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from abc import ABCMeta, abstractmethod
import itertools
from strongsup.parse_case import ParseCase
from strongsup.exploration_policy import Beam
from strongsup.rlong.predicate import RLongPredicate
from strongsup.rlong.state import RLongAlchemyObject
from strongsup.rlong.world import RLongAlchemyWorld
#############... | ContextualSP/lemon/executor/strongsup/rlong/exploration_policy.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/rlong/exploration_policy.py",
"repo_id": "ContextualSP",
"token_count": 4020
} | 242 |
"""Data structures for the tables domain.
We represent denotations with various Python data structures.
Possible denotation types include:
- Unary = set of Things
| InfiniteSet
- ScopedBinary = dict {Object: Unary, ...} (the domain must be finite)
- Relation = string (Used when the relation is mentioned befor... | ContextualSP/lemon/executor/strongsup/tables/structure.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tables/structure.py",
"repo_id": "ContextualSP",
"token_count": 6455
} | 243 |
import copy
import math
import numpy as np
import pytest
from numpy.testing import assert_allclose
from strongsup.parse_case import ParseCase, ParsePath
from strongsup.predicate import Predicate
from strongsup.tests.utils import PredicateGenerator, softmax
class ParseCaseTester(object):
def test_previous_decisi... | ContextualSP/lemon/executor/strongsup/tests/test_parse_case.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tests/test_parse_case.py",
"repo_id": "ContextualSP",
"token_count": 2109
} | 244 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import sys
from argparse import ArgumentParser
from fairseq_cli.train import cli_main as fairseq_train
from fairseq_cli.generate import cli_main as fairseq_generate
import logging
import shlex
import re
import os
sys.path.append('../')
# from mod... | ContextualSP/lemon/lemon/run_model_finetune.py/0 | {
"file_path": "ContextualSP/lemon/lemon/run_model_finetune.py",
"repo_id": "ContextualSP",
"token_count": 4412
} | 245 |
{"id":"Mercury_417466","answerKey":"A"}
{"id":"Mercury_7081673","answerKey":"B"}
{"id":"Mercury_7239733","answerKey":"D"}
{"id":"NYSEDREGENTS_2015_4_8","answerKey":"D"}
{"id":"Mercury_7037258","answerKey":"B"}
{"id":"CSZ20679","answerKey":"C"}
{"id":"Mercury_182158","answerKey":"A"}
{"id":"Mercury_7216668","answerKey":... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/arc/data-easy/question-answers.jsonl/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/arc/data-easy/question-answers.jsonl",
"repo_id": "ContextualSP",
"token_count": 41514
} | 246 |
from typing import Dict, NamedTuple, Iterable
from evaluation.metric import Metric
class EvaluationAverages(NamedTuple):
inputs: float
outputs: float
conversions: float
moves: float
overall: float
class Evaluation:
def __init__(self, scores: Dict[int, "QuestionScores"]) -> None: # type: ig... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/evaluation/evaluation.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/evaluation/evaluation.py",
"repo_id": "ContextualSP",
"token_count": 1254
} | 247 |
#!/bin/bash
set -e
export PYTHONPATH=.
echo
echo ----------------------------------
echo unit tests
echo ----------------------------------
echo
set -x
pytest
set +x
echo
echo ----------------------------------
echo mypy
echo ----------------------------------
echo
set -x
mypy $(find . -type f -name '*.py')
e... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/test.sh/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/test.sh",
"repo_id": "ContextualSP",
"token_count": 1183
} | 248 |
The file [answers.jsonl](answers.jsonl) are the answers against which predictions are evaluated on the [SciTail Leaderboard](https://leaderboard.allenai.org/).
The file [dummy-predictions.csv](dummy-predictions.csv) is a valid example prediction file that can be submitted to the [SciTail Leaderboard](https://leaderboa... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/data/test/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/data/test/README.md",
"repo_id": "ContextualSP",
"token_count": 123
} | 249 |
import json
import sys
def evaluate(answer_file, prediction_file):
answer_by_id = {}
for line in open(answer_file).readlines():
struct = json.loads(line)
answer_by_id[struct["id"]] = struct
prediction_by_id = {}
for line in open(prediction_file).readlines():
struct = json.load... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/tracie/evaluator/evaluator.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/tracie/evaluator/evaluator.py",
"repo_id": "ContextualSP",
"token_count": 1550
} | 250 |
if [ -d "./bookcorpus_premise" ]
then
rm -r ./bookcorpus_premise
fi
mkdir ./bookcorpus_premise
python corpus_construction.py --start 0 --end 500 --indicator_type premise &
python corpus_construction.py --start 500 --end 1000 --indicator_type premise &
python corpus_construction.py --start 1000 --end 1500 --indica... | ContextualSP/logigan/corpus_construction/mlm_corpus/construct_premise.sh/0 | {
"file_path": "ContextualSP/logigan/corpus_construction/mlm_corpus/construct_premise.sh",
"repo_id": "ContextualSP",
"token_count": 884
} | 251 |
# Usages:
#
# to install matchzoo dependencies:
# $ make init
#
# to run all matchzoo tests, recommended for big PRs and new versions:
# $ make test
#
# there are three kinds of tests:
#
# 1. "quick" tests
# - run in seconds
# - include all unit tests without marks and all doctests
# - for rapid prototyping
# - ... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/Makefile/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/Makefile",
"repo_id": "ContextualSP",
"token_count": 622
} | 252 |
import typing
import numpy as np
import matchzoo as mz
from matchzoo.engine.base_task import BaseTask
from matchzoo.engine.base_model import BaseModel
from matchzoo.engine.base_callback import BaseCallback
from matchzoo.engine.base_preprocessor import BasePreprocessor
from matchzoo.dataloader import DatasetBuilder
fr... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/auto/preparer/preparer.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/auto/preparer/preparer.py",
"repo_id": "ContextualSP",
"token_count": 3733
} | 253 |
import matchzoo as mz
from matchzoo.dataloader import Dataset
class DatasetBuilder(object):
"""
Dataset Bulider. In essense a wrapped partial function.
