python_code stringlengths 0 229k |
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import os
import threading
from datetime import datetime
import torch
import torch.distributed.rpc as rpc
import torch.multiprocessing as mp
import torch.nn as nn
from torch import optim
import torchvision
batch_size = 20
image_w = 64
image_h = 64
num_classes = 30
batch_update_size = 5
num_batches = 6
def timed_l... |
import argparse
import os
from threading import Lock
import torch
import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.distributed.optim import DistributedOptimi... |
import argparse
import gymnasium as gym
import numpy as np
import os
from itertools import count
import torch
import torch.distributed.rpc as rpc
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributed.rpc import RRef, rpc_sync, rpc_as... |
import os
import torch
import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
import torch.multiprocessing as mp
import torch.optim as optim
from torch.distributed.optim import DistributedOptimizer
import rnn
def _run_trainer():
r"""
The trainer creates a distributed RNNModel... |
import torch
import torch.nn as nn
import torch.distributed.rpc as rpc
from torch.distributed.rpc import RRef
def _call_method(method, rref, *args, **kwargs):
r"""
a helper function to call a method on the given RRef
"""
return method(rref.local_value(), *args, **kwargs)
def _remote_method(method, r... |
import torch
from torch.fx import symbolic_trace, replace_pattern
'''
How to Use the FX Subgraph Rewriter
For easy subgraph rewriting, FX exposes the utility function:
replace_pattern(gm : GraphModule,
pattern : Callable,
replacement : Callable)
-> Non... |
import torch
from torch.fx import symbolic_trace, Tracer, Graph, GraphModule, Node
from typing import Any, Callable, Dict, Optional, Tuple, Union
"""
How to Create and Use Custom Tracers
`Tracer`--the class that implements the symbolic tracing functionality
of `torch.fx.symbolic_trace`--can be subclassed to overrid... |
import torch
from torch.fx import symbolic_trace
import operator
"""
How to Replace One Op With Another
1. Iterate through all Nodes in your GraphModule's Graph.
2. Determine if the current Node should be replaced. (Suggested: match
on the Node's ``target`` attribute).
3. Create a replacement Node and add it to the G... |
from enum import Enum, auto
import torch
from torch.fx import GraphModule, Node, Proxy, symbolic_trace
'''
Wrap Graph Output Dynamically
The following code demonstrates how change an existing Graph based on
parameters specified at runtime. We'll let the user specify an
activation function from a predefined Enum lis... |
"""
This file demonstrates using a custom FX Tracer to override
the behavior of `torch.autograd.profiler.record_function` and
make profiler ranges appear in FX-traced code. This is done
with Python dynamic patching magic, allowing us to explicitly
emit calls to
`torch.ops.profiler._record_function_enter/_record_functio... |
"""
Recording Module Hierarchy With a Custom Tracer
In this example, we are going to define a custom `fx.Tracer` instance that--
for each recorded operation--also notes down the qualified name of the module
from which that operation originated. The _qualified name_ is the path to the
Module from the root module. More ... |
import torch
import torch.fx
"""
In this example we are going do define a library of
"composite" operations. Composite operations are those
that are defined as callable functions that are composed
of several other operations in their implementation.
Composite operations allow you to choose at what level
of abstraction... |
import torch
import torch.fx as fx
# An inverse mapping is one that takes a function f(x) and returns a function g
# such that f(g(x)) == x. For example,since log(exp(x)) == x, exp and log are
# inverses.
invert_mapping = {}
def add_inverse(a, b):
invert_mapping[a] = b
invert_mapping[b] = a
inverses = [
(... |
import torch
from torch.fx import Proxy, Graph, GraphModule
'''
How to Create a Graph Using Proxy Objects Instead of Tracing
It's possible to directly create a Proxy object around a raw Node. This
can be used to create a Graph independently of symbolic tracing.
