content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
value | license stringclasses 101
values | size int64 200 198k | sha stringlengths 40 40 | lang stringclasses 1
value | ext stringclasses 1
value |
|---|---|---|---|---|---|---|---|
import os
import os.path as osp
import numpy as np
import torch
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from skorch import NeuralNetRegressor
from torch.optim.lr_scheduler import StepLR
from skorch.callbacks.lr_scheduler import LRScheduler
from classicalgsg... | examples/mmff94/train_models.py | MIT | 3,471 | fdf133d84931286806f6504e1dc9c352218d12e1 | Python | py | |
# encoding:utf-8
'''
@Author: catnlp
@Email: wk_nlp@163.com
@Time: 2018/5/21 18:23
'''
from NER.utils.config import Config
from NER.utils.helpers import *
import sys
import argparse
import random
import torch
import numpy as np
seed_num = 100
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
... | NER/Tests/group/cc/train_joint-cc.py | MIT | 4,442 | a94fe4509e4a67bda35b2e2e1c86590fa2535014 | Python | py | |
import traceback
import sys
import logging
import os
import time
import subprocess
import socket
import json
import torch
from contextlib import closing
from carla08.tcp import TCPConnectionError
from carla08.driving_benchmark import run_driving_benchmark
from drive import CoILAgent
from logger import coil_logger... | coil_core/run_drive.py | MIT | 12,130 | 650a11832b2703bdabe15a872a03e20d25dffb3e | Python | py | |
# Author: Yan Zhang
# Email: zhangyan.cse (@) gmail.com
import sys
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
import gzip
import model
from torch.utils import data
import torch
import torch.optim as optim
from torch.autograd import Variable
from time import gmtime, strftime
import sys... | src/testConvNet.py | MIT | 3,639 | 8b45b86b3b0b09671417b191eb953207d6048dab | Python | py | |
import math
import torch.nn as nn
import torch.nn.functional as F
import torch
from timm.models.layers import variance_scaling_
def make_cross_attention_mask(query_mask, kv_mask):
batch_size, query_len = query_mask.shape
_, key_len = kv_mask.shape
mask = torch.einsum("bi,bj->bij", (query_mask, kv_mask))... | perceiver_io/transformer_primitives.py | Apache-2.0 | 16,840 | fcd93b3e87d8ab9c82d977776fc09c3ef4271491 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_module import BaseModule
from model.paragraph_module import ParagraphModule
from model.image_module import ImageModule
import math
class CrossModalModule(BaseModule):
def __init__(self, config, encoder):... | model/cross_modal_module.py | MIT | 5,178 | e62d0f597db2912823b80142c0b43368fed7f49d | Python | py | |
"""
Meta-learning Omniglot and mini-imagenet experiments with iMAML-GD (see [1] for more details).
The code is quite simple and easy to read thanks to the following two libraries which need both to be installed.
- higher: https://github.com/facebookresearch/higher (used to get stateless version of torch nn.Module-s)
-... | examples/iMAML.py | MIT | 11,822 | 8b45e71146436ec4eea05332d3b3a0c54f90d3c2 | Python | py | |
# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, s... | monai/networks/blocks/segresnet_block.py | Apache-2.0 | 4,322 | 4b6fd132037b53556b403588b1ebe0040a52f179 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Create a FAISS Index with a series of dense embeddings.
"""
import os
import random
import torch
from typing import L... | parlai/agents/rag/scripts/index_dense_embeddings.py | MIT | 4,832 | 10b32be640f4a4e965fd6f5de2f749f58a73558c | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.criterions import FairseqCriterion,... | fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py | MIT | 6,685 | 801ee2dfd2c1c4bfae07c5fecc651ae41a8fb36e | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model.utils.config import cfg
from model.faster_rcnn.faster_rcnn_MEAA import _fasterRCNN
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import mat... | lib/model/faster_rcnn/resnet_MEAA.py | MIT | 31,362 | 4ba4cf229d44a302d4a1a1dbeedd1383a4e76909 | Python | py | |
#!/usr/bin/env python
#-*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn as nn
from models.backbones.densenet.densenet_models import DenseNetModels
class NormalDensenetBackbone... | models/backbones/densenet/densenet_backbone.py | Apache-2.0 | 6,487 | 8b463593cc096189d2c276e8d3aef2d8264ca23c | Python | py | |
import math
import multiprocessing as mp
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
import torch.optim as optim
from trainer.start import *
from data.nih14 import *
from data.vin import *
from data.voc import *
from model.baseline import *
from model.fpn import *
from pylon import *
... | utils/exp_base.py | Apache-2.0 | 13,086 | fcbe4a2399d419ed7cc5ab1a93d049f8e7851eee | Python | py | |
import re
from copy import deepcopy
import torch
from torch import nn as nn
from .conv2d_helpers import *
# Default args for PyTorch BN impl
_BN_MOMENTUM_PT_DEFAULT = 0.1
_BN_EPS_PT_DEFAULT = 1e-5
BN_ARGS_PT = dict(momentum=_BN_MOMENTUM_PT_DEFAULT, eps=_BN_EPS_PT_DEFAULT)
def round_channels(channels, depth_multipli... | gen_efficientnet/efficientnet_builder.py | Apache-2.0 | 19,184 | 8b464d2ca755b028f700a993df57c0a8096c6438 | Python | py | |
# ------------------------------------------------------------------------------
# Portions of this code are from
# CornerNet (https://github.com/princeton-vl/CornerNet)
# Copyright (c) 2018, University of Michigan
# Licensed under the BSD 3-Clause License
# -------------------------------------------------------------... | src/lib/models/losses.py | MIT | 8,108 | 12a8548a6ef29c9273b43d5f96679900bddb3386 | Python | py | |
"""
This script is ported from https://github.com/MadryLab/robustness
branch: master
commit: 54f3375e428ad9a9ba4501dbbb3030f141e2ff78
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['resnet50'] # https://github.com/MadryLab/robustness only provide pre-trained models for resnet50
c... | policy_driven_attack/models/victim/cifar/madry_resnet.py | Apache-2.0 | 4,745 | 45d34af65e44ec061b31ecef48f06df419ae3436 | Python | py | |
#!/usr/bin/env python
# encoding: utf-8
"""
@Author: yangwenhao
@Contact: 874681044@qq.com
@Software: PyCharm
@File: test_accuracy.py
@Time: 19-8-6 下午1:29
@Overview: Train the resnet 10 with asoftmax.
