working demo

PTI/.gitignore

@@ -1 +0,0 @@
-

PTI/LICENSE

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

PTI/README.md

@@ -1,229 +0,0 @@
-# PTI: Pivotal Tuning for Latent-based editing of Real Images
-
-<!-- > Recently, a surge of advanced facial editing techniques have been proposed
-that leverage the generative power of a pre-trained StyleGAN. To successfully
-edit an image this way, one must first project (or invert) the image into
-the pre-trained generator’s domain. As it turns out, however, StyleGAN’s
-latent space induces an inherent tradeoff between distortion and editability,
-i.e. between maintaining the original appearance and convincingly altering
-some of its attributes. Practically, this means it is still challenging to
-apply ID-preserving facial latent-space editing to faces which are out of the
-generator’s domain. In this paper, we present an approach to bridge this
-gap. Our technique slightly alters the generator, so that an out-of-domain
-image is faithfully mapped into an in-domain latent code. The key idea is
-pivotal tuning — a brief training process that preserves the editing quality
-of an in-domain latent region, while changing its portrayed identity and
-appearance. In Pivotal Tuning Inversion (PTI), an initial inverted latent code
-serves as a pivot, around which the generator is fined-tuned. At the same
-time, a regularization term keeps nearby identities intact, to locally contain
-the effect. This surgical training process ends up altering appearance features
-that represent mostly identity, without affecting editing capabilities.
-To supplement this, we further show that pivotal tuning can also adjust the
-generator to accommodate a multitude of faces, while introducing negligible
-distortion on the rest of the domain. We validate our technique through
-inversion and editing metrics, and show preferable scores to state-of-the-art
-methods. We further qualitatively demonstrate our technique by applying
-advanced edits (such as pose, age, or expression) to numerous images of
-well-known and recognizable identities. Finally, we demonstrate resilience
-to harder cases, including heavy make-up, elaborate hairstyles and/or headwear,
-which otherwise could not have been successfully inverted and edited
-by state-of-the-art methods. -->
-
-<a href="https://arxiv.org/abs/2106.05744"><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>  
-Inference Notebook: <a href="https://colab.research.google.com/github/danielroich/PTI/blob/main/notebooks/inference_playground.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=20></a>  
-
-<p align="center">
-<img src="docs/teaser.jpg"/>  
-<br>
-Pivotal Tuning Inversion (PTI) enables employing off-the-shelf latent based
-semantic editing techniques on real images using StyleGAN. 
-PTI excels in identity preserving edits, portrayed through recognizable figures —
-Serena Williams and Robert Downey Jr. (top), and in handling faces which
-are clearly out-of-domain, e.g., due to heavy makeup (bottom).
-</br>
-</p>
-
-## Description   
-Official Implementation of our PTI paper + code for evaluation metrics. PTI introduces an optimization mechanizem for solving the StyleGAN inversion task.
-Providing near-perfect reconstruction results while maintaining the high editing abilitis of the native StyleGAN latent space W. For more details, see <a href="https://arxiv.org/abs/2106.05744"><img src="https://img.shields.io/badge/arXiv-2008.00951-b31b1b.svg"></a>
-
-## Recent Updates
-**2021.07.01**: Fixed files download phase in the inference notebook. Which might caused the notebook not to run smoothly.
-
-**2021.06.29**: Added support for CPU. In order to run PTI on CPU please change `device` parameter under `configs/global_config.py` to "cpu" instead of "cuda".
-
-**2021.06.25** : Adding mohawk edit using StyleCLIP+PTI in inference notebook.
-	      Updating documentation in inference notebook due to Google Drive rate limit reached.
-	      Currently, Google Drive does not allow to download the pretrined models using Colab automatically. Manual intervention might be needed.
-
-## Getting Started
-### Prerequisites
-- Linux or macOS
-- NVIDIA GPU + CUDA CuDNN (Not mandatory bur recommended)
-- Python 3
-
-### Installation
-- Dependencies:  
-	1. lpips
-	2. wandb
-	3. pytorch
-	4. torchvision
-	5. matplotlib
-	6. dlib
-- All dependencies can be installed using *pip install* and the package name
-
-## Pretrained Models
-Please download the pretrained models from the following links.
-
-### Auxiliary Models
-We provide various auxiliary models needed for PTI inversion task.  
-This includes the StyleGAN generator and pre-trained models used for loss computation.
-| Path | Description
-| :--- | :----------
-|[FFHQ StyleGAN](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl) | StyleGAN2-ada model trained on FFHQ with 1024x1024 output resolution.
-|[Dlib alignment](https://drive.google.com/file/d/1HKmjg6iXsWr4aFPuU0gBXPGR83wqMzq7/view?usp=sharing) | Dlib alignment used for images preproccessing.
-|[FFHQ e4e encoder](https://drive.google.com/file/d/1ALC5CLA89Ouw40TwvxcwebhzWXM5YSCm/view?usp=sharing) | Pretrained e4e encoder. Used for StyleCLIP editing.
-
-Note: The StyleGAN model is used directly from the official [stylegan2-ada-pytorch implementation](https://github.com/NVlabs/stylegan2-ada-pytorch).
-For StyleCLIP pretrained mappers, please see [StyleCLIP's official routes](https://github.com/orpatashnik/StyleCLIP/blob/main/utils.py)
-
-
-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`. 
-
-
-## Inversion
-### Preparing your Data
-In order to invert a real image and edit it you should first align and crop it to the correct size. To do so you should perform *One* of the following steps: 
-1. Run `notebooks/align_data.ipynb` and change the "images_path" variable to the raw images path
-2. Run `utils/align_data.py` and change the "images_path" variable to the raw images path
-
-
-### Weights And Biases
-The project supports [Weights And Biases](https://wandb.ai/home) framework for experiment tracking. For the inversion task it enables visualization of the losses progression and the generator intermediate results during the initial inversion and the *Pivotal Tuning*(PT) procedure.
-
-The log frequency can be adjusted using the parameters defined at `configs/global_config.py` under the "Logs" subsection.
-
-There is no no need to have an account. However, in order to use the features provided by Weights and Biases you first have to register on their site.
-
-
-### Running PTI
-The main training script is `scripts/run_pti.py`. The script receives aligned and cropped images from paths configured in the "Input info" subscetion in
- `configs/paths_config.py`. 
-Results are saved to directories found at "Dirs for output files" under `configs/paths_config.py`. This includes inversion latent codes and tuned generators. 
-The hyperparametrs for the inversion task can be found at  `configs/hyperparameters.py`. They are intilized to the default values used in the paper. 
-
-## Editing
-By default, we assume that all auxiliary edit directions are downloaded and saved to the directory `editings`. 
-However, you may use your own paths by changing the necessary values in `configs/path_configs.py` under "Edit directions" subsection.
-
-Example of editing code can be found at `scripts/latent_editor_wrapper.py`
-
-## Inference Notebooks
-To help visualize the results of PTI we provide a Jupyter notebook found in `notebooks/inference_playground.ipynb`.   
-The notebook will download the pretrained models and run inference on a sample image found online or 
-on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/github/danielroich/PTI/blob/main/notebooks/inference_playground.ipynb).
-
-The notebook demonstrates how to:
-- Invert an image using PTI
-- Visualise the inversion and use the PTI output
-- Edit the image after PTI using InterfaceGAN and StyleCLIP
-- Compare to other inversion methods
-
-## Evaluation
-Currently the repository supports qualitative evaluation for reconstruction of: PTI, SG2 (*W Space*), e4e, SG2Plus (*W+ Space*). 
-As well as editing using InterfaceGAN and GANSpace for the same inversion methods.
-To run the evaluation please see `evaluation/qualitative_edit_comparison.py`. Examples of the evaluation scripts are:
-
-<p align="center">
-<img src="docs/model_rec.jpg"/>  
-<br>
-Reconsturction comparison between different methods. The images order is: Original image, W+ inversion, e4e inversion, W inversion, PTI inversion
-</br>  
-</p>
-
-<p align="center">
-<img src="docs/stern_rotation.jpg"/>  
-<br>
-InterfaceGAN pose edit comparison between different methods. The images order is: Original, W+, e4e, W, PTI
-</br>  
-</p>
-
-<p align="center">
-<img src="docs/tyron_original.jpg" width="220" height="220"/>  
-<img src="docs/tyron_edit.jpg" width="220" height="220"/>
-<br>
-Image per edit or several edits without comparison
-</br>  
-</p>
-
-###  Coming Soon - Quantitative evaluation and StyleCLIP qualitative evaluation
-
-## Repository structure
-| Path | Description <img width=200>
-| :--- | :---
-| &boxvr;&nbsp; configs | Folder containing configs defining Hyperparameters, paths and logging
-| &boxvr;&nbsp; criteria | Folder containing various loss and regularization criterias for the optimization
-| &boxvr;&nbsp; dnnlib | Folder containing internal utils for StyleGAN2-ada
-| &boxvr;&nbsp; docs | Folder containing the latent space edit directions
-| &boxvr;&nbsp; editings | Folder containing images displayed in the README
-| &boxvr;&nbsp; environment | Folder containing Anaconda environment used in our experiments
-| &boxvr;&nbsp; licenses | Folder containing licenses of the open source projects used in this repository
-| &boxvr;&nbsp; models | Folder containing models used in different editing techniques and first phase inversion
-| &boxvr;&nbsp; notebooks | Folder with jupyter notebooks to demonstrate the usage of PTI end-to-end
-| &boxvr;&nbsp; scripts | Folder with running scripts for inversion, editing and metric computations
-| &boxvr;&nbsp; torch_utils | Folder containing internal utils for StyleGAN2-ada
-| &boxvr;&nbsp; training | Folder containing the core training logic of PTI
-| &boxvr;&nbsp; utils | Folder with various utility functions
-
-
-## Credits
-**StyleGAN2-ada model and implementation:**  
-https://github.com/NVlabs/stylegan2-ada-pytorch
-Copyright © 2021, NVIDIA Corporation.  
-Nvidia Source Code License https://nvlabs.github.io/stylegan2-ada-pytorch/license.html
-
-**LPIPS model and implementation:**  
-https://github.com/richzhang/PerceptualSimilarity  
-Copyright (c) 2020, Sou Uchida  
-License (BSD 2-Clause) https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE
-
-**e4e model and implementation:**   
-https://github.com/omertov/encoder4editing
-Copyright (c) 2021 omertov  
-License (MIT) https://github.com/omertov/encoder4editing/blob/main/LICENSE
-
-**StyleCLIP model and implementation:**   
-https://github.com/orpatashnik/StyleCLIP
-Copyright (c) 2021 orpatashnik  
-License (MIT) https://github.com/orpatashnik/StyleCLIP/blob/main/LICENSE
-
-**InterfaceGAN implementation:**   
-https://github.com/genforce/interfacegan
-Copyright (c) 2020 genforce  
-License (MIT) https://github.com/genforce/interfacegan/blob/master/LICENSE
-
-**GANSpace implementation:**   
-https://github.com/harskish/ganspace
-Copyright (c) 2020 harkish  
-License (Apache License 2.0) https://github.com/harskish/ganspace/blob/master/LICENSE
-
-
-## Acknowledgments
-This repository structure is based on [encoder4editing](https://github.com/omertov/encoder4editing) and [ReStyle](https://github.com/yuval-alaluf/restyle-encoder) repositories
-
-## Contact
-For any inquiry please contact us at our email addresses: [email protected] or [email protected]
-
-
-## Citation
-If you use this code for your research, please cite:
-```
-@article{roich2021pivotal,
-  title={Pivotal Tuning for Latent-based Editing of Real Images},
-  author={Roich, Daniel and Mokady, Ron and Bermano, Amit H and Cohen-Or, Daniel},
-  journal={arXiv preprint arXiv:2106.05744},
-  year={2021}
-}
-```