The following code demonstrates how to use Proxy with ... |
import torch
from torch.fx import Proxy, symbolic_trace
from torch.fx.node import map_arg
'''
How to Inline a Function Into an Existing Graph
One reason you might want to inline a function is to get around FX's
default tracing behavior. For example, unless you've defined a custom
Tracer, the out-of-the-box implement... |
import torch
import torch.fx
import operator
# Does this path not exist? Check that you've done the following:
# 1) Read README.md and follow the instructions to build libinterpreter.
# 2) If this file still does not exist after you've followed those instructions,
# check if it is under a different extension (e.g. ... |
import torch
import torch.nn as nn
import torch.nn.init as init
class Net(nn.Module):
def __init__(self, upscale_factor):
super(Net, self).__init__()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
... |
import torch.utils.data as data
from os import listdir
from os.path import join
from PIL import Image
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def load_img(filepath):
img = Image.open(filepath).convert('YCbCr')
y, _, _ = img.split(... |
from __future__ import print_function
import argparse
from math import log10
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from model import Net
from data import get_training_set, get_test_set
# Training settings
parser = argparse.ArgumentParser(description='Py... |
from os.path import exists, join, basename
from os import makedirs, remove
from six.moves import urllib
import tarfile
from torchvision.transforms import Compose, CenterCrop, ToTensor, Resize
from dataset import DatasetFromFolder
def download_bsd300(dest="dataset"):
output_image_dir = join(dest, "BSDS300/images"... |
from __future__ import print_function
import argparse
import torch
from PIL import Image
from torchvision.transforms import ToTensor
import numpy as np
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--input_image', type=str, required=True, help='inpu... |
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchv... |
import os
from argparse import ArgumentParser
def makedirs(name):
"""helper function for python 2 and 3 to call os.makedirs()
avoiding an error if the directory to be created already exists"""
import os, errno
try:
os.makedirs(name)
except OSError as ex:
if ex.errno == errno.EE... |
import torch
import torch.nn as nn
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = super(Bottle, self).forward(input.view(size[0]*size[1], -1))
return out.view(size[0]... |
import os
import time
import glob
import torch
import torch.optim as O
import torch.nn as nn
from torchtext.legacy import data
from torchtext.legacy import datasets
from model import SNLIClassifier
from util import get_args, makedirs
args = get_args()
if torch.cuda.is_available():
torch.cuda.set_device(args.gp... |
import argparse
import gym
import numpy as np
from itertools import count
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
# Cart Pole
parser = argparse.ArgumentParser(description='PyTorch act... |
import argparse
import gym
import numpy as np
from itertools import count
from collections import deque
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')
p... |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a... |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class Sequence(nn.Module):
def __init__(self):
super(Sequence, self).__init__()
self.lstm1 ... |
import numpy as np
import torch
np.random.seed(2)
T = 20
L = 1000
N = 100
x = np.empty((N, L), 'int64')
x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)
data = np.sin(x / 1.0 / T).astype('float64')
torch.save(data, open('traindata.pt', 'wb'))
|
import os
import zipfile
# PyTorch 1.1 moves _download_url_to_file
# from torch.utils.model_zoo to torch.hub
# PyTorch 1.0 exists another _download_url_to_file
# 2 argument
# TODO: If you remove support PyTorch 1.0 or older,
# You should remove torch.utils.model_zoo
# Ref. PyTorch #18758
# http... |
import torch
class TransformerNet(torch.nn.Module):
def __init__(self):
super(TransformerNet, self).__init__()
# Initial convolution layers
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, ... |
from collections import namedtuple
import torch
from torchvision import models
class Vgg16(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential()
... |
import argparse
import os
import sys
import time
import re
import numpy as np
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
import torch.onnx
import utils
from transformer_net import TransformerNet
from vgg import ... |
import torch
from PIL import Image
def load_image(filename, size=None, scale=None):
img = Image.open(filename).convert('RGB')
if size is not None:
img = img.resize((size, size), Image.ANTIALIAS)
elif scale is not None:
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), ... |
from __future__ import division
from __future__ import print_function
import argparse
import gzip
import os
import sys
import urllib
try:
from urllib.error import URLError
from urllib.request import urlretrieve
except ImportError:
from urllib2 import URLError
from urllib import urlretrieve
RESOURCES ... |
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import matplotlib.pyplot as plt
import torch
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--sample-file", required=True)
parser.add_argument("-o", "--out-file", default="out.png")
parser.add_argument("-d",... |
"""
This python script converts the network into Script Module
"""
import torch
from torchvision import models
# Download and load the pre-trained model
model = models.resnet18(pretrained=True)
# Set upgrading the gradients to False
for param in model.parameters():