"""
from __future__ import print_function
import argparse
import os
import sys
import time
# Version conflict
import ... | TrainAndTest/test_egs_multi.py | MIT | 29,231 | 5f78b17a1b6fea8a12cb38edb3c35a4299c983bb | Python | py | |
from typing import Optional, Any
from warnings import warn
import torch
import torch.autograd as autograd
import torch.distributed as distributed
from torch.autograd.function import once_differentiable
from . import backend
def _activation_from_name(activation):
if activation == "leaky_relu":
return bac... | modules/functions.py | BSD-3-Clause | 10,911 | 4b5564b3bd01ebce17f89d0d7d54aacb31f7a7da | Python | py | |
import torch
import torch.nn as nn
from collections import defaultdict
from copy import deepcopy
from math import log2
from ..utils import ConvModule, build_module
from .registry import BRICKS
FUSION_METHODS = ('concat', 'add')
@BRICKS.register_module
class JunctionBlock(nn.Module):
"""JunctionBlock
A basic... | vedaseg/models/decoders/bricks.py | Apache-2.0 | 7,125 | 5fb2ce38b0ccea34cf0784b2cb3e64923c14b5ae | Python | py | |
import argparse
from model_vc import Generator
import matplotlib.pyplot as plt
import torch
from torch_utils import device
from data_loader_circular import get_loader
def show_melsp(tensor, title):
fig = plt.figure(title)
plt.imshow(tensor.detach().cpu().reshape((128,80)).numpy().T, cmap='viridis')
if __name_... | visualizer.py | MIT | 2,229 | 8b46fe47108113c123c0001e48d32de4d09b0eac | Python | py | |
"""Create model."""
# coding=utf-8
#
# /************************************************************************************
# ***
# *** Copyright 2020-2022 Dell(18588220928@163.com), All Rights Reserved.
# ***
# *** File Author: Dell, 2020年 09月 09日 星期三 23:56:45 CST
# ***
# ***************************************... | project/image_clean/clean.py | MIT | 5,442 | 8b472e4fb51b5d3f78bbd61bf7177bb2778be9b3 | Python | py | |
import numpy as np
import os
from PIL import Image
from torch.utils.data import Dataset
class Dataloader(Dataset):
def __init__(self, image_dir, mask_dir, transform=None):
self.image_dir = image_dir
self.mask_dir = mask_dir
self.transform = transform
self.images = os.listdir(image_... | cv_segmentation/u-net_architecture/dataset.py | MIT | 949 | b216a76c9538ec40915e33cf847d6049ef3c67e8 | Python | py | |
import torch
from torch import nn
from smoke.modeling.smoke_coder import SMOKECoder
from smoke.layers.utils import (
nms_hm,
select_topk,
select_point_of_interest,
)
class PostProcessor(nn.Module):
def __init__(self,
smoker_coder,
reg_head,
det_thres... | smoke/modeling/heads/smoke_head/inference.py | MIT | 3,953 | b25e2b9e0c9032d7e8d3b168e8135ae234c2cd15 | Python | py | |
import copy
import os
from PIL import Image
import tqdm
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
import baseline.transforms as T
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
... | object-detection/baseline/coco_utils.py | BSD-3-Clause | 9,420 | ff3acac4e77c9756789566b1db54230305c8eb9f | Python | py | |
import logging
import math
from typing import Any, Dict, List, Tuple
import torch
import torch.nn.functional as F
from overrides import overrides
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models.model import Model
from allennlp.modules.token_embedders import Embedding
from allennlp.modules ... | allennlp/allennlp/models/coreference_resolution/coref.py | MIT | 31,342 | d7295bcf571cb3bac4a8d5b2e546d66428684675 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from botorch import fit_gpytorch_model
from botorch.models.contextual import LCEAGP, SACGP
from botorch... | test/models/test_contextual.py | MIT | 3,970 | 7c9c40b482e4086573284552cff1b3f5f5b62547 | Python | py | |
import argparse
import glob
import json
import logging
import os
import random
import math
import onnx
import onnx.numpy_helper
import numpy as np
import torch