PTI/torch_utils/custom_ops.py

@@ -1,126 +0,0 @@
-# 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.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try: # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-#----------------------------------------------------------------------------

PTI/torch_utils/misc.py

@@ -1,262 +0,0 @@
-# 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.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    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()
-    return outputs
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/bias_act.cpp

@@ -1,99 +0,0 @@
-// 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.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
-    if (x.dim() != y.dim())
-        return false;
-    for (int64_t i = 0; i < x.dim(); i++)
-    {
-        if (x.size(i) != y.size(i))
-            return false;
-        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
-            return false;
-    }
-    return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
-    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
-    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
-    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
-    TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
-    // Validate layout.
-    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
-    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
-    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
-    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
-    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    torch::Tensor y = torch::empty_like(x);
-    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
-    // Initialize CUDA kernel parameters.
-    bias_act_kernel_params p;
-    p.x     = x.data_ptr();
-    p.b     = (b.numel()) ? b.data_ptr() : NULL;
-    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
-    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
-    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
-    p.y     = y.data_ptr();
-    p.grad  = grad;
-    p.act   = act;
-    p.alpha = alpha;
-    p.gain  = gain;
-    p.clamp = clamp;
-    p.sizeX = (int)x.numel();
-    p.sizeB = (int)b.numel();
-    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
-    // Choose CUDA kernel.
-    void* kernel;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        kernel = choose_bias_act_kernel<scalar_t>(p);
-    });
-    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
-    // Launch CUDA kernel.
-    p.loopX = 4;
-    int blockSize = 4 * 32;
-    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/bias_act.cu

