text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
# def propara_executor(state, action):
import jsonlines
from tqdm import tqdm
from random import choices
import argparse
import multiprocessing
from multiprocessing import Pool
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_prefix", type=str, default='propara', help="dataset prefix")
# parser.a... | ContextualSP/lemon/corpus_generation/propara_corpus_generation.py/0 | {
"file_path": "ContextualSP/lemon/corpus_generation/propara_corpus_generation.py",
"repo_id": "ContextualSP",
"token_count": 2952
} | 234 |
from abc import abstractproperty, ABCMeta, abstractmethod
import tensorflow as tf
from keras.layers import Dense, LSTM
from gtd.ml.framework import Feedable, Model
from gtd.ml.seq_batch import FeedSequenceBatch, embed, reduce_mean, SequenceBatch, reduce_sum, weighted_sum, reduce_max
from gtd.ml.vocab import Vocab
c... | ContextualSP/lemon/executor/gtd/ml/model.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/ml/model.py",
"repo_id": "ContextualSP",
"token_count": 6965
} | 235 |
from unittest import TestCase
from gtd.graph import Graph
class TestGraph(TestCase):
def test_shortest_path(self):
triples = [
('1', '2', '3'),
('3', '4', '5'),
('1', '0', '5'),
]
self.assertEqual(
Graph(triples).shortest_path('1', '5'),
['... | ContextualSP/lemon/executor/gtd/tests/test_graph.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/tests/test_graph.py",
"repo_id": "ContextualSP",
"token_count": 255
} | 236 |
from abc import ABCMeta, abstractproperty
class ExampleFactory(object, metaclass=ABCMeta):
@abstractproperty
def examples(self):
"""Return an iterable of Examples."""
raise NotImplementedError
| ContextualSP/lemon/executor/strongsup/example_factory.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/example_factory.py",
"repo_id": "ContextualSP",
"token_count": 73
} | 237 |
"""Knowledge graph constructed from a table.
The graph is stored as a list of triples.
"""
import os
import re
import sys
from collections import Counter
from itertools import chain
from strongsup.tables.structure import parse_number, parse_date, InfiniteSet
from strongsup.tables.utils import tsv_unescape, tsv_unesca... | ContextualSP/lemon/executor/strongsup/tables/graph.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tables/graph.py",
"repo_id": "ContextualSP",
"token_count": 4157
} | 238 |
# -*- coding: utf-8 -*-
import pytest
from strongsup.tables.utils import (
tsv_unescape, tsv_unescape_list, normalize,
)
class TestStringMethods(object):
def test_tsv_unescape(self):
assert tsv_unescape(r'abn\ncd\p\\\pp') == 'abn\ncd|\\|p'
assert tsv_unescape_list(r'abn\ncd\p\\\pp|... | ContextualSP/lemon/executor/strongsup/tests/tables/test_utils.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tests/tables/test_utils.py",
"repo_id": "ContextualSP",
"token_count": 422
} | 239 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import re
from collections import defaultdict
from re import RegexFlag
from typing import List
def extract_structure_data(plain_text_content: str):
# extracts lines starts with specific flags
# map id to its related information
data... | ContextualSP/lemon/lemon/model_eval.py/0 | {
"file_path": "ContextualSP/lemon/lemon/model_eval.py",
"repo_id": "ContextualSP",
"token_count": 1290
} | 240 |
# OpenBookQA
* [evaluator](evaluator/) is the program used by the AI2 Leaderboard to evaluate submitted predictions.
* `data` have the files (and scripts to generate them) used for evaluating Leaderboard predictions.
## Example usage
To evaluate dummy predictions (every question is predicted to be `A`) against the d... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/openbookqa/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/openbookqa/README.md",
"repo_id": "ContextualSP",
"token_count": 149
} | 241 |
#!/bin/bash
echo ----------------------------------
echo removing pycache detritus
echo ----------------------------------
echo
rm -vrf $(find . -type d -name __pycache__)
echo
echo ----------------------------------
echo removing mypy detritus
echo ----------------------------------
echo
rm -vrf .mypy_cache
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/clean.sh/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/clean.sh",
"repo_id": "ContextualSP",
"token_count": 74
} | 242 |
from scoring.question import QuestionScores
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/scoring/__init__.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/scoring/__init__.py",
"repo_id": "ContextualSP",
"token_count": 9
} | 243 |
## Test case: Prediction and answer are both empty
* answers.tsv is empty.
* predictions.tsv is empty.
An evaluation on this prediction should abort.
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/testfiles-4/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/testfiles-4/README.md",
"repo_id": "ContextualSP",
"token_count": 40
} | 244 |
# QASC
* [evaluator](evaluator/) is the program used by the AI2 Leaderboard to evaluate submitted predictions.
* `data` have example prediction files
## Example usage
To evaluate your predictions against the train or dev datasets, run either of these and look at the resulting metrics.json file:
```
% python3 evalua... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/qasc/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/qasc/README.md",
"repo_id": "ContextualSP",
"token_count": 281
} | 245 |
# SciTail
* [evaluator](evaluator/) is the program used by the AI2 Leaderboard to evaluate submitted predictions.