# from torch.functional import norm
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, RandomSampler, SequentialS... | inference/nni_onnx_map.py | MIT | 7,839 | 8b47ab53f9efca0a3ace2d0cd2ca1ea7a501c41f | Python | py | |
import logging
from typing import Dict, List, Tuple
from overrides import overrides
import torch
from allennlp.data.fields.production_rule_field import ProductionRuleArray
from allennlp.data.vocabulary import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import TextFieldEmbedder, Seq2SeqEn... | allennlp/models/semantic_parsing/nlvr/nlvr_semantic_parser.py | Apache-2.0 | 12,119 | 704ef6994d40655b418bcce9448c4a5d9bcda8d1 | Python | py | |
import random
import os
import numpy as np
from PIL import Image
from collections import defaultdict
import torch
from torch.utils.data.dataloader import default_collate
import torchvision.transforms as transforms
import torch.utils.data as data
from input_pipelines import colmap_utils
def _collate_fn(batch):
_... | input_pipelines/llff/nerf_dataset.py | MIT | 10,341 | dfc7929eefba3cf5007cc04a552e0d1e9ea0158b | Python | py | |
import argparse
import os
import sys
import mmcv
from mmcv import DictAction
from torchvision import utils
# yapf: disable
sys.path.append(os.path.abspath(os.path.join(__file__, '../..'))) # isort:skip # noqa
from mmgen.apis import init_model, sample_uncoditional_model # isort:skip # noqa
# yapf: enable
def pa... | demo/unconditional_demo.py | Apache-2.0 | 2,349 | 34107c1f70ac3725755340e450cfbd9787a64682 | Python | py | |
"""
This file handles the details of the loss function during training.
This includes: LossComputeBase and the standard NMTLossCompute, and
sharded loss compute stuff.
"""
from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
import onmt
import onmt.io
... | onmt/Loss.py | MIT | 10,819 | 8b47d9542d53beb880aac49d2470abdc6b16caf1 | Python | py | |
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import RGCNConv, FastRGCNConv
'''Model skeleton for creating custom scene graph learning models'''
class CustomGraphModel(nn.Module):
def __init__(self, config):
... | roadscene2vec/learning/model/graph_model_skeleton.py | MIT | 2,262 | 8b47f1684875f895dc68f6161cf9a27ad19a4dcd | Python | py | |
try:
import binutil # required to import from dreamcoder modules
except ModuleNotFoundError:
import bin.binutil # alt import if called as module
from json.decoder import JSONDecodeError
from syntax_robustfill import SyntaxCheckingRobustFill
from image_robustfill import Image_RobustFill
import time
from dream... | bin/rbBaseline.py | Unlicense | 19,602 | 8b480e93f11ea0a7a71921fb5265968d8ab0c998 | Python | py | |
# python frozen.py --model_path scratch/frozen_bert --train --dump_path scratch/dump_bert --verbose --num_runs 100 --num_workers 4 --gpu 4
# python frozen.py --model_path scratch/frozen_glove --train --dump_path scratch/dump_word_embeddings_glove --verbose --num_runs 100 --num_workers 4 --gpu 5
import argparse
import ... | frozen.py | MIT | 6,277 | f9669848cca6cb98e0f87d3d366b058878169815 | Python | py | |
import logging
import time
import os
import sys
from argparse import ArgumentParser
import baseline
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader
from eight_mile.utils import str2bool, write_json, Average, Timer, get_num_gpus_multiworker
from baseline.pytorch.embeddings i... | mead/api_examples/pretrain_paired_pytorch.py | Apache-2.0 | 19,620 | e95bca66d49813d57a72ac5e43b4a485cdf5f572 | Python | py | |
import math
import torch
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import multiprocessing
from os.path import join
from datetime import datetime
from torch.utils.data.dataloader import DataLoader
torch.backends.cudnn.benchmark= True # Provides a speedup
import util
import test
imp... | train.py | MIT | 7,512 | 5ed656a407d12bab933f1d7fe2a78a419894ebd0 | Python | py | |
import torch
import torch.nn as nn
from abc import ABC
from abc import abstractmethod
from pathlib import Path
class Model(nn.Module, ABC):
