text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
DATASET_PATH=path_to_dataset
MODEL_PATH=path_to_bart_large
python -m bpe_encoder \
--encoder-json $MODEL_PATH/encoder.json \
--vocab-bpe $MODEL_PATH/vocab.bpe \
--inputs $DATASET_PATH/train.src \
--outputs $DATASET_PATH/train.bpe.src \
--workers 20 \
... | ContextualSP/lemon/lemon/preprocess_finetune.bat/0 | {
"file_path": "ContextualSP/lemon/lemon/preprocess_finetune.bat",
"repo_id": "ContextualSP",
"token_count": 1124
} | 241 |
CORRECT_OPTION_TAG = "correct_option"
INCORRECT_OPTION_TAG = "incorrect_option"
CORRECT_OPTION_GOLD_TAG = "gold"
CORRECT_OPTION_TAG_LIST = [CORRECT_OPTION_TAG, CORRECT_OPTION_GOLD_TAG]
ALL_OPTION_TAG_LIST = [
CORRECT_OPTION_TAG,
CORRECT_OPTION_GOLD_TAG,
INCORRECT_OPTION_TAG,
]
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/code/allennlp_reasoning_explainqa/common/constants.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/code/allennlp_reasoning_explainqa/common/constants.py",
"repo_id": "ContextualSP",
"token_count": 129
} | 242 |
from errors.errors import corrupted_action_file, corrupted_sentences_file
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/errors/__init__.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/errors/__init__.py",
"repo_id": "ContextualSP",
"token_count": 18
} | 243 |
from typing import List, NamedTuple, Callable, TypeVar, Optional
from evaluation.metric import Metric
from text import terms
from process import ProcessSummary, Conversion, Move, Input, Output
# Question types used in functions here
QType = TypeVar("QType", Input, Output, Conversion, Move)
class QuestionScores(Name... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/scoring/question.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/scoring/question.py",
"repo_id": "ContextualSP",
"token_count": 2335
} | 244 |
The file [answers.jsonl](answers.jsonl) are the dev answers against which development predictions can be evaluated.
The file [dummy-predictions.csv](dummy-predictions.csv) is an example prediction file that can be evaluated against the answers in [answers.jsonl](answers.jsonl).
This is a prediction that every pair of ... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/data/dev/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/data/dev/README.md",
"repo_id": "ContextualSP",
"token_count": 101
} | 245 |
#!/bin/bash
GPU_NUM=16
python -m torch.distributed.launch --nproc_per_node=${GPU_NUM} nli_es.py
#python nli_es.py
| ContextualSP/logigan/pre-training/run_nli_es.sh/0 | {
"file_path": "ContextualSP/logigan/pre-training/run_nli_es.sh",
"repo_id": "ContextualSP",
"token_count": 50
} | 246 |
# coding=utf-8
"""Preprocesses a specific split of the CFQ dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import os
import string
from typing import Any, Dict, List, Text, Tuple
from absl import app
from absl im... | ContextualSP/poset_decoding/preprocess_cfq.py/0 | {
"file_path": "ContextualSP/poset_decoding/preprocess_cfq.py",
"repo_id": "ContextualSP",
"token_count": 2084
} | 247 |
# Watchers and contributors to MatchZoo repo directories/packages/files
# Please see documentation of use of CODEOWNERS file at
# https://help.github.com/articles/about-codeowners/ and
# https://github.com/blog/2392-introducing-code-owners
#
# Anybody can add themselves or a team as additional watcher or contributor
#... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/CODEOWNERS/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/CODEOWNERS",
"repo_id": "ContextualSP",
"token_count": 986
} | 248 |
from .preparer import prepare
from .preparer import Preparer
from .tuner import Tuner
from .tuner import tune
from . import tuner
| ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/auto/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/auto/__init__.py",
"repo_id": "ContextualSP",
"token_count": 38
} | 249 |
"""Basic data loader."""
import typing
import math
import numpy as np
import torch
from torch.utils import data
from matchzoo.dataloader.dataset import Dataset
from matchzoo.engine.base_callback import BaseCallback
class DataLoader(object):
"""
DataLoader that loads batches of data from a Dataset.
:par... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/dataloader.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/dataloader/dataloader.py",
"repo_id": "ContextualSP",
"token_count": 2270
} | 250 |
"""Quora Question Pairs data loader."""
import typing
from pathlib import Path
import pandas as pd
import matchzoo
from matchzoo.engine.base_task import BaseTask
_url = "https://firebasestorage.googleapis.com/v0/b/mtl-sentence" \
"-representations.appspot.com/o/data%2FQQP.zip?alt=media&" \
"token=700c... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/quora_qp/load_data.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/quora_qp/load_data.py",
"repo_id": "ContextualSP",
"token_count": 1133
} | 251 |
""":class:`BasePreprocessor` define input and ouutput for processors."""
import abc
import functools
import typing
from pathlib import Path
import dill
import matchzoo as mz
def validate_context(func):
"""Validate context in the preprocessor."""
