sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bfd83901cd79a19eb8b8088f91d15de5d2c93a716e760f17ce6ab656403b2f1c | Python | 3,849 | 132 | import argparse
import gzip
import sys
from time import time
from lxml import etree
_VERSION = "20201019"
_AUTHORS = ["@keuv.grvl", "@nick-youngblut"]
def parse_args():
desc = "Convert UniRef XML to fasta. Mostly to rebuild previous UniRef databases"
epi = """DESCRIPTION: A simple script to convert UniRef X... |
ac4cf6b47f7f6e2380aa1a6e0e7de01a520b03c9dacfa143594ee8c4460698af | Python | 3,850 | 135 | from pathlib import Path
import taxopy
from genomad import utils
class Database:
def __init__(self, database_directory: Path) -> None:
self._directory = database_directory
with open(database_directory / "version.txt") as fin:
self._version = float(fin.read().strip())
@property
... |
bb2a63ad96e81edc0fc1a3c583b61957d85bc5d75d9215a696cae0ecc6e0b5ba | Python | 3,854 | 92 | import pickle
import numpy as np
from tqdm import tqdm
from rdkit import Chem
import Augmentation
import Model
from fingerprint_handler import _isglycoside
from fingerprint_handler import calculate_fingerprint
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import ... |
4e8afcdc992b5570e62052a67013b738a8cd57c62720b32cfb21530182be5d8f | Python | 3,856 | 117 | # -*- coding: utf-8 -*-
"""Process the model choice"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
from models.resnet import generate_model
from models.ltp import Model
from models.modelB import ModelB
from models.modelB_single import ModelBSingle
from models.Unet import UNet, RecombinationBlock
from torchvision... |
bdfbe4afdd2dd03e15335507dbb82f9c20322b46cb57ff378de7da1f7d5c63d7 | Python | 3,856 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
8b373fcd2cb59923ef2e342b3a9448d89361e26409af1d826c97b278147896c9 | Python | 3,857 | 103 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedd... |
a04ef67dd1b83b6c9e10f296e7a1fa46754464eae3b1f8507937d3d6edaf3315 | Python | 3,860 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
b4a9a680b7812bfdb23eb58ca20aa4ece2e673aca8a75829128a5c60d9f96802 | Python | 3,860 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
13bf867d28ed9ad80b4921078a202d32e4c77b216220c46d37c2149184c0ebca | Python | 3,861 | 81 | """Check inference and resampling after externally setting cluster sample splits."""
import numpy as np
import pytest
from sklearn.linear_model import LinearRegression
from doubleml import DoubleMLData, DoubleMLPLR
@pytest.fixture(scope="module", params=[1, 2], ids=["one-way", "two-way"])
def cluster_data(request: ... |
c2cb39a0525b2f43cba66e04fff0870a6499388be72d209d55fce16c832cc5d8 | Python | 3,861 | 100 | from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
def plot_raster(neuron_traces, vmax, ylabel):
n_neurons = neuron_traces.shape[0]
plt.figure(figsize=(15, int(n_neurons / 10) + 1))
plt.imshow(neuro... |
bdce2bffd205aaec559efd0776915de2e40f73de7ba4eb686a624e103dd4057a | Python | 3,862 | 107 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
training_splits = [0,1,4,6,7,9]
number_r... |
5d52c52a3710f30bd3048a0b1101a972f202bcd570dc54998e02f7decd8eb01f | Python | 3,863 | 100 | from typing import Annotated
from pydantic import Field, NonNegativeFloat, NonNegativeInt, PositiveInt
RegionListT = Annotated[
str | None,
Field(
description="File with a list the genes used to generate the probe sequences, leave empty if all the genes are used. For examples, see 'data/genes'."