## Example usage
To evaluate dummy predictions (every pair of sentences is predicted to entail) against the SciTail dataset, run this:
```
% python3 evaluator/evaluator.py -a data/test/answers.jsonl -p ... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/README.md",
"repo_id": "ContextualSP",
"token_count": 247
} | 246 |
{"id": "tracie-train-uniform-0000", "label": "entailment"}
{"id": "tracie-train-uniform-0001", "label": "entailment"}
{"id": "tracie-train-uniform-0002", "label": "entailment"}
{"id": "tracie-train-uniform-0003", "label": "entailment"}
{"id": "tracie-train-uniform-0004", "label": "entailment"}
{"id": "tracie-train-unif... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/tracie/data/predictions.jsonl/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/tracie/data/predictions.jsonl",
"repo_id": "ContextualSP",
"token_count": 21493
} | 247 |
#!/bin/bash
GPU_NUM=16
python -m torch.distributed.launch --nproc_per_node=${GPU_NUM} hf_generation_multi_es.py \
--model_name_or_path $1 \
--output_dir $2 \
--data_dir $3 \
--train_file $4 \
--validation_file $5 \
--per_device_train_batch_size $6 \
--gradient_accumulation_steps $7 \
--l... | ContextualSP/logigan/pre-training/run_hf.sh/0 | {
"file_path": "ContextualSP/logigan/pre-training/run_hf.sh",
"repo_id": "ContextualSP",
"token_count": 403
} | 248 |
# Local path to the dataset (after it has been downloaded).
dataset_local_path="./data/dataset.json"
if [[ ! -f "${dataset_local_path}" ]]; then
echo "ERROR: Dataset not found."
echo "Please download the dataset first from ${dataset_url}!"
echo "See further instructions in the README."
exit 1
fi
# preproc... | ContextualSP/poset_decoding/preprocess.sh/0 | {
"file_path": "ContextualSP/poset_decoding/preprocess.sh",
"repo_id": "ContextualSP",
"token_count": 325
} | 249 |
language: python
cache: pip
sudo: true
env:
global:
- PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR/tests:$TRAVIS_BUILD_DIR/matchzoo
matrix:
allow_failures:
- os: osx
include:
- os: linux
dist: xenial
python: 3.6
- os: osx
osx_image: xcode10.2
language: shell
install:
- p... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/.travis.yml/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/.travis.yml",
"repo_id": "ContextualSP",
"token_count": 328
} | 250 |
from pathlib import Path
USER_DIR = Path.expanduser(Path('~')).joinpath('.matchzoo')
if not USER_DIR.exists():
USER_DIR.mkdir()
USER_DATA_DIR = USER_DIR.joinpath('datasets')
if not USER_DATA_DIR.exists():
USER_DATA_DIR.mkdir()
USER_TUNED_MODELS_DIR = USER_DIR.joinpath('tuned_models')
from .version import __ve... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/__init__.py",
"repo_id": "ContextualSP",
"token_count": 350
} | 251 |
import typing
from collections import Iterable
import numpy as np
from matchzoo.engine.base_callback import BaseCallback
def _infer_dtype(value):
"""Infer the dtype for the features.
It is required as the input is usually array of objects before padding.
"""
while isinstance(value, (list, tuple)) a... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/callbacks/padding.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/callbacks/padding.py",
"repo_id": "ContextualSP",
"token_count": 4960
} | 252 |
from .load_data import load_data
| ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/quora_qp/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/quora_qp/__init__.py",
"repo_id": "ContextualSP",
"token_count": 10
} | 253 |
"""Base Model."""
import abc
import typing
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
from matchzoo.utils import parse_activation
from matchzoo.engine.base_callback import BaseCallback
from matchzoo.engine import hyper_spaces
from matchzoo.engine.base_preprocessor import BasePrepr... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/base_model.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/base_model.py",
"repo_id": "ContextualSP",
"token_count": 6350
} | 254 |
"""Normalized discounted cumulative gain metric for ranking."""
import numpy as np
from matchzoo.engine.base_metric import (
BaseMetric, sort_and_couple, RankingMetric
)
from .discounted_cumulative_gain import DiscountedCumulativeGain
class NormalizedDiscountedCumulativeGain(RankingMetric):
"""Normalized dis... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/metrics/normalized_discounted_cumulative_gain.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/metrics/normalized_discounted_cumulative_gain.py",
"repo_id": "ContextualSP",
"token_count": 919
} | 255 |
"""An implementation of DUET Model."""
import typing
import torch
import torch.nn as nn
import torch.nn.functional as F
from matchzoo import preprocessors
from matchzoo.engine import hyper_spaces
from matchzoo.engine.param import Param
from matchzoo.engine.base_model import BaseModel
from matchzoo.engine.param_table ... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/duet.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/duet.py",
"repo_id": "ContextualSP",
"token_count": 4688
} | 256 |
"""Gaussian kernel module."""
import typing
import torch
import torch.nn as nn
class GaussianKernel(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/gaussian_kernel.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/gaussian_kernel.py",
"repo_id": "ContextualSP",
"token_count": 365
} | 257 |
from .unit import Unit
class DigitRemoval(Unit):
"""Process unit to remove digits."""
def transform(self, input_: list) -> list:
"""
Remove digits from list of tokens.
:param input_: list of tokens to be filtered.
