reset_op = lambda: tf.assign(device.grad_acc_count, tf.zeros([]))
-                    acc_ok = (count_cur >= tf.cast(self.minibatch_multiplier, tf.float32))
-                    all_ops.append(tf.cond(acc_ok, count_reset_op, count_inc_op))
-
-                    # Track gradients.
-                    for var, grad in device.grad_clean.items():
-                        acc_var = device.grad_acc_vars[var]
-                        acc_cur = acc_var + grad
-                        device.grad_acc[var] = acc_cur
-                        with tf.control_dependencies([acc_cur]):
-                            acc_inc_op = lambda: tf.assign(acc_var, acc_cur)
-                            acc_reset_op = lambda: tf.assign(acc_var, tf.zeros(var.shape))
-                            all_ops.append(tf.cond(acc_ok, acc_reset_op, acc_inc_op))
-
-                # No overflow => apply gradients.
-                all_ok = tf.reduce_all(tf.stack([acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()]))
-                apply_op = lambda: device.optimizer.apply_gradients([(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()])
-                all_ops.append(tf.cond(all_ok, apply_op, tf.no_op))
-
-                # Adjust loss scaling.
-                if self.use_loss_scaling:
-                    ls_inc_op = lambda: tf.assign_add(device.loss_scaling_var, self.loss_scaling_inc)
-                    ls_dec_op = lambda: tf.assign_sub(device.loss_scaling_var, self.loss_scaling_dec)
-                    ls_update_op = lambda: tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op))
-                    all_ops.append(tf.cond(acc_ok, ls_update_op, tf.no_op))
-
-                # Last device => report statistics.
-                if device_idx == len(self._devices) - 1:
-                    all_ops.append(autosummary.autosummary(self.id + "/learning_rate", self.learning_rate))
-                    all_ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok))
-                    if self.use_loss_scaling:
-                        all_ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", device.loss_scaling_var))
-
-        # Initialize variables.
-        self.reset_optimizer_state()
-        if self.use_loss_scaling:
-            tfutil.init_uninitialized_vars([device.loss_scaling_var for device in self._devices.values()])
-        if self.minibatch_multiplier is not None:
-            tfutil.run([var.initializer for device in self._devices.values() for var in list(device.grad_acc_vars.values()) + [device.grad_acc_count]])
-
-        # Group everything into a single op.
-        with tfutil.absolute_name_scope(self.scope):
-            return tf.group(*all_ops, name="TrainingOp")
-
-    def reset_optimizer_state(self) -> None:
-        """Reset internal state of the underlying optimizer."""
-        tfutil.assert_tf_initialized()
-        tfutil.run([var.initializer for device in self._devices.values() for var in device.optimizer.variables()])
-
-    def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
-        """Get or create variable representing log2 of the current dynamic loss scaling factor."""
-        return self._get_device(device).loss_scaling_var
-
-    def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
-        """Apply dynamic loss scaling for the given expression."""
-        assert tfutil.is_tf_expression(value)
-        if not self.use_loss_scaling:
-            return value
-        return value * tfutil.exp2(self.get_loss_scaling_var(value.device))
-
-    def undo_loss_scaling(self, value: TfExpression) -> TfExpression:
-        """Undo the effect of dynamic loss scaling for the given expression."""
-        assert tfutil.is_tf_expression(value)
-        if not self.use_loss_scaling:
-            return value
-        return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type
-
-
-class SimpleAdam:
-    """Simplified version of tf.train.AdamOptimizer that behaves identically when used with dnnlib.tflib.Optimizer."""
-
-    def __init__(self, name="Adam", learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):
-        self.name = name
-        self.learning_rate = learning_rate
-        self.beta1 = beta1
-        self.beta2 = beta2
-        self.epsilon = epsilon
-        self.all_state_vars = []
-
-    def variables(self):
-        return self.all_state_vars
-
-    def compute_gradients(self, loss, var_list, gate_gradients=tf.train.Optimizer.GATE_NONE):
-        assert gate_gradients == tf.train.Optimizer.GATE_NONE
-        return list(zip(tf.gradients(loss, var_list), var_list))
-
-    def apply_gradients(self, grads_and_vars):
-        with tf.name_scope(self.name):
-            state_vars = []
-            update_ops = []
-
-            # Adjust learning rate to deal with startup bias.
-            with tf.control_dependencies(None):
-                b1pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
-                b2pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False)
-                state_vars += [b1pow_var, b2pow_var]
-            b1pow_new = b1pow_var * self.beta1
-            b2pow_new = b2pow_var * self.beta2
-            update_ops += [tf.assign(b1pow_var, b1pow_new), tf.assign(b2pow_var, b2pow_new)]
-            lr_new = self.learning_rate * tf.sqrt(1 - b2pow_new) / (1 - b1pow_new)
-
-            # Construct ops to update each variable.
-            for grad, var in grads_and_vars:
-                with tf.control_dependencies(None):
-                    m_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
-                    v_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False)
-                    state_vars += [m_var, v_var]
-                m_new = self.beta1 * m_var + (1 - self.beta1) * grad
-                v_new = self.beta2 * v_var + (1 - self.beta2) * tf.square(grad)
-                var_delta = lr_new * m_new / (tf.sqrt(v_new) + self.epsilon)
-                update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, v_new), tf.assign_sub(var, var_delta)]
-
-            # Group everything together.
-            self.all_state_vars += state_vars
-            return tf.group(*update_ops)

Time_TravelRephotography/dnnlib/tflib/tfutil.py

@@ -1,254 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Miscellaneous helper utils for Tensorflow."""
-
-import os
-import numpy as np
-import tensorflow as tf
-
-# Silence deprecation warnings from TensorFlow 1.13 onwards
-import logging
-logging.getLogger('tensorflow').setLevel(logging.ERROR)
-import tensorflow.contrib   # requires TensorFlow 1.x!
-tf.contrib = tensorflow.contrib
-
-from typing import Any, Iterable, List, Union
-
-TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation]
-"""A type that represents a valid Tensorflow expression."""
-
-TfExpressionEx = Union[TfExpression, int, float, np.ndarray]
-"""A type that can be converted to a valid Tensorflow expression."""
-
-
-def run(*args, **kwargs) -> Any:
-    """Run the specified ops in the default session."""
-    assert_tf_initialized()
-    return tf.get_default_session().run(*args, **kwargs)
-
-
-def is_tf_expression(x: Any) -> bool:
-    """Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation."""
-    return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation))
-
-
-def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
-    """Convert a Tensorflow shape to a list of ints. Retained for backwards compatibility -- use TensorShape.as_list() in new code."""
-    return [dim.value for dim in shape]
-
-
-def flatten(x: TfExpressionEx) -> TfExpression:
-    """Shortcut function for flattening a tensor."""
-    with tf.name_scope("Flatten"):
-        return tf.reshape(x, [-1])
-
-
-def log2(x: TfExpressionEx) -> TfExpression:
-    """Logarithm in base 2."""
-    with tf.name_scope("Log2"):
-        return tf.log(x) * np.float32(1.0 / np.log(2.0))
-
-
-def exp2(x: TfExpressionEx) -> TfExpression:
-    """Exponent in base 2."""
-    with tf.name_scope("Exp2"):
-        return tf.exp(x * np.float32(np.log(2.0)))
-
-
-def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx:
-    """Linear interpolation."""
-    with tf.name_scope("Lerp"):
-        return a + (b - a) * t
-
-
-def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression:
-    """Linear interpolation with clip."""
-    with tf.name_scope("LerpClip"):
-        return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
-
-
-def absolute_name_scope(scope: str) -> tf.name_scope:
-    """Forcefully enter the specified name scope, ignoring any surrounding scopes."""
-    return tf.name_scope(scope + "/")
-
-
-def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
-    """Forcefully enter the specified variable scope, ignoring any surrounding scopes."""
-    return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False)
-
-
-def _sanitize_tf_config(config_dict: dict = None) -> dict:
-    # Defaults.
-    cfg = dict()
-    cfg["rnd.np_random_seed"]               = None      # Random seed for NumPy. None = keep as is.
-    cfg["rnd.tf_random_seed"]               = "auto"    # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
-    cfg["env.TF_CPP_MIN_LOG_LEVEL"]         = "1"       # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
-    cfg["graph_options.place_pruned_graph"] = True      # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
-    cfg["gpu_options.allow_growth"]         = True      # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
-
-    # Remove defaults for environment variables that are already set.
-    for key in list(cfg):
-        fields = key.split(".")
-        if fields[0] == "env":
-            assert len(fields) == 2
-            if fields[1] in os.environ:
-                del cfg[key]
-
-    # User overrides.
-    if config_dict is not None:
-        cfg.update(config_dict)
-    return cfg
-
-
-def init_tf(config_dict: dict = None) -> None:
-    """Initialize TensorFlow session using good default settings."""
-    # Skip if already initialized.
-    if tf.get_default_session() is not None:
-        return
-
-    # Setup config dict and random seeds.
-    cfg = _sanitize_tf_config(config_dict)
-    np_random_seed = cfg["rnd.np_random_seed"]
-    if np_random_seed is not None:
-        np.random.seed(np_random_seed)
-    tf_random_seed = cfg["rnd.tf_random_seed"]
-    if tf_random_seed == "auto":
-        tf_random_seed = np.random.randint(1 << 31)
-    if tf_random_seed is not None:
-        tf.set_random_seed(tf_random_seed)
-
-    # Setup environment variables.
-    for key, value in cfg.items():
-        fields = key.split(".")
-        if fields[0] == "env":
-            assert len(fields) == 2
-            os.environ[fields[1]] = str(value)
-
-    # Create default TensorFlow session.
-    create_session(cfg, force_as_default=True)
-
-
-def assert_tf_initialized():
-    """Check that TensorFlow session has been initialized."""
-    if tf.get_default_session() is None:
-        raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().")
-
-
-def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session:
-    """Create tf.Session based on config dict."""
-    # Setup TensorFlow config proto.
-    cfg = _sanitize_tf_config(config_dict)
-    config_proto = tf.ConfigProto()
-    for key, value in cfg.items():
-        fields = key.split(".")
-        if fields[0] not in ["rnd", "env"]:
-            obj = config_proto
-            for field in fields[:-1]:
-                obj = getattr(obj, field)
-            setattr(obj, fields[-1], value)
-
-    # Create session.
-    session = tf.Session(config=config_proto)
-    if force_as_default:
-        # pylint: disable=protected-access
-        session._default_session = session.as_default()
-        session._default_session.enforce_nesting = False
-        session._default_session.__enter__()
-    return session
-
-
-def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
-    """Initialize all tf.Variables that have not already been initialized.
-
-    Equivalent to the following, but more efficient and does not bloat the tf graph:
-    tf.variables_initializer(tf.report_uninitialized_variables()).run()
-    """
-    assert_tf_initialized()
-    if target_vars is None:
-        target_vars = tf.global_variables()
-
-    test_vars = []
-    test_ops = []
-
-    with tf.control_dependencies(None):  # ignore surrounding control_dependencies
-        for var in target_vars:
-            assert is_tf_expression(var)
-
-            try:
-                tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0"))
-            except KeyError:
-                # Op does not exist => variable may be uninitialized.
-                test_vars.append(var)
-
-                with absolute_name_scope(var.name.split(":")[0]):
-                    test_ops.append(tf.is_variable_initialized(var))
-
-    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
-    run([var.initializer for var in init_vars])
-
-
-def set_vars(var_to_value_dict: dict) -> None:
-    """Set the values of given tf.Variables.
-
-    Equivalent to the following, but more efficient and does not bloat the tf graph:
-    tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
-    """
-    assert_tf_initialized()
-    ops = []
-    feed_dict = {}
-
-    for var, value in var_to_value_dict.items():
-        assert is_tf_expression(var)
-
-        try:
-            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0"))  # look for existing op
-        except KeyError:
-            with absolute_name_scope(var.name.split(":")[0]):
-                with tf.control_dependencies(None):  # ignore surrounding control_dependencies
-                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter")  # create new setter
-
-        ops.append(setter)
-        feed_dict[setter.op.inputs[1]] = value
-
-    run(ops, feed_dict)
-
-
-def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs):
-    """Create tf.Variable with large initial value without bloating the tf graph."""
-    assert_tf_initialized()
-    assert isinstance(initial_value, np.ndarray)
-    zeros = tf.zeros(initial_value.shape, initial_value.dtype)
-    var = tf.Variable(zeros, *args, **kwargs)
-    set_vars({var: initial_value})
-    return var
-
-
-def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
-    """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
-    Can be used as an input transformation for Network.run().
-    """
-    images = tf.cast(images, tf.float32)
-    if nhwc_to_nchw:
-        images = tf.transpose(images, [0, 3, 1, 2])
-    return images * ((drange[1] - drange[0]) / 255) + drange[0]
-
-
-def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
-    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
-    Can be used as an output transformation for Network.run().
-    """
-    images = tf.cast(images, tf.float32)
-    if shrink > 1:
-        ksize = [1, 1, shrink, shrink]
-        images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW")
-    if nchw_to_nhwc:
-        images = tf.transpose(images, [0, 2, 3, 1])
-    scale = 255 / (drange[1] - drange[0])
-    images = images * scale + (0.5 - drange[0] * scale)
-    return tf.saturate_cast(images, tf.uint8)