)... |
78911fb509b3b06616c64d40d54a8085f9f71140262da60e8794d1cf403405dd | Python | 3,863 | 119 | import jax
import jax.numpy as jnp
from collections import namedtuple
'''
the semantics of jax.lax.scan() are given roughly by:
def scan(f, init, xs, length=None):
if xs is None:
xs = [None] * length
carry = init
ys = []
for x in xs:
carry, y = f(carry, x)
ys.append(y)
return carry, np.stack(ys)... |
42dc2a3549bac6ec78ce0ba23cd378d92127f90c15a2de308903332a42193483 | Python | 3,865 | 129 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
23652fd912a3b41cdda645eeeebad9ef5bcfc34468974ba95efdce7694700b3b | Python | 3,868 | 91 | import torch
import torch.nn as nn
from mmcv.runner import load_checkpoint
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .kd_one_stage import KnowledgeDistillationSingleStageDetector
@DETECTORS.register_module()
class LAD(KnowledgeDistillationSingleStageDetector):
"""Implementation... |
79f91e265ce4b4fb146579ac6163453b01effbf0e33c38eb4d67066085bd8144 | Python | 3,868 | 108 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
normal_init)
from torch.nn import BatchNorm2d
from ..builder import NECKS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedEncoder u... |
9559a1c7e7ca4df9a1b382e765240289100c371dcf4df77007cb9e7c9191e78c | Python | 3,868 | 114 | from numpy import float64, mean
import numpy as np
import multipagetiff as mtif
import multiprocessing as mp
from tqdm import tqdm
import ants
import os
def identity(x):
return x
def stack_average(paths, chunk_size=None):
"""Calculate the average over the TIFF stacks specified in paths.
This function... |
e672c718f2e25910f4ef34c3f1dbf0afba64f80f7999356d0ea6a486bbb6decb | Python | 3,872 | 108 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
training_splits = [0,1,4,6,7,9]
number_r... |
74a5c564a16f69f8b2d84ab6dd734524a136d073a56af28df5b944e82f6bf449 | Python | 3,875 | 77 | import os
import random
import sys
import numpy as np
import torch
from sklearn.utils import shuffle
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.data_preprocessing.lending_club_loan.lending_club_dataset import loan_load_three_party_data
from fedml_api.model.finance.vfl_m... |
0f7af4f07338328d998c8cb08aa8d57db251df54261692e6c4d9b08b9bd85a3d | Python | 3,880 | 122 | # -*- coding: utf-8 -*-
"""
Created on Mon Jan 17 10:09:58 2022
@author: daniel
"""
import shelve
import os
from tempotron.main import Tempotron
import numpy as np
import matplotlib.pyplot as plt
import sqlite3
import sys
import multiprocessing
import argparse
"""Parse Command Line inputs"""
pr = argparse.ArgumentPa... |
7beade5f4e6cd4e552a352c927cc0ddd1078e6e9ba2de6226dfc015ceebad441 | Python | 3,882 | 85 | import logging
import torch
from torch import nn
from fedml_api.distributed.fedavg.utils import transform_tensor_to_list
class FedAvgRobustTrainer(object):
def __init__(self, client_index, train_data_local_dict, train_data_local_num_dict, train_data_num, device, model,
poisoned_train_loader, nu... |
4894b51b6b564a41bc128c6edc76e956165dc19fd93a6816f388c60c961f60b1 | Python | 3,884 | 78 | import logging
import torch
from fedml_api.distributed.fednas.message_define import MyMessage
from fedml_core.distributed.communication.message import Message
from fedml_core.distributed.server.server_manager import ServerManager
class FedNASServerManager(ServerManager):
def __init__(self, args, comm, rank, siz... |
5e6ba4215b3943bea4436a8f529889f2a06db1f0d802bdfe6c6818bc824db6d6 | Python | 3,888 | 114 | from .dataset import Dataset, DatasetError
import os
import numpy as np
import scipy.io
# TODO: BROKEN!
class Khushaba(Dataset):
"""A Khushaba dataset.
