content stringlengths 200 198k | repo stringlengths 4 186 | path stringclasses 1
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from PIL import Image
import numpy as np
import torch
def to_pil(array):
if isinstance(array, torch.Tensor):
array = array.cpu().detach()
if len(array.shape) > 3 and array.shape[2] != 3:
array = array.permute(1, 2, 0).numpy()
return Image.fromarray(np.uint8(array * 255))
def to_p... | src/utils.py | MIT | 893 | 24cdd28acd8f79d9c5ac3ad734f4ffaf6f960d7a | Python | py | |
import json
import argparse
from konlpy.tag import Mecab
import torch
from pytorch_pretrained_bert.tokenization import BertTokenizer, whitespace_tokenize
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
import run_squad
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def arg... | question-answering-SQuAD/main.py | MIT | 8,569 | 1eb571756cc48eeaf3c2cf9030e174f54066495c | Python | py | |
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
first_HL = 128
def conv3x3(in_channels, out_channels, stride=1):
"""3x3 kernel size with padding convolutional layer in ResNet BasicBlock."""
return nn.Conv2d(
in_channe... | model/spinalresnet.py | Unlicense,MIT | 5,342 | 4b787b89073a9bf9688527202ba75546efebec66 | Python | py | |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import shutil
from pathlib import Path
from typing import Generator
import pytest
import torch
from _pytest.fixtures import SubRequest
from _pytest.monkeypatch import MonkeyPatch
import torchgeo.datasets.utils
fro... | tests/datasets/test_gid15.py | MIT | 2,180 | 7cc90edd92403fb78d373a8b8b8a202e5c112e5d | Python | py | |
import torch
import argparse
from domainbed.lib import misc
from domainbed.datasets import PACS
from domainbed import hparams_registry
from domainbed.lib.fast_data_loader import InfiniteDataLoader, FastDataLoader
import os
from domainbed import algorithms
import torch.nn as nn
if torch.cuda.is_available():
device... | domainbed/scripts/get_features_and_logits.py | MIT | 3,482 | 703ec5e40177d501950a2cdf0398621e91221da3 | Python | py | |
# coding=utf-8
"""Inference EVA"""
import os
import torch
import torch.nn.functional as F
from arguments import get_args
from utils import load_checkpoint
from tokenization_eva import EVATokenizer
import mpu
import deepspeed
import torch.distributed as dist
from model import EVAModel, EVAConfig
from fp16 import FP16_... | src/eva_interactive.py | MIT | 6,772 | fec0a58145d0675e4644858973bc433840943552 | Python | py | |
"""
Run transfer learning on FashionProductImages dataset. This will first fine-tune
a chosen model from the ImageNet model zoo on classifying the 20 most common
product and then in a second pass will fine-tune the network further on the
remaining, less common, product classes.
"""
import os
import random
import time
i... | few_shot_learning/train_transfer.py | MIT | 14,468 | fec0b5ee72850ab5c55496b5550cefaaba06318b | Python | py | |
#!/usr/bin/env python 3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 Baidu.com, Inc. All Rights Reserved
#
"""
模型
Authors: panxu(panxu@baidu.com)
Date: 2020/05/18 10:08:00
"""
from typing import Any, Dict
import torch
from torch.nn import Module
from .model_outputs import ModelOutputs
class Model(Module):
... | easytext/model/model.py | MIT | 545 | 425454f656ec09f27f5186850c04c56a7d5e1a24 | Python | py | |
import torch
import torch.nn as nn
import numpy as np
from cnnseq import utils
from cnnseq.utils_models import set_optimizer, flatten_audio
from skimage.io import imsave
import json
import os
# Recurrent neural network (many-to-one)
class Seq2Seq(nn.Module):
def __init__(self, params, device=torch.device('cpu')):... | cnnseq/Seq2Seq.py | MIT | 11,288 | fec0bce1e5c4eb1b17d4fe3fbf1968c68e03d4c9 | Python | py | |
import logging
import six
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence
from torch.nn.utils.rnn import pad_packed_sequence
from espnet.nets.e2e_asr_common import get_vgg2l_odim
from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
from e... | espnet/nets/pytorch_backend/encoders.py | Apache-2.0 | 11,712 | fec0be0d33f071edb8bf37aa21d4e1fbab7b69c7 | Python | py | |
from __future__ import division
import torch
import random
import numpy as np
import cv2
def confidence_filter(result, confidence):
conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
result = result*conf_mask
return result
def confidence_filter_cls(result, confidence):
max_score... | detector/yolo/bbox.py | Apache-2.0 | 3,388 | b2d15ac94c5f449da53cdcd6cd237087e8eb7d03 | Python | py | |
# -*- coding: utf-8 -*-
"""Prediction workflows."""