# Must be implemented.
@abstractmethod
def get_args(self):
return []
def save(self, path, word_index):
path = Path(path)
path.parent.mkdir(exist_ok=True, par... | autowordbug/base_models/model.py | MIT | 856 | 5eba97655b7b3b596427ef4642e37e8bd02fe837 | Python | py | |
import torch
from rlpyt.algos.pg.base import PolicyGradientAlgo, OptInfo
from rlpyt.agents.base import AgentInputs, AgentInputsRnn
from rlpyt.utils.tensor import valid_mean
from rlpyt.utils.quick_args import save__init__args
from rlpyt.utils.buffer import buffer_to, buffer_method
from rlpyt.utils.collections import n... | rlpyt/algos/pg/ppo.py | MIT | 5,397 | 8b487f7ab5b757eafe62794c8ad877820c42758c | Python | py | |
# Based on https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py
"""PyTorch Temporal BERT model"""
import math
import torch
import torch.utils.checkpoint
import utils
from models.tempobert.configuration_tempobert import TempoBertConfig
from packaging import version
fro... | models/tempobert/modeling_tempobert.py | MIT | 56,530 | 1046babb8c3850ca50fce8ccaa1c7e1ffc6e6c1c | Python | py | |
import os
import logging
import numpy as np
from PIL import Image
from scipy.ndimage import convolve1d
from torch.utils import data
import torchvision.transforms as transforms
from utils import get_lds_kernel_window
print = logging.info
class AgeDB(data.Dataset):
def __init__(self, df, data_dir, img_size, split... | agedb-dir/datasets.py | MIT | 3,488 | b24f4ac2466cb12bc4acda42ddd17dae6d7e02d0 | Python | py | |
from __future__ import print_function
import sys
import time
import torch
from torchvision import datasets, transforms
import os
import dataset
import random
import math
import numpy as np
from utils import get_all_boxes, multi_bbox_ious, nms, read_data_cfg, logging
from cfg import parse_cfg
from darknet import Darknet... | eval.py | MIT | 4,606 | 8b489b37aaa277370c9580008d1ba31e49036df4 | Python | py | |
from pathlib import Path
from collections import OrderedDict
# import gpustat
import torch
from torch import autograd
import torch.nn.init as init
from torchvision import transforms, datasets
from torch.autograd import grad
from models.wgan import *
def mkdir_path(path):
path.mkdir(parents=True, exist_ok=True)
... | training_utils.py | MIT | 3,402 | e98ca720e97924134867cffd5f86b6551274dec9 | Python | py | |
import torch
from torch_sparse import coalesce
from torch_geometric.read import parse_txt_array
from torch_geometric.utils import one_hot
from torch_geometric.data import Data
elems = {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4}
def parse_sdf(src):
src = src.split('\n')[3:]
num_atoms, num_bonds = [int(item) for ... | torch_geometric/read/sdf.py | MIT | 1,213 | 8b48f745a07611f2b082571dd651fe315703a368 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, force_fp32,
images_to_levels, multi_apply, multiclass_... | mmdet/models/dense_heads/sabl_retina_head.py | Apache-2.0 | 27,068 | ff1471f7f2c1247733415622fa17f05decf34359 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Backbone modules.
"""
from collections import OrderedDict
import os
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter
from typing import Dict, List... | models/backbone.py | Apache-2.0 | 9,547 | f9a84236d09bb8b64cea330c2a38530fdf6003c0 | Python | py | |
import unittest
import os
from torchvision.datasets import ImageFolder
from torch._utils_internal import get_file_path_2
def mock_transform(return_value, arg_list):
def mock(arg):
arg_list.append(arg)
return return_value
return mock
class Tester(unittest.TestCase):
root = os.path.normp... | test/test_folder.py | BSD-3-Clause | 3,467 | 8b49518b419036c7456ce1ca76c0d638993b6db9 | Python | py | |
import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmcls.datasets.pipelines import Compose
from mmcls.models import build_classifier
def init_model(config, checkpoint=None, device='cuda:0'... | mmcls/apis/inference.py | Apache-2.0 | 5,678 | 4584ffc4df24d7f6427b34beeeff8824cd86f93d | Python | py | |
# Owner(s): ["module: tests"]
import torch
import numpy as np
import math
from typing import Dict, List, Sequence
import random
from functools import partial
from itertools import product, combinations, permutations
import warnings
from torch._six import inf, nan
from torch.testing import make_tensor
from torch.test... | test/test_reductions.py | Intel | 160,475 | 52c0a8a1d25785e9b65c830364bfec888e660fac | Python | py | |
import copy
import logging
import numpy as np
import torch
from torch.utils.data import DataLoader
from utils.toolkit import tensor2numpy
from scipy.spatial.distance import cdist
EPSILON = 1e-8
batch_size = 64
class BaseLearner(object):
def __init__(self):
self._cur_task = -1
self._known_classes ... | LLLClassify-part/models/base.py | MIT | 10,414 | 3402ea3bb5b383f0a695ad4c7ec4541bbf6e15bf | Python | py | |
import numpy as np
import os,sys,time
import torch
import torch.nn.functional as torch_F
import torchvision