@@ -1,173 +0,0 @@
-// 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.
-
-#include <c10/util/Half.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-// Helpers.
-
-template <class T> struct InternalType;
-template <> struct InternalType<double>     { typedef double scalar_t; };
-template <> struct InternalType<float>      { typedef float  scalar_t; };
-template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
-
-//------------------------------------------------------------------------
-// CUDA kernel.
-
-template <class T, int A>
-__global__ void bias_act_kernel(bias_act_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-    int G                 = p.grad;
-    scalar_t alpha        = (scalar_t)p.alpha;
-    scalar_t gain         = (scalar_t)p.gain;
-    scalar_t clamp        = (scalar_t)p.clamp;
-    scalar_t one          = (scalar_t)1;
-    scalar_t two          = (scalar_t)2;
-    scalar_t expRange     = (scalar_t)80;
-    scalar_t halfExpRange = (scalar_t)40;
-    scalar_t seluScale    = (scalar_t)1.0507009873554804934193349852946;
-    scalar_t seluAlpha    = (scalar_t)1.6732632423543772848170429916717;
-
-    // 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.
-        scalar_t x = (scalar_t)((const T*)p.x)[xi];
-        scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
-        scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
-        scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
-        scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
-        scalar_t yy = (gain != 0) ? yref / gain : 0;
-        scalar_t y = 0;
-
-        // Apply bias.
-        ((G == 0) ? x : xref) += b;
-
-        // linear
-        if (A == 1)
-        {
-            if (G == 0) y = x;
-            if (G == 1) y = x;
-        }
-
-        // relu
-        if (A == 2)
-        {
-            if (G == 0) y = (x > 0) ? x : 0;
-            if (G == 1) y = (yy > 0) ? x : 0;
-        }
-
-        // lrelu
-        if (A == 3)
-        {
-            if (G == 0) y = (x > 0) ? x : x * alpha;
-            if (G == 1) y = (yy > 0) ? x : x * alpha;
-        }
-
-        // tanh
-        if (A == 4)
-        {
-            if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
-            if (G == 1) y = x * (one - yy * yy);
-            if (G == 2) y = x * (one - yy * yy) * (-two * yy);
-        }
-
-        // sigmoid
-        if (A == 5)
-        {
-            if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
-            if (G == 1) y = x * yy * (one - yy);
-            if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
-        }
-
-        // elu
-        if (A == 6)
-        {
-            if (G == 0) y = (x >= 0) ? x : exp(x) - one;
-            if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
-            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
-        }
-
-        // selu
-        if (A == 7)
-        {
-            if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
-            if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
-            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
-        }
-
-        // softplus
-        if (A == 8)
-        {
-            if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
-            if (G == 1) y = x * (one - exp(-yy));
-            if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
-        }
-
-        // swish
-        if (A == 9)
-        {
-            if (G == 0)
-                y = (x < -expRange) ? 0 : x / (exp(-x) + one);
-            else
-            {
-                scalar_t c = exp(xref);
-                scalar_t d = c + one;
-                if (G == 1)
-                    y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
-                else
-                    y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
-                yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
-            }
-        }
-
-        // Apply gain.
-        y *= gain * dy;
-
-        // Clamp.
-        if (clamp >= 0)
-        {
-            if (G == 0)
-                y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
-            else
-                y = (yref > -clamp & yref < clamp) ? y : 0;
-        }
-
-        // Store.
-        ((T*)p.y)[xi] = (T)y;
-    }
-}
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
-{
-    if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
-    if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
-    if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
-    if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
-    if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
-    if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
-    if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
-    if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
-    if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
-    return NULL;
-}
-
-//------------------------------------------------------------------------
-// Template specializations.
-
-template void* choose_bias_act_kernel<double>       (const bias_act_kernel_params& p);
-template void* choose_bias_act_kernel<float>        (const bias_act_kernel_params& p);
-template void* choose_bias_act_kernel<c10::Half>    (const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/bias_act.h

@@ -1,38 +0,0 @@
-// 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.
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct bias_act_kernel_params
-{
-    const void* x;      // [sizeX]
-    const void* b;      // [sizeB] or NULL
-    const void* xref;   // [sizeX] or NULL
-    const void* yref;   // [sizeX] or NULL
-    const void* dy;     // [sizeX] or NULL
-    void*       y;      // [sizeX]
-
-    int         grad;
-    int         act;
-    float       alpha;
-    float       gain;
-    float       clamp;
-
-    int         sizeX;
-    int         sizeB;
-    int         stepB;
-    int         loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/bias_act.py

@@ -1,212 +0,0 @@
-# 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.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
-    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
-    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
-    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
-    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
-    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
-    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
-    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
-    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        _inited = True
-        sources = ['bias_act.cpp', 'bias_act.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=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 PyTorch ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can be of any shape.
-        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 `dim`.
-        dim:    The dimension in `x` corresponding to the elements of `b`.
-                The value of `dim` 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.
-        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
-                the clamping (default).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
-    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Add bias.
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.ndim == 1
-        assert 0 <= dim < x.ndim
-        assert b.shape[0] == x.shape[dim]
-        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
-    # Evaluate activation function.
-    alpha = float(alpha)
-    x = spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    gain = float(gain)
-    if gain != 1:
-        x = x * gain
-
-    # Clamp.
-    if clamp >= 0:
-        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
-    return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Fast CUDA implementation of `bias_act()` using custom ops.
-    """
-    # Parse arguments.
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Lookup from cache.
-    key = (dim, act, alpha, gain, clamp)
-    if key in _bias_act_cuda_cache:
-        return _bias_act_cuda_cache[key]
-
-    # Forward op.
-    class BiasActCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, b): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
-            x = x.contiguous(memory_format=ctx.memory_format)
-            b = b.contiguous() if b is not None else _null_tensor
-            y = x
-            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
-                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                y if 'y' in spec.ref else _null_tensor)
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            dy = dy.contiguous(memory_format=ctx.memory_format)
-            x, b, y = ctx.saved_tensors
-            dx = None
-            db = None
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                dx = dy
-                if act != 'linear' or gain != 1 or clamp >= 0:
-                    dx = BiasActCudaGrad.apply(dy, x, b, y)
-
-            if ctx.needs_input_grad[1]:
-                db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
-            return dx, db
-
-    # Backward op.
-    class BiasActCudaGrad(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
-            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                dy if spec.has_2nd_grad else _null_tensor,
-                x, b, y)
-            return dx
-
-        @staticmethod
-        def backward(ctx, d_dx): # pylint: disable=arguments-differ
-            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
-            dy, x, b, y = ctx.saved_tensors
-            d_dy = None
-            d_x = None
-            d_b = None
-            d_y = None
-
-            if ctx.needs_input_grad[0]:
-                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
-            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
-                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
-            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
-                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
-            return d_dy, d_x, d_b, d_y
-
-    # Add to cache.
-    _bias_act_cuda_cache[key] = BiasActCuda
-    return BiasActCuda
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/conv2d_gradfix.py