An Khushaba dataset usually consists of three files that are within a specific
subdirectory. The implementation follows this structuring, i.e.... |
097a45fa3fb56e5de44c5c96788354babffdf1ee89c2b93a85b8c957c4e3b5d9 | Python | 3,889 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
68f5f9e9adf0964f6f0a879ec9af78db9b2f0dac3fb8898bef0f21cae3a4dabc | Python | 3,890 | 117 | # -*- coding: utf-8 -*-
"""Process the model choice"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
from models.resnet import generate_model
from models.ltp import Model
from models.modelB import ModelB
from models.modelB_single import ModelBSingle
from models.Unet import UNet, RecombinationBlock
from torchvision... |
e3030d3f711715e00c485d4b7752e2964b9ce6b4b633f104304ff058292ca318 | Python | 3,890 | 115 | import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class Read_ignore(nn.Module):
def __init__(self, start_index=1):
super(Read_ignore, self).__init__()
self.start_index = start_index
def forward(self, x):
... |
069b41d97f1697b2fe2a70190e85ae836670b0b84f16ed5ad8b6ef052b7caf30 | Python | 3,894 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/nvgesture.py'
]
checkpoint_config = dict(interval=5)
evaluation = dict(interval=5, metric='AP', save_best='AP_rgb')
optimizer = dict(
type='SGD',
lr=1e-1,
momentum=0.9,
)
optimizer_config = dict(grad_clip=None)
# lear... |
0b84c425cc08a64c12bfa7b1045f87dc08f4c889bb4fe6f67c933d49b68467a4 | Python | 3,894 | 115 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Any, Callable
import torch
import torch.distributions.transforms as torch_tf
from torch import Tensor
from sbi.samplers.importance.si... |
9c59b7f886f90b44287922dd715ce65bc2568911cef3d2a9c987479ff8afd576 | Python | 3,894 | 130 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
96101749ea98742f521a046498f0d6d18e1136a17caa94ed38837eec912fb74e | Python | 3,895 | 121 | from typing import Union, Optional, Any
from typing_extensions import Literal
import eagerpy as ep
import numpy as np
from ..models import Model
from ..criteria import Criterion
from ..distances import Distance
from .base import FlexibleDistanceMinimizationAttack
from .base import T
from .base import get_is_adversa... |
0074f89fbc74817dcc1253d4a3bd6953e3ea1771ec9cd0f5ede1d0b9191923ad | Python | 3,896 | 111 | """test_checks.py
This file is part of the test suite for keras2c
Implements tests for the checks run on the model before conversion
"""
#!/usr/bin/env python3
import unittest
import tensorflow.keras as keras
from keras2c import keras2c_main
import subprocess
import time
import numpy as np
import tensorflow as tf
tf.... |
7d10203981fb6cbce8458466a51a3bd1387d2daf6082dfc196541a17b1f25b76 | Python | 3,898 | 99 | # -*- coding: utf-8 -*-
"""
This file is part of the cerebellar-task-dependent repository (Xie et al. 2023)
Copyright (C) 2023 Marjorie Xie and Ashok Litwin-Kumar
See README for more information
Script that walks through how to set up our network model of the
cerebellar mossy fiber - granule cell - Purkinje cell moti... |
25c1dc9298e04c781b180e6d7da627f2179301d354e8b5cf249b266fccf463bf | Python | 3,899 | 112 | # Copyright (C) 2024-2025 ETH Zurich, Niels Maeder and other DASH contributors.
"""Factory for dash Trees with different properties loaded."""
from serenityff.charge.data import dash_props_tree_path
from serenityff.charge.tree.dash_tree import DASHTree, TreeType
class Forest:
@staticmethod
def get_MBIS_DASH_... |
a081fae1c28752b15c892b0ba8e2d568266243b088fc8c1e978a188dc45a9cfc | Python | 3,902 | 79 | """An unavailable optional Triton module must retain genuine CUDA-graph replay."""