import torchvision.transforms.functional as torchvision_F
import tqdm
from easydict import EasyDict as edict
import matplotlib.pyplot as plt
import lpips
import util_vis
import camera
"""
nerf.py의 novel_pose pl... | visualization_novel_view.py | MIT | 6,064 | 8b4976e20464e6fd063d31cf2420f55d531cc247 | Python | py | |
from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from data import cfg_mnet, cfg_slim, cfg_rfb
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from models.retinaface import RetinaFac... | convert_to_onnx.py | MIT | 3,875 | 8b49775f49506eb66a1fe11f6702ffa8cc3d85bf | Python | py | |
from torchvision import transforms
from src.datasets.cifar10 import CIFAR10
from src.datasets.imagenet import ImageNet
from src.datasets.wikipedia import WIKIPEDIA, WIKIPEDIATwoViews
from src.datasets.wikipedia_val import WIKIPEDIAVAL
from src.datasets.newsgroup import NEWSGROUP
from src.datasets.slice import WIKIPEDIA... | src/datasets/datasets.py | MIT | 1,687 | 8b49862079a15a3a7bf7115f08d401a51ca3a8cb | Python | py | |
# coding=utf-8
import math
import torch
from torch.nn.modules import Module
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn import init
from torch.nn import Parameter
class ConvNd(Module):
__constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias',
'padding_m... | fourier_conv.py | Apache-2.0 | 7,505 | 4b97e1c84ea350382227f7a1c7fbe217dd6312a2 | Python | py | |
from flask import Flask, render_template, request,Response
import requests
from werkzeug import secure_filename
app = Flask(__name__,template_folder='templates/')
DIST = ["Hotdog", "Not hotdog"]
from torchvision import transforms
import io,base64
from PIL import Image
import torch,json
transform = transforms.Compose([... | BLOX/Examples/NotHotdog/server.py | Apache-2.0 | 1,526 | 101e3d88953e0fc727a39f387e3e1d5b9b080203 | Python | py | |
import torch.nn as nn
class AgentConvBlock(nn.Module):
def __init__(self, nin, nout, ksize=3):
super(AgentConvBlock, self).__init__()
self.conv1 = nn.Conv2d(nin, nout, ksize, padding=1)
self.lrelu1 = nn.LeakyReLU(0.2)
self.conv2 = nn.Conv2d(nout, nout, ksize, padding=1)
sel... | dsketch/models/sketch_net.py | BSD-3-Clause | 2,014 | 6b3b641d931985add5f1bd74bedab6b117b10345 | Python | py | |
#!/usr/local/bin/env python
import torch
from torch.autograd import Variable
import torch.nn.functional as F
from tqdm import tqdm
from scipy.stats import linregress
import numpy as np
from sklearn import metrics
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, sel... | ODO/discriminator/model.py | MIT | 2,753 | dfb65475b0557a808904ecda154d7aea4182df59 | Python | py | |
"""
Calculates PSNR for JPG with respect to a dataset.
Note: Currently hard-coded for Cifar10
"""
import sys
import os
sys.path.append(os.path.abspath('..'))
import argparse
import numpy as np
import torch
import cv2
from collections import namedtuple
from PIL import Image
from tqdm import tqdm
from utils import util_... | scripts/calculate_jpg_psnr.py | MIT | 3,144 | f992d724a49b68a640fbc669989450a0840a851e | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import numpy as np
from typing import List
import torch
from fvcore.nn import sigmoid_focal_loss_jit, smooth_l1_loss
from torch import nn
from torch.nn import functional as F
from detectron2.data.detection_utils import convert_image_to_... | detectron2/modeling/meta_arch/retinanet.py | Apache-2.0 | 19,618 | e6638a89e847935cd25cdbae497c2cfb55e39430 | Python | py | |
import os
import re
import json
import sys
import csv
import math
import shutil
import argparse
import torch
import random
import subprocess
from utility_functions import taskManager
import pandas as pd
import scipy.io as sio
from PIL import Image as PILImage
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
... | taskCreator.py | RSA-MD | 72,472 | b25d12b1b4b57921e6039f7c915099fcc7101c4d | Python | py | |
import torch.nn as nn
import numpy as np
import torch.nn.utils.spectral_norm as spectral_norm
import torch.nn.functional as F
from .base_network import BaseNetwork
from .sync_batchnorm import SynchronizedBatchNorm2d
from .normalization import get_nonspade_norm_layer
from ...util import util
import torch
from torch.util... | projects/data_generation/synthetic_multi_view_facial_image_generation/algorithm/Rotate_and_Render/models/networks/discriminator.py | Apache-2.0 | 12,171 | 28dbb5baa7e034df6b26c8277f53924f987469b6 | Python | py | |
import argparse
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import scipy.io as sio
import sys
import time
import torch
from numpy import dot
from scipy.stats import levy_stable
from tqdm import tqdm
import torch.nn as nn
import copy
import torch.nn.funct... | geometry_analysis/levy_geometry.py | MIT | 7,893 | 24b39ebc24862b244cfd9b8d128fd1e32afd3e6a | Python | py | |
import math
import torch
import torch.nn as nn
from torch import Tensor
class PositionalEncoding(nn.Module):
""" Encodes positional information into word embeddings.