@@ -1,170 +0,0 @@
-# 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.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-            else: # transpose
-                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/conv2d_resample.py

@@ -1,156 +0,0 @@
-# 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.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        shape = [int(sz) for sz in w.shape]
-    misc.assert_shape(w, shape)
-    return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
-    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
-    """
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-
-    # Flip weight if requested.
-    if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
-        w = w.flip([2, 3])
-
-    # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
-    # 1x1 kernel + memory_format=channels_last + less than 64 channels.
-    if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
-        if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
-            if out_channels <= 4 and groups == 1:
-                in_shape = x.shape
-                x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
-                x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
-            else:
-                x = x.to(memory_format=torch.contiguous_format)
-                w = w.to(memory_format=torch.contiguous_format)
-                x = conv2d_gradfix.conv2d(x, w, groups=groups)
-            return x.to(memory_format=torch.channels_last)
-
-    # Otherwise => execute using conv2d_gradfix.
-    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
-    return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
-    r"""2D convolution with optional up/downsampling.
-
-    Padding is performed only once at the beginning, not between the operations.
-
-    Args:
-        x:              Input tensor of shape
-                        `[batch_size, in_channels, in_height, in_width]`.
-        w:              Weight tensor of shape
-                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
-        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
-                        calling upfirdn2d.setup_filter(). None = identity (default).
-        up:             Integer upsampling factor (default: 1).
-        down:           Integer downsampling factor (default: 1).
-        padding:        Padding with respect to the upsampled image. Can be a single number
-                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                        (default: 0).
-        groups:         Split input channels into N groups (default: 1).
-        flip_weight:    False = convolution, True = correlation (default: True).
-        flip_filter:    False = convolution, True = correlation (default: False).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
-    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
-    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
-    assert isinstance(up, int) and (up >= 1)
-    assert isinstance(down, int) and (down >= 1)
-    assert isinstance(groups, int) and (groups >= 1)
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-    fw, fh = _get_filter_size(f)
-    px0, px1, py0, py1 = _parse_padding(padding)
-
-    # Adjust padding to account for up/downsampling.
-    if up > 1:
-        px0 += (fw + up - 1) // 2
-        px1 += (fw - up) // 2
-        py0 += (fh + up - 1) // 2
-        py1 += (fh - up) // 2
-    if down > 1:
-        px0 += (fw - down + 1) // 2
-        px1 += (fw - down) // 2
-        py0 += (fh - down + 1) // 2
-        py1 += (fh - down) // 2
-
-    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
-    if kw == 1 and kh == 1 and (down > 1 and up == 1):
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
-    if kw == 1 and kh == 1 and (up > 1 and down == 1):
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-        return x
-
-    # Fast path: downsampling only => use strided convolution.
-    if down > 1 and up == 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
-    if up > 1:
-        if groups == 1:
-            w = w.transpose(0, 1)
-        else:
-            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
-            w = w.transpose(1, 2)
-            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
-        px0 -= kw - 1
-        px1 -= kw - up
-        py0 -= kh - 1
-        py1 -= kh - up
-        pxt = max(min(-px0, -px1), 0)
-        pyt = max(min(-py0, -py1), 0)
-        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
-        if down > 1:
-            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-        return x
-
-    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
-    if up == 1 and down == 1:
-        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
-            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
-    # Fallback: Generic reference implementation.
-    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-    if down > 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-    return x
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/fma.py

@@ -1,60 +0,0 @@
-# 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.
-
-"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
-
-import torch
-
-#----------------------------------------------------------------------------
-
-def fma(a, b, c): # => a * b + c
-    return _FusedMultiplyAdd.apply(a, b, c)
-
-#----------------------------------------------------------------------------
-
-class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
-    @staticmethod
-    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
-        out = torch.addcmul(c, a, b)
-        ctx.save_for_backward(a, b)
-        ctx.c_shape = c.shape
-        return out
-
-    @staticmethod
-    def backward(ctx, dout): # pylint: disable=arguments-differ
-        a, b = ctx.saved_tensors
-        c_shape = ctx.c_shape
-        da = None
-        db = None
-        dc = None
-
-        if ctx.needs_input_grad[0]:
-            da = _unbroadcast(dout * b, a.shape)
-
-        if ctx.needs_input_grad[1]:
-            db = _unbroadcast(dout * a, b.shape)
-
-        if ctx.needs_input_grad[2]:
-            dc = _unbroadcast(dout, c_shape)
-
-        return da, db, dc
-
-#----------------------------------------------------------------------------
-
-def _unbroadcast(x, shape):
-    extra_dims = x.ndim - len(shape)
-    assert extra_dims >= 0
-    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
-    if len(dim):
-        x = x.sum(dim=dim, keepdim=True)
-    if extra_dims:
-        x = x.reshape(-1, *x.shape[extra_dims+1:])
-    assert x.shape == shape
-    return x
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/grid_sample_gradfix.py

@@ -1,83 +0,0 @@
-# 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.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import warnings
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------

PTI/torch_utils/ops/upfirdn2d.cpp

@@ -1,103 +0,0 @@
-// 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.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/upfirdn2d.cu