Example:
>>> import matchzoo as mz
>>> builder = mz.dataloader.DatasetBuilder(
... mode='point'
... )
>>> dat... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/dataset_builder.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/dataset_builder.py",
"repo_id": "ContextualSP",
"token_count": 432
} | 254 |
import typing
from pathlib import Path
import pandas as pd
import matchzoo
from matchzoo.engine.base_task import BaseTask
def load_data(
stage: str = 'train',
task: typing.Union[str, BaseTask] = 'ranking',
return_classes: bool = False
) -> typing.Union[matchzoo.DataPack, typing.Tuple[matchzoo.DataPack, ... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/toy/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/toy/__init__.py",
"repo_id": "ContextualSP",
"token_count": 816
} | 255 |
"""Parameter class."""
import inspect
import numbers
import typing
import hyperopt.pyll
from matchzoo.engine import hyper_spaces
# Both hyperopt native spaces and matchzoo proxies are valid spaces.
SpaceType = typing.Union[hyperopt.pyll.Apply, hyper_spaces.HyperoptProxy]
class Param(object):
"""
Parameter... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/param.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/param.py",
"repo_id": "ContextualSP",
"token_count": 3371
} | 256 |
"""An implementation of aNMM Model."""
import typing
import torch
import torch.nn as nn
from matchzoo.dataloader import callbacks
from matchzoo.engine import hyper_spaces
from matchzoo.engine.base_model import BaseModel
from matchzoo.engine.param import Param
from matchzoo.engine.param_table import ParamTable
from ma... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/anmm.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/anmm.py",
"repo_id": "ContextualSP",
"token_count": 2474
} | 257 |
"""An implementation of KNRM Model."""
import typing
import torch
import torch.nn as nn
import torch.nn.functional as F
from matchzoo.engine.param_table import ParamTable
from matchzoo.engine.param import Param
from matchzoo.engine.base_model import BaseModel
from matchzoo.engine import hyper_spaces
from matchzoo.mod... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/knrm.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/knrm.py",
"repo_id": "ContextualSP",
"token_count": 1573
} | 258 |
"""Semantic composite module for DIIN model."""
import typing
import torch
import torch.nn as nn
class SemanticComposite(nn.Module):
"""
SemanticComposite module.
Apply a self-attention layer and a semantic composite fuse gate to compute the
encoding result of one tensor.
:param in_features: Fe... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/semantic_composite.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/semantic_composite.py",
"repo_id": "ContextualSP",
"token_count": 916
} | 259 |
import numpy as np
from .unit import Unit
class MatchingHistogram(Unit):
"""
MatchingHistogramUnit Class.
:param bin_size: The number of bins of the matching histogram.
:param embedding_matrix: The word embedding matrix applied to calculate
the matching histogram.
:p... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/matching_histogram.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/matching_histogram.py",
"repo_id": "ContextualSP",
"token_count": 1080
} | 260 |
"""Base Trainer."""
import typing
from pathlib import Path
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import matchzoo
from matchzoo import tasks
from matchzoo.dataloader import DataLoader
from matchzoo.engine.base_model import Base... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/trainers/trainer.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/trainers/trainer.py",
"repo_id": "ContextualSP",
"token_count": 7080
} | 261 |
import pytest
import matchzoo as mz
from matchzoo import preprocessors
from matchzoo.dataloader import callbacks
from matchzoo.dataloader import Dataset, DataLoader
from matchzoo.datasets import embeddings
from matchzoo.embedding import load_from_file
@pytest.fixture(scope='module')
def train_raw():
return mz.da... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/dataloader/test_callbacks.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/dataloader/test_callbacks.py",
"repo_id": "ContextualSP",
"token_count": 1558
} | 262 |
import torch
import numpy as np
import pandas as pd
import matchzoo as mz
import os
print('matchzoo version', mz.__version__)
model_name = "esim-mcd1"
model_path = f"../../model/traversal_path_{model_name}/"
data_path = f"../../data/"
if not os.path.exists(model_path):
os.mkdir(model_path)
task = mz.tasks.Classific... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/train_esim.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/train_esim.py",
"repo_id": "ContextualSP",
"token_count": 884
} | 263 |
<jupyter_start><jupyter_code>%run init.ipynb
preprocessor = mz.preprocessors.BasicPreprocessor(
truncated_length_left = 10,
truncated_length_right = 40,
filter_low_freq = 2
)
train_pack_processed = preprocessor.fit_transform(train_pack_raw)
dev_pack_processed = preprocessor.transform(dev_pack_raw)
test_pack... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/knrm.ipynb/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/knrm.ipynb",
"repo_id": "ContextualSP",
"token_count": 734
} | 264 |
#!/usr/bin/env bash
export seed=1
export config_file=train_configs_bert/concat.none.jsonnet
export model_file=checkpoints_cosql/cosql_bert_concat_none_model
export tables_file=dataset_cosql/tables.json
export database_path=dataset_cosql/database
export dataset_path=dataset_cosql
export train_data_path=dataset_cosql/tra... | ContextualSP/semantic_parsing_in_context/bash_files/linux/train_cosql_bert.bash/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/bash_files/linux/train_cosql_bert.bash",
"repo_id": "ContextualSP",
"token_count": 324
} | 265 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
Utility functions for reading the standardised text2sql datasets presented in
`"Improving Text to SQL Evaluation Methodology" <https://arxiv.org/abs/1806.09029>`_
"""
import json
import os
import sqlite3
from collections import defaultdict
fr... | ContextualSP/semantic_parsing_in_context/context/utils.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/context/utils.py",
"repo_id": "ContextualSP",
"token_count": 2508
} | 266 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Callable, Dict, Generic, List, TypeVar
from allennlp.nn import util
from constant import SpecialSymbol
ActionRepresentation = TypeVar('ActionRepresentation') # pylint: disable=invalid-name
class GrammarStatelet(Generic[Acti... | ContextualSP/semantic_parsing_in_context/models/states_machine/grammar_state_let.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/models/states_machine/grammar_state_let.py",
