Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
null | grin-main/lib/datasets/pems_bay.py | import os
import numpy as np
import pandas as pd
from lib import datasets_path
from .pd_dataset import PandasDataset
from ..utils import sample_mask
class PemsBay(PandasDataset):
def __init__(self):
df, dist, mask = self.load()
self.dist = dist
super().__init__(dataframe=df, u=None, mask... | 4,199 | 36.837838 | 96 | py |
null | grin-main/lib/datasets/synthetic.py | import os.path
import numpy as np
import torch
from einops import rearrange
from torch.utils.data import Dataset, DataLoader, Subset
from lib import datasets_path
def generate_mask(shape, p_block=0.01, p_point=0.01, max_seq=1, min_seq=1, rng=None):
"""Generate mask in which 1 denotes valid values, 0 missing one... | 7,408 | 36.231156 | 112 | py |
null | grin-main/lib/fillers/__init__.py | from .filler import Filler
from .britsfiller import BRITSFiller
from .graphfiller import GraphFiller
from .rgainfiller import RGAINFiller
from .multi_imputation_filler import MultiImputationFiller
| 197 | 32 | 58 | py |
null | grin-main/lib/fillers/britsfiller.py | import torch
from . import Filler
from ..nn import BRITS
class BRITSFiller(Filler):
def training_step(self, batch, batch_idx):
# Unpack batch
batch_data, batch_preprocessing = self._unpack_batch(batch)
# Extract mask and target
mask = batch_data['mask'].clone().detach()
... | 3,431 | 39.857143 | 108 | py |
null | grin-main/lib/fillers/filler.py | import inspect
from copy import deepcopy
import pytorch_lightning as pl
import torch
from pytorch_lightning.core.decorators import auto_move_data
from pytorch_lightning.metrics import MetricCollection
from pytorch_lightning.utilities import move_data_to_device
from .. import epsilon
from ..nn.utils.metric_base import... | 12,351 | 40.589226 | 146 | py |
null | grin-main/lib/fillers/graphfiller.py | import torch
from . import Filler
from ..nn.models import MPGRUNet, GRINet, BiMPGRUNet
class GraphFiller(Filler):
def __init__(self,
model_class,
model_kwargs,
optim_class,
optim_kwargs,
loss_fn,
scaled_target=... | 5,454 | 40.015038 | 108 | py |
null | grin-main/lib/fillers/multi_imputation_filler.py | import torch
from pytorch_lightning.core.decorators import auto_move_data
from . import Filler
class MultiImputationFiller(Filler):
"""
Filler with multiple imputation outputs
"""
def __init__(self,
model_class,
model_kwargs,
optim_class,
... | 2,930 | 35.185185 | 104 | py |
null | grin-main/lib/fillers/rgainfiller.py | import torch
from torch.nn import functional as F
from .multi_imputation_filler import MultiImputationFiller
from ..nn.utils.metric_base import MaskedMetric
class MaskedBCEWithLogits(MaskedMetric):
def __init__(self,
mask_nans=False,
mask_inf=False,
compute_on_s... | 6,340 | 42.136054 | 110 | py |
null | grin-main/lib/nn/__init__.py | from .layers import *
| 22 | 10.5 | 21 | py |
null | grin-main/lib/nn/layers/__init__.py | from .rits import RITS, BRITS
from .gril import GRIL, BiGRIL
from .spatial_conv import SpatialConvOrderK
from .mpgru import MPGRUImputer
| 137 | 26.6 | 43 | py |
null | grin-main/lib/nn/layers/gcrnn.py | import torch
import torch.nn as nn
from .spatial_conv import SpatialConvOrderK
class GCGRUCell(nn.Module):
"""
Graph Convolution Gated Recurrent Unit Cell.