import itertools as itt
import logging
from typing import Optional, Tuple, Union
import numpy as np
import pandas as pd
import torch
from .base import Model
from ..triples import CoreTriplesFactory, TriplesFactory
from ..typing import MappedTriples, ScorePack
__a... | src/pykeen/models/predict.py | MIT | 20,574 | fcecaf1bd4bc6f6e21576a84f799b686071f108b | Python | py | |
import abc
import logging
import random
import numpy as np
import torch
from torch.autograd import Variable
class Algorithm(metaclass=abc.ABCMeta):
def __init__(self, module_name, name, seed, details=False):
self.logger = logging.getLogger(module_name)
self.name = name
self.seed = seed
... | src/algorithms/algorithm_utils.py | MIT | 2,010 | fec0fd6caa8b216ade1b20e117bdd713918fd59b | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import pdb
class BaseBEVBackbone(nn.Module):
def __init__(self, model_cfg, input_channels):
super().__init__()
self.model_cfg = model_cfg
if self.model_cfg.get('LAYER_NUMS', None) is not None:
assert len(self.model_cfg.LAYE... | pcdet/models/backbones_2d/base_bev_backbone.py | Apache-2.0 | 4,384 | fec13e1b9feabe4f2a9741952e9025cd6a7b25f1 | Python | py | |
import torch
import torch.nn as nn
class GCN_module(nn.Module):
def __init__(self,c,out_c,k=7):
super(GCN_module, self).__init__()
self.conv_l1 = nn.Conv2d(c, out_c, kernel_size=(k,1), padding =((k-1)//2,0))
self.conv_l2 = nn.Conv2d(out_c, out_c, kernel_size=(1,k), padding =(0,(k-1)//2))
... | model/GCN/GCN_module.py | MIT | 971 | 7c6374419cb03b7d25fc29c90cc537f35bd8c216 | Python | py | |
import torch.nn as nn
import math
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, retain_activation=True, activation='ReLU'):
super(ConvBlock, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, p... | code/protonet/model.py | MIT | 2,327 | fec15fe1e3554956795598408e1461a1b345c50b | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import spconv
from functools import partial
from ..model_utils.resnet_utils import SparseBasicBlock
from ...config import cfg
from ...utils import common_utils, loss_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
class UNetHead(nn.Mod... | pcdet/models/rpn/rpn_unet.py | Apache-2.0 | 23,189 | fec18f9633885f57eda8325a0435d63ee93edbfd | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from einops import repeat, rearrange
from quant_noise import quant_noise
class Zformer(nn.Module):
def __init__(
self,
input_dim,
num_heads,
dropout=0.0,
scalar=700.,
... | zformer_pytorch/zformer_pytorch.py | Apache-2.0 | 2,492 | fdbcee47e544409ad4772b14445f0e845272c7e7 | Python | py | |
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | torch_utils/misc.py | MIT | 11,872 | fec19dc99ceb91dedcbb3f6992028abf41026eb4 | Python | py | |
# options file
import time, os, sys, torch, torchvision
import torchnet.meter as meter
from pathlib import Path
from torch.autograd import Variable
import torch
def init_settings(force_new_model, pretrained_model_path=None):
global opt
opt = dict()
opt["dataroot"] = "../datasets/sysu_mm01"
opt["batch_... | src_py/settings.py | Apache-2.0 | 995 | 709a220013d8d68202bc6442be87ed9d5e6dd223 | Python | py | |
# pylint: disable=protected-access
from typing import List, Optional, Tuple, cast
import torch
import torch.nn.functional as F
from torch import nn
from torch.distributions import Normal
from torch.nn.utils import spectral_norm
from .encoders import EncoderWithAction