:return tokens: tokens of tokens without digits.
"""
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/digit_removal.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/digit_removal.py",
"repo_id": "ContextualSP",
"token_count": 141
} | 258 |
from .classification import Classification
from .ranking import Ranking
| ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/tasks/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/tasks/__init__.py",
"repo_id": "ContextualSP",
"token_count": 13
} | 259 |
import torch
import numpy as np
from matchzoo import losses
def test_hinge_loss():
true_value = torch.Tensor([[1.2], [1], [1], [1]])
pred_value = torch.Tensor([[1.2], [0.1], [0], [-0.3]])
expected_loss = torch.Tensor([(0 + 1 - 0.3 + 0) / 2.0])
loss = losses.RankHingeLoss()(pred_value, true_value)
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/test_losses.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/test_losses.py",
"repo_id": "ContextualSP",
"token_count": 888
} | 260 |
<jupyter_start><jupyter_code>%run init.ipynb
ranking_task = mz.tasks.Ranking(losses=mz.losses.RankCrossEntropyLoss(num_neg=4))
ranking_task.metrics = [
mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
mz.metrics.MeanAveragePrecision()
]
preprocessor = m... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/dssm.ipynb/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/dssm.ipynb",
"repo_id": "ContextualSP",
"token_count": 765
} | 261 |
# Semantic Parsing in Context <img src="https://pytorch.org/assets/images/logo-dark.svg" height = "25" align=center />
The official pytorch implementation of our paper [How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context](https://arxiv.org/pdf/2002.00652.pdf). This cod... | ContextualSP/semantic_parsing_in_context/README.md/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/README.md",
"repo_id": "ContextualSP",
"token_count": 5834
} | 262 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import os
from functools import partial
from typing import List
from typing import Optional, Dict
from typing import Tuple
import edit_distance
import numpy as np
import torch
from allennlp.training.metrics.metric import Metric
fro... | ContextualSP/semantic_parsing_in_context/models/metrics.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/models/metrics.py",
"repo_id": "ContextualSP",
"token_count": 5552
} | 263 |
import json
from context.converter import SQLConverter, SparcDBContext
import unittest
from allennlp.data.tokenizers import WordTokenizer
class TestSQLToSemQL(unittest.TestCase):
@staticmethod
def template(sql_plain, sql_text, db_id, expected_str):
sql_clause = json.loads(sql_text)
db_context... | ContextualSP/semantic_parsing_in_context/test_sql_to_semql.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/test_sql_to_semql.py",
"repo_id": "ContextualSP",
"token_count": 1419
} | 264 |
# Copyright (c) Facebook, Inc. and Microsoft Corporation.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List
import torch
from genre.trie import Trie
keyword = ['select', 'distinct', 'from', '... | ContextualSP/unified_parser_text_to_sql/genre/entity_linking.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/genre/entity_linking.py",
"repo_id": "ContextualSP",
"token_count": 2026
} | 265 |
import subprocess
import argparse
import os
def run_command(bash_command):
process = subprocess.Popen(bash_command.split())
output, error = process.communicate()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_path", type=str, default="", help="dataset pa... | ContextualSP/unified_parser_text_to_sql/train.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/train.py",
"repo_id": "ContextualSP",
"token_count": 981
} | 266 |
import math
import sys
from typing import Iterable, Optional
from timm.utils.model import unwrap_model
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from lib import utils
@torch.no_grad()
def evaluate(data_loader, model, device, amp=True):
criterion = torch.nn.CrossEntropyLo... | Cream/AutoFormerV2/engine.py/0 | {
"file_path": "Cream/AutoFormerV2/engine.py",
"repo_id": "Cream",
"token_count": 653
} | 267 |
""" Retrain cell """
import _init_paths
import os
import torch
import json
import torch.nn as nn
import numpy as np
import lib.utils.genotypes as gt
from tensorboardX import SummaryWriter
from lib.models.cdarts_controller import CDARTSController
from lib.utils import utils
from lib.config import AugmentConfig
from lib... | Cream/CDARTS/CDARTS/retrain.py/0 | {
"file_path": "Cream/CDARTS/CDARTS/retrain.py",
"repo_id": "Cream",
"token_count": 3503
} | 268 |
from abc import ABCMeta, abstractmethod
class BaseFileHandler(object):
__metaclass__ = ABCMeta # python 2 compatibility
@abstractmethod
def load_from_fileobj(self, file, **kwargs):
pass
@abstractmethod
def dump_to_fileobj(self, obj, file, **kwargs):
pass
@abstractmethod
... | Cream/CDARTS/CDARTS_detection/mmcv/fileio/handlers/base.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/fileio/handlers/base.py",
"repo_id": "Cream",
"token_count": 293
} | 269 |
import collections
import torch
import torch.nn.functional as F
from torch.utils.data.dataloader import default_collate
from .data_container import DataContainer
def collate(batch, samples_per_gpu=1):
"""Puts each data field into a tensor/DataContainer with outer dimension
batch size.