@functools.wraps(func)
def transform_wrapper(self, *args... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/base_preprocessor.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/engine/base_preprocessor.py",
"repo_id": "ContextualSP",
"token_count": 1559
} | 252 |
"""Precision for ranking."""
import numpy as np
from matchzoo.engine.base_metric import (
BaseMetric, sort_and_couple, RankingMetric
)
class Precision(RankingMetric):
"""Precision metric."""
ALIAS = 'precision'
def __init__(self, k: int = 1, threshold: float = 0.):
"""
:class:`Preci... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/metrics/precision.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/metrics/precision.py",
"repo_id": "ContextualSP",
"token_count": 887
} | 253 |
"""An implementation of ESIM Model."""
import typing
import torch
import torch.nn as nn
from torch.nn import 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.modules import RNNDropout
from matchzoo... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/esim.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/esim.py",
"repo_id": "ContextualSP",
"token_count": 3310
} | 254 |
"""Matching module."""
import typing
import torch
import torch.nn as nn
import torch.nn.functional as F
class Matching(nn.Module):
"""
Module that computes a matching matrix between samples in two tensors.
:param normalize: Whether to L2-normalize samples along the
dot product axis before taking... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/matching.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/matching.py",
"repo_id": "ContextualSP",
"token_count": 1233
} | 255 |
import collections
import typing
import numpy as np
from .stateful_unit import StatefulUnit
class FrequencyFilter(StatefulUnit):
"""
Frequency filter unit.
:param low: Lower bound, inclusive.
:param high: Upper bound, exclusive.
:param mode: One of `tf` (term frequency), `df` (document frequenc... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/frequency_filter.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/frequency_filter.py",
"repo_id": "ContextualSP",
"token_count": 1449
} | 256 |
"""Classification task."""
from matchzoo.engine.base_task import BaseTask
class Classification(BaseTask):
"""Classification task.
Examples:
>>> classification_task = Classification(num_classes=2)
>>> classification_task.metrics = ['acc']
>>> classification_task.num_classes
2
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/tasks/classification.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/tasks/classification.py",
"repo_id": "ContextualSP",
"token_count": 698
} | 257 |
import io
import os
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
# Avoids IDE errors, but actual version is read from version.py
__version__ = None
exec(open('matchzoo/version.py').read())
short_description = 'Facilitating the design, comparison and sharing' \
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/setup.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/setup.py",
"repo_id": "ContextualSP",
"token_count": 772
} | 258 |
import numpy as np
from matchzoo.engine.base_metric import sort_and_couple
from matchzoo import metrics
def test_sort_and_couple():
l = [0, 1, 2]
s = [0.1, 0.4, 0.2]
c = sort_and_couple(l, s)
assert (c == np.array([(1, 0.4), (2, 0.2), (0, 0.1)])).all()
def test_mean_reciprocal_rank():
label = [... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/test_metrics.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/test_metrics.py",
"repo_id": "ContextualSP",
"token_count": 838
} | 259 |
<jupyter_start><jupyter_code>import sys
sys.path.append('/data/users/fyx/NTMC-Community/MatchZoo-py/')
import matchzoo as mz
%run init.ipynb
preprocessor = mz.models.DUET.get_default_preprocessor(
filter_mode='df',
filter_low_freq=2,
truncated_mode='post',
truncated_length_left=10,
truncated_length_... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/duet.ipynb/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/duet.ipynb",
"repo_id": "ContextualSP",
"token_count": 938
} | 260 |
#!/usr/bin/env bash
export model_file=checkpoints_sparc/sparc_concat_none_model
python -m allennlp.service.server_simple \
--archive-path ${model_file}/model.tar.gz \
--predictor sparc \
--include-package predictor.sparc_predictor \
--include-package dataset_reader.sparc_reader \
--include-package m... | ContextualSP/semantic_parsing_in_context/bash_files/linux/demo.bash/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/bash_files/linux/demo.bash",
"repo_id": "ContextualSP",
"token_count": 166
} | 261 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Dict
from typing import List, Optional
from constant import SpecialSymbol
from context.db_context import SparcDBContext
from context.utils import Table
Keywords = ['limit', 'des', 'asc', 'and', 'or', 'sum', 'min', 'max', 'avg... | ContextualSP/semantic_parsing_in_context/context/grammar.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/context/grammar.py",
"repo_id": "ContextualSP",
"token_count": 7814
} | 262 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import re
import sys
import traceback
from collections import namedtuple
from typing import Dict, List, Any
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from allennlp.data import Vocabulary
