python_code stringlengths 0 229k |
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#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Special implementations of mathematical functions that
solve numerical issues of naive implementations.
.. [Mae... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import math
import warnings
from abc import abstractproperty
from collections imp... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# NOTE: To be removed once (if) https://github.com/pytorch/pytorch/pull/37385 lands
from __future__ import annotatio... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from copy import deepcopy
from math import pi
from typing import List, Optional
... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Callable, List, Optional, Tuple
import botorch.models.model a... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Optional, Sequence
import torch
from botorch.utils.probabilit... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from inspect import getmembers
from typing import Optional, Sequence, Union
impo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Linear Elliptical Slice Sampler.
References
.. [Gessner2020]
A. Gessner, O. Kanjilal, and P. Hennig. Integr... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.utils.probability.bvn import bvn, bvnmom
from botorch.utils.probability.lin_ess import LinearEllipticalS... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Bivariate conditioning algorithm for approximating Gaussian probabilities,
see [Genz2016numerical]_ and [Trinh20... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from dataclasses import dataclass, InitVar
from itertools import chain
from typin... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import math
from functools import lru_cache
from math import pi
from numbers imp... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Methods for computing bivariate normal probabilities and statistics.
.. [Genz2004bvnt]
A. Genz. Numerical c... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.utils.multi_objective.hypervolume import Hypervolume, infer_reference_point
from botorch.utils.multi_obj... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import torch
from torch import Tensor
# maximum tensor size for simple pareto co... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Helper utilities for constructing scalarizations.
References
.. [Knowles2005]
J. Knowles, "ParEGO: a hybri... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Hypervolume Utilities.
References
.. [Fonseca2006]
C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An impro... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Box decomposition algorithms.
References
.. [Lacour17]
R. Lacour, K. Klamroth, C. Fonseca. A box decomposit... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.utils.multi_objective.box_decompositions.box_decomposition_list import ( # noqa E501
BoxDecomposit... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Utilities for box decomposition algorithms."""
from typing import Optional, Tuple
import torch
from botorch.exc... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Algorithms for partitioning the dominated space into hyperrectangles."""
from __future__ import annotations
fro... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Box decomposition container."""
from __future__ import annotations
from typing import List, Union
import torch... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Algorithms for partitioning the non-dominated space into rectangles.
References
.. [Couckuyt2012]
I. Coucku... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
References
.. [Zhe2019hogp]
S. Zhe, W. Xing, and R. M. Kirby. Scalable high-order gaussian process regressi... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Multi-task Gaussian Process Regression models with fully Bayesian inference.
"""
from typing import Any, Dict, List, Mapping, NoRetur... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
References
.. [Feng2020HDCPS]
Q. Feng, B. Latham, H. Mao and E. Backshy. High-Dimensional Contextual Policy... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Deterministic Models: Simple wrappers that allow the usage of deterministic
mappings via the BoTorch Model and P... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Abstract model class for all GPyTorch-based botorch models.
To implement your own, simply inherit from both the... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
from botorch.models.gp_regression import FixedNoiseGP
from botorch.models.ke... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Gaussian Process Regression models with fully Bayesian inference.
Fully Bayesian models use Bayesian inference over model hyperparamet... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
References
.. [burt2020svgp]
David R. Burt and Carl Edward Rasmussen and Mark van der Wilk,
Convergence... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Utilities for converting between different models.
"""
from __future__ import annotations
from copy import dee... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.models.approximate_gp import (
ApproximateGPyTorchModel,
SingleTaskVariationalGP,
)
from botorch... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Preference Learning with Gaussian Process
.. [Chu2005preference]
Wei Chu, and Zoubin Ghahramani. Preference... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Gaussian Process Regression models based on GPyTorch models.
These models are often a good starting point and a... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Abstract base module for all BoTorch models.
This module contains `Model`, the abstract base class for all BoTor... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Ensemble Models: Simple wrappers that allow the usage of ensembles
via the BoTorch Model and Posterior APIs.
"""... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Multi-Task GP models.
References
.. [Bonilla2007MTGP]
E. Bonilla, K. Chai and C. Williams. Multi-task Gaus... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Cost models to be used with multi-fidelity optimization.
Cost are useful for defining known cost functions when... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Model List GP Regression models.
"""
from __future__ import annotations
from copy import deepcopy
from typing ... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Multi-Fidelity Gaussian Process Regression models based on GPyTorch models.