param.requires_grad = False
# Save the model excep... |
import argparse
import os
import random
import shutil
import time
import warnings
from enum import Enum
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import ... |
# This code is based on the implementation of Mohammad Pezeshki available at
# https://github.com/mohammadpz/pytorch_forward_forward and licensed under the MIT License.
# Modifications/Improvements to the original code have been made by Vivek V Patel.
import argparse
import torch
import torch.nn as nn
from torchvision... |
from __future__ import print_function
import argparse, random, copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision import transforms as T
from tor... |
import os
import torch
import torch.optim as optim
import torch.nn.functional as F
def train(rank, args, model, device, dataset, dataloader_kwargs):
torch.manual_seed(args.seed + rank)
train_loader = torch.utils.data.DataLoader(dataset, **dataloader_kwargs)
optimizer = optim.SGD(model.parameters(), lr=a... |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from torch.utils.data.sampler import Sampler
from torchvision import datasets, transforms
from train import train, test
# Training settings
parser = argparse.Argu... |
#!/usr/bin/env python
from __future__ import print_function
from itertools import count
import torch
import torch.nn.functional as F
POLY_DEGREE = 4
W_target = torch.randn(POLY_DEGREE, 1) * 5
b_target = torch.randn(1) * 5
def make_features(x):
"""Builds features i.e. a matrix with columns [x, x^2, x^3, x^4]."""... |
import os
import time
import requests
import tarfile
import numpy as np
import argparse
import torch
from torch import nn
import torch.nn.functional as F
from torch.optim import Adam
class GraphConv(nn.Module):
"""
Graph Convolutional Layer described in "Semi-Supervised Classification with Graph Convolut... |
from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
parser = argparse.ArgumentParser(description='VAE MNIST Example')
parser.add... |
#!/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 os
import sys
from setuptools import find_packages, setup
# Minimum required python version
REQUIRED_MAJOR =... |
#!/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 logging
import torch
from botorch import settings
from botorch.logging import LOG_LEVEL_DEFAULT, logger, sha... |
#!/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 itertools
import warnings
import torch
from botorch.cross_validation import batch_cross_validation, gen_loo_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.
import math
from contextlib import ExitStack, nullcontext
from itertools import filterfalse, product
from typing 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.
|
#!/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 math
import warnings
import torch
from botorch.acquisition import ExpectedImprovement, qExpectedImprovement
f... |
#!/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"""
Monolithic CUDA tests. This implements a single monolithic test for all
CUDA functionality. The main reason for ... |
#!/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 warnings
import gpytorch.settings as gp_settings
import linear_operator.settings as linop_settings
from 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.
from botorch.test_functions.multi_fidelity import (
AugmentedBranin,
AugmentedHartmann,
AugmentedRosenbro... |
#!/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.
|
# 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 botorch.test_functions.multi_objective_multi_fidelity import (
MOMFBraninCurrin,
MOMFPark,
)
from botorch.utils.te... |
#!/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 botorch.exceptions.errors import InputDataError
from botorch.test_functions.synthetic 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.