"repo_id": "ContextualSP",
"token_count": 2656
} | 267 |
# 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.
import html
import re
import xml.etree.ElementTree as ET
from collections import defaultdict
from urllib.parse impor... | ContextualSP/unified_parser_text_to_sql/genre/utils.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/genre/utils.py",
"repo_id": "ContextualSP",
"token_count": 13574
} | 268 |
import argparse
import json
import re
import subprocess
from collections import defaultdict
from re import RegexFlag
import networkx as nx
import torch
from genre.fairseq_model import GENRE, mGENRE
from genre.entity_linking import get_end_to_end_prefix_allowed_tokens_fn_fairseq as get_prefix_allowed_tokens_fn
from gen... | ContextualSP/unified_parser_text_to_sql/step3_evaluate.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/step3_evaluate.py",
"repo_id": "ContextualSP",
"token_count": 5755
} | 269 |
# model settings
input_size = 300
model = dict(
type='RetinaNet',
pretrained='/home2/hongyuan/cydas/spos/mmdetection/390.pth.tar',
backbone=dict(
type='SSDMobilenetV3',
input_size=input_size,
activation_type='relu6',
single_scale=True
),
neck=dict(
type='F... | Cream/CDARTS/CDARTS_detection/configs/CyDAS_retinanet_1x.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/configs/CyDAS_retinanet_1x.py",
"repo_id": "Cream",
"token_count": 1920
} | 270 |
from pathlib import Path
from ..utils import is_list_of, is_str
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
file_handlers = {
'json': JsonHandler(),
'yaml': YamlHandler(),
'yml': YamlHandler(),
'pickle': PickleHandler(),
'pkl': PickleHandler()
}
def load(file, ... | Cream/CDARTS/CDARTS_detection/mmcv/fileio/io.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/fileio/io.py",
"repo_id": "Cream",
"token_count": 1481
} | 271 |
import torch
from torch.nn.parallel._functions import Scatter as OrigScatter
from ._functions import Scatter
from .data_container import DataContainer
def scatter(inputs, target_gpus, dim=0):
"""Scatter inputs to target gpus.
The only difference from original :func:`scatter` is to add support for
:type:... | Cream/CDARTS/CDARTS_detection/mmcv/parallel/scatter_gather.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/parallel/scatter_gather.py",
"repo_id": "Cream",
"token_count": 838
} | 272 |
from torch.nn.utils import clip_grad
from .hook import Hook
class OptimizerHook(Hook):
def __init__(self, grad_clip=None):
self.grad_clip = grad_clip
def clip_grads(self, params):
clip_grad.clip_grad_norm_(
filter(lambda p: p.requires_grad, params), **self.grad_clip)
def aft... | Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/optimizer.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/optimizer.py",
"repo_id": "Cream",
"token_count": 466
} | 273 |
import numpy as np
from mmcv._ext import flow_warp_c
from mmcv.arraymisc import dequantize, quantize
from mmcv.image import imread, imwrite
from mmcv.utils import is_str
def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
"""Read an optical flow map.