import builtins
import pytest
import torch
from conftest import CAFFEINE_FILE, load_mol
from aimnet.calculators import AIMNet2Calculator
pytestmark = [pytest.mark.gpu, pytest.mark.weights]
if not torch.cuda.is_available():
pytes... |
2bac63a3998f5131b49cb9cd1a9046e1e899abd9ac5690d037940c516631010f | Python | 3,906 | 124 | from enum import Enum
from .logger import log_file_logger
from Bio import AlignIO
from Bio.Align import MultipleSeqAlignment
import numpy as np
from .exceptions import InvalidInputFileFormat
from .ecomp import read_ecomp
from .ecomp.reader import HEADER_MAGIC, EcompDecodeError
class FileFormat(Enum):
fasta = "f... |
8ecf7580faad96a93fa0c206eb73355c38dcb90acb145b7693b4c09758bf3393 | Python | 3,906 | 80 |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import jax
import jax.numpy as jnp
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
from collec... |
50a2a303626488c8b7087e296b5761084aa5fa3e5a2642ad9bdd7760d4f2002f | Python | 3,907 | 113 | from __future__ import print_function
import os
import argparse
import torch
import numpy as np
import torch.nn as nn
from torch.optim import Adam, lr_scheduler
import data
import utils
import metrics
import config_frequentist as cfg
from models.NonBayesianModels.AlexNet import AlexNet
from models.NonBayesianModels.... |
a5320604074fe8c722f7d78ce62aaadfd66e19c3a777ed1ed1c21f6065968407 | Python | 3,908 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/onehand10k.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
polic... |
9c13a15e89e48d4fee69696e26e1352372e6d5758054276919a7fa786e0cfdd8 | Python | 3,915 | 108 | import pandas as pd
from os.path import dirname, join
import numpy as np
from transformers import AutoModelForMaskedLM, AutoTokenizer
from datasets import load_dataset, Dataset
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
import torch
torch.manual_seed(0... |
b0ffc9ea636c45ce580321a737516d0de7467fe1e90e9bc6e5c016b3a3f318a7 | Python | 3,915 | 129 | import logging
from sklearn import svm, linear_model
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.linear_model import Ridge, ElasticNet, Lasso, SG... |
dd74bf83e72650b4da1d9c86718eed0aad539f47a478302fb0d49f7119508939 | Python | 3,915 | 101 | import os
import pickle as pkl
from cv_v1 import GridSearchCV, grid_convert
from util import path_check, Timer
dataset_path = "/home/francishe/Documents/DFTBrepulsive/Datasets/Au_energy_clean_dispersion.h5"
gammas_path = "/home/francishe/Documents/DFTBrepulsive/Datasets/gammas_50_5_full_aed.h5"
# dataset_path = "/home... |
f789e49780d073c7721cc7827a5de1a0117ada360a23014dd5ad27606b9b1a2b | Python | 3,916 | 140 | # -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow Layer modules complatible with pytorch."""
import tensorflow as tf
class TFReflectionPad1d(tf.keras.layers.Layer):
"""Tensorflow ReflectionPad1d module."""
def __init__(self... |
4ed48db0da64b341c7a035682984e4b08428ea29f7d7f6a3de23895beca593f8 | Python | 3,920 | 102 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
"""Base class of samplers."""
def __init__(self,
num,
pos_fraction,
neg_p... |
7165c4e7cde2621854d5d1a6d2aa31b57be8e7dd39911eb51daacaa7c773f3a2 | Python | 3,920 | 123 | # Copyright (c) OpenMMLab. All rights reserved.