"""
def __init__(self, d_in, num_units, support_len, order, activation='tanh'):
"""
:param num_units: the hidden dim of rnn
... | 3,184 | 36.470588 | 117 | py |
null | grin-main/lib/nn/layers/gril.py | import torch
import torch.nn as nn
from einops import rearrange
from .spatial_conv import SpatialConvOrderK
from .gcrnn import GCGRUCell
from .spatial_attention import SpatialAttention
from ..utils.ops import reverse_tensor
class SpatialDecoder(nn.Module):
def __init__(self, d_in, d_model, d_out, support_len, or... | 11,916 | 42.652015 | 133 | py |
null | grin-main/lib/nn/layers/imputation.py | import math
import torch
from torch import nn
from torch.nn import functional as F
class ImputationLayer(nn.Module):
def __init__(self, d_in, bias=True):
super(ImputationLayer, self).__init__()
self.W = nn.Parameter(torch.Tensor(d_in, d_in))
if bias:
self.b = nn.Parameter(torc... | 897 | 29.965517 | 69 | py |
null | grin-main/lib/nn/layers/mpgru.py | import torch
from torch import nn
from .gcrnn import GCGRUCell
class MPGRUImputer(nn.Module):
def __init__(self,
input_size,
hidden_size,
ff_size=None,
u_size=None,
n_layers=1,
dropout=0.,
kerne... | 4,835 | 38.317073 | 116 | py |
null | grin-main/lib/nn/layers/rits.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from ..utils.ops import reverse_tensor
class FeatureRegression(nn.Module):
def __init__(self, input_size):
super(FeatureRegression, self).__init__()... | 5,700 | 32.145349 | 108 | py |
null | grin-main/lib/nn/layers/spatial_attention.py | import torch.nn as nn
from einops import rearrange
class SpatialAttention(nn.Module):
def __init__(self, d_in, d_model, nheads, dropout=0.):
super(SpatialAttention, self).__init__()
self.lin_in = nn.Linear(d_in, d_model)
self.self_attn = nn.MultiheadAttention(d_model, nheads, dropout=drop... | 971 | 32.517241 | 81 | py |
null | grin-main/lib/nn/layers/spatial_conv.py | import torch
from torch import nn
from ... import epsilon
class SpatialConvOrderK(nn.Module):
"""
Spatial convolution of order K with possibly different diffusion matrices (useful for directed graphs)
Efficient implementation inspired from graph-wavenet codebase
"""
def __init__(self, c_in, c_o... | 2,253 | 31.2 | 106 | py |
null | grin-main/lib/nn/models/__init__.py | from .grin import GRINet
from .brits import BRITSNet
from .mpgru import MPGRUNet, BiMPGRUNet
from .var import VARImputer
from .rgain import RGAINNet
from .rnn_imputers import BiRNNImputer, RNNImputer
| 200 | 27.714286 | 50 | py |
null | grin-main/lib/nn/models/brits.py | import torch
from torch import nn
from ..layers import BRITS
class BRITSNet(nn.Module):
def __init__(self,
d_in,
d_hidden=64):
super(BRITSNet, self).__init__()
self.birits = BRITS(input_size=d_in,
hidden_size=d_hidden)
def forward... | 1,171 | 36.806452 | 110 | py |
null | grin-main/lib/nn/models/grin.py | import torch
from einops import rearrange
from torch import nn
from ..layers import BiGRIL
from ...utils.parser_utils import str_to_bool
class GRINet(nn.Module):
def __init__(self,
adj,
d_in,
d_hidden,
d_ff,
ff_dropout,
... | 3,589 | 42.253012 | 105 | py |
null | grin-main/lib/nn/models/mpgru.py | import torch
from einops import rearrange
from torch import nn
from ..layers import MPGRUImputer, SpatialConvOrderK
from ..utils.ops import reverse_tensor
from ...utils.parser_utils import str_to_bool
class MPGRUNet(nn.Module):
def __init__(self,
adj,
d_in,
d_hi... | 8,025 | 42.857923 | 120 | py |
null | grin-main/lib/nn/models/rgain.py | import torch
from torch import nn
from .rnn_imputers import BiRNNImputer
from ...utils.parser_utils import str_to_bool
class Generator(nn.Module):
def __init__(self, d_in, d_model, d_z, dropout=0., inject_noise=True):
super(Generator, self).__init__()
self.inject_noise = inject_noise
self... | 2,564 | 36.173913 | 102 | py |
null | grin-main/lib/nn/models/rnn_imputers.py | import torch
from torch import nn
from ..utils.ops import reverse_tensor
class RNNImputer(nn.Module):
"""Fill the blanks with a 1-step-ahead GRU predictor."""
