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
value |
|---|---|---|---|---|---|---|
pytorch | pytorch-main/torchgen/api/types/types_base.py | """
Where should I add a new type? `types_base.py` vs `types.py`
This file defines data model classes for torchgen typing system, as well as some base types such as int32_t.
`types.py` defines ATen Tensor type and some c10 types, along with signatures that use these types.
The difference between these two files, is ... | 8,872 | 32.108209 | 114 | py |
pytorch | pytorch-main/torchgen/shape_functions/gen_jit_shape_functions.py | #!/usr/bin/env python3
import importlib.util
import os
import sys
from itertools import chain
from pathlib import Path
# Manually importing the shape function module based on current directory
# instead of torch imports to avoid needing to recompile Pytorch before
# running the script
file_path = Path.cwd() / "torch... | 5,141 | 27.098361 | 86 | py |
pytorch | pytorch-main/torchgen/operator_versions/gen_mobile_upgraders_constant.py | MOBILE_UPGRADERS_HEADER_DESCRIPTION = """/**
* @generated
* This is an auto-generated file. Please do not modify it by hand.
* To re-generate, please run:
* cd ~/pytorch && python torchgen/operator_versions/gen_mobile_upgraders.py
*/
"""
| 243 | 29.5 | 76 | py |
pytorch | pytorch-main/torchgen/operator_versions/gen_mobile_upgraders.py | #!/usr/bin/env python3
import os
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List
import torch
from torch.jit.generate_bytecode import generate_upgraders_bytecode
from torchgen.code_template import CodeTemplate
from torchgen.operator_versions.gen_mobile_upgraders_constant import (
... | 12,671 | 31.244275 | 114 | py |
pytorch | pytorch-main/torchgen/decompositions/gen_jit_decompositions.py | #!/usr/bin/env python3
import os
from pathlib import Path
from torch.jit._decompositions import decomposition_table
# from torchgen.code_template import CodeTemplate
DECOMP_HEADER = r"""
/**
* @generated
* This is an auto-generated file. Please do not modify it by hand.
* To re-generate, please run:
* cd ~/pytor... | 2,375 | 24.010526 | 82 | py |
pytorch | pytorch-main/torchgen/dest/lazy_ir.py | import itertools
from abc import ABC
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torchgen.api.dispatcher as dispatcher
from torchgen.api.lazy import (
getValueT,
isValueType,
LazyArgument,
LazyIrProperties,
LazyIrSchema,
tensorListValueT,
... | 29,273 | 40.28914 | 129 | py |
pytorch | pytorch-main/torchgen/dest/register_dispatch_key.py | import itertools
import textwrap
from dataclasses import dataclass
from typing import List, Literal, Optional, Tuple, Union
import torchgen.api.cpp as cpp
import torchgen.api.meta as meta
import torchgen.api.structured as structured
from torchgen.api.translate import translate
from torchgen.api.types import (
Base... | 40,487 | 39.814516 | 132 | py |
pytorch | pytorch-main/torchgen/dest/lazy_ts_lowering.py | from torchgen.api.lazy import LazyArgument, LazyIrSchema
from torchgen.api.types import OptionalCType
def ts_lowering_body(schema: LazyIrSchema) -> str:
# for now, we just want one IR class decl and soon after also the method defs
# and we use the functional version not out/inplace.
emplace_arguments = []... | 1,831 | 36.387755 | 124 | py |
pytorch | pytorch-main/torchgen/dest/native_functions.py | from typing import List, Optional, Union
import torchgen.api.meta as meta
import torchgen.api.structured as structured
from torchgen.api.types import kernel_signature
from torchgen.context import with_native_function_and_index
from torchgen.model import BackendIndex, NativeFunction, NativeFunctionsGroup
from torchgen... | 2,328 | 34.830769 | 88 | py |
pytorch | pytorch-main/torchgen/dest/ufunc.py | from dataclasses import dataclass
from typing import Dict, List, Optional, Sequence, Tuple, Union
import torchgen.api.ufunc as ufunc
from torchgen.api.translate import translate
from torchgen.api.types import (
BaseCType,
Binding,
CType,
Expr,
NamedCType,
opmath_t,
scalar_t,
StructuredI... | 17,800 | 31.602564 | 116 | py |
pytorch | pytorch-main/torchgen/selective_build/operator.py | from dataclasses import dataclass
from typing import Dict, Optional, Tuple
# This class holds information about a single operator used to determine
# the outcome of a selective/custom PyTorch build that doesn't include
# registration code for all the supported operators. This is done to
# reduce the size of the gener... | 6,564 | 36.729885 | 98 | py |
pytorch | pytorch-main/torchgen/selective_build/selector.py | from collections import defaultdict
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple
import yaml
from torchgen.model import NativeFunction
from torchgen.selective_build.operator import (
merge_debug_info,
merge_operator_dicts,
Selec... | 12,606 | 35.227011 | 105 | py |
pytorch | pytorch-main/torchgen/static_runtime/gen_static_runtime_ops.py | import argparse
import itertools
import os
from typing import Sequence, TypeVar, Union
from libfb.py.log import set_simple_logging # type: ignore[import]
from torchgen import gen
from torchgen.context import native_function_manager
from torchgen.model import DispatchKey, NativeFunctionsGroup, NativeFunctionsViewGrou... | 7,353 | 31.113537 | 88 | py |
pytorch | pytorch-main/torchgen/static_runtime/config.py | from typing import Dict, Union
from torchgen.model import NativeFunctionsGroup, NativeFunctionsViewGroup
def func_name_base_str(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> str:
if isinstance(g, NativeFunctionsGroup):
return str(g.functional.func.name.name.base)
else:
return s... | 14,493 | 36.25964 | 88 | py |
pytorch | pytorch-main/torchgen/static_runtime/generator.py | import json
import logging
import math
from typing import Dict, List, Optional, Sequence, Tuple, Union
import torchgen.api.cpp as cpp
from torchgen.context import native_function_manager
from torchgen.model import (
Argument,
BackendIndex,
BaseTy,
BaseType,
FunctionSchema,
NativeFunctionsGroup... | 26,374 | 32.092848 | 122 | py |
DiagnoseRE | DiagnoseRE-master/test.py | import torch
import json
import pickle
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.mo... | 2,497 | 32.756757 | 84 | py |
DiagnoseRE | DiagnoseRE-master/train.py | import torch
import json
import pickle
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.mo... | 2,914 | 35.4375 | 84 | py |
DiagnoseRE | DiagnoseRE-master/Q2/victim.py | import sys
import os
import re
import json
import logging
import OpenAttack
import numpy as np
import opennre
import torch
import argparse
from opennre.model import SoftmaxNN
from OpenAttack.utils.dataset import Dataset, DataInstance
from tqdm import tqdm
import logging
import tensorflow as tf
class REClassifier(Open... | 6,497 | 34.124324 | 110 | py |
DiagnoseRE | DiagnoseRE-master/Q2/attack.py | import time
from multiprocessing import Queue, Process, set_start_method
from victim import *
# Enable this to allow tensors being converted to numpy arrays
tf.compat.v1.enable_eager_execution()
try:
set_start_method('spawn')
except Exception:
pass
logging.basicConfig(level=logging.INFO)
# Silent unimportant ... | 5,707 | 39.771429 | 113 | py |
DiagnoseRE | DiagnoseRE-master/Q2/filter_correct.py | from victim import *
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.modeling_utils',
'transformers.tokenization_utils_base']:
logging.getLogger(logger_name).setLevel(logging.WARN... | 1,963 | 40.787234 | 84 | py |
DiagnoseRE | DiagnoseRE-master/Q1/random_perturb.py | import os
import argparse
import json
import nltk
from collections import deque
from tqdm import tqdm