Extend default_co... | Cream/CDARTS/CDARTS_detection/mmcv/parallel/collate.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/parallel/collate.py",
"repo_id": "Cream",
"token_count": 1935
} | 270 |
import os.path as osp
import torch
from ...utils import master_only
from .base import LoggerHook
class TensorboardLoggerHook(LoggerHook):
def __init__(self,
log_dir=None,
interval=10,
ignore_last=True,
reset_flag=True):
super(Tensorboa... | Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/logger/tensorboard.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/logger/tensorboard.py",
"repo_id": "Cream",
"token_count": 996
} | 271 |
from time import time
class TimerError(Exception):
def __init__(self, message):
self.message = message
super(TimerError, self).__init__(message)
class Timer(object):
"""A flexible Timer class.
:Example:
>>> import time
>>> import mmcv
>>> with mmcv.Timer():
>>> # s... | Cream/CDARTS/CDARTS_detection/mmcv/utils/timer.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/utils/timer.py",
"repo_id": "Cream",
"token_count": 1263
} | 272 |
import mmcv
from . import assigners, samplers
def build_assigner(cfg, **kwargs):
if isinstance(cfg, assigners.BaseAssigner):
return cfg
elif isinstance(cfg, dict):
return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs)
else:
raise TypeError('Invalid type {} for buil... | Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assign_sampling.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assign_sampling.py",
"repo_id": "Cream",
"token_count": 533
} | 273 |
import torch
class SamplingResult(object):
def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
gt_flags):
self.pos_inds = pos_inds
self.neg_inds = neg_inds
self.pos_bboxes = bboxes[pos_inds]
self.neg_bboxes = bboxes[neg_inds]
self.pos_... | Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/samplers/sampling_result.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/samplers/sampling_result.py",
"repo_id": "Cream",
"token_count": 403
} | 274 |
from .bbox_nms import multiclass_nms
from .merge_augs import (merge_aug_proposals, merge_aug_bboxes,
merge_aug_scores, merge_aug_masks)
__all__ = [
'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
'merge_aug_scores', 'merge_aug_masks'
]
| Cream/CDARTS/CDARTS_detection/mmdet/core/post_processing/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/post_processing/__init__.py",
"repo_id": "Cream",
"token_count": 141
} | 275 |
import collections
from mmdet.utils import build_from_cfg
from ..registry import PIPELINES
@PIPELINES.register_module
class Compose(object):
def __init__(self, transforms):
assert isinstance(transforms, collections.abc.Sequence)
self.transforms = []
for transform in transforms:
... | Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/compose.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/compose.py",
"repo_id": "Cream",
"token_count": 492
} | 276 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import normal_init
from mmdet.core import delta2bbox
from mmdet.ops import nms
from .guided_anchor_head import GuidedAnchorHead
from ..registry import HEADS
@HEADS.register_module
class GARPNHead(GuidedAnchorHead):
"""Guided-Anchor-... | Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/ga_rpn_head.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/ga_rpn_head.py",
"repo_id": "Cream",
"token_count": 3227
} | 277 |
import math
import torch
import torch.nn as nn
from torch.autograd import Variable
from .dropblock import DropBlockScheduled, DropBlock2D
import logging
from torch.nn.modules.batchnorm import _BatchNorm
import torch.nn.functional as F
import time
import numpy as np
from ..registry import BACKBONES
def Conv_3x3(i... | Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/mnasnet.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/mnasnet.py",
"repo_id": "Cream",
"token_count": 3658
} | 278 |
from .base import BaseDetector
from .double_head_rcnn import DoubleHeadRCNN
from .single_stage import SingleStageDetector
from .two_stage import TwoStageDetector
from .rpn import RPN
from .fast_rcnn import FastRCNN
from .faster_rcnn import FasterRCNN
from .mask_rcnn import MaskRCNN
from .cascade_rcnn import CascadeRCNN... | Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/__init__.py",
"repo_id": "Cream",
"token_count": 251
} | 279 |
from .accuracy import accuracy, Accuracy
from .cross_entropy_loss import (cross_entropy, binary_cross_entropy,
mask_cross_entropy, CrossEntropyLoss)
from .focal_loss import sigmoid_focal_loss, FocalLoss
from .smooth_l1_loss import smooth_l1_loss, SmoothL1Loss
from .ghm_loss import GHMC,... | Cream/CDARTS/CDARTS_detection/mmdet/models/losses/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/losses/__init__.py",
"repo_id": "Cream",
"token_count": 409
} | 280 |
from .fpn import FPN
from .fpn_panet import PAFPN
from .bfp import BFP
from .hrfpn import HRFPN
from .nas_fpn import NASFPN
from .search_pafpn import SearchPAFPN
__all__ = ['FPN', 'BFP', 'HRFPN', 'NASFPN',
'PAFPN', 'SearchPAFPN']
| Cream/CDARTS/CDARTS_detection/mmdet/models/necks/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/necks/__init__.py",
"repo_id": "Cream",
"token_count": 99
} | 281 |
from __future__ import division
import torch
import torch.nn as nn
from mmdet import ops
from mmdet.core import force_fp32
from ..registry import ROI_EXTRACTORS
@ROI_EXTRACTORS.register_module
class SingleRoIExtractor(nn.Module):
"""Extract RoI features from a single level feature map.