Time_TravelRephotography/dnnlib/util.py

@@ -1,479 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    # print('func_name: ', func_name) #'training.dataset.ImageFolderDataset'
-    func_obj = get_obj_by_name(func_name) 
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)

Time_TravelRephotography/losses/color_transfer_loss.py

@@ -1,60 +0,0 @@
-from typing import List, Optional
-
-import torch
-from torch import nn
-from torch.nn.functional import (
-    smooth_l1_loss,
-)
-
-
-def flatten_CHW(im: torch.Tensor) -> torch.Tensor:
-    """
-    (B, C, H, W) -> (B, -1)
-    """
-    B = im.shape[0]
-    return im.reshape(B, -1)
-
-
-def stddev(x: torch.Tensor) -> torch.Tensor:
-    """
-    x: (B, -1), assume with mean normalized
-    Retuens:
-        stddev: (B)
-    """
-    return torch.sqrt(torch.mean(x * x, dim=-1))
-
-
-def gram_matrix(input_):
-    B, C = input_.shape[:2]
-    features = input_.view(B, C, -1)
-    N = features.shape[-1]
-    G = torch.bmm(features, features.transpose(1, 2))  # C x C
-    return G.div(C * N)
-
-
-class ColorTransferLoss(nn.Module):
-    """Penalize the gram matrix difference between StyleGAN2's ToRGB outputs"""
-    def __init__(
-        self,
-        init_rgbs,
-        scale_rgb: bool = False
-    ):
-        super().__init__()
-
-        with torch.no_grad():
-            init_feats = [x.detach() for x in init_rgbs]
-            self.stds = [stddev(flatten_CHW(rgb)) if scale_rgb else 1 for rgb in init_feats]  # (B, 1, 1, 1) or scalar
-            self.grams = [gram_matrix(rgb / std) for rgb, std in zip(init_feats, self.stds)]
-
-    def forward(self, rgbs: List[torch.Tensor], level: int = None):
-        if level is None:
-            level = len(self.grams)
-
-        feats = rgbs
-        loss = 0
-        for i, (rgb, std) in enumerate(zip(feats[:level], self.stds[:level])):
-            G = gram_matrix(rgb / std)
-            loss = loss + smooth_l1_loss(G, self.grams[i])
-
-        return loss
-

Time_TravelRephotography/losses/contextual_loss/.gitignore

@@ -1,104 +0,0 @@
-# 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/

Time_TravelRephotography/losses/contextual_loss/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2019 Sou Uchida
-
-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.

Time_TravelRephotography/losses/contextual_loss/__init__.py

@@ -1 +0,0 @@
-from .modules import *

Time_TravelRephotography/losses/contextual_loss/config.py

@@ -1,2 +0,0 @@
-# TODO: add supports for L1, L2 etc.
-LOSS_TYPES = ['cosine', 'l1', 'l2']