from context... | ContextualSP/semantic_parsing_in_context/models/sparc_parser.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/models/sparc_parser.py",
"repo_id": "ContextualSP",
"token_count": 49779
} | 263 |
# 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 copy
import logging
import os
from collections import defaultdict
from typing import Dict, List
import torch... | ContextualSP/unified_parser_text_to_sql/genre/fairseq_model.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/genre/fairseq_model.py",
"repo_id": "ContextualSP",
"token_count": 2925
} | 264 |
import os
import sqlite3
from semparse.worlds.evaluate import Evaluator, build_valid_col_units, rebuild_sql_val, rebuild_sql_col, \
build_foreign_key_map_from_json
from semparse.sql.process_sql import Schema, get_schema, get_sql
_schemas = {}
kmaps = None
def evaluate(gold, predict, db_name, db_dir, table, chec... | ContextualSP/unified_parser_text_to_sql/semparse/worlds/evaluate_spider.py/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/semparse/worlds/evaluate_spider.py",
"repo_id": "ContextualSP",
"token_count": 1260
} | 265 |
# AutoFormer: Searching Transformers for Visual Recognition
**This is an official implementation of AutoFormer.**
AutoFormer is new one-shot architecture search framework dedicated to vision transformer search. It entangles the weights of different vision transformer blocks in the same layers during supernet training... | Cream/AutoFormer/README.md/0 | {
"file_path": "Cream/AutoFormer/README.md",
"repo_id": "Cream",
"token_count": 2067
} | 266 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.utils import to_2tuple
import numpy as np
class PatchembedSuper(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, scale=False):
super(PatchembedSuper, self).__init__()
img_size = to_2... | Cream/AutoFormer/model/module/embedding_super.py/0 | {
"file_path": "Cream/AutoFormer/model/module/embedding_super.py",
"repo_id": "Cream",
"token_count": 948
} | 267 |
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import yaml
from pathlib import Path
from timm.data import Mixup
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer
from timm.utils import NativeScaler
from lib.d... | Cream/AutoFormerV2/evaluation.py/0 | {
"file_path": "Cream/AutoFormerV2/evaluation.py",
"repo_id": "Cream",
"token_count": 6389
} | 268 |
""" Search cell """
import _init_paths
import os
import copy
import json
import torch
import time
import math
import torch.nn as nn
import numpy as np
from tensorboardX import SummaryWriter
from lib.models.cdarts_controller import CDARTSController
from lib.utils.visualize import plot
from lib.utils import utils
from l... | Cream/CDARTS/CDARTS/search.py/0 | {
"file_path": "Cream/CDARTS/CDARTS/search.py",
"repo_id": "Cream",
"token_count": 10413
} | 269 |
import json
from .base import BaseFileHandler
class JsonHandler(BaseFileHandler):
def load_from_fileobj(self, file):
return json.load(file)
def dump_to_fileobj(self, obj, file, **kwargs):
json.dump(obj, file, **kwargs)
def dump_to_str(self, obj, **kwargs):
return json.dumps(obj... | Cream/CDARTS/CDARTS_detection/mmcv/fileio/handlers/json_handler.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/fileio/handlers/json_handler.py",
"repo_id": "Cream",
"token_count": 135
} | 270 |
import functools
import torch
def assert_tensor_type(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if not isinstance(args[0].data, torch.Tensor):
raise AttributeError('{} has no attribute {} for type {}'.format(
args[0].__class__.__name__, func.__name__, arg... | Cream/CDARTS/CDARTS_detection/mmcv/parallel/data_container.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/parallel/data_container.py",
"repo_id": "Cream",
"token_count": 946
} | 271 |
import datetime
import torch.nn.functional as F
import os
import os.path as osp
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
import mmcv
from .base import LoggerHook
class TextLoggerHook(LoggerHook):
def __init__(self, interval=10, ignore_last=True, reset... | Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/logger/text.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/logger/text.py",
"repo_id": "Cream",
"token_count": 2496
} | 272 |
__version__ = '0.2.12'
| Cream/CDARTS/CDARTS_detection/mmcv/version.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/version.py",
"repo_id": "Cream",
"token_count": 12
} | 273 |
from .base_assigner import BaseAssigner
from .max_iou_assigner import MaxIoUAssigner
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
__all__ = [
'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult'
]
| Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assigners/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/assigners/__init__.py",
"repo_id": "Cream",
"token_count": 111
} | 274 |
import mmcv
import numpy as np
import torch
def bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):
assert proposals.size() == gt.size()
proposals = proposals.float()
gt = gt.float()
px = (proposals[..., 0] + proposals[..., 2]) * 0.5
py = (proposals[..., 1] + proposals[..., 3]) * 0.... | Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/transforms.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/transforms.py",
"repo_id": "Cream",
"token_count": 2913
} | 275 |
import torch
from mmdet.ops.nms import nms_wrapper
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None):
"""NMS for multi-class bboxes.