If there are mulitpl... | Cream/CDARTS/CDARTS_detection/mmdet/models/roi_extractors/single_level.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/roi_extractors/single_level.py",
"repo_id": "Cream",
"token_count": 1852
} | 282 |
import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from ..functions.deform_conv import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
... | Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/modules/deform_conv.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/modules/deform_conv.py",
"repo_id": "Cream",
"token_count": 2682
} | 283 |
#include <ATen/ATen.h>
#include <THC/THCAtomics.cuh>
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \
i += blockDim.x * gridDim.x)
#define THREADS_PER_BLOCK 1024
inline int GET_BLOCKS(const int N) {
int optimal_block_num = (N + THR... | Cream/CDARTS/CDARTS_detection/mmdet/ops/masked_conv/src/masked_conv2d_kernel.cu/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/masked_conv/src/masked_conv2d_kernel.cu",
"repo_id": "Cream",
"token_count": 2595
} | 284 |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='roi_align_cuda',
ext_modules=[
CUDAExtension('roi_align_cuda', [
'src/roi_align_cuda.cpp',
'src/roi_align_kernel.cu',
]),
],
cmdclass={'build_ext': Build... | Cream/CDARTS/CDARTS_detection/mmdet/ops/roi_align/setup.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/roi_align/setup.py",
"repo_id": "Cream",
"token_count": 154
} | 285 |
from torch import nn
from ..functions.sigmoid_focal_loss import sigmoid_focal_loss
# TODO: remove this module
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, logits,... | Cream/CDARTS/CDARTS_detection/mmdet/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py",
"repo_id": "Cream",
"token_count": 298
} | 286 |
from argparse import ArgumentParser
from mmdet.core import coco_eval
def main():
parser = ArgumentParser(description='COCO Evaluation')
parser.add_argument('result', help='result file path')
parser.add_argument('--ann', help='annotation file path')
parser.add_argument(
'--types',
type... | Cream/CDARTS/CDARTS_detection/tools/coco_eval.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/tools/coco_eval.py",
"repo_id": "Cream",
"token_count": 330
} | 287 |
# ------------------------------------------------------------------------------
# Generates targets for Panoptic-DeepLab.
# Written by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
import numpy as np
import torch
class PanopticTargetGenerator(ob... | Cream/CDARTS/CDARTS_segmentation/dataloaders/transforms/target_transforms.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/dataloaders/transforms/target_transforms.py",
"repo_id": "Cream",
"token_count": 4642
} | 288 |
# ------------------------------------------------------------------------------
# Reference: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
import torch.nn as n... | Cream/CDARTS/CDARTS_segmentation/segmentation/model/backbone/resnet.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/model/backbone/resnet.py",
"repo_id": "Cream",
"token_count": 6389
} | 289 |
from .semantic_post_processing import get_semantic_segmentation
from .instance_post_processing import get_panoptic_segmentation
from .evaluation_format import get_cityscapes_instance_format
| Cream/CDARTS/CDARTS_segmentation/segmentation/model/post_processing/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/model/post_processing/__init__.py",
"repo_id": "Cream",
"token_count": 53
} | 290 |
# ------------------------------------------------------------------------------
# Utility functions.
# Written by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
import torch
class AverageMeter(object):
"""Computes and stores the average and cur... | Cream/CDARTS/CDARTS_segmentation/segmentation/utils/utils.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/utils/utils.py",
"repo_id": "Cream",
"token_count": 554
} | 291 |
import os
import cv2
import numpy as np
import time
from tqdm import tqdm
import torch
import torch.multiprocessing as mp
from engine.logger import get_logger
from utils.pyt_utils import load_model, link_file, ensure_dir
from utils.img_utils import pad_image_to_shape, normalize
logger = get_logger()
class Evaluato... | Cream/CDARTS/CDARTS_segmentation/tools/engine/evaluator.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/tools/engine/evaluator.py",
"repo_id": "Cream",
"token_count": 6706
} | 292 |
import numpy as np
import cv2
import scipy.io as sio
def set_img_color(colors, background, img, gt, show255=False, weight_foreground=0.55):
origin = np.array(img)
for i in range(len(colors)):
if i != background:
img[np.where(gt == i)] = colors[i]
if show255:
img[np.where(gt == ... | Cream/CDARTS/CDARTS_segmentation/tools/utils/visualize.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/tools/utils/visualize.py",
"repo_id": "Cream",
"token_count": 1350
} | 293 |
import torch
import torch.nn as nn
from torch.nn import functional as F
from builder import *
from operations import *
from operations import DropPath_
from genotypes import PRIMITIVES
from pdb import set_trace as bp
from seg_oprs import FeatureFusion, Head, Decoder
from layers import NaiveSyncBatchNorm
# BatchNorm2d ... | Cream/CDARTS/CDARTS_segmentation/train/cydas.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/train/cydas.py",
"repo_id": "Cream",
"token_count": 9355
} | 294 |
from __future__ import division
import os
import shutil
import sys
import time
import glob
import json
import logging
import argparse
import _init_paths
from utils.darts_utils import create_exp_dir, save, plot_op, plot_path_width, objective_acc_lat
parser = argparse.ArgumentParser(description='parameters for sampling'... | Cream/CDARTS/CDARTS_segmentation/train/vis_arch.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/train/vis_arch.py",
"repo_id": "Cream",
"token_count": 852
} | 295 |
""" Config class for search/augment """
import argparse
import os
from functools import partial
import torch
def get_parser(name):
""" make default formatted parser """
parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# print default value always
parser.ad... | Cream/CDARTS/lib/config.py/0 | {
"file_path": "Cream/CDARTS/lib/config.py",
"repo_id": "Cream",
"token_count": 5765
} | 296 |
""" Genotypes
- Genotype: normal/reduce gene + normal/reduce cell output connection (concat)
- gene: discrete ops information (w/o output connection)
- dag: real ops (can be mixed or discrete, but Genotype has only discrete information itself)
"""