import random
import logging
import checklist
import torch
import spacy
from checklist.perturb import Perturb
# from checklist.editor import Editor
spacy_model_path = 'en_core_web_sm-2.3.1/en_core_web_sm/en_core_web_s... | 7,255 | 39.764045 | 102 | py |
DiagnoseRE | DiagnoseRE-master/Q3/integrated_gradient.py | import numpy as np
import torch
from torch import nn
import json
import opennre
from opennre.model import SoftmaxNN
from tqdm import tqdm
import sys
import os
import argparse
import logging
device = torch.device('cuda:0')
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['tr... | 4,524 | 34.077519 | 84 | py |
DiagnoseRE | DiagnoseRE-master/Q3/train_cf.py | import torch
import json
import pickle
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.mo... | 4,391 | 33.857143 | 84 | py |
DiagnoseRE | DiagnoseRE-master/Q3/counterfactual.py | import numpy as np
import json
from tqdm import tqdm
import sys
import torch
import os
import argparse
import logging
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.modeling_utils',
... | 4,346 | 41.203883 | 106 | py |
DiagnoseRE | DiagnoseRE-master/Q3/test_cf.py | import torch
import json
import pickle
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.mo... | 3,911 | 31.6 | 84 | py |
DiagnoseRE | DiagnoseRE-master/Q4/train_weighted.py | import torch
import json
import pickle
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
import math
from torch.utils.data import WeightedRandomSampler, DataLoader
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name i... | 4,526 | 37.042017 | 100 | py |
DiagnoseRE | DiagnoseRE-master/Q5/select_debiased.py | import os
import sys
import json
import argparse
import logging
import random
import torch
import opennre
from tqdm import tqdm
logging.basicConfig(level=logging.INFO)
# Silent unimportant log messages
for logger_name in ['transformers.configuration_utils',
'transformers.modeling_utils',
... | 3,155 | 34.460674 | 90 | py |
sockeye | sockeye-main/test/unit/test_layers.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 11,578 | 40.801444 | 119 | py |
sockeye | sockeye-main/test/unit/test_average.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 3,926 | 46.313253 | 114 | py |
sockeye | sockeye-main/test/unit/test_scoring.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 1,290 | 35.885714 | 79 | py |
sockeye | sockeye-main/test/unit/test_loss.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 5,637 | 32.760479 | 94 | py |
sockeye | sockeye-main/test/unit/test_beam_search.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 18,551 | 42.858156 | 117 | py |
sockeye | sockeye-main/test/unit/test_lr_scheduler.py | # Copyright 2017--2019 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 5,453 | 49.036697 | 112 | py |
sockeye | sockeye-main/test/unit/test_deepspeed.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 1,817 | 37.680851 | 108 | py |
sockeye | sockeye-main/test/unit/test_arguments.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 19,608 | 38.855691 | 116 | py |
sockeye | sockeye-main/test/unit/test_inference.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 25,031 | 51.698947 | 148 | py |
sockeye | sockeye-main/test/unit/test_params.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 4,963 | 43.321429 | 114 | py |
sockeye | sockeye-main/test/unit/test_knn.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 7,827 | 43.477273 | 120 | py |
sockeye | sockeye-main/test/unit/test_quantize.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 3,449 | 43.805195 | 113 | py |
sockeye | sockeye-main/test/unit/test_transformer.py | # Copyright 2018--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 1,461 | 34.658537 | 79 | py |
sockeye | sockeye-main/test/unit/test_data_io.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 40,171 | 48.110024 | 118 | py |
sockeye | sockeye-main/test/unit/test_utils.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 20,867 | 47.417633 | 118 | py |
sockeye | sockeye-main/test/integration/test_seq_copy_int.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 15,783 | 52.144781 | 116 | py |
sockeye | sockeye-main/sockeye/inference.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 69,919 | 52.455657 | 473 | py |
sockeye | sockeye-main/sockeye/lr_scheduler.py | # Copyright 2017--2019 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 12,233 | 41.926316 | 120 | py |
sockeye | sockeye-main/sockeye/scoring.py | # Copyright 2018--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 9,314 | 47.015464 | 126 | py |