import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .detectors_resnet import Bottleneck as _Bottleneck
from .detectors_resnet import DetectoRS_ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init_... |
acf23a3818fbd5a764ca9fe58f298ebc28f73638aa03d9db12fd90ded2b17cba | Python | 3,921 | 69 | import logging
import numpy as np
from fedml_api.standalone.hierarchical_fl.group import Group
from fedml_api.standalone.hierarchical_fl.client import Client
from fedml_api.standalone.fedavg.fedavg_trainer import FedAvgTrainer
class Trainer(FedAvgTrainer):
def setup_clients(self, train_data_local_num_dict, train... |
cacc14526da2d07bd19a6523ffc4a572c1b40d9c04fd374d5d24c15e866ebe68 | Python | 3,922 | 95 | from rdkit import Chem
from tqdm import tqdm
import sys
import pandas as pd
from openff.toolkit.topology import Molecule
from openff.toolkit.typing.engines.smirnoff import ForceField
sdf_test_path = "/cluster/work/igc/mlehner/test142_psi4_rest/sdf_qmugs500_mbis_collect.sdf"
sdf_test = Chem.SDMolSupplier(sdf_test_path,... |
77b92d16061e0d1c696a53d4337add3feecbe1154d08b493c1deee737385d9d2 | Python | 3,924 | 142 | import torch
from torch.autograd import grad
from tqdm import tqdm
import torch.nn as nn
import numpy as np
def leapfrog(p_0, q_0, Func, T, dt, volatile=True, use_tqdm=False):
trajectories = torch.empty((T, p_0.shape[0], 2 * p_0.shape[1]), requires_grad=False).cuda()
p = p_0
q = q_0
p.requi... |
f77b03983559f9f4498709d839f5432f1858dbab21f208e07754d5324d0f196c | Python | 3,925 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/freihand2d.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learnin... |
8f70716659f5c11971369f92759ecae054d6ebe8ffc3c86d87097d1f3ae94d23 | Python | 3,931 | 113 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anchor-bas... |
f5dda3bd2b51ea0f071ef4b933a0c26118c42d1a4454bcbd0bba6c0b7b82f2e2 | Python | 3,932 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/h36m.py'
]
evaluation = dict(interval=10, metric=['mpjpe', 'p-mpjpe'], save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
426b85dbbc4ab96ead425356a414fde2d540a96b443619040797ba2182512a0a | Python | 3,933 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/rhd2d.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='st... |
729034f766128bad161b6576a422e68a3d40d620e9675689b0920a42765fa9b3 | Python | 3,935 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/h36m.py'
]
evaluation = dict(interval=10, metric=['mpjpe', 'p-mpjpe'], save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
0d325e23cde0424018999b0d4c2954963eba23d73add5c5dafa90133d7046eea | Python | 3,936 | 114 |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
from collections import OrderedDict
from ppri... |
c22f93e59109bc2107bf3611745a9b96b1b5cb49af8bc2c27cb10ad6eda2b8dc | Python | 3,936 | 89 | """
Shivai workflow for swomed
"""
from nipype import Node, Workflow, IdentityInterface, DataGrabber
from shivai.interfaces.shiva import Shivai_Contained
from shivai.utils.container_config import configure_container_node
import os
import yaml
dummy_args = {
"SUBJECT_LIST": ['BIOMIST::SUBJECT_LIST'],
"BASE_DIR... |
c3f435e8daf41ca70e1751843f020697f6264a4545ed9182e437adab09029674 | Python | 3,937 | 93 | import unittest
import json
import pathlib
import os
from PIL import Image
import random
import shutil
import torchvision.transforms as T
import torch
import numpy as np
from exsclaim import figure
from exsclaim.figures.scale.dataset import ScaleBarDataset
class TestScaleDetection(unittest.TestCase):
def setUp(s... |
171f297876017cf4e4454d3a1b48532bf8252fcbb1aa84dda4385be9e8941de1 | Python | 3,939 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/onehand10k.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
polic... |
fb118362bce3a2bd01121f72e5d69f2368e7364a5cc218b9bbe091a6e8551221 | Python | 3,939 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/h36m.py'
]
evaluation = dict(interval=10, metric=['mpjpe', 'p-mpjpe'], save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config =... |
45df4c3efc02991442b8a04b54816a487d9da3e4d747a38addd8cd303df02939 | Python | 3,943 | 126 | import os
