sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1a3fccbc9a30513011fa161b956171b56ddded90795b6368064fbf0b71de661c | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
2bccc107b54cf6ea38be451a7edd3c3897741d2e24bbca373edbd0ceecb09bd9 | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
69b492e9f03e41a0520fc63e99e01bf62097236cc6a60028e9f7102475cbf4bd | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
768d804073ab6bcede181117efb7a032773eba4057ad4b01160786c26bebd47e | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
abd0f45c7e55b798bf952c6b157c6bbd3cd9bd090cff2f9eef220cc22efdc6b1 | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
b7fd1317246796a4ff6489c46d4904575f57d3073d210c1612d87edeee3a5b37 | Python | 4,117 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
95eb86f1da6072ab3e6974e316ce263c2b81510ed2d8c7d0064274bb7ad341e6 | Python | 4,118 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
e67508432f13f1959994e7489f3c72c1dab3b42b197458a025ee97c816fc3d95 | Python | 4,118 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
52d36d06a9f4156cdb616fb4d860ade3a45f90a45cd78a5b5d68c85868f86f95 | Python | 4,119 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
9f3e8cb09b0826a1b4869db38a3890d3a5cd4956930128db3aee981808e48ff6 | Python | 4,119 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
5683eadfdc5b8bc7c7a91166af9bbb2dc55b39c827c9d09f42b8d10e0be1b820 | Python | 4,120 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
5a480dbadcb729e03d48692108eb4d00cffee832efab3465281f81fa6e0863de | Python | 4,120 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
842312c3094f37a6e99995baf983be9c79f1b6733310d0bd187ac56ce77142d6 | Python | 4,120 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
8e4f41d8392a27713be081261ab0a45476830730b610c5913e592df6e35e1e07 | Python | 4,120 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
0c6bcdf99a2062c7d344daaa36f7c05d1ebd7e18bf3b3d51932c287af9ab596e | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
9ab81afaa7773c0b48e83894377c3101cb7673b009399021961e859c0721348c | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
b41e62814853385560307af373ef34f154b8e1c161f68ffbf1bdf539a9efd030 | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
bafccce567c76b238364b30c9724a7884997b62a84dbad81fe794c58263a08e6 | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
be6b9f01b179a621042d68e123ab08251847d68204534647d78d6f36a7e17ea1 | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
d3834e936b291ab85cda36dac60c4ed2c65acd32f12d98f76aa67ef7b93cb8cf | Python | 4,121 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
516f9456788cee17a4ec5c5454ffb36ab12a4868eb0e2c28d11b32c90e1be1e7 | Python | 4,122 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
5984019abd771bf84ac1f2b8db47ae0e9e497b99807dae1c3a3841f7bb32311c | Python | 4,122 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
765749388ad28aa84ef03b98ee2ca526747da740d59b9e0014d82f1d5de0fe26 | Python | 4,122 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
8466948f6820e07435a667e5efc45b34f5c85a102deebd71a934ec1f8ba7979d | Python | 4,122 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
8744b768e757009460f6feef372569bab0d104041571337d8e815a79eeb6dbc3 | Python | 4,122 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
a14e299511901213fd1974c0f2b6f3f88d0c148ffb0f83d82c7e1c1b99a4b204 | Python | 4,122 | 91 | import pandas as pd
import os
from S_Seasonal_Forecasts import date
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 50)
'''
take extracted data,
add monthly observed cum rain and mean temp (computed from dekadal values) as forecasts,
save it as fn extraction + _ObsAsForecast
'''
# ########... |
3c071af462dc6fc093d0f84d546fff10b934e49f5646413ff2715efcefb20030 | Python | 4,123 | 98 | import torch
from torch import nn
try:
from fedml_core.trainer.model_trainer import ModelTrainer
except ImportError:
from FedML.fedml_core.trainer.model_trainer import ModelTrainer
class MyModelTrainer(ModelTrainer):
def get_model_params(self):
return self.model.cpu().state_dict()
def set_mo... |
6d82c146f6771ae81b004b4eb3227851c7f7d96ca331290c60e00e20f6efe106 | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
711bd7553221bea0bcce6574d17d608d7149d38811cf3c2c55bdb5eef8889367 | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
772d41ab39b1f4614a33e3dc990bfb020ae8693f3a349088eef6ceb36815045b | Python | 4,123 | 105 | import json
import os
import pathlib
import unittest