import math
from typing import List
import torch
from botorch.exceptions.errors import UnsupportedError
from botorc... |
#!/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 botorch.test_functions.sensitivity_analysis import Gsobol, Ishigami, Morris
from botorch.utils.test... |
#!/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 botorch.test_functions.base import BaseTestProblem, ConstrainedBaseTestProblem
from botorch.utils.t... |
#!/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 List
import torch
from botorch.acquisition import LinearMCObjective, ScalarizedPosteriorTransform... |
#!/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 itertools import product
import torch
from botorch.acquisition.predictive_entropy_search import qPredictiveEntr... |
#!/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 itertools
from unittest import mock
import torch
from botorch.acquisition.objective import GenericMCObjective... |
#!/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 botorch.acquisition import (
ExpectedImprovement,
qExpectedImprovement,
qMultiStepLook... |
#!/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 warnings
from copy import deepcopy
from functools import partial
from itertools import product
from math 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 typing import Callable, Optional
from unittest import mock
import torch
from botorch.acquisition.cost_aware 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 itertools import product
import torch
from botorch.acquisition.analytic import ExpectedImprovement
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.
import warnings
from copy import deepcopy
from itertools import product
from math import pi
from unittest import mock... |
#!/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 warnings
import torch
from botorch import settings
from botorch.acquisition.decoupled import DecoupledAcquisi... |
#!/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 warnings
import torch
from botorch import settings
from botorch.acquisition.cost_aware import (
CostAwar... |
#!/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 typing import Any, Callable, Sequence, Type
from unittest 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.
import math
from unittest import mock
import torch
from botorch.acquisition import logei, monte_carlo
from botorch.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 typing import Optional
import torch
from botorch.acquisition.objective import LinearMCObjective
from botorch.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.
|
#!/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 botorch.acquisition.acquisition import (
AcquisitionFunction,
MCSamplerMixin,
MultiMode... |
#!/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 math
import torch
from botorch.acquisition import qAnalyticProbabilityOfImprovement
from botorch.acquisition.... |
#!/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 itertools
import warnings
from typing import Optional
import torch
from botorch import settings
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.
import torch
from botorch.acquisition.analytic import ExpectedImprovement
from botorch.acquisition.monte_carlo 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 contextlib import ExitStack
from unittest import mock
import torch
from botorch.acquisition.analytic import Pos... |
#!/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 botorch.acquisition.analytic import ExpectedImprovement
from botorch.acquisition.fixed_feature 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.
import warnings
from unittest import mock
import torch
from botorch import settings
from botorch.acquisition.cached_... |
#!/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 botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.preference 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 itertools import product
import torch
from botorch.acquisition.joint_entropy_search import qJointEntropySearch
... |
#!/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 unittest import mock
import torch
from botorch.acquisition.active_learning import (
PairwiseMCPosteriorVari... |
#!/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 itertools import product
import torch
from botorch.acquisition.multi_objective.predictive_entropy_search 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.
import warnings
from itertools import product
from unittest import mock
import torch
from botorch.acquisition.multi_... |
#!/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 warnings
from unittest import mock
import torch
from botorch import settings
from botorch.acquisition.multi_o... |
#!/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 warnings
from copy import deepcopy
from itertools import product
from math import pi
from unittest import mock... |
#!/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 itertools import product
from unittest import mock
import torch
from botorch.acquisition.max_value_entropy_sear... |
#!/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 warnings
from typing import Optional
import torch
from botorch import settings
from botorch.acquisition.multi... |
#!/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.
|
#!/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 botorch.acquisition.multi_objective.analytic import (
ExpectedHypervolumeImprovement,
Mult... |
#!/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 itertools
import warnings
import torch
from botorch.acquisition.multi_objective.multi_output_risk_measures im... |
#!/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 itertools import product
import torch
from botorch.acquisition.multi_objective.joint_entropy_search 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.
import torch
from botorch.sampling.deterministic import DeterministicSampler
from botorch.utils.testing import Botorc... |
#!/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 unittest import mock
import torch
from botorch.exceptions.errors import UnsupportedError
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.
import torch
from botorch.posteriors.deterministic import DeterministicPosterior
from botorch.posteriors.gpytorch 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 unittest import mock
import torch
from botorch.posteriors.torch import TorchPosterior
from botorch.sampling.sto... |
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