def __init__(self, d_in, d_model, concat_mask=True, detach_inputs=False, state_init='zero', d_u=0):
super(RNNImputer, self).__init__()
... | 3,972 | 37.572816 | 115 | py |
null | grin-main/lib/nn/models/var.py | import torch
from einops import rearrange
from torch import nn
from lib import epsilon
class VAR(nn.Module):
def __init__(self, order, d_in, d_out=None, steps_ahead=1, bias=True):
super(VAR, self).__init__()
self.order = order
self.d_in = d_in
self.d_out = d_out if d_out is not No... | 2,612 | 35.802817 | 82 | py |
null | grin-main/lib/nn/utils/__init__.py | 0 | 0 | 0 | py | |
null | grin-main/lib/nn/utils/metric_base.py | from functools import partial
import torch
from pytorch_lightning.metrics import Metric
from torchmetrics.utilities.checks import _check_same_shape
class MaskedMetric(Metric):
def __init__(self,
metric_fn,
mask_nans=False,
mask_inf=False,
comput... | 2,660 | 33.115385 | 87 | py |
null | grin-main/lib/nn/utils/metrics.py | from .metric_base import MaskedMetric
from .ops import mape
from torch.nn import functional as F
import torch
from torchmetrics.utilities.checks import _check_same_shape
from ... import epsilon
class MaskedMAE(MaskedMetric):
def __init__(self,
mask_nans=False,
mask_inf=False,
... | 4,835 | 39.3 | 96 | py |
null | grin-main/lib/nn/utils/ops.py | import torch
import torch.nn.functional as F
from einops import reduce
from torch.autograd import Variable
from ... import epsilon
def mae(y_hat, y, reduction='none'):
return F.l1_loss(y_hat, y, reduction=reduction)
def mape(y_hat, y):
return torch.abs((y_hat - y) / y)
def wape_loss(y_hat, y):
l = to... | 1,953 | 26.138889 | 88 | py |
null | grin-main/lib/utils/__init__.py | from .utils import *
| 21 | 10 | 20 | py |
null | grin-main/lib/utils/numpy_metrics.py | import numpy as np
def mae(y_hat, y):
return np.abs(y_hat - y).mean()
def nmae(y_hat, y):
delta = np.max(y) - np.min(y) + 1e-8
return mae(y_hat, y) * 100 / delta
def mape(y_hat, y):
return 100 * np.abs((y_hat - y) / (y + 1e-8)).mean()
def mse(y_hat, y):
return np.square(y_hat - y).mean()
d... | 1,256 | 19.274194 | 77 | py |
null | grin-main/lib/utils/parser_utils.py | import inspect
from argparse import Namespace, ArgumentParser
from typing import Union
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {'false', 'f', '0', 'no', 'n', 'off'}:
return False
elif value.lower() in {'true', 't', '1', 'yes', 'y', 'on'}:
... | 3,358 | 37.170455 | 114 | py |
null | grin-main/lib/utils/utils.py | import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import haversine_distances
def sample_mask(shape, p=0.002, p_noise=0., max_seq=1, min_seq=1, rng=None):
if rng is None:
rand = np.random.random
randint = np.random.randint
else:
rand = rng.random
randint = rn... | 8,755 | 40.107981 | 119 | py |
null | grin-main/scripts/run_baselines.py | from argparse import ArgumentParser
import numpy as np
from fancyimpute import MatrixFactorization, IterativeImputer
from sklearn.neighbors import kneighbors_graph
from lib import datasets
from lib.utils import numpy_metrics
from lib.utils.parser_utils import str_to_bool
metrics = {
'mae': numpy_metrics.masked_m... | 7,437 | 33.119266 | 108 | py |
null | grin-main/scripts/run_imputation.py | import copy
import datetime
import os
import pathlib
from argparse import ArgumentParser
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
import yaml
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLog... | 12,130 | 42.636691 | 114 | py |
null | grin-main/scripts/run_synthetic.py | import copy
import datetime
import os
import pathlib
from argparse import ArgumentParser
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
import yaml
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLog... | 9,402 | 41.355856 | 120 | py |
hats | hats-master/README.md | # Hyper-Accelerated Tree Search (HATS)
This is the repo for the paper entitled
"Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems".