import numpy as np
import torchvision.transforms as T
from PIL import Image
from exsclaim import figure
class TestScaleDetection(unittest.TestCase):
def setUp(self):
"""Instantiates a test search query and FigureSeparator to test"""
self.curre... |
84fd0551b134f2f5f0a902e6433219c6ce4b7734c7c2a4c8bdeb89fa4bee202d | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
aac0700386495cfb3ff5d9ef3f056783ea1757395266eb4284570f0c9df3ccf1 | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
e4be81b9b4ec6a624dba457e95b0c96ade81122de3f9401a19c3c52405eae7e5 | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
ecfaf00fcb24c2b5ff65fc704fe633456979d9330c7f4d6ac4c2a7f358e5dcc5 | Python | 4,123 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
4d70115c59fd9e4bb2236d5ab2c0dd1eebc9c738a93d4204768133e82d599718 | Python | 4,126 | 68 | import numpy as np
import matplotlib.pyplot as plt
import jax
import jax.numpy as jnp
label_map = {
"mfc_gparam_hj_forward11": {'legend': "MFC $g$-parameter 1D -> 1D", 'linestyle': '-', 'marker': 'o', 'xlabel': r"$x$"},
"mfc_gparam_hj_forward12": {'legend': "MFC $g$-parameter 1D -> 2D", 'linestyle': '--', 'ma... |
6bfe12d45d3cdc40d9951b472d552e716e92f3d75547969cabdc870b4c9d341a | Python | 4,126 | 96 | # Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors.
from typing import List
import numpy as np
from openff.toolkit.typing.engines.smirnoff import (
ElectrostaticsHandler,
LibraryChargeHandler,
vdWHandler,
)
from openff.toolkit.typing.engines.smirnoff.parameters import _Nonbonde... |
4269050b53d9547e6123cb1bdebeb9c37b55661d564c83d05647cd159df37388 | Python | 4,129 | 75 | #!/usr/bin/env python3
import argparse
import CNVworkflowlib
description = '''
Takes a samtools depth file and a genomic interval in the format chr:start-end,
and outputs the average depth in that interval, as well as the normalized depth
(the average depth of the interval divided by the average depth in the chromoso... |
0bf11705a990029db6cb7e7fb4687ba604f2737861d35920ce708012d76521fe | Python | 4,130 | 104 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv import ConfigDict
from mmdet.models.dense_heads import DETRHead
def test_detr_head_loss():
"""Tests transformer head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_fact... |
2a3eae1beeefb3bdf37d572d0bc0b2dcb8403b4feaaa8eb63222bb879fa255a9 | Python | 4,132 | 78 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
5fe5d9c6dc0d2560c5a690d86d4c55fb6d663431e7df052bd47d425e005238be | Python | 4,132 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
c1da0ee575d787780cbec6eaf7f35d8d9d2a35076da33f0fb0e811cca3836276 | Python | 4,132 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
332078a1a1af8a51c3975a509f9852687779d5efb643d901bc92d8e0768171ad | Python | 4,133 | 123 | # -*- coding: utf-8 -*-
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Adversarial loss modules."""
import torch
import torch.nn.functional as F
class GeneratorAdversarialLoss(torch.nn.Module):
"""Generator adversarial loss module."""
def __init__(
self,
... |
cf0a9607a3d5a7a6b2ab83236a77c34625e5eade7bc733dfb8a870a7429c395c | Python | 4,133 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
c0742ce4a334e56a88d024c2e2950ce1e7884f3b89e3b3a3ec1f48db028c7843 | Python | 4,135 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
5c049037356dd11a994d50fa45bc53b24329561c66d106309c357163c01dc8d0 | Python | 4,136 | 116 | # Copyright (C) 2022-2025 ETH Zurich, Niels Maeder and other DASH contributors.
from math import ceil
from typing import List, Optional, Sequence, Tuple
import torch
from sklearn.model_selection import KFold
from sklearn.model_selection import GroupShuffleSplit
from .custom_data import CustomData
def get_split_num... |
87681a13236661f0b0ee49486819aba07cc22366878c210d31cdef174a9b0092 | Python | 4,137 | 120 | import random
import numpy as np
import torch
from PIL import Image, ImageOps, ImageFilter
from torchvision import transforms
class RandomMirror(object):
def __call__(self, sample):
img = sample['image']
mask = sample['label']
if random.random() < 0.5:
img = img.transpose(Imag... |
f4a8ac636192518b7c54e6851b520b26259a11ae06f60ed7622a1ff1c14b0c56 | Python | 4,137 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.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='... |
14e40b752842de4b6eaa7d6a7283cbfc324f5aadaa750da8f85eb26fc1ae5145 | Python | 4,138 | 120 | """Optional out-of-place small-K geometry-gradient accumulation.
