repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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pytorch-kaldi-gan | pytorch-kaldi-gan-master/parallel_dataset.py | import configparser
import sox
import logging
import torch
import torchaudio
import numpy as np
import matplotlib.pyplot as plt
import os
import random
import shutil
import subprocess
import shlex
import sys
import math
def validate_dir(dir_list):
''' Remove hidden files from directory list '''
for dir in dir... | 17,550 | 34.031936 | 143 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/plot_acc_and_loss.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import sys
import configparser
import os
from utils import create_curves
# Checking arguments
i... | 1,203 | 30.684211 | 120 | py |
pytorch-kaldi-gan | pytorch-kaldi-gan-master/save_raw_fea.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
#
# Description: This script generates kaldi ark files containing raw features.
# The file list must be a file containing "snt_id file.wav".
# Note that onl... | 4,010 | 31.877049 | 119 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/setup.py | from setuptools import setup, find_packages
setup(
name='pytorchts',
version='0.1.0',
description="PyTorch Probabilistic Time Series Modeling framework",
long_description=open("README.md").read(),
long_description_content_type="text/markdown",
url='https://github.com/kashif/pytorch-ts',
li... | 871 | 21.947368 | 74 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/examples/m5/training.py | import time
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
from pprint import pprint
from pathlib import Path
import logging
import os
from pts.model import Predictor
from pts.model.deepar import DeepAREstimator
from pts.modules import TweedieOutput
from pts.trainer import Trainer
from pts.... | 3,592 | 27.744 | 110 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/examples/m5/make_predictions.py |
import numpy as np
import torch
import os
from tqdm import tqdm
from pathlib import Path
import logging
from pts.core.logging import get_log_path
from pts.model import Predictor
from load_dataset import make_m5_dataset
from pts.evaluation.backtest import make_evaluation_predictions, make_validation_data
#test_sta... | 3,587 | 30.2 | 123 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/trainer.py | import time
from typing import List, Optional, Tuple
import logging
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import CosineAnnealingLR
from warmup_scheduler import GradualWarmupScheduler
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tq... | 6,578 | 35.55 | 137 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/scaler.py | from abc import ABC, abstractmethod
from typing import Tuple
import torch
import torch.nn as nn
class Scaler(ABC, nn.Module):
def __init__(self, keepdim: bool = False):
super().__init__()
self.keepdim = keepdim
@abstractmethod
def compute_scale(
self, data: torch.Tensor, observed... | 3,528 | 31.376147 | 91 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/lambda_layer.py | import torch.nn as nn
class LambdaLayer(nn.Module):
def __init__(self, function):
super().__init__()
self._func = function
def forward(self, x, *args):
return self._func(x, *args)
| 215 | 18.636364 | 35 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution_output.py | from abc import ABC, abstractmethod
from typing import Callable, Dict, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import (
Distribution,
Beta,
NegativeBinomial,
StudentT,
Normal,
Independent,
LowRankMultivar... | 8,743 | 25.904615 | 95 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/flows.py | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def create_masks(
input_size, hidden_size, n_hidden, input_order="sequential", input_degrees=None
):
# MADE paper sec 4:
# degrees of connections between layers -- ensure at m... | 13,996 | 32.646635 | 177 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/feature.py | from typing import List, Optional
import torch
import torch.nn as nn
class FeatureEmbedder(nn.Module):
def __init__(self, cardinalities: List[int], embedding_dims: List[int],) -> None:
super().__init__()
assert len(cardinalities) == len(embedding_dims), 'the length of two variables should match'... | 2,938 | 32.397727 | 100 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/activation.py | from typing import Optional, Union, List, Tuple
# Third-party imports
import torch.nn as nn
from torch import Tensor
class Activation(nn.Module):
"""
Activation fuction
Parameters
----------
activation
Activation function to use.