Reference: https://naokishibuya.medium.com/positional-encoding-286800cce437
"""
def __init__(self, max_positions: int, dim_embed: int, ... | simple_transformer/modules/pos_encoding.py | MIT | 1,909 | 704e4efb8047c934fc7cd93191480ce924891663 | Python | py | |
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, column_units):
super(Model, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
nn.Max... | models/clstm.py | Apache-2.0,MIT | 1,051 | 8b4a570d1354d61bb220892113d100ca24735c28 | Python | py | |
# -*- coding: utf-8 -*-
import gc
import numpy as np
import torch
import torch.distributed as dist
from pcode.create_dataset import define_dataset, load_data_batch
from pcode.utils.checkpoint import save_to_checkpoint
from pcode.utils.logging import (
display_training_stat,
display_test_stat,
dispaly_best... | distributed_code/pcode/distributed_running_cv.py | Apache-2.0 | 5,979 | a90aac476a86e990715f81be7e077a020be65a46 | Python | py | |
from typing import Dict, Optional, Tuple, Union
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import tiny_value_of_dtype
import torch
_emb_dim = -1
def get_unit_normalized_vector(vec: Optional[torch.FloatTensor],
) -> Optional[torch.FloatTensor]:
"""... | interlens/nn/util.py | MIT | 4,609 | 8b4a5e6a3a3c7a0fe858f6690f83c5626d23dd1d | Python | py | |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import contextlib
import io
import numpy as np
import os
import unittest
from collections import defaultdict
import torch
import tqdm
from cvpods.utils import benchmark
from pycocotools.coco import COCO
from tabulate import ... | tests/layers/test_mask_ops.py | Apache-2.0 | 6,993 | 8b4a60984663c43aac15739d18328c09a2f76405 | Python | py | |
import torch
import torch.nn as nn
from torch.autograd import Variable
import pdb
''' NOTES
- steps are how many times you recurse over the data! (eg the length of the trajectory)
- effective_steps is a list of which step outputs you want to save
- the format of the data as-is is [batch size, input channels, h, w, d]
... | convolution_lstm.py | MIT | 10,196 | 8b4aa1ec412e1720eb36920fc5d5c00b07f7f147 | Python | py | |
import os
import torch
import argparse
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import numpy as np
from core import models
from core.utils import flow as flo
from core.utils.disp import labelcolormap
from core.utils.config import Config
from core.utils.bbox import gen_bbox, label... | davis_test.py | BSD-2-Clause | 23,238 | 6b19790e87087adfa53efd5bfa5079c481cb9d04 | Python | py | |
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from torch.utils.data import DataLoader
from paww.augmentation import get_transforms
from paww.dataloader import PawwDataset
def get_dataloader(cfg):
train = pd.read_csv("data/petfinder-pawpularity-score/train.csv")
... | tests/utils.py | MIT | 1,366 | f93a04769d098144b1ac03c1d0753b64878c178c | Python | py | |
import torch
import torch.nn.functional as F
import numpy as np
from rlpyt.models.mlp import MlpModel
class VaeHeadModel(torch.nn.Module):
def __init__(self, latent_size, action_size, hidden_sizes):
super().__init__()
self.head = MlpModel(
input_size=latent_size + action_size,
... | rlpyt/ul/models/ul/vae_models.py | MIT | 3,435 | 45fe5b4269d41395edea4e25ea2ffdcc88e02abb | Python | py | |
# Copyright (c) 2020 fortiss GmbH
#
# Authors: Patrick Hart, Julian Bernhard, Klemens Esterle,
# Tobias Kessler and Mansoor Nasir
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
# The code is adapted from opensource implementation - https://github.com/ku2482/fqf-iqn-qrdqn.pyt... | bark_ml/library_wrappers/lib_fqf_iqn_qrdqn/agent/iqn_agent.py | MIT | 13,603 | 4bc0f073a6631e869456dbd9bfc78995b4b0f74d | Python | py | |
import math
import argparse
import uuid
import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torchvision import transforms
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
import wandb
from som import SomLayer
from autoencoder impor... | som-diffusion/train_som.py | MIT | 7,187 | 2481a0d81e3db4fe179a9fdf8992d2d0065e34e2 | Python | py | |
import math
import random
import time
import os
import sys
import copy
from datetime import datetime
from os.path import join, exists
from os import mkdir
import collections
from collections import namedtuple
from collections import deque
from itertools import count
import torch
import torch.nn as nn
import torch.op... | pyvs/LatentARTraining (copy).py | Apache-2.0 | 46,962 | 8b4ac290a8170d2194e0f1d0cfbe54b49fc86312 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from typing import List, Dict, Tuple, Union
from fairdiplomacy.agents.base_agent import BaseAgent
from fairdi... | fairdiplomacy/agents/base_search_agent.py | MIT | 4,800 | 5ec989f9285bdbfdd270f375e9f94fdc0472e8d6 | Python | py | |
""" Optimizers class """
import torch
import torch.optim as optim
from torch.nn.utils import clip_grad_norm_
from onmt.utils import use_gpu
def build_optim(model, opt, checkpoint):
""" Build optimizer """
saved_optimizer_state_dict = None
if opt.train_from and opt.reset_optim != 'all':
optim = c... | onmt/utils/optimizers.py | MIT | 10,448 | f97b01637dd04c7aaa7ff902fd2b4e467d69cadb | Python | py | |
""" Quick n simple image folder dataset
Copyright 2020 Ross Wightman
"""
import torch.utils.data as data
import os
import re
import torch
from PIL import Image
IMG_EXTENSIONS = ['.png', '.jpg', '.jpeg']
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s... | pruning/pruning-model-zoo/ImageNet/data/dataset.py | MIT | 3,000 | 8b4b07f0eb57dbe517714e170796647db8d7a6ed | Python | py | |
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#############################################################
# Borrow the idea of https://github.com/arogozhnikov/einops #
#############################################################
import torch
import torch.... | xautodl/xlayers/super_rearrange.py | MIT | 7,584 | ff9ff35f6b64e2e65d7f7e7f7a8d5be602b1e059 | Python | py | |
from abc import ABC
from torch.optim import SGD
class VROptimizer(ABC):
def __init__(self, name, model, train_set, batch_size, lr, device, num_workers, weight_decay):
self.name = name
self.model = model
self.train_set = train_set
self.batch_size = batch_size
self.optimiser ... | vr_optimizers/optims/base_class.py | Apache-2.0 | 508 | 8b4b67faf96424a6dc540685c6db6cdab76b0f37 | Python | py | |
import time
import logging
import numpy as np
import emcee
import torch
import torch.nn as nn
import torch.optim as optim
from scipy import optimize
from pybnn.base_model import BaseModel
from pybnn.util.normalization import zero_mean_unit_var_normalization, zero_mean_unit_var_denormalization
from pybnn.bayesian_lin... | pybnn/dngo.py | BSD-3-Clause | 14,202 | 8b4b7f761f6fbcf65100ecd48c6ae08cd41a93c6 | Python | py | |
"""Dataset utils for different data settings for GLUE."""