Compute ``accum + left @ right`` with one read of ``accum`` and a fresh output.
Unlike an out-of-place BLAS beta operation, the kernel does not first copy the
accumulator into its output. No incoming gradient or saved tensor is mutated.
This module is i... |
e75b5ff7a4f62741efc5792a36448c06dd0e7fcc011952012a1a4fc6a088c34a | Python | 4,138 | 103 | import os.path as osp
import argparse
import numpy as np
import pandas as pd
import sklearn
import collections
from graphrepr.explain.utils import get_all_subfolders, get_all_files, get_configs
from graphrepr.explain.plot_utils import kolorex
from graphrepr.data import load_data_from_smiles
from graphrepr.config impor... |
f45e6c0b25e6ff1f8a35eff6a214757b6587b582d46504f8d5c9da7e0c2aff7b | Python | 4,139 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.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='... |
7d4e2fdb282b5477ab67666589e32ab14e3a3c36a65aa4c2b16b1c1feeea0401 | Python | 4,140 | 146 | import copy
import pytest
import torch
from daart.models import Segmenter
from daart.models.rnn import RNN
def test_lstm(hparams, data_generator, check_batch):
hp = copy.deepcopy(hparams)
hp['backbone'] = 'lstm'
hp['variational'] = False
model = Segmenter(hp)
model.to(hp['device'])
# load ... |
06067f1c50247980183f173fd3f672428bed10c0b71c1ed0153378ab5fc5b074 | Python | 4,141 | 137 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
e... |
b8da5a1cb7a5e7d147f8318369b9aa7ce7a898704f1a6e06741c07082e7cc4aa | Python | 4,141 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/macaque.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... |
c617328647e89a633dfd0e6ca9efa28093ae0890fa9a69ecb3b53da1d7cae391 | Python | 4,142 | 104 | import os
import sys
import cv2
THIS_DIR = os.path.dirname(os.path.abspath(sys.argv[0]))
class Config:
FIGURE = None
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
IMG_PATH = None
HOME = os.path.expanduser('~')
TESSDATA_PATH = f'{THIS_DIR}/../../tessdata'
SINGLE_BOND_LENGTH = 1.54
cla... |
29a6ed7762e8ce519c4ac6cc8c507fa581046735e747c45fdc91f2b15a96ded8 | Python | 4,143 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/macaque.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... |
2fb7f123365c88454804bd56ce43199cce3ac8f5e5eea94e1a088c5655c65fc6 | Python | 4,143 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/macaque.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... |
88058175afac3f0c559bea2072dfcb992499a63f1577b059918bd4cb0dd9cd36 | Python | 4,144 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
b2c7b5d414167ea6c193637d73f13c15ef977da1ebbf6afea3a79adc62693e13 | Python | 4,144 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
0212ebff673cb6fe4bb9b18f428f36930921587759c3a268bd177c3d49de5802 | Python | 4,146 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
a985fdb4ce89a29a4ef7cafe61ba97a2d2600d2bec8ed5361b7a4f8cd06c5a2e | Python | 4,146 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
d4db7b5b06201f13478e3ce5e6c952481c6761b84aa54b1f1380a968f5d8f757 | Python | 4,146 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
e53c7c4ff4a98154fd1da1aecc508a4f893e75091c9ce4c3e802578e046c54f0 | Python | 4,146 | 129 | from torchmd.forcefields.forcefield import _ForceFieldBase
from math import radians
import numpy as np
def load_parmed_parameters(fname):
"""Convenience method for reading parameter files with parmed
Parameters
----------
fname : str
Parameter file name
Returns
-------
prm : Para... |
ffc2fd7a4b1940b5ff367ad1459f8d6278cdf808a83c21bc44e77f6f245849ef | Python | 4,146 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
4a33e87b44e3c9ef58b045d4aaf7600c09c99650426ef54913691abb646e5522 | Python | 4,147 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.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',
war... |
c92c7399cdd2cbfdcb1cb314988f5178596dcf197e5192b656090dfbc5d92ac0 | Python | 4,148 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
d8d6c36b783c6c8ee75751e145effb30bbb1bde5b5407d4bc4b24f32c0ddf1ba | Python | 4,148 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
58ebe46553ae2b218013605aa8b8c756428ca872c2dac0046939c8678a194056 | Python | 4,149 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_lower.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',... |
6c00f9ae2db8481fb3a5624b4422067dda17d2dcf30b64ed0e7cfbb7f24c984e | Python | 4,149 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.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',
war... |
e89271c40d8eca0bf7c945eef9238bf3f520605111470cd76f18ac558a51a98b | Python | 4,149 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.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',
war... |
3c99a6ce418c4a1a88f1b01f5b9748d1806a760c92e82bd12e0d5f0b91733c65 | Python | 4,150 | 117 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class FusedSemanticHead(BaseModu... |
3f6ff2463ac53ef5b2ed657a096a9697a3c03b28b2cddb95e449b4f50e8d02b3 | Python | 4,150 | 101 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.runner import load_checkpoint
from .. import build_detector
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class KnowledgeDistillationSingleStageDetector(SingleStageDetector)... |
aadcd3ec4936d9c9531256c499da19953b3a383f5d22a3883246045a2be0559d | Python | 4,150 | 158 | """
Methods for plotting distributions.