sockeye | sockeye-main/sockeye/average.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 5,742 | 33.184524 | 117 | py |
sockeye | sockeye-main/sockeye/arguments.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 82,594 | 53.018967 | 122 | py |
sockeye | sockeye-main/sockeye/constants.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 13,965 | 34.090452 | 122 | py |
sockeye | sockeye-main/sockeye/checkpoint_decoder.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 11,359 | 46.731092 | 117 | py |
sockeye | sockeye-main/sockeye/optimizers.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 3,278 | 36.689655 | 118 | py |
sockeye | sockeye-main/sockeye/nvs.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 2,055 | 37.792453 | 118 | py |
sockeye | sockeye-main/sockeye/loss.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 15,168 | 37.113065 | 145 | py |
sockeye | sockeye-main/sockeye/embeddings.py | # Copyright 2017--2021 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 5,152 | 37.455224 | 119 | py |
sockeye | sockeye-main/sockeye/training.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 49,875 | 47.51751 | 129 | py |
sockeye | sockeye-main/sockeye/utils.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 29,031 | 34.404878 | 122 | py |
sockeye | sockeye-main/sockeye/model.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 42,829 | 49.566706 | 119 | py |
sockeye | sockeye-main/sockeye/encoder.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 9,165 | 38.852174 | 132 | py |
sockeye | sockeye-main/sockeye/convert_deepspeed.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 5,580 | 44.008065 | 120 | py |
sockeye | sockeye-main/sockeye/log.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 4,642 | 28.01875 | 102 | py |
sockeye | sockeye-main/sockeye/beam_search.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 57,038 | 47.792985 | 129 | py |
sockeye | sockeye-main/sockeye/layers.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 39,877 | 44.315909 | 175 | py |
sockeye | sockeye-main/sockeye/generate_decoder_states.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 12,146 | 41.472028 | 119 | py |
sockeye | sockeye-main/sockeye/transformer.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 16,758 | 45.812849 | 112 | py |
sockeye | sockeye-main/sockeye/decoder.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 14,845 | 46.43131 | 126 | py |
sockeye | sockeye-main/sockeye/data_io.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 100,351 | 47.107383 | 137 | py |
sockeye | sockeye-main/sockeye/quantize.py | # Copyright 2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | 2,490 | 30.935897 | 116 | py |
sockeye | sockeye-main/sockeye/train.py | # Copyright 2017--2022 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://aws.amazon.com/apache2.0/
#
# or in the "license" fi... | 65,203 | 52.184339 | 120 | py |
EvidentialSparsification | EvidentialSparsification-main/BehaviorPrediction/code/dst/run_dst.py | import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
from copy import deepcopy
import pickle as pkl
import os
from tqdm import tqdm
import matplotlib2tikz
import torch
from sparsemax import Sparsemax
from d... | 9,290 | 38.875536 | 159 | py |
EvidentialSparsification | EvidentialSparsification-main/BehaviorPrediction/code/dst/sparsemax.py | """Sparsemax activation function.
Pytorch implementation of Sparsemax function from:
-- "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification"
-- André F. T. Martins, Ramón Fernandez Astudillo (http://arxiv.org/abs/1602.02068)
"""
from __future__ import division
import torch
import t... | 2,767 | 31.186047 | 90 | py |
EvidentialSparsification | EvidentialSparsification-main/BehaviorPrediction/code/model/dyn_stg.py | import numpy as np
import torch
from model.node_model import MultimodalGenerativeCVAE
class SpatioTemporalGraphCVAEModel(object):
def __init__(self, robot_node, model_registrar,
hyperparams, log_writer,
device):
super(SpatioTemporalGraphCVAEModel, self).__init__()
... | 11,097 | 47.252174 | 123 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/dataloader.py | # From: https://github.com/Aftaab99/PyTorchImageClassifier/blob/master/dataloader.py
import os
import numpy as np
import torch
from PIL import Image
from torch.utils.data.dataset import Dataset
from scipy.misc import imread
from torch import Tensor
"""
Loads the train/test set.