from collections import namedtuple
import torch
import torch... | Cream/CDARTS/lib/utils/genotypes.py/0 | {
"file_path": "Cream/CDARTS/lib/utils/genotypes.py",
"repo_id": "Cream",
"token_count": 2551
} | 297 |
# This file is downloaded from https://github.com/rwightman/pytorch-image-models
# This file is to define the inverted residual block which is the base operation in our search space.
import torch.nn as nn
from timm.models.layers import create_conv2d
from timm.models.efficientnet_blocks import make_divisible, resolve_... | Cream/Cream/lib/models/blocks/inverted_residual_block.py/0 | {
"file_path": "Cream/Cream/lib/models/blocks/inverted_residual_block.py",
"repo_id": "Cream",
"token_count": 1519
} | 298 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Written by Hao Du and Houwen Peng
# email: haodu8-c@my.cityu.edu.hk and houwen.peng@microsoft.com
import os
import warnings
import datetime
import torch
import torch.nn as nn
import _init_paths
from torch.utils.tensorboard import SummaryWriter... | Cream/Cream/tools/test.py/0 | {
"file_path": "Cream/Cream/tools/test.py",
"repo_id": "Cream",
"token_count": 2845
} | 299 |
# dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetric... | Cream/EfficientViT/downstream/configs/_base_/datasets/wider_face.py/0 | {
"file_path": "Cream/EfficientViT/downstream/configs/_base_/datasets/wider_face.py",
"repo_id": "Cream",
"token_count": 1019
} | 300 |
# Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import time
from tempfile import TemporaryDirectory
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.checkpoint import weights_to_cpu, get_state_dict
try:
import apex
exce... | Cream/EfficientViT/downstream/mmcv_custom/runner/checkpoint.py/0 | {
"file_path": "Cream/EfficientViT/downstream/mmcv_custom/runner/checkpoint.py",
"repo_id": "Cream",
"token_count": 1165
} | 301 |
import torch
from timm.models.registry import register_model
from models import deit_tiny_patch16_224,\
deit_small_patch16_224,\
deit_base_patch16_224,\
deit_base_patch16_384
def get_deit_rpe_config():
from irpe import get_rpe_config as _get_rpe_config
rpe_config = _get_rpe_config(
ratio=1... | Cream/MiniViT/Mini-DeiT/mini_deit_models.py/0 | {
"file_path": "Cream/MiniViT/Mini-DeiT/mini_deit_models.py",
"repo_id": "Cream",
"token_count": 1244
} | 302 |
MODEL:
TYPE: swin_minivit_distill
NAME: swin_base_patch4_window7_224_minivit
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 7
MINIVIT:
SEPARATE_LAYERNUM_LIST: [1, 1, 9, 1] | Cream/MiniViT/Mini-Swin/configs/swin_base_patch4_window7_224_minivit_sharenum2.yaml/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/configs/swin_base_patch4_window7_224_minivit_sharenum2.yaml",
"repo_id": "Cream",
"token_count": 140
} | 303 |
from .build import build_model | Cream/MiniViT/Mini-Swin/models/__init__.py/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/models/__init__.py",
"repo_id": "Cream",
"token_count": 7
} | 304 |
include src/open_clip/bpe_simple_vocab_16e6.txt.gz
include src/open_clip/model_configs/*.json
| Cream/TinyCLIP/MANIFEST.in/0 | {
"file_path": "Cream/TinyCLIP/MANIFEST.in",
"repo_id": "Cream",
"token_count": 38
} | 305 |
import ast
import json
import logging
import math
import os
import random
import sys
import braceexpand
from dataclasses import dataclass
from multiprocessing import Value
import numpy as np
import pandas as pd
import torch
import torchvision.datasets as datasets
import webdataset as wds
from PIL import Image
from tor... | Cream/TinyCLIP/src/training/data.py/0 | {
"file_path": "Cream/TinyCLIP/src/training/data.py",
"repo_id": "Cream",
"token_count": 9288
} | 306 |
MODEL:
TYPE: clip_vit_large14_224
TRAIN:
EPOCHS: 90
DATA:
MEAN_AND_STD_TYPE: clip
DATASET: imagenet22k
AUG:
MIXUP: 0.0
CUTMIX: 0.0
| Cream/TinyViT/configs/teacher/clip_vit_large_patch14_22k.yaml/0 | {
"file_path": "Cream/TinyViT/configs/teacher/clip_vit_large_patch14_22k.yaml",
"repo_id": "Cream",
"token_count": 83
} | 307 |
from .parser_factory import create_parser
| Cream/TinyViT/data/augmentation/parsers/__init__.py/0 | {
"file_path": "Cream/TinyViT/data/augmentation/parsers/__init__.py",
"repo_id": "Cream",
"token_count": 11
} | 308 |
# --------------------------------------------------------
# TinyViT Utils
# Copyright (c) 2022 Microsoft
# --------------------------------------------------------
import torch