import os
import copy
import logging
import torch
import numpy as np
import time
from filelock import FileLock
import json
import itertools
import random
import transformers
from transformers.data.processors.utils import InputFeatures
from transformers import D... | src/processors.py | MIT | 38,901 | 10fcfce05c403c97e1aca25795c4d038a47ca92d | Python | py | |
#encoding:utf-8
import torch
import warnings
from torch.utils.data import DataLoader
from pybert.io.dataset import CreateDataset
from pybert.io.data_transformer import DataTransformer
from pybert.utils.logginger import init_logger
from pybert.utils.utils import seed_everything
from pybert.config.basic_config import con... | Code/HypoBertClas/inference.py | MIT | 3,598 | 5eb717f1b60abe45f76b88215a9b63d217bcdaf6 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import json
import numpy as np
import os
import torch
from pycocotools.cocoeval import COCOeval, maskUtils
from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.file_io import PathManager
from .coco_evalu... | detectron2/evaluation/rotated_coco_evaluation.py | Apache-2.0 | 7,556 | 8b4bab3cad5f4b1951e5dddd5b4caa90e9a19394 | Python | py | |
from typing import Callable
import torch
from torch import Tensor
from torch.nn import functional as F
from torch import distributed as dist
class SimpleContrastiveLoss:
def __init__(self, n_hard_negatives: int = 0):
self.target_per_qry = n_hard_negatives + 1
def __call__(self, x: Tensor, y: Tensor,... | src/grad_cache/loss.py | Apache-2.0 | 2,282 | 10c858f4dcf53ec27d5ad6222c2ba0c6a2babe12 | Python | py | |
# Owner(s): ["oncall: quantization"]
import torch
import math
from typing import Tuple
from torch.ao.quantization import (
FakeQuantize,
MovingAverageMinMaxObserver,
default_observer,
default_affine_fixed_qparams_fake_quant,
)
from torch.ao.quantization._learnable_fake_quantize import _LearnableFakeQu... | test/quantization/core/test_workflow_ops.py | Intel | 58,794 | 8b4baf10d45d1f2d4794b8860924703c27d54cea | Python | py | |
from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.nn as nn
import argparse
from tensorboardX import SummaryWriter
import os
import tokenization
import models
from tqdm import tqdm
import optim
import train
from torch.utils.data import Dataset, DataLoader... | data.py | Apache-2.0 | 8,259 | 459f1bc446f3a8d70c430cd3906905d6419e1cb4 | Python | py | |
from baseline_data import IMDB_Dataset
import torch
from torch.utils.data import DataLoader
from torch import nn
from baseline_model import Baseline_Bert, Baseline_LSTM
from baseline_tools import logging
from baseline_config import Baseline_Config, dataset_config, model_path
from tqdm import tqdm
evaluate_device = tor... | baseline/baseline_evaluate.py | Apache-2.0 | 3,512 | 8b4bbeb73a3498ffc2ab639b20ad3d49cf44ad11 | Python | py | |
import torchxrayvision as xrv
import torchvision as tv
import os
from os.path import join as pjoin
import vis_utils
import matplotlib.pyplot as plt
import torch
from arch.lightning.xray import XRayLightningModel
from arch.lightning.base_imagenet import ImageNetLightningModel
import pandas as pd
import json
from arch.li... | cal_calibration.py | Apache-2.0 | 3,074 | 343f66ead42c356b2b526f4620edaa6044dbb441 | Python | py | |
import torch as tc
from drl.agents.preprocessing import Preprocessing
def one_hot(ys: tc.Tensor, depth: int) -> tc.Tensor:
"""
Applies one-hot encoding to a batch of vectors.