"""
import matplotlib.pyplot as plt
import numpy as np
from numpy import nan
from .statistics import *
def plot_ccdf(data, xmin=None, xmax=None, ax=None, **kwargs):
"""
Plots the complementary cumulative distribution function (CDF) of the data
to a new figure or to... |
f4ba7696869bf340c301f1f840526826d19e3ce4d9eba05ab6e422388c70a973 | Python | 4,150 | 133 |
__author__ = "Grzegorz Chojnowski"
__date__ = "21 Apr 2021"
import os,sys,re
ROOT = os.path.dirname(os.path.realpath(__file__))
import ctypes
from numpy.ctypeslib import ndpointer
import numpy as np
USE_TORCH = 0
if sys.platform.startswith('win'):
NN_LIBNAME = os.path.join(ROOT, 'doublehelix_nnmodel_win64... |
da39fd7ca0011c8e4a005de90b12138763e2a6093cab54c4d0405ba3a2445758 | Python | 4,154 | 135 | import numpy as np
import tensorflow as tf
import random as python_random
import os
import tensorflow_datasets as tfds
from tensorflow.keras.models import Model, load_model
from gradient_accumulator import GradientAccumulateModel
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, UpSampling2D,\
MaxP... |
b062a9f97360a3fed4e9f53d25817208ff54eb6a38ae8d95aa6a1624637b6559 | Python | 4,158 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco_wholebody.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',
... |
1d3fb584bb48a9e9a90059a7881056564ecc734861fa1d0c6b88c79a800d2b58 | Python | 4,161 | 115 | from .dataset import Dataset, DatasetError
import os
import numpy as np
import scipy.io
class GrazB(Dataset):
"""An NST dataset.
An NST dataset usually consists of three files that are within a specific
subdirectory. The implementation follows this structuring, i.e. the user
needs to pass ... |
4b92a7211370ceb3730469a8b7088a0c6c83ec4c68d881ab3a200d6a292eb66b | Python | 4,161 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_upper.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',... |
f3b8dceddb5815d615130c0d03a927f5392dbd8caa0eeb5c943fb00d6a4503f6 | Python | 4,162 | 115 | 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]
# numb... |
9092ae826ccdbcdfaea7d78b81cb81ac86c5fbeab2dbb0a40593e905641c09e9 | Python | 4,166 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/deepfashion_full.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',
... |
c731b4e352a063f6981244575599e5614d435ec36ae7bdbfb18cb6bb0824cf04 | Python | 4,168 | 118 | from rdkit import Chem
from rdkit.Chem import AllChem
from tqdm import tqdm
# import time
import sys
import pandas as pd
from openff.toolkit.topology import Molecule
from openff.toolkit.typing.engines.smirnoff import ForceField
from serenityff.charge.tree.tree import Tree
sdf_test_path = "./test.sdf"
sdf_test = Chem.... |
aaf27d11af5a232b448ca59bc22d0a3adf22aefcb35e71bef9d62ad44d1d2668 | Python | 4,170 | 90 | '''
Predictions for the whole channel
k-fold CV, with ensemble model and ensemble fit error estimation
BK V1/2 predictor with random forest
ebn jun 2022
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
'''
import numpy as np
import pandas as pd
import csv
import scipy
impo... |
512f6131465669c02e5036551da347db9b015a2190685a6c78bf839a0980fd1b | Python | 4,171 | 137 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.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... |
c823630a1840a8824973b6b82564857ad9f689ff757812f4da894ecd58590a49 | Python | 4,175 | 100 | # Copyright (c) OpenMMLab. All rights reserved.