Time_TravelRephotography/losses/contextual_loss/functional.py

@@ -1,198 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from .config import LOSS_TYPES
-
-__all__ = ['contextual_loss', 'contextual_bilateral_loss']
-
-
-def contextual_loss(x: torch.Tensor,
-                    y: torch.Tensor,
-                    band_width: float = 0.5,
-                    loss_type: str = 'cosine',
-                    all_dist: bool = False):
-    """
-    Computes contextual loss between x and y.
-    The most of this code is copied from
-        https://gist.github.com/yunjey/3105146c736f9c1055463c33b4c989da.
-
-    Parameters
-    ---
-    x : torch.Tensor
-        features of shape (N, C, H, W).
-    y : torch.Tensor
-        features of shape (N, C, H, W).
-    band_width : float, optional
-        a band-width parameter used to convert distance to similarity.
-        in the paper, this is described as :math:`h`.
-    loss_type : str, optional
-        a loss type to measure the distance between features.
-        Note: `l1` and `l2` frequently raises OOM.
-
-    Returns
-    ---
-    cx_loss : torch.Tensor
-        contextual loss between x and y (Eq (1) in the paper)
-    """
-
-    assert x.size() == y.size(), 'input tensor must have the same size.'
-    assert loss_type in LOSS_TYPES, f'select a loss type from {LOSS_TYPES}.'
-
-    N, C, H, W = x.size()
-
-    if loss_type == 'cosine':
-        dist_raw = compute_cosine_distance(x, y)
-    elif loss_type == 'l1':
-        dist_raw = compute_l1_distance(x, y)
-    elif loss_type == 'l2':
-        dist_raw = compute_l2_distance(x, y)
-
-    dist_tilde = compute_relative_distance(dist_raw)
-    cx = compute_cx(dist_tilde, band_width)
-    if all_dist:
-        return cx
-
-    cx = torch.mean(torch.max(cx, dim=1)[0], dim=1)  # Eq(1)
-    cx_loss = torch.mean(-torch.log(cx + 1e-5))  # Eq(5)
-
-    return cx_loss
-
-
-# TODO: Operation check
-def contextual_bilateral_loss(x: torch.Tensor,
-                              y: torch.Tensor,
-                              weight_sp: float = 0.1,
-                              band_width: float = 1.,
-                              loss_type: str = 'cosine'):
-    """
-    Computes Contextual Bilateral (CoBi) Loss between x and y,
-        proposed in https://arxiv.org/pdf/1905.05169.pdf.
-
-    Parameters
-    ---
-    x : torch.Tensor
-        features of shape (N, C, H, W).
-    y : torch.Tensor
-        features of shape (N, C, H, W).
-    band_width : float, optional
-        a band-width parameter used to convert distance to similarity.
-        in the paper, this is described as :math:`h`.
-    loss_type : str, optional
-        a loss type to measure the distance between features.
-        Note: `l1` and `l2` frequently raises OOM.
-
-    Returns
-    ---
-    cx_loss : torch.Tensor
-        contextual loss between x and y (Eq (1) in the paper).
-    k_arg_max_NC : torch.Tensor
-        indices to maximize similarity over channels.
-    """
-
-    assert x.size() == y.size(), 'input tensor must have the same size.'
-    assert loss_type in LOSS_TYPES, f'select a loss type from {LOSS_TYPES}.'
-
-    # spatial loss
-    grid = compute_meshgrid(x.shape).to(x.device)
-    dist_raw = compute_l2_distance(grid, grid)
-    dist_tilde = compute_relative_distance(dist_raw)
-    cx_sp = compute_cx(dist_tilde, band_width)
-
-    # feature loss
-    if loss_type == 'cosine':
-        dist_raw = compute_cosine_distance(x, y)
-    elif loss_type == 'l1':
-        dist_raw = compute_l1_distance(x, y)
-    elif loss_type == 'l2':
-        dist_raw = compute_l2_distance(x, y)
-    dist_tilde = compute_relative_distance(dist_raw)
-    cx_feat = compute_cx(dist_tilde, band_width)
-
-    # combined loss
-    cx_combine = (1. - weight_sp) * cx_feat + weight_sp * cx_sp
-
-    k_max_NC, _ = torch.max(cx_combine, dim=2, keepdim=True)
-
-    cx = k_max_NC.mean(dim=1)
-    cx_loss = torch.mean(-torch.log(cx + 1e-5))
-
-    return cx_loss
-
-
-def compute_cx(dist_tilde, band_width):
-    w = torch.exp((1 - dist_tilde) / band_width)  # Eq(3)
-    cx = w / torch.sum(w, dim=2, keepdim=True)  # Eq(4)
-    return cx
-
-
-def compute_relative_distance(dist_raw):
-    dist_min, _ = torch.min(dist_raw, dim=2, keepdim=True)
-    dist_tilde = dist_raw / (dist_min + 1e-5)
-    return dist_tilde
-
-
-def compute_cosine_distance(x, y):
-    # mean shifting by channel-wise mean of `y`.
-    y_mu = y.mean(dim=(0, 2, 3), keepdim=True)
-    x_centered = x - y_mu
-    y_centered = y - y_mu
-
-    # L2 normalization
-    x_normalized = F.normalize(x_centered, p=2, dim=1)
-    y_normalized = F.normalize(y_centered, p=2, dim=1)
-
-    # channel-wise vectorization
-    N, C, *_ = x.size()
-    x_normalized = x_normalized.reshape(N, C, -1)  # (N, C, H*W)
-    y_normalized = y_normalized.reshape(N, C, -1)  # (N, C, H*W)
-
-    # consine similarity
-    cosine_sim = torch.bmm(x_normalized.transpose(1, 2),
-                           y_normalized)  # (N, H*W, H*W)
-
-    # convert to distance
-    dist = 1 - cosine_sim
-
-    return dist
-
-
-# TODO: Considering avoiding OOM.
-def compute_l1_distance(x: torch.Tensor, y: torch.Tensor):
-    N, C, H, W = x.size()
-    x_vec = x.view(N, C, -1)
-    y_vec = y.view(N, C, -1)
-
-    dist = x_vec.unsqueeze(2) - y_vec.unsqueeze(3)
-    dist = dist.abs().sum(dim=1)
-    dist = dist.transpose(1, 2).reshape(N, H*W, H*W)
-    dist = dist.clamp(min=0.)
-
-    return dist
-
-
-# TODO: Considering avoiding OOM.
-def compute_l2_distance(x, y):
-    N, C, H, W = x.size()
-    x_vec = x.view(N, C, -1)
-    y_vec = y.view(N, C, -1)
-    x_s = torch.sum(x_vec ** 2, dim=1)
-    y_s = torch.sum(y_vec ** 2, dim=1)
-
-    A = y_vec.transpose(1, 2) @ x_vec
-    dist = y_s - 2 * A + x_s.transpose(0, 1)
-    dist = dist.transpose(1, 2).reshape(N, H*W, H*W)
-    dist = dist.clamp(min=0.)
-
-    return dist
-
-
-def compute_meshgrid(shape):
-    N, C, H, W = shape
-    rows = torch.arange(0, H, dtype=torch.float32) / (H + 1)
-    cols = torch.arange(0, W, dtype=torch.float32) / (W + 1)
-
-    feature_grid = torch.meshgrid(rows, cols)
-    feature_grid = torch.stack(feature_grid).unsqueeze(0)
-    feature_grid = torch.cat([feature_grid for _ in range(N)], dim=0)
-
-    return feature_grid

Time_TravelRephotography/losses/contextual_loss/modules/__init__.py

@@ -1,5 +0,0 @@
-from .contextual import ContextualLoss
-from .contextual_bilateral import ContextualBilateralLoss
-from .vgg import VGG19
-
-__all__ = ['ContextualLoss', 'ContextualBilateralLoss', 'VGG19']

Time_TravelRephotography/losses/contextual_loss/modules/contextual.py

@@ -1,122 +0,0 @@
-import random
-from typing import (
-    Iterable,
-    List,
-    Optional,
-)
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from .vgg import VGG19
-from .. import functional as F
-from ..config import LOSS_TYPES
-
-
-class ContextualLoss(nn.Module):
-    """
-    Creates a criterion that measures the contextual loss.
-
-    Parameters
-    ---
-    band_width : int, optional
-        a band_width parameter described as :math:`h` in the paper.
-    use_vgg : bool, optional
-        if you want to use VGG feature, set this `True`.
-    vgg_layer : str, optional
-        intermidiate layer name for VGG feature.
-        Now we support layer names:
-            `['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4']`
-    """
-
-    def __init__(
-            self,
-            band_width: float = 0.5,
-            loss_type: str = 'cosine',
-            use_vgg: bool = False,
-            vgg_model: nn.Module = None,
-            vgg_layers: List[str] = ['relu3_4'],
-            feature_1d_size: int = 64,
-    ):
-
-        super().__init__()
-
-        assert band_width > 0, 'band_width parameter must be positive.'
-        assert loss_type in LOSS_TYPES,\
-            f'select a loss type from {LOSS_TYPES}.'
-
-        self.loss_type = loss_type
-        self.band_width = band_width
-        self.feature_1d_size = feature_1d_size
-
-        if use_vgg:
-            self.vgg_model = VGG19() if vgg_model is None else vgg_model
-            self.vgg_layers = vgg_layers
-            self.register_buffer(
-                name='vgg_mean',
-                tensor=torch.tensor(
-                    [[[0.485]], [[0.456]], [[0.406]]], requires_grad=False)
-            )
-            self.register_buffer(
-                name='vgg_std',
-                tensor=torch.tensor(
-                    [[[0.229]], [[0.224]], [[0.225]]], requires_grad=False)
-            )
-
-    def forward(self, x: torch.Tensor, y: torch.Tensor, all_dist: bool = False):
-        if not hasattr(self, 'vgg_model'):
-            return self.contextual_loss(x, y, self.feature_1d_size, self.band_width, all_dist=all_dist)
-
-
-        x = self.forward_vgg(x)
-        y = self.forward_vgg(y)
-
-        loss = 0
-        for layer in self.vgg_layers:
-            # picking up vgg feature maps
-            fx = getattr(x, layer)
-            fy = getattr(y, layer)
-            loss = loss + self.contextual_loss(
-                fx, fy, self.feature_1d_size, self.band_width, all_dist=all_dist, loss_type=self.loss_type
-            )
-        return loss
-
-    def forward_vgg(self, x: torch.Tensor):
-        assert x.shape[1] == 3, 'VGG model takes 3 chennel images.'
-        # [-1, 1] -> [0, 1]
-        x = (x + 1) * 0.5
-
-        # normalization
-        x = x.sub(self.vgg_mean.detach()).div(self.vgg_std)
-        return self.vgg_model(x)
-
-    @classmethod
-    def contextual_loss(
-            cls,
-            x: torch.Tensor, y: torch.Tensor,
-            feature_1d_size: int,
-            band_width: int,
-            all_dist: bool = False,
-            loss_type: str = 'cosine',
-    ) -> torch.Tensor:
-        feature_size = feature_1d_size ** 2
-        if np.prod(x.shape[2:]) > feature_size or np.prod(y.shape[2:]) > feature_size:
-            x, indices = cls.random_sampling(x, feature_1d_size=feature_1d_size)
-            y, _ = cls.random_sampling(y, feature_1d_size=feature_1d_size, indices=indices)
-
-        return F.contextual_loss(x, y, band_width, all_dist=all_dist, loss_type=loss_type)
-
-    @staticmethod
-    def random_sampling(
-            tensor_NCHW: torch.Tensor, feature_1d_size: int, indices: Optional[List] = None
-    ):
-        N, C, H, W = tensor_NCHW.shape
-        S = H * W
-        tensor_NCS = tensor_NCHW.reshape([N, C, S])
-        if indices is None:
-            all_indices = list(range(S))
-            random.shuffle(all_indices)
-            indices = all_indices[:feature_1d_size**2]
-        res = tensor_NCS[:, :, indices].reshape(N, -1, feature_1d_size, feature_1d_size)
-        return res, indices