Args:
ys (torch.Tensor): Torch tensor with shape batch_shape, and dtype int64.
depth (int): The number of possibl... | drl/agents/preprocessing/tabular.py | MIT | 1,065 | 5f135b149d139fff32fb1bb3880ab0f46fef20b4 | Python | py | |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import torch
import numpy
import random
import pdb
import os
import cv2
import math
from scipy.io import wavfile
def loadWAV(filename, max_frames, start_frame=0, evalmode=False, num_eval=10):
# Maximum audio length
max_audio = max_frames*160+240
# self.nMaxFrames = 40 的情况... | dataLoader.py | MIT | 1,871 | e9dd23723cf1c990ba298643f0325366631878c3 | Python | py | |
from torch import nn
from torch.optim import Optimizer, RMSprop
from typing import Callable, Dict, Optional, Tuple
from .envs import ClassicalControl, DummyParallelEnv, EnvExt, EnvGen, ParallelEnv
from .net import actor_critic, value
from .lib.explore import DummyCooler, Cooler, LinearCooler, Explorer, EpsGreedy
from .... | rainy/config.py | Apache-2.0 | 6,002 | 29899921f8a5326f4d574610badf2a9fdf07cbca | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftIoULoss(nn.Module):
def __init__(self, n_classes):
super(SoftIoULoss, self).__init__()
self.n_classes = n_classes
@staticmethod
def to_one_hot(tensor, n_classes):
n, h, w = tensor.size()
one_hot =... | src/losses/multi/softiou_loss.py | MIT | 1,145 | 2989a9f2aad9ddfb2d332dbf055cae4c772d6f39 | Python | py | |
import torch
from torch_geometric.nn import GravNetConv
def test_gravnet_conv():
x1 = torch.randn(8, 16)
x2 = torch.randn(4, 16)
batch1 = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1])
batch2 = torch.tensor([0, 0, 1, 1])
conv = GravNetConv(16, 32, space_dimensions=4, propagate_dimensions=8, k=2)
asse... | test/nn/conv/test_gravnet_conv.py | MIT | 1,044 | 2989d6264a4ea5dc8fe9440a17c8a4b1177c48f4 | Python | py | |
import argparse
from itertools import product
import torch
from torch_geometric import seed_everything
from torch_geometric.loader import DataLoader
from torch_geometric.profile import profileit, timeit, get_stats_summary
from datasets import get_dataset
from train_eval import train, eval_acc
from gcn import GCN
fr... | benchmark/kernel/main_performance.py | MIT | 2,622 | d77254f62e3719585c29c6d14aa1d5c7497d172f | Python | py | |
# -*- coding: utf-8 -*-
from os import walk
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import os
from PIL import Image
import cv2
from torchsummary import summary
from torch.utils.data import Dataset, DataLoader, random_split
import pandas as pd
import nu... | src/data/make_dataset.py | FTL | 4,234 | 7c38cdb27c7d945b0e8934aba6c770f455685545 | Python | py | |
import os
import copy
import numpy as np
from os.path import isfile
import torch
#import binvox_rw
import torch.nn.functional as F
EPS = 1e-6
use_cuda = torch.cuda.is_available()
DEVICE = torch.device("cuda" if use_cuda else "cpu")
def assert_same_shape(t1, t2):
for (x, y) in zip(list(t1.shape), list(t2.shape)):
... | net_utils/basic.py | MIT | 14,495 | 298a130500b258ab2eae35451b04138e3cc3fa71 | Python | py | |
"""
Copyright (c) 2020-2021 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in w... | nncf/torch/model_creation.py | Apache-2.0 | 11,396 | 803369dff77c9e1b7cb8853af230baf77eaca465 | Python | py | |
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torchvision
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
class ResNet18(nn.Module):
"""
18: ([2, 2, 2, 2], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, ... | bird/resnet.py | Apache-2.0 | 5,024 | 298a1867c71f62d17a2dbf24dc8471d2108a5b24 | Python | py | |
from typing import Any, Dict, List
import torch
from allennlp.common import Registrable
from allennlp.common.util import pad_sequence_to_length
from allennlp.data.tokenizers.token import Token
from allennlp.data.vocabulary import Vocabulary
# An indexed token list represents the arguments that will be passed to a To... | allennlp/data/token_indexers/token_indexer.py | Apache-2.0 | 5,714 | d782d3392b14607b950119bb7882cdf756b702db | Python | py | |
from PIL import Image
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
from scipy.interpolate import interp1d
from misc_functions import get_vide_example_params, save_class_activation_videos
class CamExtractor():
"""
Extracts cam features from the model
"... | src/unet3d_scorecam2.py | MIT | 5,857 | 298a5c0210628bcb66e032378aef62a6574bc2c3 | Python | py | |
import torch
from torch import nn
import pdb
from models import c3d_BN
def generate_model(opt):
assert opt.model in ['c3d']
if opt.model == 'c3d':
assert opt.model_depth in [10]
from models.c3d_BN import get_fine_tuning_parameters
if opt.model_depth == 10:
model = c3d... | model.py | MIT | 3,352 | 24d96955948ce5c3bf7a12e7034e17a6005e89b4 | Python | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.