"""
def __init__(
self,
activati... | 979 | 19.416667 | 55 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/cnn.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,557 | 26.37931 | 83 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/mlp.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,023 | 26.726027 | 94 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/encoder.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 12,696 | 26.188437 | 118 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/enc2dec.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,929 | 25.636364 | 77 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/decoder.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,368 | 25.979899 | 100 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/quantile_output.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,592 | 25.258216 | 79 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution/constant.py | import torch
from torch.distributions.distribution import Distribution
class ConstantDistribution(Distribution):
r"""
Creates a constant distribution, i.e. Var(x) = 0
Args:
loss_type: L1 or L2
mu (Tensor): mean
"""
def __init__(self, loss_type, mu, validate_args=None):
... | 1,045 | 25.15 | 92 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution/tweedie.py | import torch
import numpy as np
from torch.distributions.distribution import Distribution
def est_lambda(mu, p):
return mu ** (2 - p) / (2 - p)
def est_alpha(p):
return (2 - p) / (p - 1)
def est_beta(mu, p):
return mu ** (1 - p) / (p - 1)
class Tweedie(Distribution):
r"""
Creates a Tweedie ... | 1,660 | 24.166667 | 79 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/serde.py | import itertools
import json
import math
import textwrap
from functools import singledispatch
from pydoc import locate
from typing import Any, Optional, cast, NamedTuple
import numpy as np
import torch
from pts.core import fqname_for
bad_type_msg = textwrap.dedent(
"""
Cannot serialize type {}. See the docum... | 10,036 | 26.49863 | 78 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/component.py | import functools
import inspect
from collections import OrderedDict
from typing import Any
import torch
from pydantic import BaseConfig, BaseModel, create_model
from pts.core.serde import dump_code
class BaseValidatedInitializerModel(BaseModel):
"""
Base Pydantic model for components with :func:`validated` ... | 5,487 | 32.668712 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/transformed_iterable_dataset.py | import itertools
from typing import Dict, Iterable, Iterator, Optional
import numpy as np
import torch
from pts.transform.transform import Transformation
from .common import DataEntry, Dataset
class TransformedIterableDataset(torch.utils.data.IterableDataset):
def __init__(
self, dataset: Dataset, is_tr... | 2,707 | 30.488372 | 99 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/loader.py | import itertools
from collections import defaultdict
from typing import Any, Dict, Iterable, Iterator, List, Optional # noqa: F401
import numpy as np
# Third-party imports
import torch
from pts.transform.transform import Transformation
# First-party imports
from .common import DataEntry, Dataset
DataBatch = Dict[st... | 7,684 | 30.756198 | 87 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/list_dataset.py | import random
import torch
from typing import Iterable
from .common import DataEntry, Dataset, SourceContext
from .process import ProcessDataEntry
class ListDataset(Dataset):
def __init__(
self,
data_iter: Iterable[DataEntry],
freq: str,
one_dim_target: bool = True,
shuffl... | 945 | 26.028571 | 78 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_util.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,063 | 25.126582 | 75 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/convert.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 22,563 | 30.602241 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/predictor.py | import json
from abc import ABC, abstractmethod
from pathlib import Path
from pydoc import locate
from typing import Iterator, Callable, Optional
import numpy as np
import torch
import torch.nn as nn
import pts
from pts.core.serde import dump_json, fqname_for, load_json
from pts.dataset import Dataset, DataEntry, Inf... | 6,040 | 33.129944 | 81 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/forecast_generator.py | from abc import ABC, abstractmethod
from typing import Any, Callable, Iterator, List, Optional
import numpy as np
import torch
import torch.nn as nn
from pts.core.component import validated
from pts.dataset import InferenceDataLoader, DataEntry, FieldName
from pts.modules import DistributionOutput
from .forecast impo... | 6,330 | 33.785714 | 125 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/utils.py | import inspect
from typing import Optional
import torch
import torch.nn as nn
def get_module_forward_input_names(module: nn.Module):
params = inspect.signature(module.forward).parameters
return list(params)
def copy_parameters(net_source: nn.Module, net_dest: nn.Module) -> None:
net_dest.load_state_dic... | 1,032 | 26.918919 | 74 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/forecast.py | from abc import ABC, abstractmethod
from enum import Enum
from typing import Dict, List, Optional, Set, Union, Callable
import numpy as np
import pandas as pd
import torch
from pydantic import BaseModel, Field