import os.path as osp
import PIL
from PIL import Image
from pathlib import Path
import numpy as np
import numpy.random as npr
import torch
import torchvision.transforms as tvtrans
from lib.cfg_helper import model_cfg_bank
from lib.model_zoo import get_model
from lib.model_zoo.ddim_vd import DDIMSampler_VD
fr... |
60cf6d42df1df01b25b025291bdcdbccd6546797f58b9e5f39155c691abfad4e | Python | 3,946 | 135 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_face.py'
]
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['NME'], save_best='NME')
optimizer = dict(
type='Adam',
lr=2e-3,
)
optimizer_config = dict(grad_clip=None)
# learning pol... |
73809b01b50ea36df4354884b6ac6a8efa7d68811334438377ce36e556055f8f | Python | 3,948 | 76 | # import pandas as pd
# import os
# pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', 50)
#
# '''
# take extracted data,
# add observed rain and prec (taken from monthly features) as forecasts,
# save it as fn extraction + _ObsAsForecast
# '''
# # ZA
# # fn_extraction = r'V:\foodsec\Project... |
ababbe4c645364fb5424c7844cb0bbddde8c6e886313f7bdfc1042f8bb03a38f | Python | 3,948 | 116 |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
from collections import OrderedDict
from ppri... |
7eafb260e0b9f3d60c82f94a962e37b9dd67d0c293ccba6a719258328e1f4305 | Python | 3,950 | 99 | import logging
import numpy as np
import wandb
from fedml_api.standalone.decentralized.client_dsgd import ClientDSGD
from fedml_api.standalone.decentralized.client_pushsum import ClientPushsum
from fedml_api.standalone.decentralized.topology_manager import TopologyManager
def cal_regret(client_list, client_number, ... |
21088bce36f9c57a0b553c4f6484e097f8a595c7cad1a588cc3199a2b74e762d | Python | 3,953 | 135 | #!/usr/bin/env python3
import argparse
import math
import os
import sys
class LabelInfo(object):
def __init__(self, start, end, label_id):
self.label_id = label_id
self.start = start
self.end = end
class SegInfo(object):
def __init__(self):
self.segs = []
self.start =... |
3a93fd9cad13ab4b5e5fe2f5e9b7223fa304497c7c01fb0b0f39fa53be920274 | Python | 3,956 | 120 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch.nn as nn
from mmpose.core.evaluation.top_down_eval import keypoints_from_heatmaps
class TopdownHeatmapBaseHead(nn.Module):
"""Base class for top-down heatmap heads.
All top-down heatmap h... |
77592013e6ec450a1ce5b76796364f30b1a67152370a77db8e0237bcf4fba256 | Python | 3,957 | 78 | """
dataset: MLR electrical stimulation, different params, while recording tail angle of the zebrafish larvae
input: from fig2_3_behavior_syllabus.py
output: for each stimulation produced, long format dataframe, with electrode's position, stimulation properties and summary of behavior elicited
function: adds to pre-ex... |
7376821e4e6b845aa943ceaa3d3110ea5bb05f3f101642c3d0ce94a510a51a60 | Python | 3,959 | 113 | """Test-tube helper functions for use in fitting scripts."""
import os
import sys
from test_tube import HyperOptArgumentParser
import yaml
from daart.io import export_expt_info_to_csv
from daart.io import find_experiment
from daart.io import get_expt_dir, get_model_dir, get_subdirs
# to ignore imports for sphix-auto... |
5e39e3e04df2328283a0895987342240ae2c2f1c397443493ad2f8cfe4fa4e65 | Python | 3,961 | 106 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from joblib import Parallel, delayed
from scipy.stats import gaussian_... |
db62909066eee8a2755e7c4304def21604089036b55ee888216cfa23061ed8e0 | Python | 3,964 | 107 | """Utility functions for daart package."""
import logging
import os
from daart.data import compute_sequence_pad, DataGenerator
from daart.transforms import ZScore
# to ignore imports for sphix-autoapidoc
__all__ = ['build_data_generator']
def build_data_generator(hparams: dict) -> DataGenerator:
"""Helper fun... |
bd061f977142064ef2997716866a7ea1ab8a138dc34f4501df1879343858db28 | Python | 3,967 | 125 | import numpy as np
import torch
TIMEFACTOR = 48.88821
BOLTZMAN = 0.001987191
def kinetic_energy(masses, vel, batch=None):
"""
Kinetic energy calculation for molecular dynamics format.