The key idea of this paper is to train the network towards the optimal heuristic. You can check the [Preprint](https://arxiv.org/abs/2101.02420) online i... | 1,938 | 43.068182 | 174 | md |
hats | hats-master/draw.py | import matplotlib.pyplot as plt
import itertools
import numpy as np
from prelude import *
omega = [-3, -1, 1, 3]
constellation = np.array([x for x in itertools.product(omega, repeat=2)])
constellation = np.expand_dims(constellation, 2)
plt.figure()
plt.subplot(221)
plt.xticks(omega)
plt.yticks(omega)
plt.scatter(con... | 876 | 22.702703 | 76 | py |
hats | hats-master/model.py | import torch.nn as nn
from prelude import *
class RadiusNet(nn.Module):
def __init__(self, n_ant: int):
super(RadiusNet, self).__init__()
self.n_ant = n_ant
n = n_ant * 2
self.layer_1 = nn.Linear(n + n * n, 128)
self.layer_2 = nn.Linear(128, 64)
self.layer_3 = nn.... | 3,545 | 30.945946 | 75 | py |
hats | hats-master/prelude.py | import itertools
import numpy as np
import torch
def torch_flat(x):
return torch.flatten(x, start_dim=1)
def class_name(o):
return o.__class__.__name__
def get_bits(x: np.ndarray):
return np.where(x <= 0, 0, 1)
def count_nonzero(t: np.ndarray):
return np.count_nonzero(t)
def count_zero(t: np.... | 2,803 | 20.569231 | 96 | py |
hats | hats-master/search.py | from typing import List
from prelude import *
from tree import TreeNode, PriorityQueue, AVLTree
class AStar:
def __init__(self, heuristic=None):
self.__queue = PriorityQueue(capacity=np.inf)
self.nodes_expanded = 0
self.nodes_generated = 0
self.__heuristic = heuristic
def ma... | 4,587 | 28.6 | 73 | py |
hats | hats-master/test.py | import time
from tabulate import tabulate
from model import HeuristicNet
from search import *
from prelude import *
import matplotlib.pyplot as plt
def get_heurisitc(n_ant):
dnn = HeuristicNet(n_ant)
dnn.load()
return lambda y, R, psv: dnn.compute(y, R, psv)
def get_algorithms(n_ant, mem_size_list):
... | 5,749 | 32.625731 | 119 | py |
hats | hats-master/train.py | import random
import time
from prelude import *
from model import HeuristicNet
import torch
import torch.optim as optim
import torch.nn.functional as F
def gen_level(y, R, s, lv):
cost = np.sum(np.square(y - R @ s))
n = np.copy(s)
n[:-lv] = 0
g = tree_g(y, R, n)
lv_par = lv - 1
if lv_par ==... | 5,359 | 28.450549 | 97 | py |
hats | hats-master/tree.py | import heapq
from prelude import *
from typing import List
from sortedcontainers import SortedList
def get_path(data):
lv = tree_level(data)
if lv == 0:
path = "root"
else:
symbols = data[-lv:, :].flat
path = "".join("{:d}".format(int(x)) for x in symbols)
return path
class... | 6,218 | 24.487705 | 77 | py |
PlagueDotTxt | PlagueDotTxt-master/README.md | # Plague Dot Text
The Plague.TXT project repository contains data related to the Third Plague Pandemic. This is an ongoing project at the University of Edinburgh involving computer scientists, medical historicans and staff at the University of Edinburgh Library.
More information about the project can be found here:
... | 366 | 51.428571 | 245 | md |
PlagueDotTxt | PlagueDotTxt-master/source-data/README.md | This directory contains the source data compiled and used as part of the Plague.TXT project.
The Third Plague Pandemic was documented in over 100 outbreak reports for most major citiesaround the world. Many of them have been digitised, converted to text via optical characterrecognition (OCR) and are available via the... | 504 | 83.166667 | 279 | md |
nftopt | nftopt-master/.travis.yml | language: python
dist: trusty
sudo: false
python:
- 2.7
- 3.6
install:
- pip install -e .
script:
- pytest
| 120 | 6.5625 | 20 | yml |
nftopt | nftopt-master/README.md | # Nakanishi-Fujii-Todo method for scipy.optimize
[](https://travis-ci.org/ken-nakanishi/nftopt)
This is Nakanishi-Fujii-Todo method (arXiv:1903.12166) for `scipy.optimize.minimize`.
The optimization function have to satisfy three conditions... | 883 | 20.560976 | 123 | md |
nftopt | nftopt-master/setup.py | # -*- coding: utf-8 -*-
from setuptools import setup, find_packages
with open('README.md') as f:
readme = f.read()
with open('LICENSE') as f:
license = f.read()
setup(
name='nftopt',
version='0.0.1',
description='Nakanishi-Fujii-Todo method for scipy.optimize',
long_description=readme,
a... | 579 | 23.166667 | 65 | py |
nftopt | nftopt-master/nftopt/__init__.py | from .nft import nakanishi_fujii_todo | 37 | 37 | 37 | py |
nftopt | nftopt-master/nftopt/nft.py | from __future__ import division
import numpy as np
from scipy.optimize import OptimizeResult
def nakanishi_fujii_todo(fun, x0, args=(), maxfev=1024, reset_interval=32, eps=1e-32, callback=None, **_):
"""
Find the global minimum of a function using the nakanishi_fujii_todo
algorithm [1].