Args:
multi_bboxes (Ten... | Cream/CDARTS/CDARTS_detection/mmdet/core/post_processing/bbox_nms.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/post_processing/bbox_nms.py",
"repo_id": "Cream",
"token_count": 1182
} | 276 |
from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..registry import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.... | Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/formating.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/datasets/pipelines/formating.py",
"repo_id": "Cream",
"token_count": 2590
} | 277 |
from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import (AnchorGenerator, anchor_target, anchor_inside_flags,
ga_loc_target, ga_shape_target, delta2bbox,
multi_apply, multiclass_nms, f... | Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/guided_anchor_head.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/anchor_heads/guided_anchor_head.py",
"repo_id": "Cream",
"token_count": 13135
} | 278 |
import logging
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from torch.nn.modules.batchnorm import _BatchNorm
from mmcv.cnn import constant_init, kaiming_init
from .utils import load_checkpoint
from ..registry import BACKBONES
norm_cfg = {
'BN': nn.BatchNorm2d,
'SyncBN': nn.SyncBat... | Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/mobilenetv2.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/mobilenetv2.py",
"repo_id": "Cream",
"token_count": 3779
} | 279 |
import logging
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
import torch.nn as nn
import pycocotools.mask as maskUtils
from mmdet.core import tensor2imgs, get_classes, auto_fp16
class BaseDetector(nn.Module):
"""Base class for detectors"""
__metaclass__ = ABCMeta
def __init__... | Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/base.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/base.py",
"repo_id": "Cream",
"token_count": 3061
} | 280 |
import torch.nn as nn
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t(... | Cream/CDARTS/CDARTS_detection/mmdet/models/losses/accuracy.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/losses/accuracy.py",
"repo_id": "Cream",
"token_count": 359
} | 281 |
from .res_layer import ResLayer
__all__ = ['ResLayer']
| Cream/CDARTS/CDARTS_detection/mmdet/models/shared_heads/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/shared_heads/__init__.py",
"repo_id": "Cream",
"token_count": 19
} | 282 |
from torch import nn
from ..functions.deform_pool import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/modules/deform_pool.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/dcn/modules/deform_pool.py",
"repo_id": "Cream",
"token_count": 4013
} | 283 |
from .nms_wrapper import nms, soft_nms
__all__ = ['nms', 'soft_nms']
| Cream/CDARTS/CDARTS_detection/mmdet/ops/nms/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/nms/__init__.py",
"repo_id": "Cream",
"token_count": 31
} | 284 |
#include <torch/extension.h>
#include <cmath>
#include <vector>
int ROIAlignForwardLaucher(const at::Tensor features, const at::Tensor rois,
const float spatial_scale, const int sample_num,
const int channels, const int height,
const int... | Cream/CDARTS/CDARTS_detection/mmdet/ops/roi_align/src/roi_align_cuda.cpp/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/roi_align/src/roi_align_cuda.cpp",
"repo_id": "Cream",
"token_count": 1461
} | 285 |
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='SigmoidFocalLoss',
ext_modules=[
CUDAExtension('sigmoid_focal_loss_cuda', [
'src/sigmoid_focal_loss.cpp',
'src/sigmoid_focal_loss_cuda.cu',
]),
],
cmdcla... | Cream/CDARTS/CDARTS_detection/mmdet/ops/sigmoid_focal_loss/setup.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/ops/sigmoid_focal_loss/setup.py",
"repo_id": "Cream",
"token_count": 164
} | 286 |
import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import numpy as np
from mmdet.core import voc_classes
label_ids = {name: i + 1 for i, name in enumerate(voc_classes())}
def parse_xml(args):
xml_path, img_path = args
tree = ET.parse(xml_path)
root = tree.getroot()
... | Cream/CDARTS/CDARTS_detection/tools/convert_datasets/pascal_voc.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/tools/convert_datasets/pascal_voc.py",
"repo_id": "Cream",
"token_count": 2230
} | 287 |
import os
import numpy as np
from PIL import Image
from torch.utils import data
from dataloaders import custom_transforms as tr
def twoTrainSeg(args, root):
images_base = os.path.join(root, 'leftImg8bit', 'train')
train_files = [os.path.join(looproot, filename) for looproot, _, filenames in os.walk(images_bas... | Cream/CDARTS/CDARTS_segmentation/dataloaders/datasets/cityscapes.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/dataloaders/datasets/cityscapes.py",
"repo_id": "Cream",
"token_count": 3075
} | 288 |
# ------------------------------------------------------------------------------
# Data augmentation following DeepLab
# (https://github.com/tensorflow/models/blob/master/research/deeplab/input_preprocess.py#L28).
# Written by Bowen Cheng (bcheng9@illinois.edu)
# --------------------------------------------------------... | Cream/CDARTS/CDARTS_segmentation/dataloaders/transforms/transforms.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/dataloaders/transforms/transforms.py",
"repo_id": "Cream",
"token_count": 2884
} | 289 |
# ------------------------------------------------------------------------------
# Reference: https://github.com/LikeLy-Journey/SegmenTron/blob/master/segmentron/models/backbones/xception.py
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------... | Cream/CDARTS/CDARTS_segmentation/segmentation/model/backbone/xception.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/model/backbone/xception.py",
"repo_id": "Cream",
"token_count": 5751
} | 290 |
# ------------------------------------------------------------------------------
# Generates the correct format for official evaluation code.
# Written by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
from collections import OrderedDict
import nump... | Cream/CDARTS/CDARTS_segmentation/segmentation/model/post_processing/evaluation_format.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/segmentation/model/post_processing/evaluation_format.py",
"repo_id": "Cream",
"token_count": 901
} | 291 |
import os
import sys
import logging
_default_level_name = os.getenv('ENGINE_LOGGING_LEVEL', 'INFO')
_default_level = logging.getLevelName(_default_level_name.upper())
class LogFormatter(logging.Formatter):
log_fout = None
date_full = '[%(asctime)s %(lineno)d@%(filename)s:%(name)s] '
date = '%(asctime)s '... | Cream/CDARTS/CDARTS_segmentation/tools/engine/logger.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/tools/engine/logger.py",
"repo_id": "Cream",
"token_count": 1250
} | 292 |
import torch
import cv2
cv2.setNumThreads(0)
from torch.utils import data
from utils.img_utils import random_scale, random_mirror, normalize, generate_random_crop_pos, random_crop_pad_to_shape
class TrainPre(object):
def __init__(self, config, img_mean, img_std):
self.img_mean = img_mean
self.img... | Cream/CDARTS/CDARTS_segmentation/train/dataloader.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_segmentation/train/dataloader.py",