Args:
flow_or_path (ndarr... | Cream/CDARTS/CDARTS_detection/mmcv/video/optflow.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/video/optflow.py",
"repo_id": "Cream",
"token_count": 2719
} | 274 |
from abc import ABCMeta, abstractmethod
class BaseAssigner(metaclass=ABCMeta):
@abstractmethod
def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
pass
| Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assigners/base_assigner.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assigners/base_assigner.py",
"repo_id": "Cream",
"token_count": 78
} | 275 |
import mmcv
def wider_face_classes():
return ['face']
def voc_classes():
return [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
]
... | Cream/CDARTS/CDARTS_detection/mmdet/core/evaluation/class_names.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/evaluation/class_names.py",
"repo_id": "Cream",
"token_count": 2389
} | 276 |
from collections import OrderedDict
import os
import torch.distributed as dist
from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
_take_tensors)
from mmcv.runner import OptimizerHook, OptimizerArchHook
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1)... | Cream/CDARTS/CDARTS_detection/mmdet/core/utils/dist_utils.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/utils/dist_utils.py",
"repo_id": "Cream",
"token_count": 1517
} | 277 |
import inspect
import albumentations
import mmcv
import numpy as np
from albumentations import Compose
from imagecorruptions import corrupt
from numpy import random
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from ..registry import PIPELINES
@PIPELINES.register_module
class Resize(object):
"""... | Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/transforms.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/transforms.py",
"repo_id": "Cream",
"token_count": 15385
} | 278 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from mmdet.core import AnchorGenerator, anchor_target, multi_apply
from .anchor_head import AnchorHead
from ..losses import smooth_l1_loss
from ..registry import HEADS
# TODO: add loss evaluator for... | Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/ssd_head.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/ssd_head.py",
"repo_id": "Cream",
"token_count": 4283
} | 279 |
import math
import torch.nn as nn
from mmdet.ops import DeformConv, ModulatedDeformConv
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, pl... | Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/resnext.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/resnext.py",
"repo_id": "Cream",
"token_count": 4190
} | 280 |
from .two_stage import TwoStageDetector
from ..registry import DETECTORS
@DETECTORS.register_module
class FastRCNN(TwoStageDetector):
def __init__(self,
backbone,
bbox_roi_extractor,
bbox_head,
train_cfg,
test_cfg,
... | Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/fast_rcnn.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/fast_rcnn.py",
"repo_id": "Cream",
"token_count": 969
} | 281 |
import torch.nn as nn
import torch.nn.functional as F
from mmdet.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from .utils import weight_reduce_loss
from ..registry import LOSSES
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
wei... | Cream/CDARTS/CDARTS_detection/mmdet/models/losses/focal_loss.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/losses/focal_loss.py",
"repo_id": "Cream",
"token_count": 1448
} | 282 |
import warnings
import torch.nn as nn
from mmcv.cnn import kaiming_init, constant_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
from .quant_conv import QuantConv
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
# TODO: octave conv
'QuantConv': QuantConv,
}
def build_conv_... | Cream/CDARTS/CDARTS_detection/mmdet/models/utils/conv_module.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/utils/conv_module.py",
"repo_id": "Cream",
"token_count": 2780
} | 283 |
/*!
******************* BEGIN Caffe Copyright Notice and Disclaimer ****************
*
* COPYRIGHT
*
* All contributions by the University of California:
* Copyright (c) 2014-2017 The Regents of the University of California (Regents)
* All rights reserved.
*
* All other contributions:
* Copyright (c) 2014-201... | Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu",
"repo_id": "Cream",
"token_count": 19385
} | 284 |
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#include <torch/extension.h>
template <typename scalar_t>
at::Tensor nms_cpu_kernel(const at::Tensor& dets, const float threshold) {
AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
if (dets.numel() == 0) {
return at::emp... | Cream/CDARTS/CDARTS_detection/mmdet/ops/nms/src/nms_cpu.cpp/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/nms/src/nms_cpu.cpp",
"repo_id": "Cream",
"token_count": 1055
} | 285 |
from .collect_env import collect_env
from .flops_counter import get_model_complexity_info
from .logger import get_root_logger, print_log
from .registry import Registry, build_from_cfg
__all__ = [
'Registry', 'build_from_cfg', 'get_model_complexity_info',
'get_root_logger', 'print_log', 'collect_env'
] | Cream/CDARTS/CDARTS_detection/mmdet/utils/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/utils/__init__.py",
"repo_id": "Cream",
"token_count": 110
} | 286 |
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = pars... | Cream/CDARTS/CDARTS_detection/tools/publish_model.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/tools/publish_model.py",
"repo_id": "Cream",
"token_count": 361
} | 287 |
import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from torchvision import transforms
from dataloaders import custom_transforms as tr
import pandas as pd
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 7
def __init__(self, args, root, split="train"... | Cream/CDARTS/CDARTS_segmentation/dataloaders/datasets/kd.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/dataloaders/datasets/kd.py",
"repo_id": "Cream",
"token_count": 2218
} | 288 |
import os
from yacs.config import CfgNode as CN
_C = CN()
# -----------------------------------------------------------------------------
# Misc
# -----------------------------------------------------------------------------
_C.OUTPUT_DIR = ''
_C.GPUS = (0,)
_C.WORKERS = 4
# Logging frequency
_C.PRINT_FREQ = 20
# Ch... | Cream/CDARTS/CDARTS_segmentation/segmentation/config/default.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/config/default.py",
"repo_id": "Cream",
"token_count": 3736
} | 289 |
# ------------------------------------------------------------------------------
# Reference: https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
impo... | Cream/CDARTS/CDARTS_segmentation/segmentation/model/decoder/aspp.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/model/decoder/aspp.py",
"repo_id": "Cream",
"token_count": 1233
} | 290 |
from .build import build_optimizer, build_lr_scheduler
from .lr_scheduler import WarmupMultiStepLR, WarmupCosineLR, WarmupPolyLR
from .utils import get_lr_group_id
| Cream/CDARTS/CDARTS_segmentation/segmentation/solver/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/solver/__init__.py",
"repo_id": "Cream",
"token_count": 54
} | 291 |
from .bdd import BDD
__all__ = ['BDD']
| Cream/CDARTS/CDARTS_segmentation/tools/datasets/bdd/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/tools/datasets/bdd/__init__.py",
"repo_id": "Cream",
"token_count": 18
} | 292 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from engine.logger import get_logger
logger = get_logger()
L1Loss = nn.L1Loss
MSELoss = nn.MSELoss
CrossEntropyLoss = nn.CrossEntropyLoss
class SigmoidFocalLoss(nn.Module):
def __init__(self, ignore_label, gamma=2.0, alpha=0.25,
... | Cream/CDARTS/CDARTS_segmentation/tools/seg_opr/loss_opr.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/tools/seg_opr/loss_opr.py",