Parameters
... | 3,156 | 28.783019 | 106 | py |
nftopt | nftopt-master/tests/__init__.py | 0 | 0 | 0 | py | |
nftopt | nftopt-master/tests/test_nft.py | from __future__ import division
import numpy as np
import scipy.optimize as opt
from nftopt import nakanishi_fujii_todo
import pytest
def test_nakanishi_fujii_todo():
param = np.linspace(0, 2 * np.pi, 100)
fun = lambda x: np.sum(np.cos(x))
res = opt.minimize(
fun,
param,
options=... | 527 | 23 | 72 | py |
nni | nni-master/.readthedocs.yaml | # .readthedocs.yaml
# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the version of Python and other tools you might need
build:
os: ubuntu-20.04
tools:
python: "3.9"
nodejs: "16" # specified but actually not used
... | 1,047 | 30.757576 | 90 | yaml |
nni | nni-master/README.md | <div align="center">
<img src="docs/img/nni_logo.png" width="600"/>
</div>
<br/>
[](LICENSE)
[](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
[, [Azure](https://github.com/Azure), ... | 2,781 | 65.238095 | 435 | md |
nni | nni-master/crowdin.yml | project_id_env: CROWDIN_PROJECT_ID
api_token_env: CROWDIN_PERSONAL_TOKEN
preserve_hierarchy: true
files:
- source: /docs/en_US/**/*
ignore:
- /docs/zh_CN/**/*
translation: /docs/%locale_with_underscore%/**/%original_file_name%
- source: '/**/*.[mM][dD]'
ignore:
- '/**/*_%locale_with_undersco... | 467 | 28.25 | 73 | yml |
nni | nni-master/setup.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
Script for installation and distribution.
You can use environment variable `NNI_RELEASE` to set release version.
If release version is not set, default to a development build whose version string will be `999.dev0`.
## Prepare Environment... | 9,762 | 31.435216 | 144 | py |
nni | nni-master/setup_ts.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
Script for building TypeScript modules.
This script is called by `setup.py` and common users should avoid using this directly.
It compiles TypeScript source files in `ts` directory,
and copies (or links) JavaScript output as well as dependen... | 12,270 | 35.84985 | 137 | py |
nni | nni-master/.github/pull_request_template.md | ### Description ###
#### Test Options ####
- [ ] fast test
- [ ] full test - HPO
- [ ] full test - NAS
- [ ] full test - compression
### Checklist ###
- [ ] test case
- [ ] doc
### How to test ###
| 214 | 12.4375 | 31 | md |
nni | nni-master/.github/ISSUE_TEMPLATE/bug-report.md | ---
name: Create an issue
about: Report an issue or question while using nni instance (deployment).
---
**Describe the issue**:
**Environment**:
- NNI version:
- Training service (local|remote|pai|aml|etc):
- Client OS:
- Server OS (for remote mode only):
- Python version:
- PyTorch/TensorFlow version:
- Is conda... | 818 | 19.475 | 108 | md |
nni | nni-master/.github/ISSUE_TEMPLATE/enhancement.md | ---
name: Enhancement Request
about: Suggest an enhancement to the nni project
---
<!-- Please only use this template for submitting enhancement requests -->
**What would you like to be added**:
**Why is this needed**:
**Without this feature, how does current nni work**：
**Components that may involve changes**:
... | 367 | 19.444444 | 74 | md |
nni | nni-master/.github/ISSUE_TEMPLATE/model-compression-bug-report.md | ---
name: Create a model compression/speedup issue
about: Report an issue or question while using nni instance (deployment).