Every image in the dataset is 28x28 pi... | 1,718 | 29.696429 | 84 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/utils.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 123
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
from torch.autograd import Variable
def to_var(x, volatile=False):
if torch.cuda.is_available():
x = x.cuda()
return Va... | 1,975 | 28.492537 | 131 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/run_dst_training.py | '''This file computes the training evolution performance.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
import pdb
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
import os
# From:... | 19,384 | 58.281346 | 218 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/models_conv.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 123
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
import torch.nn as nn
from sparsemax import Sparsemax
from utils import to_var, idx2onehot, sample_q, sample_p
class View(nn.Module):
... | 7,640 | 37.786802 | 155 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/train.py | # Code modified from: "https://github.com/timbmg/VAE-CVAE-MNIST/
# Discrete latent variable code modelled after: https://github.com/EmilienDupont/vae-concrete/
import os
import time
seed = 123
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
from copy import ... | 9,183 | 44.465347 | 172 | py |
EvidentialSparsification | EvidentialSparsification-main/NotMNIST/run_dst_bar_plot.py | '''This file computes the qualitative observations for the bar plots.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
# From: https://... | 5,495 | 35.397351 | 114 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/run_dst_filtered_training.py | '''This file computes the training evolution performance.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
# Function from: https://git... | 14,922 | 51.178322 | 204 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/train_tops_bottoms.py | # Code modified from: "https://github.com/timbmg/VAE-CVAE-MNIST/
# Discrete latent variable code modelled after: https://github.com/EmilienDupont/vae-concrete/
import os
import time
seed = 2
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
from copy import de... | 10,517 | 45.955357 | 178 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/utils.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 2
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
from torch.autograd import Variable
def to_var(x, volatile=False):
if torch.cuda.is_available():
x = x.cuda()
return Vari... | 1,973 | 28.462687 | 131 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/sparsemax.py | """Sparsemax activation function.
Pytorch implementation of Sparsemax function from:
-- "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification"
-- André F. T. Martins, Ramón Fernandez Astudillo (http://arxiv.org/abs/1602.02068)
Code used from: https://github.com/gokceneraslan/SparseMax... | 2,834 | 31.215909 | 90 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/models.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 2
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
import torch.nn as nn
from sparsemax import Sparsemax
from utils import to_var, idx2onehot, sample_q, sample_p
class VAE(nn.Module):
... | 4,759 | 29.909091 | 136 | py |
EvidentialSparsification | EvidentialSparsification-main/FashionMNIST/run_dst_bar_plot.py | '''This file computes the qualitative observations for the bar plots.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
# From: https://... | 4,974 | 33.548611 | 104 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/utils.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 123
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
from torch.autograd import Variable
def to_var(x, volatile=False):
if torch.cuda.is_available():
x = x.cuda()
return Va... | 2,012 | 29.5 | 131 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/sparsemax.py | """Sparsemax activation function.
Pytorch implementation of Sparsemax function from:
-- "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification"
-- André F. T. Martins, Ramón Fernandez Astudillo (http://arxiv.org/abs/1602.02068)
Code used from: https://github.com/gokceneraslan/SparseMax... | 2,834 | 31.215909 | 90 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/models.py | # Code modified from: https://github.com/timbmg/VAE-CVAE-MNIST/
seed = 123
import numpy as np
np.random.seed(seed)
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
import torch.nn as nn
from sparsemax import Sparsemax
from utils import to_var, idx2onehot, sample_q, sample_p
class VAE(nn.Module):
... | 5,059 | 31.435897 | 136 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/run_dst_training.py | '''This file computes the training evolution performance.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
# From: https://github.com/s... | 14,365 | 49.230769 | 201 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/train.py | # Code modified from: "https://github.com/timbmg/VAE-CVAE-MNIST/
# Discrete latent variable code modelled after: https://github.com/EmilienDupont/vae-concrete/
import os
import time
seed = 123
import numpy as np
np.random.seed(seed) # original: 123, 135
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed(see... | 10,593 | 45.464912 | 178 | py |
EvidentialSparsification | EvidentialSparsification-main/MNIST/run_dst_bar_plot.py | '''This file computes the qualitative observations for the bar plots.'''