"repo_id": "Cream",
"token_count": 4080
} | 293 |
# ------------------------------------------------------------------------------
# Adds `segmentation` package into Python path.
# Written by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
import os.path as osp
import sys
def add_path(path):
if... | Cream/CDARTS/CDARTS_segmentation/train/_init_paths.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/train/_init_paths.py",
"repo_id": "Cream",
"token_count": 157
} | 294 |
## NAS-Bench-201
* Main python file is
```buildoutcfg
${ROOT}/benchmark201/search.py
```
* Here we present our search script on NAS-Bench-201.
```buildoutcfg
cd benchmark201
bash run_search_cifar_1gpu.sh
```
| Cream/CDARTS/benchmark201/README.md/0 | {
"file_path": "Cream/CDARTS/benchmark201/README.md",
"repo_id": "Cream",
"token_count": 99
} | 295 |
""" Search cell """
import os
import copy
import apex
import json
import torch
import time
import math
import torch.nn as nn
import numpy as np
import torch.distributed as dist
from tensorboardX import SummaryWriter
from models.cdarts_controller import CDARTSController
from utils.visualize import plot
from utils impor... | Cream/CDARTS/benchmark201/search.py/0 | {
"file_path": "Cream/CDARTS/benchmark201/search.py",
"repo_id": "Cream",
"token_count": 5741
} | 296 |
AUTO_RESUME: True
DATA_DIR: './data/imagenet'
MODEL: 'Childnet_Testing'
RESUME_PATH: './experiments/workspace/ckps/42.pth.tar'
SAVE_PATH: './experiments/workspace/test'
SEED: 42
LOG_INTERVAL: 50
RECOVERY_INTERVAL: 0
WORKERS: 4
NUM_GPU: 2
SAVE_IMAGES: False
AMP: False
OUTPUT: 'None'
EVAL_METRICS: 'prec1'
TTA: 0
LOCAL_RA... | Cream/Cream/experiments/configs/test/test.yaml/0 | {
"file_path": "Cream/Cream/experiments/configs/test/test.yaml",
"repo_id": "Cream",
"token_count": 317
} | 297 |
from lib.utils.builder_util import *
from lib.models.builders.build_childnet import *
from timm.models.layers import SelectAdaptivePool2d
from timm.models.layers.activations import hard_sigmoid
# ChildNet Structures
class ChildNet(nn.Module):
def __init__(
self,
block_args,
nu... | Cream/Cream/lib/models/structures/childnet.py/0 | {
"file_path": "Cream/Cream/lib/models/structures/childnet.py",
"repo_id": "Cream",
"token_count": 2414
} | 298 |
# EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
:pushpin: This is an official PyTorch implementation of **[CVPR 2023]** - EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
> [**EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attenti... | Cream/EfficientViT/README.md/0 | {
"file_path": "Cream/EfficientViT/README.md",
"repo_id": "Cream",
"token_count": 1125
} | 299 |
"""
Testing the speed of different models
"""
import os
import torch
import torchvision
import time
import timm
from model.build import EfficientViT_M0, EfficientViT_M1, EfficientViT_M2, EfficientViT_M3, EfficientViT_M4, EfficientViT_M5
import torchvision
import utils
torch.autograd.set_grad_enabled(False)
T0 = 10
T1... | Cream/EfficientViT/classification/speed_test.py/0 | {
"file_path": "Cream/EfficientViT/classification/speed_test.py",
"repo_id": "Cream",
"token_count": 1400
} | 300 |
import argparse
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
... | Cream/EfficientViT/downstream/test.py/0 | {
"file_path": "Cream/EfficientViT/downstream/test.py",
"repo_id": "Cream",
"token_count": 4144
} | 301 |
MODEL:
TYPE: swin
NAME: swin_small_patch4_window7_224
DROP_PATH_RATE: 0.3
SWIN:
EMBED_DIM: 96
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 3, 6, 12, 24 ]
WINDOW_SIZE: 7 | Cream/MiniViT/Mini-Swin/configs/swin_small_patch4_window7_224.yaml/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/configs/swin_small_patch4_window7_224.yaml",
"repo_id": "Cream",
"token_count": 102
} | 302 |
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_, to_2tuple, DropPath
from .swin_transformer import Mlp, window_partition, window_reverse, PatchEmbed, PatchMerging
import torch.utils.checkpoint as checkpoint
class WindowAttentionDISTILL(nn.Module):
r""" Window based multi-head self a... | Cream/MiniViT/Mini-Swin/models/swin_transformer_distill.py/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/models/swin_transformer_distill.py",
"repo_id": "Cream",
"token_count": 10285
} | 303 |
# Preparation
### Install the dependencies
```bash
pip install -r requirements-training.txt
pip install -v -e .
```
### Data Preparation
We need to prepare [ImageNet-1k](http://www.image-net.org/) datasets to do zero-shot classification task.