def _compute_ensemble_variance(
observation... | d3rlpy/models/torch/dynamics.py | MIT | 8,035 | 038573122c6c4f2464d3d63194eeccca4988fba2 | Python | py | |
import torch
import torch.nn as nn
import torchvision.models as models
#case1: numlayers =2, dropout=0.2 in lstm; train bn parameters; Adam(with L2 regularization]; xavier initialization
class EncoderCNN(nn.Module):
def __init__(self, embed_size):
super(EncoderCNN, self).__init__()
resnet = models.... | model_case1.py | MIT | 5,062 | 342c07224e7b9ab416444af5468a95332be71c67 | Python | py | |
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, smooth_l1_s, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (t... | mmdet/models/losses/smooth_l1_loss.py | Apache-2.0 | 6,100 | fec2126697061b46572ed3fcb20d72dcb004e645 | Python | py | |
import torch
import json
import os
import argparse
import copy
import torch.optim as optim
import torch.nn as nn
from torch.utils.data import DataLoader
from datetime import datetime
from collections import defaultdict
from tqdm import tqdm
import datasets.edge_imageprovider as image_provider
import math
import cv2
imp... | CODE/test.py | MIT | 10,301 | fec22043e39a0ab60e81f1ee7fc03adb33318a41 | Python | py | |
import logging
from functools import partial
from typing import Dict, Iterable, Optional, Sequence, Union
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import _CONSTANTS
from scvi._compat import Literal
from scvi._docs import doc_differential_expression
from scvi._utils imp... | scvi/model/base/_rnamixin.py | BSD-3-Clause | 19,789 | fec2259114c6512404f80dba0d9f3d285f707780 | Python | py | |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# 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 appli... | nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py | Apache-2.0 | 15,204 | 03bd60e208d06fbf25cbf852207ff00e87a68bc6 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(module):
for n, m in module.named_children():
print('initialize: '+n)
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
if m.bias is not None:... | fine/net.py | MIT | 14,573 | fec249498c6069b4e8b94a4d138bd0a0eb98fae7 | Python | py | |
"""
Copyright 2019 The Microsoft DeepSpeed Team
"""
import os
import re
import stat
import math
import torch
import warnings
import hashlib
import torch.distributed as dist
from collections import defaultdict, OrderedDict
from shutil import copyfile
from torch.nn.modules import Module
from torch.nn.parameter import P... | deepspeed/runtime/engine.py | MIT | 126,051 | f99d3effcb5c2625d0eb2450428e13047bebcd51 | Python | py | |
import importlib
import numpy as np
import time
import torch
def load_model(script, model, config):
module = importlib.import_module(script)
Network = getattr(module, model)
return Network(config)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.v... | M2_util_train_test.py | MIT | 4,940 | fcdf24891d5ffd04762b156481dad669b1f05984 | Python | py | |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from tests import utils
class SimpleToModel(torch.nn.Module):
def __init__(self, *conversions):
super(SimpleToModel, self).__init__()
self.conversions = conversions
def forward(self, tensor):
... | torch_glow/tests/nodes/to_test.py | Apache-2.0 | 3,138 | fec26d07452b97f599dbb2df6d8594d4e4f006d7 | Python | py | |
#!/usr/bin/env python3
import time
import math
from datetime import datetime
from time import sleep