For more on Multi-Fidelity BO, see ... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import warnings
from typing import Any, Callable, Dict, List, Optional
import to... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Optional
import torch
from gpytorch.constraints import Interv... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from gpytorch.kernels.kernel import Kernel
from torch import Tensor
class CategoricalKernel(Kernel):
... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import List, Optional, Tuple
import numpy
import torch
from botorch.exceptions.errors import UnsupportedError
from ... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.models.kernels.categorical import CategoricalKernel
from botorch.models.kernels.downsampling import Down... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict, List, Optional
import torch
from gpytorch.kernels.kernel import Kernel
from gpytorch.k... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Any, List, Optional
import torch
from botorch.exceptions impo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict, List, Optional
import torch
from gpytorch.constraints import Positive
from gpytorch.ke... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Optional
import torch
from gpytorch.constraints import Interv... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import torch
from gpytorch.constraints.constraints import GreaterThan
from gpytorch.kern... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Functionality for allocating the inducing points of sparse Gaussian
process models.
References
.. [chen2018dpp... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Parsing rules for BoTorch datasets."""
from __future__ import annotations
from typing import Any, Dict, Hashab... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.models.utils.assorted import (
_make_X_full,
add_output_dim,
check_min_max_scaling,
chec... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""Assorted helper methods and objects for working with BoTorch models."""
from __future__ import annotations
impo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.models.likelihoods.pairwise import (
PairwiseLogitLikelihood,
PairwiseProbitLikelihood,
)
__al... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Pairwise likelihood for pairwise preference model (e.g., PairwiseGP).
"""
from __future__ import annotations
i... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Outcome transformations for automatically transforming and un-transforming
model outputs. Outcome transformation... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.models.transforms.factory import get_rounding_input_transform
from botorch.models.transforms.input impor... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from collections import OrderedDict
from typing import Dict, List, Optional
from... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from functools import wraps
from typing import Tuple
import torch
from torch imp... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Input Transformations.
These classes implement a variety of transformations for
input parameters including: lea... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Botorch Warnings.
"""
class BotorchWarning(Warning):
r"""Base botorch warning."""
pass
class BadIni... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.exceptions.errors import (
BotorchError,
BotorchTensorDimensionError,
CandidateGenerationErr... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Botorch Errors.
"""
from typing import Any
import numpy as np
class BotorchError(Exception):
r"""Base bo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Tuple
import torch
from botorch.exceptions.errors import BotorchTensorDimensionError
fr... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Deterministic (degenerate) posteriors. Used in conjunction with deterministic
models.
"""
from __future__ impor... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Posterior module to be used with GPyTorch models.
"""
from __future__ import annotations
import warnings
from... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Callable, Optional, Tuple
import torch
from botorch.posteriors.gpytorch import GPyTo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.posteriors.deterministic import DeterministicPosterior
from botorch.posteriors.fully_bayesian import Ful... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Abstract base module for all botorch posteriors.
"""
from __future__ import annotations
from functools import ... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import torch
from gpytorch.distributions.multitask_multivariate_normal import (
... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Ensemble posteriors. Used in conjunction with ensemble models.
"""
from __future__ import annotations
from typ... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Abstract base module for all botorch posteriors.
"""
from __future__ import annotations
import warnings
from ... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Tuple, Union
import torch
from botorch.exceptions.errors import BotorchTensorDimensionError
from botorch.poste... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Posterior module to be used with PyTorch distributions.
"""
from __future__ import annotations
from typing imp... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Callable, Optional, Tuple
import torch
from botorch.posterior... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Candidate generation utilities.
"""
from __future__ import annotations
import time
import warnings
from functo... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from botorch.generation.gen import (
gen_candidates_scipy,
gen_candidates_torch,
get_best_candidates,
)
... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from dataclasses import dataclass
from typing import Callable, Dict, List, Option... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Sampling-based generation strategies.