Args:
masses: Mass tensor of shape (natoms, 1)
vel: Velocity tensor of shape (nreplicas, natoms, 3)
... |
3d244780dca5b8acb14859998fe5355338a85aadd8f5ab263d8c13a02b5b857d | Python | 3,968 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
a8eb4d419242c0ae56f177ba21d9473df74a6bcf38c34f5a7c40358673b64eee | Python | 3,968 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
ba0db47770b346e500b814c192e89c61fed953ea89540b985966ef83c2c4709e | Python | 3,968 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
424d2b834a83bdf444008c61a80ef2fb42527bc273b3363250eb3e680ceef18b | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
4563ad5da18e842ca5a29775a68b72f19795b16767e16ef6ac30b432225f2bba | Python | 3,970 | 129 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
@LOSSES.register_module()
class AdaptiveWingLoss(nn.Module):
"""Adaptive wing loss. paper ref: 'Adaptive Wing Loss for Robust Face
Alignment via Heatmap Regression' Wang et al. ICCV'2019.
Args... |
5d9d26a20ebe7477cfbaa6a7846cfa09128ca3eae9b3fa351cd559cbd148149c | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
92b8e6dc8b6547ad08c4c625df72c030545aae80222f1005339800d94c3884b5 | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
982386b8afb01c9ebe4d693415502373097b370d34f8350fbdd3d3f8232ce277 | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
b3ec2a3ad3bf4cdf938b739f9a76470cb1b0fa971d0e0c2852b7463b509af686 | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
e1795bd60335da5e893b5ab966f6aaea22ae075f8103aa780c63e75e06dc883b | Python | 3,970 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmu... |
607c6bcd88352c4a84c03899c00ddb82fc4f8285685a58462877230b3016054e | Python | 3,975 | 100 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class ChannelMapper(BaseModule):
r"""Channel Mapper to reduce/increase channels of backbone features.
This is used to ... |
10c1fcee93e8ad37d50a762617f735979d5f432f5681eb536bd3ca0e2aeadea4 | Python | 3,976 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
312d19b4597cb3a8410e0852123322645b1a5ebdc5cfe2ffae4dd4c85ca24b28 | Python | 3,978 | 66 | """General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from --checkpoints_dir and save the results to --results_dir.
It first creates model and dataset given the option. It will hard-code some... |
6c91f472c61b04b9ca9781388ca3a28dc8706568e0e5aa608873e1503825e8c5 | Python | 3,978 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
a73dfa1248b229bb30498f28c303d2d3f59973f0fdc52eb0bd67e5aecd5e0f25 | Python | 3,978 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
03cdd4112b41187a2a2e029cf2dd891b0410ee6393a3a6af0f91b02e64527cb2 | Python | 3,979 | 115 | # -*- coding: utf-8 -*-
"""
This file is part of the cerebellar-task-dependent repository (Xie et al. 2023)
Copyright (C) 2023 Marjorie Xie and Ashok Litwin-Kumar
See README for more information
A small library for drawing functions from Gaussian processes with squared exponential
covariance function.
"""
import nu... |
e336f3504d90946aa317f44894fba55e34fdaef79817830faff08aa6f76347b1 | Python | 3,979 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mhp.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
3563326fbc3b7b2578982bf8bf688ee9739441e3c497faeb8eb0024537366024 | Python | 3,981 | 88 | import argparse
import os
import typing
import numpy as np
import pandas as pd
import sklearn
import sklearn.tree
import models.sklearn
import models.sklearn.metrics
from models.abstract import ModelClassMetaData, ReportOptions, EvaluateResults, FitResults
class RegressionTreeModelMeta(models.sklearn.SklearnModelMe... |
8653269f9027541ccf482f3021f8429ba6bcec2911fe7c97cbbfcbd63efb1944 | Python | 3,981 | 134 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/onehand10k.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
polic... |
55c005f756049f1cffc3053209dd99b02d75710fd58ca30c28e698ba23971b91 | Python | 3,983 | 133 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody_hand.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
... |
fa2704dd4d60f9557b2bbf75ea2a2be6639dc2b58e414fda73e22df5c7ea76fe | Python | 3,983 | 91 |
"""
Evaluation for Seq Design Models that maximize p(struct=S|seq) - i.e. sequence
design models made to design a sequence that matches a backbone.