import numpy as np
import random
import cv2
import os
import argparse
import torch
import sys
sys.path.append('./Eval')
sys.path.append('./')
import matplotlib.pyplot as plt
from scipy.spatial.transform import Rotation... | simulation/env_152.py | MIT | 5,293 | fec26f7d124dc2012d0754c4038883854e9a65b9 | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | tests/loggers/test_tensorboard.py | Apache-2.0 | 11,635 | 5e82c79463598818c2994579d9eae77b29a498cb | Python | py | |
import os
import sys
import traceback
import types
import torch
import torch.nn as nn
import torch.optim as optim
from diora.net.diora import DioraTreeLSTM
from diora.net.diora import DioraMLP
from diora.net.diora import DioraMLPShared
from diora.logging.configuration import get_logger
import transformers
import t... | pytorch/diora/net/trainer.py | Apache-2.0 | 36,137 | 3455c317e4ffbf7cad030a65fc3e164789fcebbc | Python | py | |
import copy
import importlib
import os
import os.path
import subprocess
import sys
import time
import numpy
import ray
import torch
from torch.utils.tensorboard import SummaryWriter
import models
import replay_buffer
import self_play
import shared_storage
import trainer
class MuZero:
"""
Main class to manag... | muzero.py | MIT | 6,371 | fec2b0b10eaf9668204f383d2aa64805dba748f0 | Python | py | |
from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from mmdet import ops
class OBBBaseRoIExtractor(nn.Module, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify RoI layer type and arguments.
o... | mmdet/models/roi_heads/roi_extractors/obb/obb_base_roi_extractor.py | Apache-2.0 | 2,656 | 12dd9afae40b8f3bf361bce13020c14d05ee5def | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | tests/strategies/test_ddp_spawn.py | Apache-2.0 | 2,570 | fec2e71ee91af7a543505fe9533c3d61438982ac | Python | py | |
# Copyright The PyTorch Lightning team.
#
# 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 i... | pytorch_lightning/trainer/distrib_parts.py | Apache-2.0 | 12,105 | 10f26d3be0aa5ba3a8d7435785046195ca87b9bf | Python | py | |
from typing import List, Union # isort:skip
from collections import OrderedDict
from pathlib import Path
import torch
import torch.nn as nn
import torchvision
from catalyst_rl import utils
from .core import _take, EncoderSpec
RESNET_PARAMS = {
"resnet18": {
"channels": [64, 64, 128, 256, 512],
"... | catalyst_rl/contrib/models/cv/segmentation/encoder/resnet.py | Apache-2.0 | 3,915 | fec2f11b0ee0eb6a595b9049658b831a5704b3b8 | Python | py | |
import os
import torch
from pytorch_pretrained_bert.modeling import BertModel
from torch import nn
class BertModule(nn.Module):
def __init__(self, bert_model_name, cache_dir="./cache/"):
super().__init__()
# Create cache directory if not exists
if not os.path.exists(cache_dir):
... | superglue/modules/bert_module.py | MIT | 1,621 | 7ca4d2f75abfbb7f6f14b0677381588969580448 | Python | py | |
from copy import deepcopy
import numpy as np
import torch
from ignite.engine import Engine
from ignite.engine import Events
from ignite.metrics import RunningAverage
from ignite.contrib.handlers.tqdm_logger import ProgressBar
from mnist_classification.utils import get_grad_norm, get_parameter_norm
VERBOSE_SILENT = 0
V... | src/05_cnn/mnist_classification/trainer.py | MIT | 6,539 | fec307dd3290d84492d13fc7fb06f4a227f03616 | Python | py | |
"""
A Gaussian mechanism used in Axiothea to ensure differential privacy.