Time_TravelRephotography/losses/contextual_loss/modules/contextual_bilateral.py

@@ -1,69 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vgg import VGG19
-from .. import functional as F
-from ..config import LOSS_TYPES
-
-
-class ContextualBilateralLoss(nn.Module):
-    """
-    Creates a criterion that measures the contextual bilateral loss.
-
-    Parameters
-    ---
-    weight_sp : float, optional
-        a balancing weight between spatial and feature loss.
-    band_width : int, optional
-        a band_width parameter described as :math:`h` in the paper.
-    use_vgg : bool, optional
-        if you want to use VGG feature, set this `True`.
-    vgg_layer : str, optional
-        intermidiate layer name for VGG feature.
-        Now we support layer names:
-            `['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4']`
-    """
-
-    def __init__(self,
-                 weight_sp: float = 0.1,
-                 band_width: float = 0.5,
-                 loss_type: str = 'cosine',
-                 use_vgg: bool = False,
-                 vgg_layer: str = 'relu3_4'):
-
-        super(ContextualBilateralLoss, self).__init__()
-
-        assert band_width > 0, 'band_width parameter must be positive.'
-        assert loss_type in LOSS_TYPES,\
-            f'select a loss type from {LOSS_TYPES}.'
-
-        self.band_width = band_width
-
-        if use_vgg:
-            self.vgg_model = VGG19()
-            self.vgg_layer = vgg_layer
-            self.register_buffer(
-                name='vgg_mean',
-                tensor=torch.tensor(
-                    [[[0.485]], [[0.456]], [[0.406]]], requires_grad=False)
-            )
-            self.register_buffer(
-                name='vgg_std',
-                tensor=torch.tensor(
-                    [[[0.229]], [[0.224]], [[0.225]]], requires_grad=False)
-            )
-
-    def forward(self, x, y):
-        if hasattr(self, 'vgg_model'):
-            assert x.shape[1] == 3 and y.shape[1] == 3,\
-                'VGG model takes 3 chennel images.'
-
-            # normalization
-            x = x.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
-            y = y.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
-
-            # picking up vgg feature maps
-            x = getattr(self.vgg_model(x), self.vgg_layer)
-            y = getattr(self.vgg_model(y), self.vgg_layer)
-
-        return F.contextual_bilateral_loss(x, y, self.band_width)

Time_TravelRephotography/losses/contextual_loss/modules/vgg.py

@@ -1,48 +0,0 @@
-from collections import namedtuple
-
-import torch.nn as nn
-import torchvision.models.vgg as vgg
-
-
-class VGG19(nn.Module):
-    def __init__(self, requires_grad=False):
-        super(VGG19, self).__init__()
-        vgg_pretrained_features = vgg.vgg19(pretrained=True).features
-        self.slice1 = nn.Sequential()
-        self.slice2 = nn.Sequential()
-        self.slice3 = nn.Sequential()
-        self.slice4 = nn.Sequential()
-        self.slice5 = nn.Sequential()
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 18):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(18, 27):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(27, 36):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_4 = h
-        h = self.slice4(h)
-        h_relu4_4 = h
-        h = self.slice5(h)
-        h_relu5_4 = h
-
-        vgg_outputs = namedtuple(
-            "VggOutputs", ['relu1_2', 'relu2_2',
-                           'relu3_4', 'relu4_4', 'relu5_4'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2,
-                          h_relu3_4, h_relu4_4, h_relu5_4)
-
-        return out

Time_TravelRephotography/losses/joint_loss.py

@@ -1,167 +0,0 @@
-from argparse import (
-    ArgumentParser,
-    Namespace,
-)
-from typing import (
-    Dict,
-    Iterable,
-    Optional,
-    Tuple,
-)
-
-import numpy as np
-import torch
-from torch import nn
-
-from utils.misc import (
-    optional_string,
-    iterable_to_str,
-)
-
-from .contextual_loss import ContextualLoss
-from .color_transfer_loss import ColorTransferLoss
-from .regularize_noise import NoiseRegularizer
-from .reconstruction import (
-    EyeLoss,
-    FaceLoss,
-    create_perceptual_loss,
-    ReconstructionArguments,
-)
-
-class LossArguments:
-    @staticmethod
-    def add_arguments(parser: ArgumentParser):
-        ReconstructionArguments.add_arguments(parser)
-
-        parser.add_argument("--color_transfer", type=float, default=1e10, help="color transfer loss weight")
-        parser.add_argument("--eye", type=float, default=0.1, help="eye loss weight")
-        parser.add_argument('--noise_regularize', type=float, default=5e4)
-        # contextual loss
-        parser.add_argument("--contextual", type=float, default=0.1, help="contextual loss weight")
-        parser.add_argument("--cx_layers", nargs='*', help="contextual loss layers",
-                            choices=['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4'],
-                            default=['relu3_4', 'relu2_2', 'relu1_2'])
-
-    @staticmethod
-    def to_string(args: Namespace) -> str:
-        return (
-            ReconstructionArguments.to_string(args)
-            + optional_string(args.eye > 0, f"-eye{args.eye}")
-            + optional_string(args.color_transfer, f"-color{args.color_transfer:.1e}")
-            + optional_string(
-                args.contextual,
-                f"-cx{args.contextual}({iterable_to_str(args.cx_layers)})"
-            )
-            #+ optional_string(args.mse, f"-mse{args.mse}")
-            + optional_string(args.noise_regularize, f"-NR{args.noise_regularize:.1e}")
-        )
-
-
-class BakedMultiContextualLoss(nn.Module):
-    """Random sample different image patches for different vgg layers."""
-    def __init__(self, sibling: torch.Tensor, args: Namespace, size: int = 256):
-        super().__init__()
-
-        self.cxs = nn.ModuleList([ContextualLoss(use_vgg=True, vgg_layers=[layer])
-            for layer in args.cx_layers])
-        self.size = size
-        self.sibling = sibling.detach()
-
-    def forward(self, img: torch.Tensor):
-        cx_loss = 0
-        for cx in self.cxs:
-            h, w = np.random.randint(0, high=img.shape[-1] - self.size, size=2)
-            cx_loss = cx(self.sibling[..., h:h+self.size, w:w+self.size], img[..., h:h+self.size, w:w+self.size]) + cx_loss
-        return cx_loss
-
-
-class BakedContextualLoss(ContextualLoss):
-    def __init__(self, sibling: torch.Tensor, args: Namespace, size: int = 256):
-        super().__init__(use_vgg=True, vgg_layers=args.cx_layers)
-        self.size = size
-        self.sibling = sibling.detach()
-
-    def forward(self, img: torch.Tensor):
-        h, w = np.random.randint(0, high=img.shape[-1] - self.size, size=2)
-        return super().forward(self.sibling[..., h:h+self.size, w:w+self.size], img[..., h:h+self.size, w:w+self.size])
-
-
-class JointLoss(nn.Module):
-    def __init__(
-            self,
-            args: Namespace,
-            target: torch.Tensor,
-            sibling: Optional[torch.Tensor],
-            sibling_rgbs: Optional[Iterable[torch.Tensor]] = None,
-    ):
-        super().__init__()
-
-        self.weights = {
-            "face": 1., "eye": args.eye,
-            "contextual": args.contextual, "color_transfer": args.color_transfer,
-            "noise": args.noise_regularize,
-        }
-
-        reconstruction = {}
-        if args.vgg > 0 or args.vggface > 0:
-            percept = create_perceptual_loss(args)
-            reconstruction.update(
-                {"face": FaceLoss(target, input_size=args.generator_size, size=args.recon_size, percept=percept)}
-            )
-            if args.eye > 0:
-                reconstruction.update(
-                    {"eye": EyeLoss(target, input_size=args.generator_size, percept=percept)}
-                )
-        self.reconstruction = nn.ModuleDict(reconstruction)
-
-        exemplar = {}
-        if args.contextual > 0 and len(args.cx_layers) > 0:
-            assert sibling is not None
-            exemplar.update(
-                {"contextual": BakedContextualLoss(sibling, args)}
-            )
-        if args.color_transfer > 0:
-            assert sibling_rgbs is not None
-            self.sibling_rgbs = sibling_rgbs
-            exemplar.update(
-                {"color_transfer": ColorTransferLoss(init_rgbs=sibling_rgbs)}
-            )
-        self.exemplar = nn.ModuleDict(exemplar)
-
-        if args.noise_regularize > 0:
-            self.noise_criterion = NoiseRegularizer()
-
-    def forward(
-            self, img, degrade=None, noises=None, rgbs=None, rgb_level: Optional[int] = None
-    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Args:
-            rgbs: results from the ToRGB layers
-        """
-        # TODO: add current optimization resolution for noises
-
-        losses = {}
-
-        # reconstruction losses
-        for name, criterion in self.reconstruction.items():
-            losses[name] = criterion(img, degrade=degrade)
-
-        # exemplar losses
-        if 'contextual' in self.exemplar:
-            losses["contextual"] = self.exemplar["contextual"](img)
-        if "color_transfer" in self.exemplar:
-            assert rgbs is not None
-            losses["color_transfer"] = self.exemplar["color_transfer"](rgbs, level=rgb_level)
-
-        # noise regularizer
-        if self.weights["noise"] > 0:
-            losses["noise"] = self.noise_criterion(noises)
-
-        total_loss = 0
-        for name, loss in losses.items():
-            total_loss = total_loss + self.weights[name] * loss
-        return total_loss, losses
-
-    def update_sibling(self, sibling: torch.Tensor):
-        assert "contextual" in self.exemplar
-        self.exemplar["contextual"].sibling = sibling.detach()