@@ -1,350 +0,0 @@
-// 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.
-
-#include <c10/util/Half.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-// Helpers.
-
-template <class T> struct InternalType;
-template <> struct InternalType<double>     { typedef double scalar_t; };
-template <> struct InternalType<float>      { typedef float  scalar_t; };
-template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
-
-static __device__ __forceinline__ int floor_div(int a, int b)
-{
-    int t = 1 - a / b;
-    return (a + t * b) / b - t;
-}
-
-//------------------------------------------------------------------------
-// Generic CUDA implementation for large filters.
-
-template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-
-    // Calculate thread index.
-    int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
-    int outY = minorBase / p.launchMinor;
-    minorBase -= outY * p.launchMinor;
-    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
-    int majorBase = blockIdx.z * p.loopMajor;
-    if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
-        return;
-
-    // Setup Y receptive field.
-    int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
-    int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
-    int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
-    int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
-    if (p.flip)
-        filterY = p.filterSize.y - 1 - filterY;
-
-    // Loop over major, minor, and X.
-    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
-    for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
-    {
-        int nc = major * p.sizeMinor + minor;
-        int n = nc / p.inSize.z;
-        int c = nc - n * p.inSize.z;
-        for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
-        {
-            // Setup X receptive field.
-            int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
-            int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
-            int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
-            int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
-            if (p.flip)
-                filterX = p.filterSize.x - 1 - filterX;
-
-            // Initialize pointers.
-            const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
-            const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
-            int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
-            int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
-
-            // Inner loop.
-            scalar_t v = 0;
-            for (int y = 0; y < h; y++)
-            {
-                for (int x = 0; x < w; x++)
-                {
-                    v += (scalar_t)(*xp) * (scalar_t)(*fp);
-                    xp += p.inStride.x;
-                    fp += filterStepX;
-                }
-                xp += p.inStride.y - w * p.inStride.x;
-                fp += filterStepY - w * filterStepX;
-            }
-
-            // Store result.
-            v *= p.gain;
-            ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
-        }
-    }
-}
-
-//------------------------------------------------------------------------
-// Specialized CUDA implementation for small filters.
-
-template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
-static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-    const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
-    const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
-    __shared__ volatile scalar_t sf[filterH][filterW];
-    __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
-
-    // Calculate tile index.
-    int minorBase = blockIdx.x;
-    int tileOutY = minorBase / p.launchMinor;
-    minorBase -= tileOutY * p.launchMinor;
-    minorBase *= loopMinor;
-    tileOutY *= tileOutH;
-    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
-    int majorBase = blockIdx.z * p.loopMajor;
-    if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
-        return;
-
-    // Load filter (flipped).
-    for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
-    {
-        int fy = tapIdx / filterW;
-        int fx = tapIdx - fy * filterW;
-        scalar_t v = 0;
-        if (fx < p.filterSize.x & fy < p.filterSize.y)
-        {
-            int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
-            int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
-            v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
-        }
-        sf[fy][fx] = v;
-    }
-
-    // Loop over major and X.
-    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
-    {
-        int baseNC = major * p.sizeMinor + minorBase;
-        int n = baseNC / p.inSize.z;
-        int baseC = baseNC - n * p.inSize.z;
-        for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
-        {
-            // Load input pixels.
-            int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
-            int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
-            int tileInX = floor_div(tileMidX, upx);
-            int tileInY = floor_div(tileMidY, upy);
-            __syncthreads();
-            for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
-            {
-                int relC = inIdx;
-                int relInX = relC / loopMinor;
-                int relInY = relInX / tileInW;
-                relC -= relInX * loopMinor;
-                relInX -= relInY * tileInW;
-                int c = baseC + relC;
-                int inX = tileInX + relInX;
-                int inY = tileInY + relInY;
-                scalar_t v = 0;
-                if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
-                    v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
-                sx[relInY][relInX][relC] = v;
-            }
-
-            // Loop over output pixels.
-            __syncthreads();
-            for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
-            {
-                int relC = outIdx;
-                int relOutX = relC / loopMinor;
-                int relOutY = relOutX / tileOutW;
-                relC -= relOutX * loopMinor;
-                relOutX -= relOutY * tileOutW;
-                int c = baseC + relC;
-                int outX = tileOutX + relOutX;
-                int outY = tileOutY + relOutY;
-
-                // Setup receptive field.
-                int midX = tileMidX + relOutX * downx;
-                int midY = tileMidY + relOutY * downy;
-                int inX = floor_div(midX, upx);
-                int inY = floor_div(midY, upy);
-                int relInX = inX - tileInX;
-                int relInY = inY - tileInY;
-                int filterX = (inX + 1) * upx - midX - 1; // flipped
-                int filterY = (inY + 1) * upy - midY - 1; // flipped
-
-                // Inner loop.
-                if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
-                {
-                    scalar_t v = 0;
-                    #pragma unroll
-                    for (int y = 0; y < filterH / upy; y++)
-                        #pragma unroll
-                        for (int x = 0; x < filterW / upx; x++)
-                            v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
-                    v *= p.gain;
-                    ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
-                }
-            }
-        }
-    }
-}
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
-{
-    int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
-
-    upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
-    if (s == 1)           spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
-
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  64,16,1>, 64,16,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,  64,16,1>, 64,16,1, 1};
-        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,  64,16,1>, 64,16,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  64,16,1>, 64,16,1, 1};
-        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,  64,16,1>, 64,16,1, 1};
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  16,16,8>,  16,16,8,  1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
-    }
-    if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 64,16,1>, 64,16,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 64,16,1>, 64,16,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 64,16,1>, 64,16,1, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 64,16,1>, 64,16,1, 1};
-    }
-    if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 16,16,8>, 16,16,8, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 16,16,8>, 16,16,8, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 16,16,8>, 16,16,8, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 16,16,8>, 16,16,8, 1};
-    }
-    if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  32,8,1>, 32,8,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  32,8,1>, 32,8,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  32,8,1>, 32,8,1, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  32,8,1>, 32,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  8,8,8>, 8,8,8, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  8,8,8>, 8,8,8, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  8,8,8>, 8,8,8, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  8,8,8>, 8,8,8, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,8,1>, 64,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,1,8>, 64,1,8, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  32,16,1>, 32,16,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  1,64,8>, 1,64,8, 1};
-    }
-    return spec;
-}
-
-//------------------------------------------------------------------------
-// Template specializations.
-
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double>   (const upfirdn2d_kernel_params& p);
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float>    (const upfirdn2d_kernel_params& p);
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/upfirdn2d.h

@@ -1,59 +0,0 @@
-// 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.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