"""
from collections import OrderedDict
import math
import torch
import numpy as np
from plato.config import Config
def gaussian_mechanism(weights, channel_type):
""""Apply Gaussian mechanism."""
clipped_weights, clipping_bound = cli... | plato/utils/dp_gaussian.py | Apache-2.0 | 3,690 | f9e1afc9bdf8777bf95de867b8eb2f8f6e5ee6ed | Python | py | |
import numpy as np
import gym
import random
import torch
import cv2
from config import *
class Atari(object):
"""
Wrapper used to simplify interactions with the environment
"""
def __init__(self, device, save_gif = False):
self._env = gym.make('Breakout-ramNoFrameskip-v4')
if save_gif... | atari/atari.py | MIT | 3,009 | 29f79ff9d63915612a3689ad6727eec428203ed5 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from omegaconf import II
from dataclasses import dataclass, field
from fairseq.tasks import FairseqTask
from . import register_task
from .translation import TranslationTask, TranslationConfig
from fairseq.optim.amp_optimizer import AMPOptimizer
from f... | fairseq/tasks/translation_with_actor_critic.py | MIT | 3,945 | 7019c63d96d083b3650b2d55e8d30ec60ac11de2 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_size, action_size, seed, fc_units=600, fc1_units=300):
super(Actor, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size,... | BipedalWalker/pretrained/model.py | MIT | 1,879 | 70a894b959656cd3ffeb475cb1d7fb45ec9806ae | Python | py | |
import torch
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from .utils import box_iou
def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
# Precision-recall curve
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
py = np.stack(py, axis=1)
if ... | red0orange/object_detection/metrics.py | MIT | 14,583 | fec35a6ff2883c228022fd9ce314f11234963a4b | Python | py | |
"""Abstract base class for evaluating models"""
from abc import ABC, abstractmethod
from datasets import Dataset
from transformers.models.auto.auto_factory import _BaseAutoModelClass
import transformers.utils.logging as tf_logging
from transformers import (
PreTrainedTokenizerBase,
AutoTokenizer,
AutoConfi... | scandeval/benchmarks/abstract/base.py | MIT | 46,034 | fec360f5b58126a9710d4879449081368fb51b94 | Python | py | |
# encoding: utf-8
"""
@author: l1aoxingyu
@contact: sherlockliao01@gmail.com
"""
import logging
import os
import torch
from torch._six import string_classes
from collections import Mapping
from project.fastreid.config import configurable
from project.fastreid.utils import comm
from . import samplers
from .common im... | project/fastreid/data/build.py | MIT | 6,190 | 5e11829647ab52073f5f42dfaa54ca34ac800cad | Python | py | |
# Copyright (c) Meta Platforms, Inc
from typing import Optional
import flowtorch
import torch
from flowtorch.bijectors.elementwise import Elementwise
from flowtorch.bijectors.ops.affine import Affine as AffineOp
class Affine(AffineOp, Elementwise):
r"""
Elementwise bijector via the affine mapping :math:`\ma... | flowtorch/bijectors/affine.py | MIT | 953 | 42a58bc2ee39ffb8adc9809d5c4cde65f7be786a | Python | py | |
from __future__ import absolute_import
from __future__ import print_function
import torch
AVAI_SCH = ['single_step', 'multi_step', 'cosine', 'warmup_db', 'warmup_sb']
def warmup_db(ep, stepsize):
ep +=1
if ep < stepsize[0]:
lr = 1e-4*(ep//5+1)
elif ep < stepsize[1]:
lr = 1e-3
elif ep... | torchreid/optim/lr_scheduler.py | MIT | 4,206 | fec393d5fe8bd139d3727684bdcc031b4397bc30 | Python | py | |
import unittest
import os
import random
import torch
import apex
from torch.autograd import Variable
class TestFusedLayerNorm(unittest.TestCase):
def setUp(self):