---
**Describe the bug**:
**Environment**:
- NNI version:
- Training service (local|remote|pai|aml|etc):
- Python version:
- PyTorch version:
- Cpu or cuda version:
**Reproduce the proble... | 361 | 14.083333 | 73 | md |
nni | nni-master/.github/ISSUE_TEMPLATE/model-compression-enhancement.md | ---
name: Enhancement Request(model compression/speedup)
about: Suggest an enhancement to the nni project
---
<!-- Please only use this template for submitting model compression/speedup enhancement requests -->
**Describe the feature**:
**Motivations**:
**Alternatives**:
**Components that may involve changes**:
... | 367 | 19.444444 | 100 | md |
nni | nni-master/.github/ISSUE_TEMPLATE/studentProgram.md | ---
name: Question for NNI Student Program China / NNI 学生项目问题表单
about: NNI Student Program China issue template on Github
---
<!--
Here is an issue template for NNI student program China. You are encouraged to raise concerns about any issue and share your ideas of NNI or our student program. Both Chinese and Engli... | 1,454 | 31.333333 | 206 | md |
nni | nni-master/.github/workflows/cffconvert.yml | name: cffconvert
on:
push:
paths:
- CITATION.cff
jobs:
validate:
name: "validate"
runs-on: ubuntu-latest
steps:
- name: Check out a copy of the repository
uses: actions/checkout@v2
- name: Check whether the citation metadata from CITATION.cff is valid
uses: citat... | 410 | 19.55 | 76 | yml |
nni | nni-master/.github/workflows/main.yml | # This is a basic workflow to help you get started with Actions
name: CI
# Controls when the action will run. Triggers the workflow on push or pull request
# events but only for the master branch
on:
push:
branches: [ master ]
paths:
- '**.md'
- '**.rst'
- '/docs/en_US/**'
- '/crowdi... | 3,968 | 43.595506 | 184 | yml |
nni | nni-master/docs/_removed/Release_v1.0.md |
<p align="center">
<img src=".././img/release-1-title-1.png" width="100%" />
</p>
From September 2018 to September 2019, We are still moving on …
**Great news!** With the tag of **Scalability** and **Ease of Use**, NNI v1.0 is comming. Based on the various types of [Tuning Algorithms](./Tuner/BuiltinTu... | 2,094 | 64.46875 | 640 | md |
nni | nni-master/docs/_removed/Tutorial/NNSpider.md | <!-- <style>
table, tr, td{
border: none;
}
div{
width: 300px;
height: 200px;
border: 1px solid grey;
background: #ccc;
box-sizing: border-box;
}
img{
width: 260px;
height: 160px;
margin: 20px;
}
</style> -->
<table>
<tr>
<td>
<div>
<img style="
... | 2,305 | 17.448 | 48 | md |
nni | nni-master/docs/extension/cardlinkitem.py | """
Directive "cardlinkitem" used in tutorials navigation page.
"""
import os
from docutils.parsers.rst import Directive, directives
from docutils.statemachine import StringList
from docutils import nodes
from sphinx.addnodes import pending_xref
TAG_TEMPLATE = """<span class="card-link-tag">{tag}</span>"""
TAGS_TEM... | 3,505 | 25.560606 | 126 | py |
nni | nni-master/docs/extension/codesnippetcard.py | """
Code snippet card, used in index page.
"""
from docutils.parsers.rst import Directive, directives
from docutils.statemachine import StringList
from docutils import nodes
from sphinx.addnodes import pending_xref
CARD_TEMPLATE_HEADER = """
.. raw:: html
<div class="codesnippet-card admonition">
<div cla... | 3,111 | 26.06087 | 131 | py |
nni | nni-master/docs/extension/getpartialtext.py | """
Basically same as
`sphinx gettext buidler <https://www.sphinx-doc.org/en/master/_modules/sphinx/builders/gettext.html>`_,
but only get texts from files in a whitelist.
"""
import re
from docutils import nodes
from sphinx.application import Sphinx
from sphinx.builders.gettext import MessageCatalogBuilder
class P... | 880 | 27.419355 | 103 | py |
nni | nni-master/docs/extension/inplace_translation.py | """
Sphinx inplace translation.
Please put `xxx_zh_CN.rst` alongside `xxx.rst`. When language is set to `zh_CN`,
`xxx_zh_CN.rst` will be used in place of `xxx.rst`.
If translation does not exist, it will automatically fallback to the original files, without warning.
I write this based on the example of:
https://github... | 1,218 | 30.25641 | 101 | py |
nni | nni-master/docs/extension/patch_autodoc.py | """Hack autodoc to get more fine-grained docstring rendering contol.
autodoc and autosummary didn't expose many of their controls to sphinx users via config.
To customize them, the "correct" approach seems to copy and paste all their code and rewrite some part.
To avoid doing this, I monkey-patched some of the functio... | 7,792 | 42.536313 | 129 | py |
nni | nni-master/docs/extension/toctree_check.py | """
Make sure pages that contain toctree only has a toctree,
because, if our theme is used, other contents will not be visible.
"""
import re
from docutils import nodes
from sphinx.application import Sphinx
from sphinx.addnodes import toctree
from sphinx.util.logging import getLogger
logger = getLogger('toctree_chec... | 1,480 | 26.943396 | 86 | py |
nni | nni-master/docs/extension/tutorial_links.py | """Creating hard links for tutorials in each individual topics."""
import os
import re
HEADER = """.. THIS FILE IS A COPY OF {} WITH MODIFICATIONS.
.. TO MAKE ONE TUTORIAL APPEAR IN MULTIPLE PLACES.
"""
def flatten_filename(filename):
return filename.replace('/', '_').replace('.', '_')
def copy_tutorials(app):... | 1,797 | 38.086957 | 120 | py |
nni | nni-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 13,869 | 31.712264 | 141 | py |
nni | nni-master/docs/source/tutorials/darts.py | """
Searching in DARTS search space
===============================
In this tutorial, we demonstrate how to search in the famous model space proposed in `DARTS`_.