- ImageNet-1k
ImageNet-1k contains 1.28 M images for training and 50 K i... | Cream/TinyCLIP/docs/PREPARATION.md/0 | {
"file_path": "Cream/TinyCLIP/docs/PREPARATION.md",
"repo_id": "Cream",
"token_count": 268
} | 304 |
import functools
import logging
import os
import json
import math
import random
from datetime import datetime
import numpy as np
import torch
from torch import optim
import torch.nn.functional as F
from torch.cuda.amp import GradScaler
from open_clip.model import convert_to_new_checkpoint, load_pruned_model
from open... | Cream/TinyCLIP/src/training/main.py/0 | {
"file_path": "Cream/TinyCLIP/src/training/main.py",
"repo_id": "Cream",
"token_count": 11089
} | 305 |
# --------------------------------------------------------
# TinyViT Config
# Copyright (c) 2022 Microsoft
# Based on the code: Swin Transformer
# (https://github.com/microsoft/swin-transformer)
# Adapted for TinyViT
# --------------------------------------------------------
import os
import yaml
from yacs.config im... | Cream/TinyViT/config.py/0 | {
"file_path": "Cream/TinyViT/config.py",
"repo_id": "Cream",
"token_count": 2954
} | 306 |
import numpy as np
from numpy.random import Generator, PCG64
RNG = None
class AugRandomContext:
def __init__(self, seed):
self.seed = seed
def __enter__(self):
global RNG
assert RNG is None
RNG = Generator(PCG64(seed=self.seed))
def __exit__(self, *_):
global RNG... | Cream/TinyViT/data/augmentation/aug_random.py/0 | {
"file_path": "Cream/TinyViT/data/augmentation/aug_random.py",
"repo_id": "Cream",
"token_count": 724
} | 307 |
import os
from .parser_image_folder import ParserImageFolder
from .parser_image_tar import ParserImageTar
from .parser_image_in_tar import ParserImageInTar
def create_parser(name, root, split='train', **kwargs):
name = name.lower()
name = name.split('/', 2)
prefix = ''
if len(name) > 1:
prefi... | Cream/TinyViT/data/augmentation/parsers/parser_factory.py/0 | {
"file_path": "Cream/TinyViT/data/augmentation/parsers/parser_factory.py",
"repo_id": "Cream",
"token_count": 410
} | 308 |
# The tutorial of saving teacher sparse logits
This document shows how to save and check teacher sparse soft labels.
We provide an example to store the sparse soft labels of **CLIP-ViT-Large/14-22k** on ImageNet-22k. With the pretrained teacher, **TinyViT-5/11/21M** will achieve the Top-1 accuracy of **80.7/83.2/84.8... | Cream/TinyViT/docs/SAVE_TEACHER_LOGITS.md/0 | {
"file_path": "Cream/TinyViT/docs/SAVE_TEACHER_LOGITS.md",
"repo_id": "Cream",
"token_count": 780
} | 309 |
import unittest
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import timm
from models import tiny_vit
class ModelsTestCase(unittest.TestCase):
"""Test for models.py"""
def setUp(self):
self.ckpt_names = [
('tiny_vit_5m_22... | Cream/TinyViT/tests/test_models.py/0 | {
"file_path": "Cream/TinyViT/tests/test_models.py",
"repo_id": "Cream",
"token_count": 1413
} | 310 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR model and criterion classes.
"""
import torch
import torch.nn.functional as F
from torch import nn
from util import box_ops
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_siz... | Cream/iRPE/DETR-with-iRPE/models/detr.py/0 | {
"file_path": "Cream/iRPE/DETR-with-iRPE/models/detr.py",
"repo_id": "Cream",
"token_count": 7219
} | 311 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
| Cream/iRPE/DETR-with-iRPE/util/__init__.py/0 | {
"file_path": "Cream/iRPE/DETR-with-iRPE/util/__init__.py",
"repo_id": "Cream",
"token_count": 17
} | 312 |
from functools import partial
from itertools import repeat
from torch._six import container_abcs
import logging
import os
from collections import OrderedDict
import numpy as np
import scipy
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import ... | CvT/lib/models/cls_cvt.py/0 | {
"file_path": "CvT/lib/models/cls_cvt.py",
"repo_id": "CvT",
"token_count": 11826
} | 313 |
name: project_environment
channels:
- defaults
dependencies:
- python=3.6.8
- cython=0.29.2
- numpy=1.18.1
- pip:
- azureml-sdk==0.1.0.*
- --index-url https://azuremlsdktestpypi.azureedge.net/dev/aml/office/134157926D8F
- --extra-index-url https://pypi.org/simple
- pandas==0.25.3
- pyarrow... | anomalydetector/aml_component/conda.yaml/0 | {
"file_path": "anomalydetector/aml_component/conda.yaml",
"repo_id": "anomalydetector",
"token_count": 199
} | 314 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from collections import OrderedDict
from numbers import Number
from typing import Iterable, Mapping, Sequence
import torch
import torch.nn as nn
def summary(model, input_size):
result, params_info = summary_string(model, input_... | archai/archai/common/model_summary.py/0 | {
"file_path": "archai/archai/common/model_summary.py",
"repo_id": "archai",
"token_count": 2585
} | 315 |
# 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 CocoCaptions, CocoDetection
from torchvision.transforms import ToTensor
from archai.api.dataset_provider... | archai/archai/datasets/cv/coco_dataset_provider.py/0 | {
"file_path": "archai/archai/datasets/cv/coco_dataset_provider.py",
"repo_id": "archai",
"token_count": 975
} | 316 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from overrides import overrides
from archai.api.dataset_provider import DatasetProvider
from archai.common.ordered_dict_logger import OrderedDictLogger
f... | archai/archai/discrete_search/algos/bananas.py/0 | {