Time_TravelRephotography/losses/perceptual_loss.py

@@ -1,111 +0,0 @@
-"""
-Code borrowed from https://gist.github.com/alper111/8233cdb0414b4cb5853f2f730ab95a49#file-vgg_perceptual_loss-py-L5
-"""
-import torch
-import torchvision
-from models.vggface import VGGFaceFeats
-
-
-def cos_loss(fi, ft):
-    return 1 - torch.nn.functional.cosine_similarity(fi, ft).mean()
-
-
-class VGGPerceptualLoss(torch.nn.Module):
-    def __init__(self, resize=False):
-        super(VGGPerceptualLoss, self).__init__()
-        blocks = []
-        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
-        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
-        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())
-        blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval())
-        for bl in blocks:
-            for p in bl:
-                p.requires_grad = False
-        self.blocks = torch.nn.ModuleList(blocks)
-        self.transform = torch.nn.functional.interpolate
-        self.mean = torch.nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1))
-        self.std = torch.nn.Parameter(torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1))
-        self.resize = resize
-
-    def forward(self, input, target, max_layer=4, cos_dist: bool = False):
-        target = (target + 1) * 0.5
-        input = (input + 1) * 0.5
-
-        if input.shape[1] != 3:
-            input = input.repeat(1, 3, 1, 1)
-            target = target.repeat(1, 3, 1, 1)
-        input = (input-self.mean) / self.std
-        target = (target-self.mean) / self.std
-        if self.resize:
-            input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False)
-            target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False)
-        x = input
-        y = target
-        loss = 0.0
-        loss_func = cos_loss if cos_dist else torch.nn.functional.l1_loss
-        for bi, block in enumerate(self.blocks[:max_layer]):
-            x = block(x)
-            y = block(y)
-            loss += loss_func(x, y.detach())
-        return loss
-
-
-class VGGFacePerceptualLoss(torch.nn.Module):
-    def __init__(self, weight_path: str = "checkpoint/vgg_face_dag.pt", resize: bool = False):
-        super().__init__()
-        self.vgg = VGGFaceFeats()
-        self.vgg.load_state_dict(torch.load(weight_path))
-
-        mean = torch.tensor(self.vgg.meta["mean"]).view(1, 3, 1, 1) / 255.0
-        self.register_buffer("mean", mean)
-
-        self.transform = torch.nn.functional.interpolate
-        self.resize = resize
-
-    def forward(self, input, target, max_layer: int = 4, cos_dist: bool = False):
-        target = (target + 1) * 0.5
-        input = (input + 1) * 0.5
-
-        # preprocessing
-        if input.shape[1] != 3:
-            input = input.repeat(1, 3, 1, 1)
-            target = target.repeat(1, 3, 1, 1)
-        input = input - self.mean
-        target = target - self.mean
-        if self.resize:
-            input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False)
-            target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False)
-
-        input_feats = self.vgg(input)
-        target_feats = self.vgg(target)
-
-        loss_func = cos_loss if cos_dist else torch.nn.functional.l1_loss
-        # calc perceptual loss
-        loss = 0.0
-        for fi, ft in zip(input_feats[:max_layer], target_feats[:max_layer]):
-            loss = loss + loss_func(fi, ft.detach())
-        return loss
-
-
-class PerceptualLoss(torch.nn.Module):
-    def __init__(
-            self, lambda_vggface: float = 0.025 / 0.15, lambda_vgg: float = 1,  eps: float = 1e-8, cos_dist: bool = False
-    ):
-        super().__init__()
-        self.register_buffer("lambda_vggface", torch.tensor(lambda_vggface))
-        self.register_buffer("lambda_vgg", torch.tensor(lambda_vgg))
-        self.cos_dist = cos_dist
-
-        if lambda_vgg > eps:
-            self.vgg = VGGPerceptualLoss()
-        if lambda_vggface > eps:
-            self.vggface = VGGFacePerceptualLoss()
-
-    def forward(self, input, target, eps=1e-8, use_vggface: bool = True, use_vgg=True, max_vgg_layer=4):
-        loss = 0.0
-        if self.lambda_vgg > eps and use_vgg:
-            loss = loss + self.lambda_vgg * self.vgg(input, target, max_layer=max_vgg_layer)
-        if self.lambda_vggface > eps and use_vggface:
-            loss = loss + self.lambda_vggface * self.vggface(input, target, cos_dist=self.cos_dist)
-        return loss
-

Time_TravelRephotography/losses/reconstruction.py

@@ -1,119 +0,0 @@
-from argparse import (
-    ArgumentParser,
-    Namespace,
-)
-from typing import Optional
-
-import numpy as np
-import torch
-from torch import nn
-
-from losses.perceptual_loss import PerceptualLoss
-from models.degrade import Downsample
-from utils.misc import optional_string
-
-
-class ReconstructionArguments:
-    @staticmethod
-    def add_arguments(parser: ArgumentParser):
-        parser.add_argument("--vggface", type=float, default=0.3, help="vggface")
-        parser.add_argument("--vgg", type=float, default=1, help="vgg")
-        parser.add_argument('--recon_size', type=int, default=256, help="size for face reconstruction loss")
-
-    @staticmethod
-    def to_string(args: Namespace) -> str:
-        return (
-            f"s{args.recon_size}"
-            + optional_string(args.vgg > 0, f"-vgg{args.vgg}")
-            + optional_string(args.vggface > 0, f"-vggface{args.vggface}")
-        )
-
-
-def create_perceptual_loss(args: Namespace):
-    return PerceptualLoss(lambda_vgg=args.vgg, lambda_vggface=args.vggface, cos_dist=False)
-
-
-class EyeLoss(nn.Module):
-    def __init__(
-            self,
-            target: torch.Tensor,
-            input_size: int = 1024,
-            input_channels: int = 3,
-            percept: Optional[nn.Module] = None,
-            args: Optional[Namespace] = None
-    ):
-        """
-        target: target image
-        """
-        assert not (percept is None and args is None)
-
-        super().__init__()
-
-        self.target = target
-
-        target_size = target.shape[-1]
-        self.downsample = Downsample(input_size, target_size, input_channels) \
-                if target_size != input_size else (lambda x: x)
-
-        self.percept = percept if percept is not None else create_perceptual_loss(args)
-
-        eye_size = np.array((224, 224))
-        btlrs = []
-        for sgn in [1, -1]:
-            center = np.array((480, 384 * sgn))   # (y, x)
-            b, t = center[0] - eye_size[0] // 2, center[0] + eye_size[0] // 2
-            l, r = center[1] - eye_size[1] // 2, center[1] + eye_size[1] // 2
-            btlrs.append((np.array((b, t, l, r)) / 1024 * target_size).astype(int))
-        self.btlrs = np.stack(btlrs, axis=0)
-
-    def forward(self, img: torch.Tensor, degrade: nn.Module = None):
-        """
-        img: it should be the degraded version of the generated image
-        """
-        if degrade is not None:
-            img = degrade(img, downsample=self.downsample)
-
-        loss = 0
-        for (b, t, l, r) in self.btlrs:
-            loss = loss + self.percept(
-                img[:, :, b:t, l:r], self.target[:, :, b:t, l:r],
-                use_vggface=False, max_vgg_layer=4,
-                # use_vgg=False,
-            )
-        return loss
-
-
-class FaceLoss(nn.Module):
-    def __init__(
-            self,
-            target: torch.Tensor,
-            input_size: int = 1024,
-            input_channels: int = 3,
-            size: int = 256,
-            percept: Optional[nn.Module] = None,
-            args: Optional[Namespace] = None
-    ):
-        """
-        target: target image
-        """
-        assert not (percept is None and args is None)
-
-        super().__init__()
-
-        target_size = target.shape[-1]
-        self.target = target if target_size == size \
-                else Downsample(target_size, size, target.shape[1]).to(target.device)(target)
-
-        self.downsample = Downsample(input_size, size, input_channels) \
-                if size != input_size else (lambda x: x)
-
-        self.percept = percept if percept is not None else create_perceptual_loss(args)
-
-    def forward(self, img: torch.Tensor, degrade: nn.Module = None):
-        """
-        img: it should be the degraded version of the generated image
-        """
-        if degrade is not None:
-            img = degrade(img, downsample=self.downsample)
-        loss = self.percept(img, self.target)
-        return loss

Time_TravelRephotography/losses/regularize_noise.py

@@ -1,37 +0,0 @@
-from typing import Iterable
-
-import torch
-from torch import nn
-
-
-class NoiseRegularizer(nn.Module):
-    def forward(self, noises: Iterable[torch.Tensor]):
-        loss = 0
-
-        for noise in noises:
-            size = noise.shape[2]
-
-            while True:
-                loss = (
-                    loss
-                    + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2)
-                    + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2)
-                )
-
-                if size <= 8:
-                    break
-
-                noise = noise.reshape([1, 1, size // 2, 2, size // 2, 2])
-                noise = noise.mean([3, 5])
-                size //= 2
-
-        return loss
-
-    @staticmethod
-    def normalize(noises: Iterable[torch.Tensor]):
-        for noise in noises:
-            mean = noise.mean()
-            std = noise.std()
-
-            noise.data.add_(-mean).div_(std)
-