"repo_id": "Cream",
"token_count": 1672
} | 293 |
# 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
# This file is to define the architecture of the residual block.
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3... | Cream/Cream/lib/models/blocks/residual_block.py/0 | {
"file_path": "Cream/Cream/lib/models/blocks/residual_block.py",
"repo_id": "Cream",
"token_count": 1474
} | 294 |
# 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 sys
import datetime
import torch
import numpy as np
import torch.nn as nn
import _init_paths
# import timm packages
from timm.u... | Cream/Cream/tools/train.py/0 | {
"file_path": "Cream/Cream/tools/train.py",
"repo_id": "Cream",
"token_count": 3892
} | 295 |
'''
Build the EfficientViT model family
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from .efficientvit import EfficientViT
from timm.models.registry import register_model
EfficientViT_m0 = {
'img_size': 224,
'patch_size': 16,
'embed_dim': [64, 128, 192],
'dept... | Cream/EfficientViT/classification/model/build.py/0 | {
"file_path": "Cream/EfficientViT/classification/model/build.py",
"repo_id": "Cream",
"token_count": 2999
} | 296 |
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = No... | Cream/EfficientViT/downstream/configs/_base_/default_runtime.py/0 | {
"file_path": "Cream/EfficientViT/downstream/configs/_base_/default_runtime.py",
"repo_id": "Cream",
"token_count": 156
} | 297 |
# model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type... | Cream/EfficientViT/downstream/configs/_base_/models/rpn_r50_caffe_c4.py/0 | {
"file_path": "Cream/EfficientViT/downstream/configs/_base_/models/rpn_r50_caffe_c4.py",
"repo_id": "Cream",
"token_count": 1039
} | 298 |
# Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.runner import RUNNERS, EpochBasedRunner
from .checkpoint import save_checkpoint
try:
import apex
except:
print('apex is not installed')
@... | Cream/EfficientViT/downstream/mmcv_custom/runner/epoch_based_runner.py/0 | {
"file_path": "Cream/EfficientViT/downstream/mmcv_custom/runner/epoch_based_runner.py",
"repo_id": "Cream",
"token_count": 1873
} | 299 |
import torch
import torch.nn as nn
from functools import partial
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helpers import load_pretrained
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.resnet import resnet26d, resnet50d
from timm.models.regis... | Cream/MiniViT/Mini-DeiT/mini_vision_transformer.py/0 | {
"file_path": "Cream/MiniViT/Mini-DeiT/mini_vision_transformer.py",
"repo_id": "Cream",
"token_count": 5763
} | 300 |
DATA:
IMG_SIZE: 384
MODEL:
TYPE: swin_minivit_distill
NAME: swin_base_patch4_window7_224to384_minivit
DROP_PATH_RATE: 0.2
SWIN:
EMBED_DIM: 128
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 4, 8, 16, 32 ]
WINDOW_SIZE: 12
MINIVIT:
SEPARATE_LAYERNUM_LIST: [1, 1, 9, 1]
TRAIN:
EPOCHS: 30
WARMUP_EPOCHS... | Cream/MiniViT/Mini-Swin/configs/swin_base_patch4_window7_224to384_minivit_sharenum2_adamw.yaml/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/configs/swin_base_patch4_window7_224to384_minivit_sharenum2_adamw.yaml",
"repo_id": "Cream",
"token_count": 211
} | 301 |
from .swin_transformer import SwinTransformer
from .swin_transformer_minivit import SwinTransformerMiniViT
from .swin_transformer_minivit_distill import SwinTransformerMiniViTDistill
from .swin_mlp import SwinMLP
def build_model(config):
model_type = config.MODEL.TYPE
if model_type == 'swin':
model = ... | Cream/MiniViT/Mini-Swin/models/build.py/0 | {
"file_path": "Cream/MiniViT/Mini-Swin/models/build.py",
"repo_id": "Cream",
"token_count": 3821
} | 302 |
install: ## [Local development] Upgrade pip, install requirements, install package.
python -m pip install -U pip
python -m pip install -e .