Through this process, you will learn:
* How to use the built-in model spaces from NNI's model space hub.
* How to use one-shot exploration strategies to e... | 27,483 | 37.013831 | 136 | py |
nni | nni-master/docs/source/tutorials/hello_nas.py | """
Hello, NAS!
===========
This is the 101 tutorial of Neural Architecture Search (NAS) on NNI.
In this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.
We use multi-trial NAS as an example to show how to construct and explore a model space.
... | 13,690 | 38.684058 | 138 | py |
nni | nni-master/docs/source/tutorials/nasbench_as_dataset.py | """
Use NAS Benchmarks as Datasets
==============================
In this tutorial, we show how to use NAS Benchmarks as datasets.
For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
rather than train them one by one from scratch.
NNI has provided query tools so that users ca... | 5,557 | 28.88172 | 123 | py |
nni | nni-master/docs/source/tutorials/new_pruning_bert_glue.py | """
Pruning Bert on Task MNLI
=========================
This is a new tutorial on pruning transformer in nni v3.0 (`old tutorial <https://nni.readthedocs.io/en/v2.9/tutorials/pruning_bert_glue.html>`__).
The main difference between this tutorial and the previous is that it integrates the feature of fusion compression ... | 23,836 | 37.571197 | 169 | py |
nni | nni-master/docs/source/tutorials/pruning_quick_start.py | """
Pruning Quickstart
==================
Model pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.
There are three common practices for pruning a DNN model:
#. Pre-training a model -> Pruning the model -> Fine-tuning the pruned model
#. Pruning a ... | 3,639 | 32.703704 | 128 | py |
nni | nni-master/docs/source/tutorials/pruning_speedup.py | """
Speedup Model with Mask
========================
Introduction
------------
Pruning algorithms usually use weight masks to simulate the real pruning. Masks can be used
to check model performance of a specific pruning (or sparsity), but there is no real speedup.
Since model speedup is the ultimate goal of model pru... | 6,827 | 27.569038 | 153 | py |
nni | nni-master/docs/source/tutorials/quantization_bert_glue.py | """
Quantize BERT on Task GLUE
==========================
Here we show an effective transformer simulated quantization process that NNI team has tried, and users can use NNI to discover better process.
We use the BERT model and the trainer pipeline in the Transformers to do some experiments.
The entire quantization p... | 11,273 | 36.832215 | 163 | py |
nni | nni-master/docs/source/tutorials/quantization_quick_start.py | """
Quantization Quickstart
=======================
Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.
In NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.
Here we use `QATQua... | 5,582 | 31.649123 | 137 | py |
nni | nni-master/docs/source/tutorials/quantization_speedup.py | """
Speed Up Quantized Model with TensorRT
======================================
Quantization algorithms quantize a deep learning model usually in a simulated way. That is, to simulate the effect of low-bit computation with float32 operators, the tensors are quantized to the targeted bit number and dequantized back t... | 8,548 | 43.295337 | 593 | py |
nni | nni-master/docs/source/tutorials/hpo_nnictl/model.py | """
Port PyTorch Quickstart to NNI
==============================
This is a modified version of `PyTorch quickstart`_.
It can be run directly and will have the exact same result as original version.
Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
It is recom... | 3,954 | 30.388889 | 106 | py |
nni | nni-master/docs/source/tutorials/hpo_quickstart_pytorch/main.py | """
HPO Quickstart with PyTorch
===========================
This tutorial optimizes the model in `official PyTorch quickstart`_ with auto-tuning.
The tutorial consists of 4 steps:
1. Modify the model for auto-tuning.
2. Define hyperparameters' search space.
3. Configure the experiment.
4. Run the experiment.
.. _of... | 5,626 | 35.070513 | 108 | py |
nni | nni-master/docs/source/tutorials/hpo_quickstart_pytorch/model.py | """
Port PyTorch Quickstart to NNI
==============================
This is a modified version of `PyTorch quickstart`_.
It can be run directly and will have the exact same result as original version.
Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
It is recom... | 3,954 | 30.388889 | 106 | py |
nni | nni-master/docs/source/tutorials/hpo_quickstart_tensorflow/main.py | """
HPO Quickstart with TensorFlow
==============================
This tutorial optimizes the model in `official TensorFlow quickstart`_ with auto-tuning.