Time_TravelRephotography/model.py

@@ -1,697 +0,0 @@
-import math
-import random
-import functools
-import operator
-import numpy as np
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch_utils.ops.bias_act import bias_act,bias_act_relu
-from torch_utils.ops.upfirdn2d import upfirdn2d
-
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, padding=[self.pad[0], self.pad[1], self.pad[0], self.pad[1]])
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, padding=[self.pad[0], self.pad[1], self.pad[0], self.pad[1]])
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer('kernel', kernel)
-
-        self.pad = pad
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, padding=[self.pad[0], self.pad[1], self.pad[0], self.pad[1]])
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = F.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
-            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = bias_act(out)
-
-        else:
-            out = F.linear(
-                input, self.weight * self.scale, bias=self.bias * self.lr_mul
-            )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
-        )
-
-
-class ScaledLeakyReLU(nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
-        return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        demodulate=True,
-        upsample=False,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
-            f'upsample={self.upsample}, downsample={self.downsample})'
-        )
-
-    def forward(self, input, style):
-        batch, in_channel, height, width = input.shape
-
-        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise is None:
-            batch, _, height, width = image.shape
-            noise = image.new_empty(batch, 1, height, width).normal_()
-
-        return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input):
-        batch = input.shape[0]
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        upsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        demodulate=True,
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-        )
-
-        self.noise = NoiseInjection()
-        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        # self.activate = ScaledLeakyReLU(0.2)
-        self.activate =bias_act_relu(out_channel)
-
-    def forward(self, input, style, noise=None):
-        out = self.conv(input, style)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None):
-        out = self.conv(input, style)
-        style_modulated = out
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out, style_modulated
-
-
-class Generator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-    ):
-        super().__init__()
-
-        self.size = size
-
-        self.style_dim = style_dim
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
-        )
-        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
-        self.log_size = int(math.log(size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channel = self.channels[4]
-
-        for layer_idx in range(self.num_layers):
-            res = (layer_idx + 5) // 2
-            shape = [1, 1, 2 ** res, 2 ** res]
-            self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-    @property
-    def device(self):
-        # TODO if multi-gpu is expected, could use the following more expensive version
-        #device, = list(set(p.device for p in self.parameters()))
-        return next(self.parameters()).device
-
-    @staticmethod
-    def get_latent_size(size):
-        log_size = int(math.log(size, 2))
-        return log_size * 2 - 2
-
-    @staticmethod
-    def make_noise_by_size(size: int, device: torch.device):
-        log_size = int(math.log(size, 2))
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-
-    def make_noise(self):
-        return self.make_noise_by_size(self.size, self.input.input.device)
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def forward(
-        self,
-        styles,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-    ):
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers
-            else:
-                noise = [
-                    getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
-                ]
-
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-
-        if len(styles) < 2:
-            inject_index = self.n_latent
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-            else:
-                latent = styles[0]
-
-        else:
-            if inject_index is None:
-                inject_index = random.randint(1, self.n_latent - 1)
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-            latent = torch.cat([latent, latent2], 1)
-
-        out = self.input(latent)
-        out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-        skip, rgb_mod = self.to_rgb1(out, latent[:, 1])
-
-
-        rgbs = [rgb_mod] # all but the last skip
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip, rgb_mod = to_rgb(out, latent[:, i + 2], skip)
-            rgbs.append(rgb_mod)
-
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent, rgbs
-
-        else:
-            return image, None, rgbs
-
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            if bias:
-                layers.append(bias_act_relu(out_channel))
-
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-
-        return out
-

Time_TravelRephotography/models/degrade.py

@@ -1,122 +0,0 @@
-from argparse import (
-    ArgumentParser,
-    Namespace,
-)
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from utils.misc import optional_string
-
-from .gaussian_smoothing import GaussianSmoothing
-
-
-class DegradeArguments:
-    @staticmethod
-    def add_arguments(parser: ArgumentParser):
-        parser.add_argument('--spectral_sensitivity', choices=["g", "b", "gb"], default="g",
-            help="Type of spectral sensitivity. g: grayscale (panchromatic), b: blue-sensitive, gb: green+blue (orthochromatic)")
-        parser.add_argument('--gaussian', type=float, default=0,
-            help="estimated blur radius in pixels of the input photo if it is scaled to 1024x1024")
-
-    @staticmethod
-    def to_string(args: Namespace) -> str:
-        return (
-            f"{args.spectral_sensitivity}"
-            + optional_string(args.gaussian > 0, f"-G{args.gaussian}")
-        )
-
-
-class CameraResponse(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.register_parameter("gamma", nn.Parameter(torch.ones(1)))
-        self.register_parameter("offset", nn.Parameter(torch.zeros(1)))
-        self.register_parameter("gain", nn.Parameter(torch.ones(1)))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = torch.clamp(x, max=1, min=-1+1e-2)
-        x = (1 + x) * 0.5
-        x = self.offset + self.gain * torch.pow(x, self.gamma)
-        x = (x - 0.5) * 2
-        # b = torch.clamp(b, max=1, min=-1)
-        return x
-
-
-class SpectralResponse(nn.Module):
-    # TODO: use enum instead for color mode
-    def __init__(self, spectral_sensitivity: str = 'b'):
-        assert spectral_sensitivity in ("g", "b", "gb"), f"spectral_sensitivity {spectral_sensitivity} is not implemented."
-
-        super().__init__()
-
-        self.spectral_sensitivity = spectral_sensitivity
-
-        if self.spectral_sensitivity == "g":
-            self.register_buffer("to_gray", torch.tensor([0.299, 0.587, 0.114]).reshape(1, -1, 1, 1))
-
-    def forward(self, rgb: torch.Tensor) -> torch.Tensor:
-        if self.spectral_sensitivity == "b":
-            x = rgb[:, -1:]
-        elif self.spectral_sensitivity == "gb":
-            x = (rgb[:, 1:2] + rgb[:, -1:]) * 0.5
-        else:
-            assert self.spectral_sensitivity == "g"
-            x = (rgb * self.to_gray).sum(dim=1, keepdim=True)
-        return x
-
-
-class Downsample(nn.Module):
-    """Antialiasing downsampling"""
-    def __init__(self, input_size: int, output_size: int, channels: int):
-        super().__init__()
-        if input_size % output_size == 0:
-            self.stride = input_size // output_size
-            self.grid = None
-        else:
-            self.stride = 1
-            step = input_size / output_size
-            x = torch.arange(output_size) * step
-            Y, X = torch.meshgrid(x, x)
-            grid = torch.stack((X, Y), dim=-1)
-            grid /= torch.Tensor((input_size - 1, input_size - 1)).view(1, 1, -1)
-            grid = grid * 2 - 1
-            self.register_buffer("grid", grid)
-        sigma = 0.5 * input_size / output_size
-        #print(f"{input_size} -> {output_size}: sigma={sigma}")
-        self.blur = GaussianSmoothing(channels, int(2 * (sigma * 2) + 1 + 0.5), sigma)
-
-    def forward(self, im: torch.Tensor):
-        out = self.blur(im, stride=self.stride)
-        if self.grid is not None:
-            out = F.grid_sample(out, self.grid[None].expand(im.shape[0], -1, -1, -1))
-        return out
-
-
-
-class Degrade(nn.Module):
-    """
-    Simulate the degradation of antique film
-    """
-    def __init__(self, args:Namespace):
-        super().__init__()
-        self.srf = SpectralResponse(args.spectral_sensitivity)
-        self.crf = CameraResponse()
-        self.gaussian = None
-        if args.gaussian is not None and args.gaussian > 0:
-            self.gaussian = GaussianSmoothing(3, 2 * int(args.gaussian * 2 + 0.5) + 1, args.gaussian)
-
-    def forward(self, img: torch.Tensor, downsample: nn.Module = None):
-        if self.gaussian is not None:
-            img = self.gaussian(img)
-        if downsample is not None:
-            img = downsample(img)
-        img = self.srf(img)
-        img = self.crf(img)
-        # Note that I changed it back to 3 channels
-        return img.repeat((1, 3, 1, 1)) if img.shape[1] == 1 else img
-
-
-

Time_TravelRephotography/models/encoder.py

@@ -1,66 +0,0 @@
-from argparse import Namespace, ArgumentParser
-from functools import partial
-
-from torch import nn
-
-from .resnet import ResNetBasicBlock, activation_func, norm_module, Conv2dAuto
-
-
-def add_arguments(parser: ArgumentParser) -> ArgumentParser:
-    parser.add_argument("--latent_size", type=int, default=512, help="latent size")
-    return parser
-
-
-def create_model(args) -> nn.Module:
-    in_channels = 3 if "rgb" in args and args.rgb else 1
-    return Encoder(in_channels, args.encoder_size, latent_size=args.latent_size)
-
-
-class Flatten(nn.Module):
-    def forward(self, input_):
-        return input_.view(input_.size(0), -1)
-
-
-class Encoder(nn.Module):
-    def __init__(
-            self, in_channels: int, size: int, latent_size: int = 512,
-            activation: str = 'leaky_relu', norm: str = "instance"
-    ):
-        super().__init__()
-
-        out_channels0 = 64
-        norm_m = norm_module(norm)
-        self.conv0 = nn.Sequential(
-            Conv2dAuto(in_channels, out_channels0, kernel_size=5),
-            norm_m(out_channels0),
-            activation_func(activation),
-        )
-
-        pool_kernel = 2
-        self.pool = nn.AvgPool2d(pool_kernel)
-
-        num_channels = [128, 256, 512, 512]
-        # FIXME: this is a hack
-        if size >= 256:
-            num_channels.append(512)
-
-        residual = partial(ResNetBasicBlock, activation=activation, norm=norm, bias=True)
-        residual_blocks = nn.ModuleList()
-        for in_channel, out_channel in zip([out_channels0] + num_channels[:-1], num_channels):
-            residual_blocks.append(residual(in_channel, out_channel))
-            residual_blocks.append(nn.AvgPool2d(pool_kernel))
-        self.residual_blocks = nn.Sequential(*residual_blocks)
-
-        self.last = nn.Sequential(
-            nn.ReLU(),
-            nn.AvgPool2d(4),    # TODO: not sure whehter this would cause problem
-            Flatten(),
-            nn.Linear(num_channels[-1], latent_size, bias=True)
-        )
-
-    def forward(self, input_):
-        out = self.conv0(input_)
-        out = self.pool(out)
-        out = self.residual_blocks(out)
-        out = self.last(out)
-        return out

Time_TravelRephotography/models/encoder4editing/.gitignore

@@ -1,133 +0,0 @@
-# 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/
-pip-wheel-metadata/
-share/python-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/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# 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/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# Custom dataset
-pretrained_models
-results_test

Time_TravelRephotography/models/encoder4editing/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2021 omertov
-
-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.