PTI/torch_utils/ops/upfirdn2d.py

@@ -1,384 +0,0 @@
-# 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.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import traceback
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-def _parse_scaling(scaling):
-    if isinstance(scaling, int):
-        scaling = [scaling, scaling]
-    assert isinstance(scaling, (list, tuple))
-    assert all(isinstance(x, int) for x in scaling)
-    sx, sy = scaling
-    assert sx >= 1 and sy >= 1
-    return sx, sy
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, int) for x in padding)
-    if len(padding) == 2:
-        padx, pady = padding
-        padding = [padx, padx, pady, pady]
-    padx0, padx1, pady0, pady1 = padding
-    return padx0, padx1, pady0, pady1
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    fw = f.shape[-1]
-    fh = f.shape[0]
-    with misc.suppress_tracer_warnings():
-        fw = int(fw)
-        fh = int(fh)
-    misc.assert_shape(f, [fh, fw][:f.ndim])
-    assert fw >= 1 and fh >= 1
-    return fw, fh
-
-#----------------------------------------------------------------------------
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
-    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
-    Args:
-        f:           Torch tensor, numpy array, or python list of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable),
-                     `[]` (impulse), or
-                     `None` (identity).
-        device:      Result device (default: cpu).
-        normalize:   Normalize the filter so that it retains the magnitude
-                     for constant input signal (DC)? (default: True).
-        flip_filter: Flip the filter? (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        separable:   Return a separable filter? (default: select automatically).
-
-    Returns:
-        Float32 tensor of the shape
-        `[filter_height, filter_width]` (non-separable) or
-        `[filter_taps]` (separable).
-    """
-    # Validate.
-    if f is None:
-        f = 1
-    f = torch.as_tensor(f, dtype=torch.float32)
-    assert f.ndim in [0, 1, 2]
-    assert f.numel() > 0
-    if f.ndim == 0:
-        f = f[np.newaxis]
-
-    # Separable?
-    if separable is None:
-        separable = (f.ndim == 1 and f.numel() >= 8)
-    if f.ndim == 1 and not separable:
-        f = f.ger(f)
-    assert f.ndim == (1 if separable else 2)
-
-    # Apply normalize, flip, gain, and device.
-    if normalize:
-        f /= f.sum()
-    if flip_filter:
-        f = f.flip(list(range(f.ndim)))
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(device=device)
-    return f
-
-#----------------------------------------------------------------------------
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    2. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by keeping every Nth pixel (`down`).
-
-    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 PyTorch ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the upsampled image. Can be a single number
-                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
-    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    if f is None:
-        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    assert f.dtype == torch.float32 and not f.requires_grad
-    batch_size, num_channels, in_height, in_width = x.shape
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Upsample by inserting zeros.
-    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
-    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
-    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
-    # Pad or crop.
-    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
-    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
-
-    # Setup filter.
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(x.dtype)
-    if not flip_filter:
-        f = f.flip(list(range(f.ndim)))
-
-    # Convolve with the filter.
-    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
-    if f.ndim == 4:
-        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
-    else:
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
-
-    # Downsample by throwing away pixels.
-    x = x[:, :, ::downy, ::downx]
-    return x
-
-#----------------------------------------------------------------------------
-
-_upfirdn2d_cuda_cache = dict()
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
-    """
-    # Parse arguments.
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Lookup from cache.
-    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-    if key in _upfirdn2d_cuda_cache:
-        return _upfirdn2d_cuda_cache[key]
-
-    # Forward op.
-    class Upfirdn2dCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, f): # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-            if f is None:
-                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-            y = x
-            if f.ndim == 2:
-                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-            else:
-                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
-                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
-            ctx.save_for_backward(f)
-            ctx.x_shape = x.shape
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            f, = ctx.saved_tensors
-            _, _, ih, iw = ctx.x_shape
-            _, _, oh, ow = dy.shape
-            fw, fh = _get_filter_size(f)
-            p = [
-                fw - padx0 - 1,
-                iw * upx - ow * downx + padx0 - upx + 1,
-                fh - pady0 - 1,
-                ih * upy - oh * downy + pady0 - upy + 1,
-            ]
-            dx = None
-            df = None
-
-            if ctx.needs_input_grad[0]:
-                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
-
-            assert not ctx.needs_input_grad[1]
-            return dx, df
-
-    # Add to cache.
-    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
-    return Upfirdn2dCuda
-
-#----------------------------------------------------------------------------
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Filter a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape matches the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + fw // 2,
-        padx1 + (fw - 1) // 2,
-        pady0 + fh // 2,
-        pady1 + (fh - 1) // 2,
-    ]
-    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a multiple of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    upx, upy = _parse_scaling(up)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw + upx - 1) // 2,
-        padx1 + (fw - upx) // 2,
-        pady0 + (fh + upy - 1) // 2,
-        pady1 + (fh - upy) // 2,
-    ]
-    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a fraction of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the input. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw - downx + 1) // 2,
-        padx1 + (fw - downx) // 2,
-        pady0 + (fh - downy + 1) // 2,
-        pady1 + (fh - downy) // 2,
-    ]
-    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------