install-dev: ## [Local development] Install test requirements
python -m pip install -r requirements-test.txt
test: ## [Local development] Run unit tests
python -m pytest -x -... | Cream/TinyCLIP/Makefile/0 | {
"file_path": "Cream/TinyCLIP/Makefile",
"repo_id": "Cream",
"token_count": 91
} | 303 |
""" Setup
"""
from setuptools import setup, find_packages
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
exec(open('src/open_clip/ve... | Cream/TinyCLIP/setup.py/0 | {
"file_path": "Cream/TinyCLIP/setup.py",
"repo_id": "Cream",
"token_count": 711
} | 304 |
import os
import torch
try:
import horovod.torch as hvd
except ImportError:
hvd = None
def is_global_master(args):
return args.rank == 0
def is_local_master(args):
return args.local_rank == 0
def is_master(args, local=False):
return is_local_master(args) if local else is_global_master(args)
... | Cream/TinyCLIP/src/training/distributed.py/0 | {
"file_path": "Cream/TinyCLIP/src/training/distributed.py",
"repo_id": "Cream",
"token_count": 1846
} | 305 |
from .build import build_loader, build_transform
from .imagenet_classnames import imagenet_classnames
| Cream/TinyViT/data/__init__.py/0 | {
"file_path": "Cream/TinyViT/data/__init__.py",
"repo_id": "Cream",
"token_count": 28
} | 306 |
import os
def load_class_map(map_or_filename, root=''):
if isinstance(map_or_filename, dict):
assert dict, 'class_map dict must be non-empty'
return map_or_filename
class_map_path = map_or_filename
if not os.path.exists(class_map_path):
class_map_path = os.path.join(root, class_map... | Cream/TinyViT/data/augmentation/parsers/class_map.py/0 | {
"file_path": "Cream/TinyViT/data/augmentation/parsers/class_map.py",
"repo_id": "Cream",
"token_count": 318
} | 307 |
# --------------------------------------------------------
# TinyViT Data Sampler
# Copyright (c) 2022 Microsoft
# Refer to https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
# --------------------------------------------------------
import torch
from typing import TypeVar, Optional, Iterato... | Cream/TinyViT/data/sampler.py/0 | {
"file_path": "Cream/TinyViT/data/sampler.py",
"repo_id": "Cream",
"token_count": 2830
} | 308 |
# --------------------------------------------------------
# Optimizer
# Copyright (c) 2022 Microsoft
# Based on the code: Swin Transformer
# (https://github.com/microsoft/swin-transformer)
# --------------------------------------------------------
from torch import optim as optim
# Modified for TinyViT
from tinyvit... | Cream/TinyViT/optimizer.py/0 | {
"file_path": "Cream/TinyViT/optimizer.py",
"repo_id": "Cream",
"token_count": 902
} | 309 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import datasets
import util.misc as utils
from datasets impo... | Cream/iRPE/DETR-with-iRPE/main.py/0 | {
"file_path": "Cream/iRPE/DETR-with-iRPE/main.py",
"repo_id": "Cream",
"token_count": 5086
} | 310 |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial
from rpe_vision_transformer import VisionTransformer, _cfg
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
__all__ = [
'deit_tiny_patch... | Cream/iRPE/DeiT-with-iRPE/models.py/0 | {
"file_path": "Cream/iRPE/DeiT-with-iRPE/models.py",
"repo_id": "Cream",
"token_count": 3142
} | 311 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from timm.data import create_transform
from PIL import ImageFilter
import logging
import random
import torchvision.transforms as T
class GaussianBlur(object):
"""Gaussian blur augmentation in SimCLR http... | CvT/lib/dataset/transformas/build.py/0 | {
"file_path": "CvT/lib/dataset/transformas/build.py",
"repo_id": "CvT",
"token_count": 2206
} | 312 |
# Spectral Residual Anomaly Detection Component
This folder specifies the Spectral Residual Anomaly Detection component that can be used in Azure Machine Learning designer. The details of the Spectral Residual algorithm can be found at https://arxiv.org/pdf/1906.03821.pdf.
## Component Specification
This section des... | anomalydetector/aml_component/README.md/0 | {
"file_path": "anomalydetector/aml_component/README.md",
"repo_id": "anomalydetector",
"token_count": 1343
} | 313 |
import bisect
import numpy as np
from msanomalydetector._anomaly_kernel_cython import median_filter
# pseudo - code to generate the factors.
# factors = [1]
# for i in range(50):
# if i < 40:
# factors.append(factors[-1] / (1.15 + 0.001 * i))
# else:
# factors.append(factors[-1] / (1.25 + 0.01... | anomalydetector/msanomalydetector/boundary_utils.py/0 | {
"file_path": "anomalydetector/msanomalydetector/boundary_utils.py",
"repo_id": "anomalydetector",
"token_count": 2224
} | 314 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Unwanted files and folders
confs/
devops/
docker/
docs/
research/
scripts/
tasks/
tests/ | archai/.amltignore/0 | {
"file_path": "archai/.amltignore",
"repo_id": "archai",
"token_count": 52
} | 315 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from __future__ import annotations
import os
import pathlib
import re
import shutil
import tempfile
from pathlib import Path
from typing import Optional
from types import TracebackType
import torch
# File-related constants
CHECKPOINT_FOLDER_PREF... | archai/archai/common/file_utils.py/0 | {
"file_path": "archai/archai/common/file_utils.py",
"repo_id": "archai",
"token_count": 2198
} | 316 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Callable, List, Optional, Union
from overrides import overrides
from torch.utils.data import Dataset
from torchvision.datasets import Caltech101, Caltech256
from torchvision.transforms import ToTensor
from archai.api.dataset_... | archai/archai/datasets/cv/caltech_dataset_provider.py/0 | {
"file_path": "archai/archai/datasets/cv/caltech_dataset_provider.py",
"repo_id": "archai",
"token_count": 1021
} | 317 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# https://github.com/quark0/darts/blob/master/cnn/utils.py
import numpy as np
import torch
class CustomCutout:
"""Custom-based cutout transform."""