import torch.distributed as dist
def get_dist_backend():
if not dist.is_available():
return None
if not dist.is_initialized... | Cream/TinyViT/my_meter.py/0 | {
"file_path": "Cream/TinyViT/my_meter.py",
"repo_id": "Cream",
"token_count": 866
} | 309 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
... | Cream/iRPE/DETR-with-iRPE/engine.py/0 | {
"file_path": "Cream/iRPE/DETR-with-iRPE/engine.py",
"repo_id": "Cream",
"token_count": 2985
} | 310 |
OUTPUT_DIR: 'OUTPUT/'
WORKERS: 6
PRINT_FREQ: 500
AMP:
ENABLED: true
MODEL:
NAME: cls_cvt
SPEC:
INIT: 'trunc_norm'
NUM_STAGES: 3
PATCH_SIZE: [7, 3, 3]
PATCH_STRIDE: [4, 2, 2]
PATCH_PADDING: [2, 1, 1]
DIM_EMBED: [64, 192, 384]
NUM_HEADS: [1, 3, 6]
DEPTH: [1, 2, 10]
MLP_RATIO: [4... | CvT/experiments/imagenet/cvt/cvt-13-224x224.yaml/0 | {
"file_path": "CvT/experiments/imagenet/cvt/cvt-13-224x224.yaml",
"repo_id": "CvT",
"token_count": 981
} | 311 |
from .build import build_transforms
| CvT/lib/dataset/transformas/__init__.py/0 | {
"file_path": "CvT/lib/dataset/transformas/__init__.py",
"repo_id": "CvT",
"token_count": 9
} | 312 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import logging
import os
import pprint
import time
import torch
import torch.nn.parallel
import torch.optim
from torch.utils.collect_env import get_pretty_env_info
from tensorboardX import Summ... | CvT/tools/train.py/0 | {
"file_path": "CvT/tools/train.py",
"repo_id": "CvT",
"token_count": 2935
} | 313 |
import numpy as np
cimport numpy as np
import array
import bisect
cpdef float sorted_median(float[:] data, int i, int j):
cdef int n = j - i
cdef int mid
if n == 0:
raise Exception("no median for empty data")
if n % 2 == 1:
return data[i + n // 2]
else:
mid = i + n // 2
... | anomalydetector/msanomalydetector/_anomaly_kernel_cython.pyx/0 | {
"file_path": "anomalydetector/msanomalydetector/_anomaly_kernel_cython.pyx",
"repo_id": "anomalydetector",
"token_count": 1096
} | 314 |
# -*- coding: utf-8 -*-
"""
Version string and parsed tuple. Keeps it all in one place.
"""
__version__ = '1.1'
VERSION = tuple(int(x) for x in __version__.split('.'))
| anomalydetector/version.py/0 | {
"file_path": "anomalydetector/version.py",
"repo_id": "anomalydetector",
"token_count": 64
} | 315 |
# Copyright (c) 2019-2020, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0.
# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/utils/distributed.py
import os
from contextlib import contextmanager
from typing import Generator, Optional, Unio... | archai/archai/common/distributed_utils.py/0 | {
"file_path": "archai/archai/common/distributed_utils.py",
"repo_id": "archai",
"token_count": 1755
} | 316 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Callable, Optional
from overrides import overrides
from torch.utils.data import Dataset
from torchvision.datasets import FGVCAircraft
from torchvision.transforms import ToTensor
from archai.api.dataset_provider import Dataset... | archai/archai/datasets/cv/aircraft_dataset_provider.py/0 | {
"file_path": "archai/archai/datasets/cv/aircraft_dataset_provider.py",
"repo_id": "archai",
"token_count": 943
} | 317 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import random
from typing import Tuple
import torch
class Brightness:
"""Brightness transform."""
def __init__(self, value: float) -> None:
"""Initialize the brightness transform.
Args:
value: Brightness f... | archai/archai/datasets/cv/transforms/brightness.py/0 | {
"file_path": "archai/archai/datasets/cv/transforms/brightness.py",
"repo_id": "archai",
"token_count": 560
} | 318 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from abc import abstractmethod
from typing import List, Optional
import torch
from overrides import EnforceOverrides
from archai.common.ordered_dict_logger import OrderedDictLogger
from archai.datasets.nlp.tokenizer_utils.token_config import Sp... | archai/archai/datasets/nlp/tokenizer_utils/tokenizer_base.py/0 | {
"file_path": "archai/archai/datasets/nlp/tokenizer_utils/tokenizer_base.py",
"repo_id": "archai",
"token_count": 2190
} | 319 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from abc import abstractmethod
from typing import List
import numpy as np
from overrides import EnforceOverrides
from archai.discrete_search.api.archai_model import ArchaiModel
class DiscreteSearchSpace(EnforceOverrides):