The tutorial consists of 4 steps:
1. Modify the model for auto-tuning.
2. Define hyperparameters' search space.
3. Configure the experiment.
4. Run the experiment... | 5,895 | 36.316456 | 108 | py |
nni | nni-master/docs/source/tutorials/hpo_quickstart_tensorflow/model.py | """
Port TensorFlow Quickstart to NNI
=================================
This is a modified version of `TensorFlow quickstart`_.
It can be run directly and will have the exact same result as original version.
Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
It... | 2,988 | 32.58427 | 106 | py |
nni | nni-master/docs/static/css/index_page.css | /* The following are for index.html */
.wy-table-responsive table td, .wy-table-responsive table th {
white-space: normal
}
.gap {
margin-top: 24px;
}
.gap2 {
margin-top: 12px;
}
.rowHeight {
line-height: 24px;
}
.title {
padding-bottom: 6px;
border-bottom: 1px solid #ccc;
}
.second-title ... | 3,167 | 13.334842 | 105 | css |
nni | nni-master/docs/static/css/material_custom.css | /* Global font */
body, input {
font-family: "Roboto", "Noto Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
}
code, kbd, pre {
font-family: "Roboto Mono", "Consolas", "Courier New", Courier, monospace;
}
h1, h2, h3, h4, .md-header, .md-tabs, .md-hero {
font-family: "Google Sans", "Noto Sans", "Hel... | 9,884 | 19.091463 | 123 | css |
nni | nni-master/docs/static/css/material_dropdown.css | /* https://codepen.io/mildrenben/pen/RPwQEY */
@media only screen and (max-width:44.9375em) {
.drop {
display: none;
}
}
.drop {
width: 5.3rem;
vertical-align: middle;
}
@media only screen and (min-width:60em) and (max-width:70em) {
.drop {
width: 4.7rem;
}
/* also narrow ... | 1,442 | 17.5 | 81 | css |
nni | nni-master/docs/static/css/material_theme.css | /* Theme related customization */
/* https://github.com/bashtage/sphinx-material/pull/122 */
/* first part */
button[data-md-color-primary=custom] {
background-color: var(--custom-color-primary)
}
[data-md-color-primary=custom] .md-typeset a {
color: var(--custom-color-primary)
}
[data-md-color-primary=custo... | 3,134 | 30.989796 | 89 | css |
nni | nni-master/docs/static/css/sphinx_gallery.css | /* Customization to sphinx-gallery generated notebooks */
p.sphx-glr-script-out {
margin: 0;
padding: 0;
font-size: .7rem;
}
.sphx-glr-script-out .highlight {
margin: 0 !important;
}
.sphx-glr-script-out .highlight pre {
background-color: #f0f6fa !important;
padding: .6rem !important;
mar... | 2,670 | 16.688742 | 99 | css |
nni | nni-master/docs/static/img/README.md | This folder is deprecated. Please do not put files here in future!
| 67 | 33 | 66 | md |
nni | nni-master/docs/templates/globaltoc.html | {% set toctree = toctree(maxdepth=theme_globaltoc_depth|toint, collapse=theme_globaltoc_collapse|tobool, includehidden=theme_globaltoc_includehidden|tobool, titles_only=True) %}
{% if toctree and sidebars and 'globaltoc.html' in sidebars %}
{% set toctree_nodes = derender_toc(toctree, False) %}
<ul class="md-nav__l... | 1,664 | 44 | 177 | html |
nni | nni-master/docs/templates/header.html | <header class="md-header" data-md-component="header">
<nav class="md-header-nav md-grid">
<div class="md-flex navheader">
<div class="md-flex__cell md-flex__cell--shrink">
<a href="{{ pathto(master_doc)|e }}" title="{{ docstitle|e }}"
class="md-header-nav__button md-logo">
<img... | 1,562 | 42.416667 | 92 | html |
nni | nni-master/docs/templates/hero.html | {% if pagename in theme_heroes %}
{% set hero = theme_heroes[pagename] %}
<div class="md-hero{% if nav_bar_tabs %} md-hero--expand{% endif %}" data-md-component="hero">
<div class="md-hero__inner md-grid">
<p>{{ hero }}</p>
</div>
<div class="md-hero__background">
<img src="{{ pathto('_static/img/hero-bac... | 372 | 30.083333 | 94 | html |
nni | nni-master/docs/templates/layout.html | {% extends "!layout.html" %}
{#- SPECIFY PARTICULAR FONTS FOR HEADERS #}
{% block font %}
<link href="https://fonts.gstatic.com/" rel="preconnect" crossorigin>
<link href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,500,700|Roboto:400,400i,500,700|Google+Sans:400,500,600|Material+Icons&display=fallback... | 1,632 | 32.326531 | 252 | html |