# bias and weight are set to 0 and 1 respectively, so no need to copy parameters from cpu module to the gpu one
self.module_cpu_ = ape... | tests/L0/run_fused_layer_norm/test_fused_layer_norm.py | BSD-3-Clause | 4,138 | fec3b764eb1575b4136f7b4068af4150785ecacd | Python | py | |
import torch
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler, SequentialSampler
import math
class Dataloader():
def __init__(self, dataset, batch_size, small=False, shuffle_indices=False):
self.batch_size = batch_size
self.dataset ... | dataset/dataloader.py | Apache-2.0 | 1,326 | fec3c780c8e8f3c77b280d6329ee2025cb802ae5 | Python | py | |
import torch
import torch.nn as nn
from models import DiscreteVAE, return_mask_lengths
class VAETrainer(object):
def __init__(self, args):
self.args = args
self.clip = args.clip
self.device = args.device
self.vae = DiscreteVAE(args).to(self.device)
params = filter(lambda ... | vae/trainer.py | Apache-2.0 | 2,368 | fec3c7bbbd4972cc5cf1a689b8748f86771e05d5 | Python | py | |
# -*- coding: utf-8 -*-
import numpy as np
import os
import pickle
import argparse
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision.transforms as trn
import torchvision.datasets as dset
import torch.nn.functional as F
from tqdm import tqdm
from models.allconv import... | CIFAR/oe_scratch.py | Apache-2.0 | 9,585 | e94d292965c51ad863406246ef444cac0ca2a8cc | Python | py | |
# -*- coding: utf-8 -*-
# A Survey on Negative Transfer
# https://github.com/chamwen/NT-Benchmark
import numpy as np
import argparse
import torch as tr
import torch.optim as optim
import torch.utils.data as Data
import os.path as osp
import os
from utils import network, loss, utils
from utils.loss import CELabelSmooth
... | NT_SSDA/source_train_seed_finetune.py | MIT | 6,008 | 28efc67c5c0b4a76e676ecb37551261ad32236c7 | Python | py | |
import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import BroadcastingList2
from torchvision.extension import _assert_has_ops
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
def roi_pool(
input: Tensor,
boxes: Tensor,
outp... | torchvision/ops/roi_pool.py | BSD-3-Clause | 2,392 | fec3fe7febe748e7bcd0a182190fec5df58a1b77 | Python | py | |
import time
import torch
import typing
import datetime
from fba import logger, metrics, utils
from .build import HOOK_REGISTRY, HookBase
from fba.utils.vis_utils import draw_segmentation_masks
@HOOK_REGISTRY.register_module
class TimeLoggerHook(HookBase):
def __init__(
self,
num_ims_per_l... | fba/engine/hooks/stat_logger.py | MIT | 8,043 | fec40a9ccd1ed1654f9a96a6a6fbf6b24b342b82 | Python | py | |
import os
import random
import math
import torch
import numpy as np
import torch.optim as optim
from typing import List, Optional, Tuple, Any, Dict
from torch import nn
from scipy.special import softmax
from medcat.config_meta_cat import ConfigMetaCAT
from medcat.tokenizers.meta_cat_tokenizers import TokenizerWrapperBa... | medcat/utils/meta_cat/ml_utils.py | MIT | 10,744 | 4223b4cbd00722f5302a3fa19fe6f54a71e24ec7 | Python | py | |
import argparse
import numpy as np
from pathlib import Path
import re
import matplotlib.pyplot as plt
from analyse_result import double_visualize
plt.rcParams.update({'font.size': 6})
from adjustText import adjust_text
import torch
import torch.nn.functional as F
from metrics import compute_all_metric... | analyse_rec.py | MIT | 2,883 | fec43829282548305cd92bbdd7320ba67c8a5012 | Python | py | |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__all__ = ['PacConv2d', 'PacConvTra... | lib/extensions/pacnet/pac.py | MIT | 42,415 | e622f03e18aee71e0ff4d107e3d577283a46fb0e | Python | py | |
# Pytorch Tabular
# Author: Manu Joseph <manujoseph@gmail.com>
# For license information, see LICENSE.TXT
"""Tabular Model"""
from collections import defaultdict
from pytorch_lightning.utilities.seed import seed_everything
import logging
import os