"""Abstract clas... | archai/archai/discrete_search/api/search_space.py/0 | {
"file_path": "archai/archai/discrete_search/api/search_space.py",
"repo_id": "archai",
"token_count": 1989
} | 320 |
# Copyright (c) DeepSpeed Team - Microsoft Corporation.
# Licensed under the MIT License.
# https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/profiling/flops_profiler/profiler.py
import time
from functools import partial
from typing import List, Optional
import torch
from archai.discrete_search.evaluators... | archai/archai/discrete_search/evaluators/pt_profiler_utils/pt_profiler_model.py/0 | {
"file_path": "archai/archai/discrete_search/evaluators/pt_profiler_utils/pt_profiler_model.py",
"repo_id": "archai",
"token_count": 3755
} | 321 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from typing import Dict, Optional
import torch
import torch.nn as nn
from flash_attn.modules.mha import MHA
from flash_attn.modules.mlp import FusedMLP
from transformers.modeling_outputs import CausalLMOutput
from transformers.models... | archai/archai/discrete_search/search_spaces/nlp/tfpp/modeling_codegen_flash.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/modeling_codegen_flash.py",
"repo_id": "archai",
"token_count": 4153
} | 322 |
# TD: [2023-01-05]: Extracted the SSKernelDiag class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We make a small change to use the log_vandermonde CUDA code.
"""SSKernelDiag is the S4D kernel, a simpler algorithm for computing the... | archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/ssm_utils/ss_kernel_diag.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/ssm_utils/ss_kernel_diag.py",
"repo_id": "archai",
"token_count": 6457
} | 323 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
#
# Copyright (c) 2018, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0.
from typing import Optional
import torch
import torch.nn as nn
class PositionWiseFF(nn.Module):
def __init__(
self,
d_model: int,... | archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/mem_transformer_utils/position_wise_ff.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/mem_transformer_utils/position_wise_ff.py",
"repo_id": "archai",
"token_count": 1133
} | 324 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
def gpt2_onnx_forward(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[Tuple[torch.FloatTensor, ...]] = None,
) -> Dict[str, torch.F... | archai/archai/onnx/onnx_forward.py/0 | {
"file_path": "archai/archai/onnx/onnx_forward.py",
"repo_id": "archai",
"token_count": 437
} | 325 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Dict, List
import torch
from overrides import overrides
from torch import nn
from archai.common.common import get_conf
from archai.common.ordered_dict_logger import get_global_logger
from archai.supergraph.algos.divnas.analys... | archai/archai/supergraph/algos/divnas/divnas_finalizers.py/0 | {
"file_path": "archai/archai/supergraph/algos/divnas/divnas_finalizers.py",
"repo_id": "archai",
"token_count": 2476
} | 326 |
# NASBench 101 Implementation
##Credits
Code in this directory is from https://github.com/romulus0914/NASBench-PyTorch authored by Romulus Hong. | archai/archai/supergraph/algos/nasbench101/README.md/0 | {
"file_path": "archai/archai/supergraph/algos/nasbench101/README.md",
"repo_id": "archai",
"token_count": 40
} | 327 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
import glob
import math
import os
import pathlib
from typing import Optional
# only works on linux
import ray
import yaml
from overrides import overrides
from archai.common import common, ml_utils, utils
from archai.common.config im... | archai/archai/supergraph/algos/petridish/evaluater_petridish.py/0 | {
"file_path": "archai/archai/supergraph/algos/petridish/evaluater_petridish.py",
"repo_id": "archai",
"token_count": 3092
} | 328 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
from collections import defaultdict
from typing import List, Union
import numpy as np
import PIL
impor... | archai/archai/supergraph/datasets/augmentation.py/0 | {
"file_path": "archai/archai/supergraph/datasets/augmentation.py",
"repo_id": "archai",
"token_count": 10051
} | 329 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torchvision
from overrides import overrides
from torchvision.transforms import transforms
from archai.common import utils
from archai.common.config import Config
from archai.supergraph.datasets.dataset_provider import (
DatasetProvide... | archai/archai/supergraph/datasets/providers/mnist_provider.py/0 | {
"file_path": "archai/archai/supergraph/datasets/providers/mnist_provider.py",
"repo_id": "archai",
"token_count": 753
} | 330 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
Note: All classes in this file needs to be deepcopy compatible because
descs are used as template to create copies by macro builder.
"""
import copy
import os
import pathlib
from enum import Enum
from typing import List, Mapping, Optio... | archai/archai/supergraph/nas/model_desc.py/0 | {
"file_path": "archai/archai/supergraph/nas/model_desc.py",
"repo_id": "archai",
"token_count": 5656
} | 331 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Callable, Optional, Tuple
import torch
from overrides import EnforceOverrides
from torch import Tensor, nn
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
from torch.utils.data imp... | archai/archai/supergraph/utils/trainer.py/0 | {
"file_path": "archai/archai/supergraph/utils/trainer.py",
"repo_id": "archai",
"token_count": 6886
} | 332 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT licen
import os
from dataclasses import asdict, dataclass, field
from typing import Any, Dict, Optional, Tuple
import numpy as np
import torch
from archai.common.distributed_utils import (
get_world_size,
init_distributed,
sync_workers,
)
fr... | archai/archai/trainers/nlp/nvidia_training_args.py/0 | {
"file_path": "archai/archai/trainers/nlp/nvidia_training_args.py",
"repo_id": "archai",
"token_count": 3759
} | 333 |
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