Time_TravelRephotography/models/encoder4editing/README.md

@@ -1,143 +0,0 @@
-# Designing an Encoder for StyleGAN Image Manipulation (SIGGRAPH 2021)
-  <a href="https://arxiv.org/abs/2102.02766"><img src="https://img.shields.io/badge/arXiv-2008.00951-b31b1b.svg"></a>
-  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
-  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/omertov/encoder4editing/blob/main/notebooks/inference_playground.ipynb)
-
-> Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
-
-<p align="center">
-<img src="docs/teaser.jpg" width="800px"/>
-</p>
-
-## Description   
-Official Implementation of "<a href="https://arxiv.org/abs/2102.02766">Designing an Encoder for StyleGAN Image Manipulation</a>" paper for both training and evaluation. 
-The e4e encoder is specifically designed to complement existing image manipulation techniques performed over StyleGAN's latent space.
-
-## Recent Updates
-`2021.08.17`: Add single style code encoder (use `--encoder_type SingleStyleCodeEncoder`). <br />
-`2021.03.25`: Add pose editing direction.
-
-## Getting Started
-### Prerequisites
-- Linux or macOS
-- NVIDIA GPU + CUDA CuDNN (CPU may be possible with some modifications, but is not inherently supported)
-- Python 3
-
-### Installation
-- Clone the repository:
-``` 
-git clone https://github.com/omertov/encoder4editing.git
-cd encoder4editing
-```
-- Dependencies:  
-We recommend running this repository using [Anaconda](https://docs.anaconda.com/anaconda/install/). 
-All dependencies for defining the environment are provided in `environment/e4e_env.yaml`.
-
-### Inference Notebook
-We provide a Jupyter notebook found in `notebooks/inference_playground.ipynb` that allows one to encode and perform several editings on real images using StyleGAN.   
-
-### Pretrained Models
-Please download the pre-trained models from the following links. Each e4e model contains the entire pSp framework architecture, including the encoder and decoder weights.
-| Path | Description
-| :--- | :----------
-|[FFHQ Inversion](https://drive.google.com/file/d/1cUv_reLE6k3604or78EranS7XzuVMWeO/view?usp=sharing)  | FFHQ e4e encoder.
-|[Cars Inversion](https://drive.google.com/file/d/17faPqBce2m1AQeLCLHUVXaDfxMRU2QcV/view?usp=sharing)  | Cars e4e encoder.
-|[Horse Inversion](https://drive.google.com/file/d/1TkLLnuX86B_BMo2ocYD0kX9kWh53rUVX/view?usp=sharing)  | Horse e4e encoder.
-|[Church Inversion](https://drive.google.com/file/d/1-L0ZdnQLwtdy6-A_Ccgq5uNJGTqE7qBa/view?usp=sharing) | Church e4e encoder.
-
-If you wish to use one of the pretrained models for training or inference, you may do so using the flag `--checkpoint_path`.
-
-In addition, we provide various auxiliary models needed for training your own e4e model from scratch.
-| Path | Description
-| :--- | :----------
-|[FFHQ StyleGAN](https://drive.google.com/file/d/1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT/view?usp=sharing) | StyleGAN model pretrained on FFHQ taken from [rosinality](https://github.com/rosinality/stylegan2-pytorch) with 1024x1024 output resolution.
-|[IR-SE50 Model](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view?usp=sharing) | Pretrained IR-SE50 model taken from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) for use in our ID loss during training.
-|[MOCOv2 Model](https://drive.google.com/file/d/18rLcNGdteX5LwT7sv_F7HWr12HpVEzVe/view?usp=sharing) | Pretrained ResNet-50 model trained using MOCOv2 for use in our simmilarity loss for domains other then human faces during training.
-
-By default, we assume that all auxiliary models are downloaded and saved to the directory `pretrained_models`. However, you may use your own paths by changing the necessary values in `configs/path_configs.py`. 
-
-## Training
-To train the e4e encoder, make sure the paths to the required models, as well as training and testing data is configured in `configs/path_configs.py` and `configs/data_configs.py`.
-#### **Training the e4e Encoder**
-```
-python scripts/train.py \
---dataset_type cars_encode \
---exp_dir new/experiment/directory \
---start_from_latent_avg \
---use_w_pool \
---w_discriminator_lambda 0.1 \
---progressive_start 20000 \
---id_lambda 0.5 \
---val_interval 10000 \
---max_steps 200000 \
---stylegan_size 512 \
---stylegan_weights path/to/pretrained/stylegan.pt \
---workers 8 \
---batch_size 8 \
---test_batch_size 4 \
---test_workers 4 
-```
-
-#### Training on your own dataset
-In order to train the e4e encoder on a custom dataset, perform the following adjustments:
-1. Insert the paths to your train and test data into the `dataset_paths` variable defined in `configs/paths_config.py`:
-```
-dataset_paths = {
-    'my_train_data': '/path/to/train/images/directory',
-    'my_test_data': '/path/to/test/images/directory'
-}
-```
-2. Configure a new dataset under the DATASETS variable defined in `configs/data_configs.py`:
-```
-DATASETS = {
-   'my_data_encode': {
-        'transforms': transforms_config.EncodeTransforms,
-        'train_source_root': dataset_paths['my_train_data'],
-        'train_target_root': dataset_paths['my_train_data'],
-        'test_source_root': dataset_paths['my_test_data'],
-        'test_target_root': dataset_paths['my_test_data']
-    }
-}
-```
-Refer to `configs/transforms_config.py` for the transformations applied to the train and test images during training. 
-
-3. Finally, run a training session with `--dataset_type my_data_encode`.
-
-## Inference
-Having trained your model, you can use `scripts/inference.py` to apply the model on a set of images.   
-For example, 
-```
-python scripts/inference.py \
---images_dir=/path/to/images/directory \
---save_dir=/path/to/saving/directory \
-path/to/checkpoint.pt 
-```
-
-## Latent Editing Consistency (LEC)
-As described in the paper, we suggest a new metric, Latent Editing Consistency (LEC), for evaluating the encoder's 
-performance.
-We provide an example for calculating the metric over the FFHQ StyleGAN using the aging editing direction in 
-`metrics/LEC.py`.     
-
-To run the example: 
-```
-cd metrics
-python LEC.py \
---images_dir=/path/to/images/directory \
-path/to/checkpoint.pt 
-```
-
-## Acknowledgments
-This code borrows heavily from [pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel)
-
-## Citation
-If you use this code for your research, please cite our paper <a href="https://arxiv.org/abs/2102.02766">Designing an Encoder for StyleGAN Image Manipulation</a>:
-
-```
-@article{tov2021designing,
-  title={Designing an Encoder for StyleGAN Image Manipulation},
-  author={Tov, Omer and Alaluf, Yuval and Nitzan, Yotam and Patashnik, Or and Cohen-Or, Daniel},
-  journal={arXiv preprint arXiv:2102.02766},
-  year={2021}
-}
-```

Time_TravelRephotography/models/encoder4editing/__init__.py

@@ -1,15 +0,0 @@
-from .utils.model_utils import setup_model
-
-
-def get_latents(net, x, is_cars=False):
-    codes = net.encoder(x)
-    if net.opts.start_from_latent_avg:
-        if codes.ndim == 2:
-            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
-        else:
-            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)
-    if codes.shape[1] == 18 and is_cars:
-        codes = codes[:, :16, :]
-    return codes
-
-

Time_TravelRephotography/models/encoder4editing/bash_scripts/inference.sh

@@ -1,15 +0,0 @@
-set -exo
-
-list="$1"
-ckpt="${2:-pretrained_models/e4e_ffhq_encode.pt}"
-
-base_dir="$REPHOTO/dataset/historically_interesting/aligned/manual_celebrity_in_19th_century/tier1/${list}/"
-save_dir="results_test/${list}/"
-
-
-TORCH_EXTENSIONS_DIR=/tmp/torch_extensions
-PYTHONPATH="" \
-python scripts/inference.py \
-    --images_dir="${base_dir}" \
-    --save_dir="${save_dir}" \
-    "${ckpt}"