import numpy as np
import scipy
import scipy.stats
import itertools
import matplotlib.pyplot as plt
from copy import deepcopy
from tqdm import tqdm
import matplotlib2tikz
import torch
from dst_utils import *
from dst import *
# From: https://... | 5,009 | 34.034965 | 155 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/classifier/test.py | # Code adapted from: https://github.com/huyvnphan/PyTorch_CIFAR10
import os, shutil
import torch
from argparse import ArgumentParser
from pytorch_lightning import Trainer, seed_everything
from module import MiniImagenet_Generated_Module
def main(hparams):
seed_everything(0)
# If only train on 1 GPU. Must ... | 1,620 | 44.027778 | 113 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/classifier/module.py | # Code adapted from: https://github.com/huyvnphan/PyTorch_CIFAR10
import torch
import pytorch_lightning as pl
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
from torchvision import models
from datasets import MiniImagenet, get_project_root
i... | 7,495 | 53.715328 | 173 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/classifier/datasets.py | # Code heavily based on: https://github.com/ritheshkumar95/pytorch-vqvae
import os
import csv
import torch.utils.data as data
from PIL import Image
import pdb
from matplotlib import pyplot as plt
from pathlib import Path
def get_project_root() -> Path:
"""Returns project root folder."""
return Path(__file__).... | 3,570 | 36.589474 | 101 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/classifier/train.py | # Code adapted from: https://github.com/huyvnphan/PyTorch_CIFAR10
import os
import torch
from argparse import ArgumentParser
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import LearningRateLogger
from pytorch_lightning.loggers import TensorBoardLogger
from module import MiniI... | 2,328 | 42.943396 | 146 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/vqvae.py | # Code from: https://github.com/ritheshkumar95/pytorch-vqvae
import numpy as np
import torch
import torch.nn.functional as F
from torchvision import transforms, datasets
from torchvision.utils import save_image, make_grid
from modules import VectorQuantizedVAE, to_scalar
from datasets import MiniImagenet
from tensor... | 8,259 | 39.097087 | 101 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/functions.py | # Code from: https://github.com/ritheshkumar95/pytorch-vqvae
import torch
from torch.autograd import Function
class VectorQuantization(Function):
@staticmethod
def forward(ctx, inputs, codebook):
with torch.no_grad():
embedding_size = codebook.size(1)
inputs_size = inputs.size(... | 2,561 | 35.6 | 77 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/vae.py | # Code from: https://github.com/ritheshkumar95/pytorch-vqvae
import numpy as np
import time
import torch
import torch.nn.functional as F
from torch.distributions.normal import Normal
from torchvision import datasets, transforms
from torchvision.utils import save_image
from modules import VAE
BATCH_SIZE = 32
N_EPOC... | 3,890 | 25.469388 | 85 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/datasets.py | # Code adapted from: https://github.com/ritheshkumar95/pytorch-vqvae
import os
import csv
import torch.utils.data as data
from PIL import Image
import pdb
from pathlib import Path
# From: https://stackoverflow.com/questions/25389095/python-get-path-of-root-project-structure/40227116
def get_project_root() -> Path:
... | 3,209 | 34.666667 | 103 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/sparsemax.py | # Code from: https://github.com/KrisKorrel/sparsemax-pytorch
"""Sparsemax activation function.
Pytorch implementation of Sparsemax function from:
-- "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification"
-- André F. T. Martins, Ramón Fernandez Astudillo (http://arxiv.org/abs/1602.0206... | 2,907 | 32.045455 | 100 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/generated_dataset.py | # Code adapted from: https://github.com/ritheshkumar95/pytorch-vqvae
import numpy as np
from matplotlib import pyplot as plt
import torch
import torch.nn.functional as F
import json
from torchvision import transforms, datasets
from torchvision.utils import save_image, make_grid
from modules import VectorQuantizedVAE,... | 12,400 | 45.100372 | 180 | py |
EvidentialSparsification | EvidentialSparsification-main/VQVAE/VQVAE/conv_linear_test.py | import torch
inputs = torch.tensor([[[[1., 2.],[3., 4.]]]])
print("linear inputs", inputs.shape)
fc = torch.nn.Linear(4,2)
weights = torch.tensor([[1.1, 1.2, 1.3, 1.4],
[1.5, 1.6, 1.7, 1.8]])
bias = torch.tensor([1.9, 2.0])
print("linear weights", weights.shape)
fc.weight.data = weights
fc.bias.data = bias
outp... | 748 | 33.045455 | 72 | py |
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