from torch.distributions import Distribution
from .quantile import Quantile
class OutputType(str, Enum):
... | 16,436 | 29.495362 | 99 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/estimator.py | from abc import ABC, abstractmethod
from typing import NamedTuple, Optional
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from pts.core.component import validated
from pts import Trainer
from pts.dataset import Dataset, TransformedIterableDataset, TransformedListDataset... | 4,526 | 26.436364 | 83 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/deepar/deepar_network.py | from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.distributions import Distribution
from pts.core.component import validated
from pts.model import weighted_average
from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
def pr... | 28,509 | 41.936747 | 179 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/deepar/deepar_estimator.py | from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
from pts.core.component import validated
from pts import Trainer
from pts.dataset import FieldName
from pts.feature import (
TimeFeature,
get_lags_for_frequency,
time_features_from_frequency_str,
)
from pts.model impor... | 9,762 | 38.686992 | 144 | py |
NM-sparsity | NM-sparsity-main/devkit/core/dist_utils.py | import os
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
__all__ = [
'init_dist', 'broadcast_params','average_gradients']
def init_dist(backend='nccl',
master_ip='127.0.0.1',
port=29500):
if mp.get_start_method(allow_none=True) is None:
mp.... | 945 | 28.5625 | 60 | py |
NM-sparsity | NM-sparsity-main/devkit/core/utils.py | import torch
import os
import shutil
def save_checkpoint(model_dir, state, is_best):
epoch = state['epoch']
path = os.path.join(model_dir, 'model.pth-' + str(epoch))
torch.save(state, path)
checkpoint_file = os.path.join(model_dir, 'checkpoint')
checkpoint = open(checkpoint_file, 'w+')
checkpo... | 2,861 | 40.478261 | 102 | py |
NM-sparsity | NM-sparsity-main/devkit/dataset/imagenet_dataset.py | from torch.utils.data import Dataset
from PIL import Image
import torch
def pil_loader(filename):
with Image.open(filename) as img:
img = img.convert('RGB')
return img
class ImagenetDataset(Dataset):
def __init__(self, root_dir, meta_file, transform=None):
self.root_dir = root_dir
... | 1,758 | 29.327586 | 71 | py |
NM-sparsity | NM-sparsity-main/devkit/sparse_ops/sparse_ops.py | import torch
from torch import autograd, nn
import torch.nn.functional as F
from itertools import repeat
from torch._six import container_abcs
class Sparse(autograd.Function):
"""" Prune the unimprotant weight for the forwards phase but pass the gradient to dense weight using SR-STE in the backwards phase"""
... | 3,245 | 26.982759 | 159 | py |
NM-sparsity | NM-sparsity-main/devkit/sparse_ops/syncbn_layer.py | import torch
from torch.autograd import Function
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.distributed as dist
import torch.nn as nn
class SyncBNFunc(Function):
@staticmethod
def forward(ctx, in_data, scale_data, shift_data, running_mean, running_var, eps... | 3,824 | 37.25 | 159 | py |
NM-sparsity | NM-sparsity-main/classification/train_imagenet.py | from __future__ import division
import argparse
import os
import time
import torch.distributed as dist
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data.distributed import DistributedSampler
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
i... | 10,642 | 33.003195 | 131 | py |
NM-sparsity | NM-sparsity-main/classification/models/resnet.py | import torch.nn as nn
import math
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
#from devkit.ops import SyncBatchNorm2d
import torch
import torch.nn.functional as F
from torch import autograd
from torch.nn.modules.utils import _pair as pair
from torch.nn import init
from... | 5,446 | 28.603261 | 95 | py |
NM-sparsity | NM-sparsity-main/RAFT/evaluate.py | import sys
sys.path.append('core')
from PIL import Image
import argparse
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import datasets
from utils import flow_viz
from utils import frame_utils
from raft import RAFT
from utils.utils import InputPa... | 6,618 | 32.429293 | 112 | py |
NM-sparsity | NM-sparsity-main/RAFT/demo.py | import sys
sys.path.append('core')
import argparse
import os
import cv2
import glob
import numpy as np
import torch
from PIL import Image
from raft import RAFT
from utils import flow_viz
from utils.utils import InputPadder
DEVICE = 'cuda'
def load_image(imfile):
img = np.array(Image.open(imfile)).astype(np.ui... | 2,073 | 26.289474 | 112 | py |
NM-sparsity | NM-sparsity-main/RAFT/train.py | from __future__ import print_function, division
import sys
sys.path.append('core')
import argparse
import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader... | 8,244 | 31.333333 | 103 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/lr_scheduler.py | import types
import math
from torch._six import inf
from functools import wraps
import warnings
import weakref
from collections import Counter
from bisect import bisect_right
#from torch.optim.optimizer import Optimizer
class _LRScheduler(object):
def __init__(self, optimizer, last_epoch=-1, verbose=False):
... | 19,353 | 43.800926 | 128 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_update.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
from devkit.sparse_ops import SparseConv