def __init__(self, length: int) -> None:
"""Initialize the custom-based cutout ... | archai/archai/datasets/cv/transforms/custom_cutout.py/0 | {
"file_path": "archai/archai/datasets/cv/transforms/custom_cutout.py",
"repo_id": "archai",
"token_count": 466
} | 318 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from collections import Counter, OrderedDict
from typing import List, Optional
from overrides import overrides
from archai.common.distributed_utils import sync_workers
from archai.common.file_utils import get_full_path
from archai.com... | archai/archai/datasets/nlp/tokenizer_utils/word_tokenizer.py/0 | {
"file_path": "archai/archai/datasets/nlp/tokenizer_utils/word_tokenizer.py",
"repo_id": "archai",
"token_count": 3554
} | 319 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from abc import abstractmethod
from typing import Callable
from overrides import EnforceOverrides
from archai.discrete_search.api.search_results import SearchResults
class Searcher(EnforceOverrides):
"""Abstract class for searchers.
T... | archai/archai/discrete_search/api/searcher.py/0 | {
"file_path": "archai/archai/discrete_search/api/searcher.py",
"repo_id": "archai",
"token_count": 581
} | 320 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Callable, List, Optional, Union
import ray
from overrides import overrides
from archai.discrete_search.api.archai_model import ArchaiModel
from archai.discrete_search.api.model_evaluator import (
AsyncModelEvaluator,
... | archai/archai/discrete_search/evaluators/ray.py/0 | {
"file_path": "archai/archai/discrete_search/evaluators/ray.py",
"repo_id": "archai",
"token_count": 1365
} | 321 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from __future__ import annotations
import json
import math
from collections import OrderedDict
from copy import deepcopy
from hashlib import sha1
from pathlib import Path
from typing import Any, Dict, List, MutableMapping, Optional, Tuple, Union... | archai/archai/discrete_search/search_spaces/cv/segmentation_dag/model.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/cv/segmentation_dag/model.py",
"repo_id": "archai",
"token_count": 6196
} | 322 |
from collections import namedtuple
from .mha import MHA
from .causal_self_attn import CausalSelfAttention
from .sep_conv1d import SeparableConv1d
from .sgconv import SGConv
from .sgconv3 import SGConv3
from .local_attention import LocalMHA
from .lsh_attn import LSHAttention
OP = namedtuple(
'Operation', ['cls', '... | archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/__init__.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/__init__.py",
"repo_id": "archai",
"token_count": 316
} | 323 |
# 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_shift.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/tfpp/ops/ssm_utils/ss_kernel_shift.py",
"repo_id": "archai",
"token_count": 1470
} | 324 |
# 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 PositionalEmbedding(nn.Module):
def __init__(self, d_model: int) -> None:
... | archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/mem_transformer_utils/positional_embedding.py/0 | {
"file_path": "archai/archai/discrete_search/search_spaces/nlp/transformer_flex/models/mem_transformer_utils/positional_embedding.py",
"repo_id": "archai",
"token_count": 417
} | 325 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from os import environ
from typing import List, Optional
from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions
from archai.common.ordered_dict_logger import OrderedDictLogger
logger = OrderedDictLogger(source=__name_... | archai/archai/onnx/onnx_loader.py/0 | {
"file_path": "archai/archai/onnx/onnx_loader.py",
"repo_id": "archai",
"token_count": 481
} | 326 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
from typing import List, Tuple
from overrides import overrides
from archai.common.config import Config
from archai.supergraph.algos.divnas.divop import DivOp
from archai.supergraph.nas.model_desc import (
CellType,
ConvMacro... | archai/archai/supergraph/algos/divnas/divnas_model_desc_builder.py/0 | {
"file_path": "archai/archai/supergraph/algos/divnas/divnas_model_desc_builder.py",
"repo_id": "archai",
"token_count": 1071
} | 327 |
- support only one stem in model
- enable model init strategies
- Output node is vertex_op(sum(edges)) + projected input
- Yaml:
- every cell reduction, cell stem = max_pool, model stem
- Model desc: model matrix, vetex ops
- Do we need alphas?
- Do we need to modify finalizers?