"file_path": "archai/archai/discrete_search/algos/bananas.py",
"repo_id": "archai",
"token_count": 5026
} | 317 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import re
from typing import Any, Dict, Optional
import nats_bench
from overrides import overrides
from archai.discrete_search.api.archai_model import ArchaiModel
from archai.discrete_search.api.model_evaluator import ModelEvaluator
from archai... | archai/archai/discrete_search/evaluators/benchmark/natsbench_tss.py/0 | {
"file_path": "archai/archai/discrete_search/evaluators/benchmark/natsbench_tss.py",
"repo_id": "archai",
"token_count": 1543
} | 318 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as f
from overrides import overrides
from tqdm import tqdm
from archai.discrete_search.api.predictor import MeanVar, Predictor
class ... | archai/archai/discrete_search/predictors/dnn_ensemble.py/0 | {
"file_path": "archai/archai/discrete_search/predictors/dnn_ensemble.py",
"repo_id": "archai",
"token_count": 2505
} | 319 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from archai.discrete_search.search_spaces.nlp.transformer_flex.search_space import TransformerFlexSearchSpace
from archai.discrete_search.search_spaces.nlp.tfpp import TfppSearchSpace
| archai/archai/discrete_search/search_spaces/nlp/__init__.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/__init__.py",
"repo_id": "archai",
"token_count": 77
} | 320 |
'''Adapted from https://github.com/lucidrains/local-attention.'''
import math
from typing import Optional
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat, pack, unpack
from archai.discrete_search.search_spaces.config import ArchConfig
TOKEN_SELF_ATTN_V... | archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/local_attention.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/local_attention.py",
"repo_id": "archai",
"token_count": 3729
} | 321 |
# Downloaded from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/functional/toeplitz.py
""" Utilities for computing convolutions.
There are 3 equivalent views:
1. causal convolution
2. multiplication of (lower) triangular Toeplitz matrices
3. polynomial... | archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/ssm_utils/ssm_ops/toeplitz.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/ssm_utils/ssm_ops/toeplitz.py",
"repo_id": "archai",
"token_count": 2744
} | 322 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional
import torch
import torch.nn as nn
from transformers.activations import ACT2FN
from transformers.models.gpt2.modeling_gpt2 import (
GPT2MLP,
GPT2Attention,
GPT2Block,
GPT2LMHeadModel,
GPT2Model,
... | archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/modeling_gpt2_flex.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/modeling_gpt2_flex.py",
"repo_id": "archai",
"token_count": 2327
} | 323 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional
class AttentionMaskFormat:
"""Enumerate the attention mask shape."""
MaskIndexEnd = 0
MaskIndexEndAndStart = 1
AttentionMask = 2
NoMask = 3
class FusionOptions:
"""Options to control the fu... | archai/archai/onnx/optimization_utils/fusion_options.py/0 | {
"file_path": "archai/archai/onnx/optimization_utils/fusion_options.py",
"repo_id": "archai",
"token_count": 738
} | 324 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
from typing import Iterator
import torch
from torch import Tensor, autograd, nn
from torch.nn.modules.loss import _Loss
from torch.optim.optimizer import Optimizer
from archai.common import ml_utils
from archai.common.config import ... | archai/archai/supergraph/algos/darts/bilevel_optimizer.py/0 | {
"file_path": "archai/archai/supergraph/algos/darts/bilevel_optimizer.py",
"repo_id": "archai",
"token_count": 3742
} | 325 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import List
import numpy as np
from archai.supergraph.algos.divnas.wmr import Wmr
class SeqOpt:
""" Implements SeqOpt
TODO: Later on we might want to refactor this class
to be able to handle bandit feedback """... | archai/archai/supergraph/algos/divnas/seqopt.py/0 | {
"file_path": "archai/archai/supergraph/algos/divnas/seqopt.py",
"repo_id": "archai",
"token_count": 1492
} | 326 |
# Copyright 2019 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | archai/archai/supergraph/algos/nasbench101/config.py/0 | {
"file_path": "archai/archai/supergraph/algos/nasbench101/config.py",
"repo_id": "archai",
"token_count": 1453
} | 327 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import bisect
import math
import os
import random
from enum import Enum
from typing import List, Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
import tensorwatch as tw
import yaml
from tensorwatch import ModelStats
from arc... | archai/archai/supergraph/algos/petridish/petridish_utils.py/0 | {
"file_path": "archai/archai/supergraph/algos/petridish/petridish_utils.py",
"repo_id": "archai",
"token_count": 10156
} | 328 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional, Union
from torch.utils.data import ConcatDataset, Dataset, Subset
class LimitDataset(Dataset):
def __init__(self, dataset, n):
self.dataset = dataset
self.n = n
if hasattr(dataset, 'targ... | archai/archai/supergraph/datasets/limit_dataset.py/0 | {
"file_path": "archai/archai/supergraph/datasets/limit_dataset.py",
"repo_id": "archai",
"token_count": 233