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__i... | 5,385 | 36.402778 | 88 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_raft.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sparse_update import BasicUpdateBlock, SmallUpdateBlock
from sparse_extractor import BasicEncoder, SmallEncoder
from corr import CorrBlock, AlternateCorrBlock
from utils.utils import bilinear_sampler, coords_grid, upflow8
try:
... | 4,950 | 33.144828 | 102 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/corr.py | import torch
import torch.nn.functional as F
from utils.utils import bilinear_sampler, coords_grid
try:
import alt_cuda_corr
except:
# alt_cuda_corr is not compiled
pass
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius... | 3,085 | 32.543478 | 74 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/update.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
... | 5,227 | 36.342857 | 87 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
s... | 8,847 | 32.014925 | 93 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/datasets.py | # Data loading based on https://github.com/NVIDIA/flownet2-pytorch
import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import os
import math
import random
from glob import glob
import os.path as osp
from utils import frame_utils
from utils.augmentor import FlowAugmentor, S... | 9,242 | 38.165254 | 111 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/raft.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from update import BasicUpdateBlock, SmallUpdateBlock
from extractor import BasicEncoder, SmallEncoder
from corr import CorrBlock, AlternateCorrBlock
from utils.utils import bilinear_sampler, coords_grid, upflow8
try:
autocast =... | 4,924 | 32.965517 | 102 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
from devkit.sparse_ops import SparseConv
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
supe... | 8,997 | 31.959707 | 94 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/utils.py | import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... | 2,489 | 29 | 93 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/augmentor.py | import numpy as np
import random
import math
from PIL import Image
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import torch
from torchvision.transforms import ColorJitter
import torch.nn.functional as F
class FlowAugmentor:
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=Tru... | 9,108 | 35.878543 | 97 | py |
NM-sparsity | NM-sparsity-main/RAFT/alt_cuda_corr/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='correlation',
ext_modules=[
CUDAExtension('alt_cuda_corr',
sources=['correlation.cpp', 'correlation_kernel.cu'],
extra_compile_args={'cxx': [], 'nvcc': ['-O3']}),
]... | 381 | 22.875 | 67 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.4/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.4/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.4/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.3/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.3/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.3/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.3/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.3/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.3/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.2/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.2/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.2/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.1/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.1/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.1/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.0/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.0/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.0/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.6/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.6/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.6/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.6/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.6/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.6/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.5/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.5/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.5/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.7/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.7/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.7/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.1/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.1/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.1/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.6/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.6/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_0.6/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.7/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.7/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_2.7/grad_hacked.py | from tequila.circuit.compiler import CircuitCompiler
from tequila.objective.objective import Objective, ExpectationValueImpl, Variable, \
assign_variable, identity, FixedVariable
from tequila import TequilaException
from tequila.objective import QTensor
from tequila.simulators.simulator_api import compile
import ty... | 9,886 | 38.548 | 132 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/data/h2/h2_bl_1.2/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
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