- Ops() overriding in NasBench1... | archai/archai/supergraph/algos/nasbench101/TODO.md/0 | {
"file_path": "archai/archai/supergraph/algos/nasbench101/TODO.md",
"repo_id": "archai",
"token_count": 108
} | 328 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import shutil
from overrides import overrides
from archai.common import utils
from archai.supergraph.algos.petridish.evaluater_petridish import EvaluaterPetridish
from archai.supergraph.algos.petridish.petridish_model_desc_builder imp... | archai/archai/supergraph/algos/petridish/petridish_exp_runner.py/0 | {
"file_path": "archai/archai/supergraph/algos/petridish/petridish_exp_runner.py",
"repo_id": "archai",
"token_count": 1059
} | 329 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Optional, Tuple
from torch.utils.data import DataLoader, Dataset, Sampler
from archai.common import apex_utils, utils
from archai.common.config import Config
from archai.common.ordered_dict_logger import get_global_logger
fro... | archai/archai/supergraph/datasets/data.py/0 | {
"file_path": "archai/archai/supergraph/datasets/data.py",
"repo_id": "archai",
"token_count": 3932
} | 330 |
# -*- coding: utf-8 -*-
import math
import torch.nn as nn
import torch.nn.functional as F
from archai.supergraph.models.shakeshake.shakeshake import ShakeShake, Shortcut
class ShakeBlock(nn.Module):
def __init__(self, in_ch, out_ch, stride=1):
super(ShakeBlock, self).__init__()
self.equal_io =... | archai/archai/supergraph/models/shakeshake/shake_resnet.py/0 | {
"file_path": "archai/archai/supergraph/models/shakeshake/shake_resnet.py",
"repo_id": "archai",
"token_count": 1442
} | 331 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
from typing import List, Optional, Tuple
from overrides import EnforceOverrides
from archai.common.config import Config
from archai.supergraph.nas.model_desc import (
AuxTowerDesc,
CellDesc,
CellType,
ConvMacroParams... | archai/archai/supergraph/nas/model_desc_builder.py/0 | {
"file_path": "archai/archai/supergraph/nas/model_desc_builder.py",
"repo_id": "archai",
"token_count": 8352
} | 332 |
__include__: "../datasets/cifar10.yaml" # default dataset settings are for cifar
common:
experiment_name: 'throwaway' # you should supply from command line
experiment_desc: 'throwaway'
logdir: '~/logdir'
log_prefix: 'log' # prefix for log files that will becreated (log.log and log.yaml), no log files if ''
l... | archai/confs/algos/darts.yaml/0 | {
"file_path": "archai/confs/algos/darts.yaml",
"repo_id": "archai",
"token_count": 4599
} | 333 |
# use this as overriding config for quick compile testing
common:
detect_anomaly: False # if True, PyTorch code will run 6X slower
resume: False
nas:
search:
data_parallel: False
resume: False # ignore checkpoint file if it exist
search_iters: 1
pareto:
max_reductions: 2
max_cells: 5... | archai/confs/algos/toy_common.yaml/0 | {
"file_path": "archai/confs/algos/toy_common.yaml",
"repo_id": "archai",
"token_count": 833
} | 334 |
!!python/object:archai.nas.model_desc.ModelDesc
_cell_descs:
- !!python/object:archai.nas.model_desc.CellDesc
_nodes:
- !!python/object:archai.nas.model_desc.NodeDesc
conv_params: &id001 !!python/object:archai.nas.model_desc.ConvMacroParams
ch_in: 16
ch_out: 16
edges:
- !!python/object:archa... | archai/confs/darts_models/final_model_desc1.yaml/0 | {
"file_path": "archai/confs/darts_models/final_model_desc1.yaml",
"repo_id": "archai",
"token_count": 21621
} | 335 |
Cloud-Based Search
==================
This section contains information about using Archai in the cloud.
.. toctree::
:maxdepth: 2
Azure <cloud/azure>
| archai/docs/advanced_guide/cloud.rst/0 | {
"file_path": "archai/docs/advanced_guide/cloud.rst",
"repo_id": "archai",
"token_count": 47
} | 336 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import json
from azure.ai.ml import command
from azure.ai.ml import Input, Output
from azure.ai.ml.entities import UserIdentityConfiguration
def make_train_model_command(output_path, code_dir, environment_name, id,
s... | archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/scripts/commands.py/0 | {
"file_path": "archai/docs/advanced_guide/cloud/azure/notebooks/multi_node_search/scripts/commands.py",
"repo_id": "archai",
"token_count": 2271
} | 337 |
display_name: Train a Pareto architecture from Transformer-Flex
type: command
compute: nas-gpu-cluster-NC6
inputs:
arch_config_path:
type: uri_file
path: azureml://full/path/to/architecture/configuration/file
outputs:
output_dir:
type: uri_folder
code: .
environment:
azureml:aml-archai:0.0.1
command: ... | archai/docs/advanced_guide/cloud/azure/notebooks/text_generation/src/train.yaml/0 | {
"file_path": "archai/docs/advanced_guide/cloud/azure/notebooks/text_generation/src/train.yaml",
"repo_id": "archai",
"token_count": 161
} | 338 |
Installation
============
There are various methods to install Archai, but it is recommended to use it within a virtual environment, such as ``conda`` or ``pyenv``. This ensures that the software runs in a consistent and isolated environment, and allows for easy management of installed packages and dependencies.
.. a... | archai/docs/getting_started/installation.rst/0 | {
"file_path": "archai/docs/getting_started/installation.rst",
"repo_id": "archai",
"token_count": 499
} | 339 |
from torch import nn
class MyModel(nn.Module):
def __init__(self, nb_layers: int = 5, kernel_size: int = 3, hidden_dim: int = 32):
super().__init__()
self.nb_layers = nb_layers
self.kernel_size = kernel_size
self.hidden_dim = hidden_dim
layer_list = []
for i in r... | archai/docs/getting_started/notebooks/discrete_search/model.py/0 | {
"file_path": "archai/docs/getting_started/notebooks/discrete_search/model.py",
"repo_id": "archai",
"token_count": 492
} | 340 |
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