A SamplingStrategy returns samples from the input points (i.e. Tensors in... |
#!/usr/bin/env python
import confu
parser = confu.standard_parser("cpuinfo configuration script")
parser.add_argument("--log", dest="log_level",
choices=("none", "fatal", "error", "warning", "info", "debug"), default="error")
parser.add_argument("--mock", dest="mock", action="store_true")
def main(args):
op... |
#!/usr/bin/env python
import os
import sys
import argparse
import shutil
parser = argparse.ArgumentParser(description='Android system files extractor')
parser.add_argument("-p", "--prefix", metavar="NAME", required=True,
help="Prefix for stored files, e.g. galaxy-s7-us")
SYSTEM_FILES = [
"/... |
#!/usr/bin/env python
from __future__ import print_function
import argparse
import sys
import re
parser = argparse.ArgumentParser(description='x86 CPUID dump parser')
parser.add_argument("input", metavar="INPUT", nargs=1,
help="Path to CPUID dump log")
def main(args):
options = parser.pars... |
#!/usr/bin/env python
import os
import sys
import string
import argparse
import subprocess
import tempfile
root_dir = os.path.abspath(os.path.dirname(__file__))
parser = argparse.ArgumentParser(description='Android system files extractor')
parser.add_argument("-p", "--prefix", metavar="NAME", required=True,
... |
#!/usr/bin/env python
import confu
parser = confu.standard_parser("clog configuration script")
def main(args):
options = parser.parse_args(args)
build = confu.Build.from_options(options)
build.export_cpath("include", ["clog.h"])
with build.options(source_dir="src", extra_include_dirs="src"):
... |
from typing import Dict, List, Optional, Tuple
import json
import math
from fairseq.data import Dictionary
import torch
import torchaudio
from torchaudio.pipelines import EMFORMER_RNNT_BASE_LIBRISPEECH
from torchaudio.models import Hypothesis
def get_hypo_tokens(hypo: Hypothesis) -> List[int]:
return hypo[0]
d... |
import torch
import torchaudio
from torch.utils.mobile_optimizer import optimize_for_mobile
def get_demo_wrapper():
wrapper = torch.jit.load("scripted_wrapper_tuple.pt")
return wrapper
wrapper = get_demo_wrapper()
scripted_model = torch.jit.script(wrapper)
optimized_model = optimize_for_mobile(scripted_model)... |
import pyaudio
import queue
import numpy as np
import torch
import torchaudio
def get_demo_wrapper():
wrapper = torch.jit.load("scripted_wrapper_tuple.pt")
return wrapper
wrapper = get_demo_wrapper()
################################################################
data_queue = queue.Queue()
def callba... |
import torch
import torch.utils.cpp_extension
print(torch.version.__version__)
op_source = """
#include <opencv2/opencv.hpp>
#include <torch/script.h>
torch::Tensor warp_perspective(torch::Tensor image, torch::Tensor warp) {
cv::Mat image_mat(/*rows=*/image.size(0),
/*cols=*/image.size(1),
... |
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile
model = torch.hub.load('pytorch/vision:v0.11.0', 'deeplabv3_resnet50', pretrained=True)
model.eval()
scripted_module = torch.jit.script(model)
optimized_scripted_module = optimize_for_mobile(scripted_module)
# Export full jit version model (no... |
import torch
from torch import Tensor
from torch.utils.mobile_optimizer import optimize_for_mobile
import torchaudio
from torchaudio.models.wav2vec2.utils.import_huggingface import import_huggingface_model
from transformers import Wav2Vec2ForCTC
# Wav2vec2 model emits sequences of probability (logits) distributions ov... |
import torch
from pytorchvideo.accelerator.deployment.mobile_cpu.utils.model_conversion import (
convert_to_deployable_form,
)
from pytorchvideo.models.accelerator.mobile_cpu.efficient_x3d import EfficientX3d
from torch.hub import load_state_dict_from_url
from torch.utils.mobile_optimizer import (
optimize_for_... |
import torch
from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering
from torch.utils.mobile_optimizer import optimize_for_mobile
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad')
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncas... |
# based on https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional a... |
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile
model = torch.hub.load('facebookresearch/deit:main', 'deit_base_patch16_224', pretrained=True)
quantized_model = torch.quantization.quantize_dynamic(model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
ts_model = torch.jit.script(quantized_... |
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PreNorm(nn.Module):
def __init__... |
import torch
import torchvision
import time
from vit_pytorch import *
from torch.utils.mobile_optimizer import optimize_for_mobile
torch.manual_seed(42)
DOWNLOAD_PATH = 'data/mnist'
BATCH_SIZE_TRAIN = 100
BATCH_SIZE_TEST = 1000
# 0.1307 and 0.3081 are the mean and std computed on the MNIST training set
transform_mn... |
#!/usr/bin/env python3
import contextlib
import copy
import os
import unittest
from PIL import Image
import torch
from d2go.export.api import convert_and_export_predictor
from d2go.export.d2_meta_arch import patch_d2_meta_arch
from d2go.runner import create_runner, GeneralizedRCNNRunner
from d2go.model_zoo import mod... |
import torch
import torchvision
from torch.utils.mobile_optimizer import optimize_for_mobile
model = torchvision.models.mobilenet_v3_small(pretrained=True)
model.eval()
example = torch.rand(1, 3, 224, 224)
traced_script_module = torch.jit.trace(model, example)
optimized_traced_model = optimize_for_mobile(traced_script... |
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