} | 329 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torchvision
from overrides import overrides
from torch.utils.data import ConcatDataset
from torchvision.transforms import transforms
from archai.common import utils
from archai.common.config import Config
from archai.supergraph.datasets.d... | archai/archai/supergraph/datasets/providers/svhn_provider.py/0 | {
"file_path": "archai/archai/supergraph/datasets/providers/svhn_provider.py",
"repo_id": "archai",
"token_count": 850
} | 330 |
import os
import torch
import torch.nn as nn
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
class VGG(nn.Module):
def __init__(self, features, num_classes=10, init_weights=True):
super(VGG, self).__init__()
self.features = feature... | archai/archai/supergraph/models/vgg.py/0 | {
"file_path": "archai/archai/supergraph/models/vgg.py",
"repo_id": "archai",
"token_count": 2552
} | 331 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import random
from overrides import overrides
from torch import nn
from archai.supergraph.nas.finalizers import Finalizers
from archai.supergraph.nas.model_desc import EdgeDesc, NodeDesc
from archai.supergraph.nas.operations import Zero
class... | archai/archai/supergraph/nas/random_finalizers.py/0 | {
"file_path": "archai/archai/supergraph/nas/random_finalizers.py",
"repo_id": "archai",
"token_count": 480
} | 332 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional, Union
from overrides import overrides
from pytorch_lightning import LightningDataModule, LightningModule
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.utilities.types import (
_EVALUATE_OUT... | archai/archai/trainers/cv/pl_trainer.py/0 | {
"file_path": "archai/archai/trainers/cv/pl_trainer.py",
"repo_id": "archai",
"token_count": 1029
} | 333 |
__include__: 'darts.yaml' # just use darts defaults
| archai/confs/aug/aug_cifar.yaml/0 | {
"file_path": "archai/confs/aug/aug_cifar.yaml",
"repo_id": "archai",
"token_count": 17
} | 334 |
name: sample-nas-env
channels:
- conda-forge
- pytorch
- nvidia
dependencies:
- python=3.10
- pip
- pip:
- azure-ai-ml==1.5.0
- azure-storage-blob
- azure-data-tables
- azure-identity
- azureml-mlflow
- matplotlib
- mldesigner
- mlflow
- torch
- torchvision
- torc... | archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/conda.yaml/0 | {
"file_path": "archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/conda.yaml",
"repo_id": "archai",
"token_count": 186
} | 335 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import argparse
import torch
import json
import time
import os
from archai.discrete_search.api import SearchObjectives
from archai.discrete_search.evaluators import AvgOnnxLatency, TorchFlops
from archai.discrete_search.evaluators import TorchNumP... | archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/scripts/search.py/0 | {
"file_path": "archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/scripts/search.py",
"repo_id": "archai",
"token_count": 3129
} | 336 |
Natural Language Processing
===========================
.. toctree::
:maxdepth: 2
Fast HF Dataset Provider <nlp/fast_hf_dataset_provider.ipynb>
HF Dataset Provider <nlp/hf_dataset_provider.ipynb>
HF Trainer <nlp/hf_trainer.ipynb>
NVIDIA Dataset Provider <nlp/nvidia_dataset_provider.ipynb>
NVIDIA Tra... | archai/docs/getting_started/notebooks/nlp.rst/0 | {
"file_path": "archai/docs/getting_started/notebooks/nlp.rst",
"repo_id": "archai",
"token_count": 206
} | 337 |
API
===
Dataset Provider
----------------
.. automodule:: archai.api.dataset_provider
:members:
:undoc-members:
Trainer (Base Class)
--------------------
.. automodule:: archai.api.trainer_base
:members:
:undoc-members:
| archai/docs/reference/api/archai.api.rst/0 | {
"file_path": "archai/docs/reference/api/archai.api.rst",
"repo_id": "archai",
"token_count": 86
} | 338 |
Configuration-Based
===================
Architecture Configuration
--------------------------
.. automodule:: archai.discrete_search.search_spaces.config.arch_config
:members:
:undoc-members:
Architecture Parameter Tree
---------------------------
.. automodule:: archai.discrete_search.search_spaces.config.ar... | archai/docs/reference/api/archai.discrete_search.search_spaces.config.rst/0 | {
"file_path": "archai/docs/reference/api/archai.discrete_search.search_spaces.config.rst",
"repo_id": "archai",
"token_count": 292
} | 339 |
Gumbel-Softmax
==============
Architecture Trainer
--------------------
.. automodule:: archai.supergraph.algos.gumbelsoftmax.gs_arch_trainer
:members:
:undoc-members:
Experiment Runner
-----------------
.. automodule:: archai.supergraph.algos.gumbelsoftmax.gs_exp_runner
:members:
:undoc-members:
Final... | archai/docs/reference/api/archai.supergraph.algos.gumbelsoftmax.rst/0 | {
"file_path": "archai/docs/reference/api/archai.supergraph.algos.gumbelsoftmax.rst",
"repo_id": "archai",
"token_count": 256
} | 340 |
Trainers
========
.. toctree::
:maxdepth: 2
archai.trainers.cv
archai.trainers.nlp
Coin-Betting Optimizer
----------------------
.. automodule:: archai.trainers.coin_betting_optimizer
:members:
:undoc-members:
Cyclic Cosine Scheduler
-----------------------
.. automodule:: archai.trainers.cyclic_co... | archai/docs/reference/api/archai.trainers.rst/0 | {
"file_path": "archai/docs/reference/api/archai.trainers.rst",
"repo_id": "archai",
"token_count": 262
} | 341 |
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