executor_cfg = dict(
# Basic configurations of the executor
name='Pose Estimation',
camera_id=0,
synchronous=False,
# Define nodes.
# The configuration of a node usually includes:
# 1. 'type': Node class name
# 2. 'name': Node name
... |
993e9ee09951b84acd2438d7b5919ec3ef09c9ea2a8f4390b836b6c9f1141ed7 | Python | 4,176 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
e4cbb4c0458bf4792543c06b435ef076a8d596975bb9d4452ddca568eb3d2392 | Python | 4,176 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
89d3264c1f5b0894c46dafeafb1fe245113f4dac8ecfca3dea8241d5e657fe3d | Python | 4,177 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
105a5ec559ec5fee146cfdcad0a5aef0d26a79225895609acdbb418ca585b88c | Python | 4,178 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
581b446b72483fb4e55ef0c4edb75d94737f4daa1af7e97877535aba35806333 | Python | 4,178 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
a6aa7c41c41452df4ab7fd23f54efb44d04225010cf142b7d864c2f41d58746e | Python | 4,178 | 141 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
import utils
import argparse
import data_loader
from ipdb import set_trace
from sklearn import metrics
SEQ_LEN = 48
def binary_cros... |
e39e70f0815f2e88303c1189fae1f61ea6c24a66d0f611aa02e78bb6121492f2 | Python | 4,178 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
e68ad21012a7647177921d5dcd479712b47d0a938ce2f94a6f3060f5123e04bc | Python | 4,178 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ochuman.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... |
dc382e1407786fe9811406eeb21a23fbb83bede88de4cace5317244ee1aa025b | Python | 4,179 | 139 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
55e9684bcc0c5a59f472352e5acaa405ac42c98e23570a950629bad0147e4b45 | Python | 4,180 | 110 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from snf import compute
rs = np.random.RandomState(1234)
def test_check_data_metric(simdata, digits):
# vanilla tests to check outputs
d, m = zip(*list(compute._check_data_metric(simdata.data, 'euclidean')))
assert len(d) == len(m) == 2
d, m =... |
5fb6de275f66c6651da0c4cb9f125c9fc5527ef2bdd8d4c2a2d0420890e491d6 | Python | 4,180 | 125 | #!/usr/bin/env python3
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the ho... |
03cd880a964e571125fcd14375a6315dfc1e3824f49f0912d20b4c990381fd59 | Python | 4,181 | 111 | import logging
import numpy as np
# import tensorflow as tf
from sklearn.base import BaseEstimator
from sklearn.metrics import accuracy_score
from sklearn.utils.extmath import softmax
from transformers import BertModel
from transformers import Trainer
import torch
from model.bert_model_utils import TorchDataset
from mo... |
0fb467612d5a4b8d60df1cd8f5121b3409f0f3194560cd85235c0ccaccab5547 | Python | 4,184 | 114 | 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... |
47e33b82daafe8f821b6c2de5c0e12f2242bbf2c98a41b3f5dc3f73f5a545a87 | Python | 4,188 | 106 | from .base import Attack # noqa: F401
# FixedEpsilonAttack subclasses
from .contrast import L2ContrastReductionAttack # noqa: F401
from .virtual_adversarial_attack import VirtualAdversarialAttack # noqa: F401
from .ddn import DDNAttack # noqa: F401
from .projected_gradient_descent import ( # noqa: F401
L1Proj... |
90088ff9683821d132e1f8175202837850939b8c4af69715144f262dd2873449 | Python | 4,195 | 141 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.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='l... |
8e76cd515ac940d77f29e52b9617035d725c6c1d95964044cc12db329258195a | Python | 4,196 | 125 | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmcv import Config
from xtcocotools.cocoeval import COCOeval
from ...builder import DATASETS
from .topdown_coco_dataset import TopDownCocoDataset
@DATASETS.register_module()
class TopDownMhpDataset(TopDownCocoDataset):
"""MHPv2.0 dataset for t... |
0788f5b182ed9a7080206e13d9303368d0a68ba7e83bcee75cbeb71786e2e435 | Python | 4,199 | 125 |
__author__ = "Grzegorz Chojnowski"
__date__ = "01 Sep 2021"
import os,sys
from optparse import OptionParser, OptionGroup, SUPPRESS_HELP
import cmstools
sys.path.insert(2, '%s/../findmysequence/fmslib' % cmstools.ROOT)
sys.path.insert(2, '%s/../findmysequence' % cmstools.ROOT)
from fmslib import xyz_utils
from fmsl... |
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