"""
import torch
import numpy as np
import math
from bayes_design.model import model_dict
from bayes_design.utils import get_protein, AMINO_ACID_ORDER
def evaluate_log_... |
2bf5323a01c09f5e382c8d30551e00bb09b7cf59475d3ff3f743fe15dfe88b6c | Python | 3,984 | 126 | import os
import os.path as osp
import PIL
from PIL import Image
from pathlib import Path
import numpy as np
import numpy.random as npr
import torch
import torchvision.transforms as tvtrans
from lib.cfg_helper import model_cfg_bank
from lib.model_zoo import get_model
from lib.model_zoo.ddim_vd import DDIMSampler_VD
fr... |
82ffb89e1a60a73997199205ae3a1ebb5941c86fe3f4d8316faad27d3494a9d6 | Python | 3,984 | 102 |
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
from collections import OrderedDict
from ppri... |
52dfbeb6e641671f3063ec397e1e6a35dc13749011f0d11292413a23fffc22b0 | Python | 3,986 | 132 | import os
import cv2
import torch
from PIL import Image
import numpy as np
import model
from torchvision import transforms
from torch.functional import F
torch.set_grad_enabled(False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trans_forward = transforms.ToTensor()
trans_backward = transform... |
e576bb23249ae2dd8b0d3025d554b4b5199780ba0fd4910c3660186599eef7d5 | Python | 3,993 | 107 | import torch
import torch.nn as nn
OPS = {
'none': lambda C, stride, affine: Zero(stride),
'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect': l... |
381c33903103569e28f07cf4b79489f8a93f8c4c722fb49c212915bcf0c73ebb | Python | 3,994 | 150 | '''
Author: ----
Date: 2022-04-29 09:41:02
LastEditors: ----
LastEditTime: 2022-04-29 09:47:18
'''
import torch
import torch.nn as nn
import numpy as np
import math
def heaviside(x: torch.Tensor):
return (x >= 0.0).to(x)
class SigmoidSurrogate(torch.autograd.Function):
@staticmethod
def forward(
... |
5401b8ac03f5ba5fa5d038369d060bc2d21e850881866e51db33fd09b91b3c75 | Python | 3,998 | 98 | """CPU smoke test: custom ops register correctly across torch versions.
Registration of op schema + fake kernel happens at import time regardless of
device (the kernels are gated to CUDA only for *execution*, and wp.init() does
not require a GPU), so this runs on a CPU CI runner and catches torch-version
drift in torc... |
c646afa93ba3c931f65f404fcd1d946c6fd3b2a9bb9380bc3cc3ef35c2b4e05a | Python | 4,001 | 113 | # Author: Fabi Prezja <faprezja@fairn.fi>
# Copyright (C) 2023 Fabi Prezja
# License: MIT License (see LICENSE.txt for details)
import uuid
import os
import pandas as pd
import pprint
def export(model, kwargs, results, base_path="exports", export_model=False, additive=True):
"""
Exports the results of the tr... |
b8a2fa066b709f63371cadac68328dfc2d084c63213b5164ea6aac88792b6998 | Python | 4,005 | 110 | from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
import torch
import numpy as np
import random
import decord
from decord import VideoReader
import json
import os
from data.utils import pre_caption
decord.bridge.set_bridge("torch")
class ImageNorm(object):... |
fb4d5f685dd564eef28bb923002e40d597bec15c41954ddf8980ffedead9c396 | Python | 4,005 | 115 | import threading
from logging import getLogger
import numpy as np
from base.config import Config
logger = getLogger(__name__)
class DelayedExecutor:
"""
Executes a function after a specified delay, canceling pending calls.
This class is useful for batching frequent operations like file writes.
Whe... |
07a2ba2d3daa91a48b261b2e70a428778647a7f42504d91d8920b2cd01f94fca | Python | 4,007 | 136 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_mpii_256x256-418ffc88_20200812.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_... |
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