Time_TravelRephotography/models/encoder4editing/configs/data_configs.py

@@ -1,41 +0,0 @@
-from configs import transforms_config
-from configs.paths_config import dataset_paths
-
-
-DATASETS = {
-	'ffhq_encode': {
-		'transforms': transforms_config.EncodeTransforms,
-		'train_source_root': dataset_paths['ffhq'],
-		'train_target_root': dataset_paths['ffhq'],
-		'test_source_root': dataset_paths['celeba_test'],
-		'test_target_root': dataset_paths['celeba_test'],
-	},
-	'cars_encode': {
-		'transforms': transforms_config.CarsEncodeTransforms,
-		'train_source_root': dataset_paths['cars_train'],
-		'train_target_root': dataset_paths['cars_train'],
-		'test_source_root': dataset_paths['cars_test'],
-		'test_target_root': dataset_paths['cars_test'],
-	},
-	'horse_encode': {
-		'transforms': transforms_config.EncodeTransforms,
-		'train_source_root': dataset_paths['horse_train'],
-		'train_target_root': dataset_paths['horse_train'],
-		'test_source_root': dataset_paths['horse_test'],
-		'test_target_root': dataset_paths['horse_test'],
-	},
-	'church_encode': {
-		'transforms': transforms_config.EncodeTransforms,
-		'train_source_root': dataset_paths['church_train'],
-		'train_target_root': dataset_paths['church_train'],
-		'test_source_root': dataset_paths['church_test'],
-		'test_target_root': dataset_paths['church_test'],
-	},
-	'cats_encode': {
-		'transforms': transforms_config.EncodeTransforms,
-		'train_source_root': dataset_paths['cats_train'],
-		'train_target_root': dataset_paths['cats_train'],
-		'test_source_root': dataset_paths['cats_test'],
-		'test_target_root': dataset_paths['cats_test'],
-	}
-}

Time_TravelRephotography/models/encoder4editing/configs/paths_config.py

@@ -1,28 +0,0 @@
-dataset_paths = {
-	#  Face Datasets (In the paper: FFHQ - train, CelebAHQ - test)
-	'ffhq': '',
-	'celeba_test': '',
-
-	#  Cars Dataset (In the paper: Stanford cars)
-	'cars_train': '',
-	'cars_test': '',
-
-	#  Horse Dataset (In the paper: LSUN Horse)
-	'horse_train': '',
-	'horse_test': '',
-
-	#  Church Dataset (In the paper: LSUN Church)
-	'church_train': '',
-	'church_test': '',
-
-	#  Cats Dataset (In the paper: LSUN Cat)
-	'cats_train': '',
-	'cats_test': ''
-}
-
-model_paths = {
-	'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
-	'ir_se50': 'pretrained_models/model_ir_se50.pth',
-	'shape_predictor': 'pretrained_models/shape_predictor_68_face_landmarks.dat',
-	'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth'
-}

Time_TravelRephotography/models/encoder4editing/configs/transforms_config.py

@@ -1,62 +0,0 @@
-from abc import abstractmethod
-import torchvision.transforms as transforms
-
-
-class TransformsConfig(object):
-
-	def __init__(self, opts):
-		self.opts = opts
-
-	@abstractmethod
-	def get_transforms(self):
-		pass
-
-
-class EncodeTransforms(TransformsConfig):
-
-	def __init__(self, opts):
-		super(EncodeTransforms, self).__init__(opts)
-
-	def get_transforms(self):
-		transforms_dict = {
-			'transform_gt_train': transforms.Compose([
-				transforms.Resize((256, 256)),
-				transforms.RandomHorizontalFlip(0.5),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
-			'transform_source': None,
-			'transform_test': transforms.Compose([
-				transforms.Resize((256, 256)),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
-			'transform_inference': transforms.Compose([
-				transforms.Resize((256, 256)),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
-		}
-		return transforms_dict
-
-
-class CarsEncodeTransforms(TransformsConfig):
-
-	def __init__(self, opts):
-		super(CarsEncodeTransforms, self).__init__(opts)
-
-	def get_transforms(self):
-		transforms_dict = {
-			'transform_gt_train': transforms.Compose([
-				transforms.Resize((192, 256)),
-				transforms.RandomHorizontalFlip(0.5),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
-			'transform_source': None,
-			'transform_test': transforms.Compose([
-				transforms.Resize((192, 256)),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
-			'transform_inference': transforms.Compose([
-				transforms.Resize((192, 256)),
-				transforms.ToTensor(),
-				transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
-		}
-		return transforms_dict

Time_TravelRephotography/models/encoder4editing/criteria/id_loss.py

@@ -1,47 +0,0 @@
-import torch
-from torch import nn
-from configs.paths_config import model_paths
-from models.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        for module in [self.facenet, self.face_pool]:
-            for param in module.parameters():
-                param.requires_grad = False
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        id_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            id_logs.append({'diff_target': float(diff_target),
-                            'diff_input': float(diff_input),
-                            'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            id_diff = float(diff_target) - float(diff_views)
-            sim_improvement += id_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, id_logs

Time_TravelRephotography/models/encoder4editing/criteria/lpips/lpips.py

@@ -1,35 +0,0 @@
-import torch
-import torch.nn as nn
-
-from criteria.lpips.networks import get_network, LinLayers
-from criteria.lpips.utils import get_state_dict
-
-
-class LPIPS(nn.Module):
-    r"""Creates a criterion that measures
-    Learned Perceptual Image Patch Similarity (LPIPS).
-    Arguments:
-        net_type (str): the network type to compare the features:
-                        'alex' | 'squeeze' | 'vgg'. Default: 'alex'.
-        version (str): the version of LPIPS. Default: 0.1.
-    """
-    def __init__(self, net_type: str = 'alex', version: str = '0.1'):
-
-        assert version in ['0.1'], 'v0.1 is only supported now'
-
-        super(LPIPS, self).__init__()
-
-        # pretrained network
-        self.net = get_network(net_type).to("cuda")
-
-        # linear layers
-        self.lin = LinLayers(self.net.n_channels_list).to("cuda")
-        self.lin.load_state_dict(get_state_dict(net_type, version))
-
-    def forward(self, x: torch.Tensor, y: torch.Tensor):
-        feat_x, feat_y = self.net(x), self.net(y)
-
-        diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)]
-        res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)]
-
-        return torch.sum(torch.cat(res, 0)) / x.shape[0]

Time_TravelRephotography/models/encoder4editing/criteria/lpips/networks.py

@@ -1,96 +0,0 @@
-from typing import Sequence
-
-from itertools import chain
-
-import torch
-import torch.nn as nn
-from torchvision import models
-
-from criteria.lpips.utils import normalize_activation
-
-
-def get_network(net_type: str):
-    if net_type == 'alex':
-        return AlexNet()
-    elif net_type == 'squeeze':
-        return SqueezeNet()
-    elif net_type == 'vgg':
-        return VGG16()
-    else:
-        raise NotImplementedError('choose net_type from [alex, squeeze, vgg].')
-
-
-class LinLayers(nn.ModuleList):
-    def __init__(self, n_channels_list: Sequence[int]):
-        super(LinLayers, self).__init__([
-            nn.Sequential(
-                nn.Identity(),
-                nn.Conv2d(nc, 1, 1, 1, 0, bias=False)
-            ) for nc in n_channels_list
-        ])
-
-        for param in self.parameters():
-            param.requires_grad = False
-
-
-class BaseNet(nn.Module):
-    def __init__(self):
-        super(BaseNet, self).__init__()
-
-        # register buffer
-        self.register_buffer(
-            'mean', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
-        self.register_buffer(
-            'std', torch.Tensor([.458, .448, .450])[None, :, None, None])
-
-    def set_requires_grad(self, state: bool):
-        for param in chain(self.parameters(), self.buffers()):
-            param.requires_grad = state
-
-    def z_score(self, x: torch.Tensor):
-        return (x - self.mean) / self.std
-
-    def forward(self, x: torch.Tensor):
-        x = self.z_score(x)
-
-        output = []
-        for i, (_, layer) in enumerate(self.layers._modules.items(), 1):
-            x = layer(x)
-            if i in self.target_layers:
-                output.append(normalize_activation(x))
-            if len(output) == len(self.target_layers):
-                break
-        return output
-
-
-class SqueezeNet(BaseNet):
-    def __init__(self):
-        super(SqueezeNet, self).__init__()
-
-        self.layers = models.squeezenet1_1(True).features
-        self.target_layers = [2, 5, 8, 10, 11, 12, 13]
-        self.n_channels_list = [64, 128, 256, 384, 384, 512, 512]
-
-        self.set_requires_grad(False)
-
-
-class AlexNet(BaseNet):
-    def __init__(self):
-        super(AlexNet, self).__init__()
-
-        self.layers = models.alexnet(True).features
-        self.target_layers = [2, 5, 8, 10, 12]
-        self.n_channels_list = [64, 192, 384, 256, 256]
-
-        self.set_requires_grad(False)
-
-
-class VGG16(BaseNet):
-    def __init__(self):
-        super(VGG16, self).__init__()
-
-        self.layers = models.vgg16(True).features
-        self.target_layers = [4, 9, 16, 23, 30]
-        self.n_channels_list = [64, 128, 256, 512, 512]
-
-        self.set_requires_grad(False)

Time_TravelRephotography/models/encoder4editing/criteria/lpips/utils.py

@@ -1,30 +0,0 @@
-from collections import OrderedDict
-
-import torch
-
-
-def normalize_activation(x, eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
-    return x / (norm_factor + eps)
-
-
-def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
-    # build url
-    url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \
-        + f'master/lpips/weights/v{version}/{net_type}.pth'
-
-    # download
-    old_state_dict = torch.hub.load_state_dict_from_url(
-        url, progress=True,
-        map_location=None if torch.cuda.is_available() else torch.device('cpu')
-    )
-
-    # rename keys
-    new_state_dict = OrderedDict()
-    for key, val in old_state_dict.items():
-        new_key = key
-        new_key = new_key.replace('lin', '')
-        new_key = new_key.replace('model.', '')
-        new_state_dict[new_key] = val
-
-    return new_state_dict