PTI/torch_utils/persistence.py

@@ -1,251 +0,0 @@
-# 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.
-
-"""Facilities for pickling Python code alongside other data.
-
-The pickled code is automatically imported into a separate Python module
-during unpickling. This way, any previously exported pickles will remain
-usable even if the original code is no longer available, or if the current
-version of the code is not consistent with what was originally pickled."""
-
-import sys
-import pickle
-import io
-import inspect
-import copy
-import uuid
-import types
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-_version            = 6         # internal version number
-_decorators         = set()     # {decorator_class, ...}
-_import_hooks       = []        # [hook_function, ...]
-_module_to_src_dict = dict()    # {module: src, ...}
-_src_to_module_dict = dict()    # {src: module, ...}
-
-#----------------------------------------------------------------------------
-
-def persistent_class(orig_class):
-    r"""Class decorator that extends a given class to save its source code
-    when pickled.
-
-    Example:
-
-        from torch_utils import persistence
-
-        @persistence.persistent_class
-        class MyNetwork(torch.nn.Module):
-            def __init__(self, num_inputs, num_outputs):
-                super().__init__()
-                self.fc = MyLayer(num_inputs, num_outputs)
-                ...
-
-        @persistence.persistent_class
-        class MyLayer(torch.nn.Module):
-            ...
-
-    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
-    source code alongside other internal state (e.g., parameters, buffers,
-    and submodules). This way, any previously exported pickle will remain
-    usable even if the class definitions have been modified or are no
-    longer available.
-
-    The decorator saves the source code of the entire Python module
-    containing the decorated class. It does *not* save the source code of
-    any imported modules. Thus, the imported modules must be available
-    during unpickling, also including `torch_utils.persistence` itself.
-
-    It is ok to call functions defined in the same module from the
-    decorated class. However, if the decorated class depends on other
-    classes defined in the same module, they must be decorated as well.
-    This is illustrated in the above example in the case of `MyLayer`.
-
-    It is also possible to employ the decorator just-in-time before
-    calling the constructor. For example:
-
-        cls = MyLayer
-        if want_to_make_it_persistent:
-            cls = persistence.persistent_class(cls)
-        layer = cls(num_inputs, num_outputs)
-
-    As an additional feature, the decorator also keeps track of the
-    arguments that were used to construct each instance of the decorated
-    class. The arguments can be queried via `obj.init_args` and
-    `obj.init_kwargs`, and they are automatically pickled alongside other
-    object state. A typical use case is to first unpickle a previous
-    instance of a persistent class, and then upgrade it to use the latest
-    version of the source code:
-
-        with open('old_pickle.pkl', 'rb') as f:
-            old_net = pickle.load(f)
-        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
-        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
-    """
-    assert isinstance(orig_class, type)
-    if is_persistent(orig_class):
-        return orig_class
-
-    assert orig_class.__module__ in sys.modules
-    orig_module = sys.modules[orig_class.__module__]
-    orig_module_src = _module_to_src(orig_module)
-
-    class Decorator(orig_class):
-        _orig_module_src = orig_module_src
-        _orig_class_name = orig_class.__name__
-
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            self._init_args = copy.deepcopy(args)
-            self._init_kwargs = copy.deepcopy(kwargs)
-            assert orig_class.__name__ in orig_module.__dict__
-            _check_pickleable(self.__reduce__())
-
-        @property
-        def init_args(self):
-            return copy.deepcopy(self._init_args)
-
-        @property
-        def init_kwargs(self):
-            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
-
-        def __reduce__(self):
-            fields = list(super().__reduce__())
-            fields += [None] * max(3 - len(fields), 0)
-            if fields[0] is not _reconstruct_persistent_obj:
-                meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
-                fields[0] = _reconstruct_persistent_obj # reconstruct func
-                fields[1] = (meta,) # reconstruct args
-                fields[2] = None # state dict
-            return tuple(fields)
-
-    Decorator.__name__ = orig_class.__name__
-    _decorators.add(Decorator)
-    return Decorator
-
-#----------------------------------------------------------------------------
-
-def is_persistent(obj):
-    r"""Test whether the given object or class is persistent, i.e.,
-    whether it will save its source code when pickled.
-    """
-    try:
-        if obj in _decorators:
-            return True
-    except TypeError:
-        pass
-    return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
-
-#----------------------------------------------------------------------------
-
-def import_hook(hook):
-    r"""Register an import hook that is called whenever a persistent object
-    is being unpickled. A typical use case is to patch the pickled source
-    code to avoid errors and inconsistencies when the API of some imported
-    module has changed.
-
-    The hook should have the following signature:
-
-        hook(meta) -> modified meta
-
-    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
-
-        type:       Type of the persistent object, e.g. `'class'`.
-        version:    Internal version number of `torch_utils.persistence`.
-        module_src  Original source code of the Python module.
-        class_name: Class name in the original Python module.
-        state:      Internal state of the object.
-
-    Example:
-
-        @persistence.import_hook
-        def wreck_my_network(meta):
-            if meta.class_name == 'MyNetwork':
-                print('MyNetwork is being imported. I will wreck it!')
-                meta.module_src = meta.module_src.replace("True", "False")
-            return meta
-    """
-    assert callable(hook)
-    _import_hooks.append(hook)
-
-#----------------------------------------------------------------------------
-
-def _reconstruct_persistent_obj(meta):
-    r"""Hook that is called internally by the `pickle` module to unpickle
-    a persistent object.
-    """
-    meta = dnnlib.EasyDict(meta)
-    meta.state = dnnlib.EasyDict(meta.state)
-    for hook in _import_hooks:
-        meta = hook(meta)
-        assert meta is not None
-
-    assert meta.version == _version
-    module = _src_to_module(meta.module_src)
-
-    assert meta.type == 'class'
-    orig_class = module.__dict__[meta.class_name]
-    decorator_class = persistent_class(orig_class)
-    obj = decorator_class.__new__(decorator_class)
-
-    setstate = getattr(obj, '__setstate__', None)
-    if callable(setstate):
-        setstate(meta.state) # pylint: disable=not-callable
-    else:
-        obj.__dict__.update(meta.state)
-    return obj
-
-#----------------------------------------------------------------------------
-
-def _module_to_src(module):
-    r"""Query the source code of a given Python module.
-    """
-    src = _module_to_src_dict.get(module, None)
-    if src is None:
-        src = inspect.getsource(module)
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-    return src
-
-def _src_to_module(src):
-    r"""Get or create a Python module for the given source code.
-    """
-    module = _src_to_module_dict.get(src, None)
-    if module is None:
-        module_name = "_imported_module_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-        exec(src, module.__dict__) # pylint: disable=exec-used
-    return module
-
-#----------------------------------------------------------------------------
-
-def _check_pickleable(obj):
-    r"""Check that the given object is pickleable, raising an exception if
-    it is not. This function is expected to be considerably more efficient
-    than actually pickling the object.
-    """
-    def recurse(obj):
-        if isinstance(obj, (list, tuple, set)):
-            return [recurse(x) for x in obj]
-        if isinstance(obj, dict):
-            return [[recurse(x), recurse(y)] for x, y in obj.items()]
-        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
-            return None # Python primitive types are pickleable.
-        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']:
-            return None # NumPy arrays and PyTorch tensors are pickleable.
-        if is_persistent(obj):
-            return None # Persistent objects are pickleable, by virtue of the constructor check.
-        return obj
-    with io.BytesIO() as f:
-        pickle.dump(recurse(obj), f)
-
-#----------------------------------------------------------------------------

PTI/torch_utils/training_stats.py

@@ -1,268 +0,0 @@
-# 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.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype  = torch.float64 # Data type to use for the internal counters.
-_rank           = 0             # Rank of the current process.
-_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
-_sync_called    = False         # Has _sync() been called yet?
-_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
-        return stats
-
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------

app.py

@@ -1,40 +1,89 @@
 import gradio as gr
-import utils
+import utils.utils as utils
 from PIL import Image
 import torch
 import math
 from torchvision import transforms
-
-
+from run_pti import run_PTI
 device = "cpu"
 years = [str(y) for y in range(1880, 2020, 10)]
+decades = [y + "s" for y in years]
 
 
+transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
 orig_models = {}
 
 for year in years:
     G, w_avg = utils.load_stylegan2(f"pretrained_models/{year}.pkl", device)
-    orig_models[year] = { "G": G.eval()}
+    orig_models[year] = { "G": G.eval().float()}
     
 
 def run_alignment(image_path,idx=None):
     import dlib
     from align_all_parallel import align_face
     predictor = dlib.shape_predictor("pretrained_models/shape_predictor_68_face_landmarks.dat")
-    aligned_image = align_face(filepath=image_path, predictor=predictor, idx=idx) 
-    print("Aligned image has shape: {}".format(aligned_image.size))
+    aligned_image = align_face(filepath=image_path, predictor=predictor, idx=idx)     
+    
 
     return aligned_image 
 
-def predict(inp):
+def predict(inp, in_decade):
   #with torch.no_grad():     
   inp.save("imgs/input.png")
-  out = run_alignment("imgs/input.png", idx=0)
-  return out
+  inversion = run_alignment("imgs/input.png", idx=0)
+  inversion.save("imgs/cropped/input.png")
+  run_PTI(run_name="gradio_demo", use_wandb=False, use_multi_id_training=False)
+  #inversion = Image.open("imgs/cropped/input.png")
+  
+  in_year = in_decade[:-1]
+  pti_models = {}
+
+  for year in years:
+      G, w_avg = utils.load_stylegan2(f"pretrained_models/{year}.pkl", device)
+      pti_models[year] = { "G": G.eval().float()}
+
+
+  pti_models[in_year]['G'] = torch.load(f"checkpoints/model_gradio_demo_input.pt", device).eval().float()
+
+  for year in years:
+      if year != in_year:
+          for p_pti, p_orig, (names, p) in zip(pti_models[in_year]['G'].parameters(),orig_models[in_year]['G'].parameters(), pti_models[year]['G'].named_parameters()):
+              with torch.no_grad():
+                  delta = p_pti - p_orig
+                  p += delta 
+
+  space = 0
+  dst = Image.new("RGB", (256 * (len(years) + 1) + (space * len(years)), 256), color='white')
+
+
+  w_pti = torch.load(f"embeddings/{in_year}/PTI/input/0.pt", map_location=device)  
+
+  border_width = 10
+  #fill_color = 'red'
+  dst.paste(inversion, (0, 0))
+
+
+
+  for i in range(0, len(years)):
+      year = str(years[i])
+      with torch.no_grad():
+          child_tensor = pti_models[year]["G"].synthesis(w_pti.view(1, 14, 512), noise_mode="const", force_fp32=True)
+      img = utils.tensor2im(child_tensor.squeeze(0))
+      #     if year == in_year:
+      #         img = img.crop((border_width, border_width, 256 - border_width, 256-border_width))
+      #         img = PIL.ImageOps.expand(img, border=border_width, fill=fill_color)
+      dst.paste(img, ((256 + space) * (i+1), 0))  
+  dst
+  return dst
 
 
 gr.Interface(fn=predict, 
-             inputs=gr.Image(type="pil"),
-             outputs=gr.Image(type="pil"),
-             #examples=["lion.jpg", "cheetah.jpg"]
-             ).launch()
+             inputs=[gr.Image(label="Input Image", type="pil"), gr.Dropdown(label="Input Decade", choices=decades, value="2010s")],
+             outputs=gr.Image(label="Decade Transformations", type="pil"),
+             examples=[["imgs/Steven-Yeun.jpg", "2010s"]]
+             
+             ).launch() #.launch(server_name="0.0.0.0", server_port=8098)

checkpoints/model_gradio_demo_input.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65ee5644ec8ab0966a4eb51971995c071f8178db765750d45e32e0ed18a09738
+size 99867041

{PTI/configs → configs}/global_config.py

@@ -1,6 +1,6 @@
 ## Device
-cuda_visible_devices = "1"
-device = "cuda:0"
+cuda_visible_devices = "0"
+device = "cpu"
 
 ## Logs
 training_step = 1

{PTI/configs → configs}/hyperparameters.py

@@ -28,4 +28,4 @@ max_images_to_invert = 10
 pti_learning_rate = 3e-4
 first_inv_lr = 5e-3
 train_batch_size = 1
-use_last_w_pivots = True
+use_last_w_pivots = False

{PTI/configs → configs}/paths_config.py

@@ -4,12 +4,12 @@ year = "2010"
 e4e = "./pretrained_models/e4e_ffhq_encode.pt"
 
 
-stylegan2_ada_ffhq = f"../pretrained_models/{year}.pkl"
+stylegan2_ada_ffhq = f"pretrained_models/{year}.pkl"
 
 style_clip_pretrained_mappers = ""
-ir_se50 = "/share/phoenix/nfs04/S7/wikitime_models/model_ir_se50.pth"
+ir_se50 = "pretrained_models/model_ir_se50.pth"
 dlib = "./pretrained_models/align.dat"
-deeplab = "/share/phoenix/nfs04/S7/wikitime_models/deeplab_model/deeplab_model.pth"
+deeplab = "pretrained_models/deeplab_model/deeplab_model.pth"
 
 ## Dirs for output files
 checkpoints_dir = "./checkpoints"
@@ -20,7 +20,7 @@ experiments_output_dir = "./output"
 ## Input info
 ### Input dir, where the images reside
 input_data_path = (
-    f"/share/phoenix/nfs04/S7/emc348/WikiFaces/datasets/new_crops/test/{year}"
+    f"imgs/cropped"
 )
 input_data_id = f"{year}"
 

embeddings/2010/PTI/input/0.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb9b3fc3d08f6dd3ee5c87299fff8cd932e4a6afaa90fe06f3d5c5f9503ebf26
+size 29419

models/StyleCLIP/criteria/clip_loss.py

@@ -0,0 +1,17 @@
+
+import torch
+import clip
+
+
+class CLIPLoss(torch.nn.Module):
+
+    def __init__(self, opts):
+        super(CLIPLoss, self).__init__()
+        self.model, self.preprocess = clip.load("ViT-B/32", device="cuda")
+        self.upsample = torch.nn.Upsample(scale_factor=7)
+        self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32)
+
+    def forward(self, image, text):
+        image = self.avg_pool(self.upsample(image))
+        similarity = 1 - self.model(image, text)[0] / 100
+        return similarity

models/StyleCLIP/criteria/id_loss.py

@@ -0,0 +1,39 @@
+import torch
+from torch import nn
+
+from models.facial_recognition.model_irse import Backbone
+
+
+class IDLoss(nn.Module):
+    def __init__(self, opts):
+        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(opts.ir_se50_weights))
+        self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
+        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
+        self.facenet.eval()
+        self.opts = opts
+
+    def extract_feats(self, x):
+        if x.shape[2] != 256:
+            x = self.pool(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):
+        n_samples = y.shape[0]
+        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
+        count = 0
+        for i in range(n_samples):
+            diff_target = y_hat_feats[i].dot(y_feats[i])
+            loss += 1 - diff_target
+            count += 1
+
+        return loss / count, sim_improvement / count