from typing import Callable, Dict, Iterable, List, Optional, Tuple, Unio... | pytorch_tabular/tabular_model.py | MIT | 35,335 | 12f9b5f6156f06884b16c2c8cdf199c80bf6c8eb | Python | py | |
import configs as ori_configs
from torch.utils.data import DataLoader, TensorDataset
from Model.matching import FT_Match
from sklearn.metrics import f1_score, accuracy_score
import transformers as trans
import _pickle as pk
import torch
import torch.nn as nn
import random
from UtilPack.ExpUtil import set_random_seed
fr... | BERT_Main.py | MIT | 6,577 | 42834956118bc7c512e89f5bb1e14995d6fba2f0 | Python | py | |
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
from pythia.modules.layers import ReLUWithWeightNormFC, LinearTransform
class Hard_Output(Module):
def __init__(self, l_dim, i_dim, s_dim, inter_dim, vocab_size):... | pythia/modules/hard_output.py | BSD-3-Clause | 2,516 | fec47a20c5bb3c00aff3f8c1992aef337643a8ba | 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 numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from f... | fairseq/models/insertion_transformer.py | MIT | 10,278 | 1296333b5e5ef2cf3bcfb86919da447924520058 | Python | py | |
import os
import time
import torch
import pickle
import numpy as np
from torch import nn
from sklearn import metrics
from models.utils import generateBatch, classListToTensor, updateProgress, valueToKey
from models.BiLstmCRF.decoder import Decoder
from models.BiLstmCRF.encoder import BiLSTMEncoder
class Model:
de... | src/models/BiLstmCRF/model.py | MIT | 16,831 | fec4a970ec6ccd0b8c891889ffd694a471f7b66b | Python | py | |
# -*- coding: utf-8 -*-
from functools import lru_cache
import numpy as np
import torch
from torch.utils.data import Dataset
from ..utils.misc import pbar
class NumpySequenceDataset(Dataset):
"""Read a sequence of numpy arrays.
Arguments:
fname (str or Path): Path to a list of paths to Numpy `.npy` f... | nmtpytorch/datasets/numpy_sequence.py | MIT | 3,168 | fec4ac967f7d6ac018d87e69297096c803add25f | Python | py | |
import torch
import torch.nn.functional as F
from typing import Optional
from src.dl.utils import one_hot
from ..weighted_base_loss import WeightedBaseLoss
# This is adapted from: Catalyst package
class WeightedSCELoss(WeightedBaseLoss):
def __init__(
self,
alpha: float=0.5,
b... | src/dl/losses/criterions/sce.py | MIT | 3,374 | 1edbe561e8e757a72a079ce9034f7feb8fecc838 | Python | py | |
#!/usr/bin/env python
# coding: utf-8
import matplotlib.pyplot as plt
import numpy as np
import time
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import helper
#from network_class import Network
# Define a transform to normal... | adaptive_learning_rate_stop.py | MIT | 5,146 | e97674503b4ab400445ba487a2213303806ae56c | Python | py | |
import numpy as np
import torch.nn as nn
from catalyst.contrib import registry
@registry.Model
class SimpleGenerator(nn.Module):
def __init__(
self,
noise_dim=10,
hidden_dim=256,
image_resolution=(28, 28),
channels=1
):
super().__init__()
self.noise_di... | examples/mnist_gan/model.py | Apache-2.0 | 1,584 | fec4b90ca9118d562126a0bedebf56590f074544 | Python | py | |
import sys
import skorch
import torch.nn as nn
import numpy as np
import pandas as pd
from torch import optim
from Optimization import MedRecGRUCover, MedRecGRUCoverTrainer
from skopt.space import Real, Integer, Categorical
from skopt.utils import use_named_args
from skopt import gp_minimize
from skopt import load as ... | codes/GRUCoverOptim.py | Apache-2.0 | 8,619 | 706dc8be7ed8707c88d26bfe9fb8b9fc0d8c29d1 | Python | py | |
import pytest
import torch
import treetensor.torch as ttorch
from treetensor.utils import replaceable_partial
from ..tests import choose_mark_with_existence_check
choose_mark = replaceable_partial(choose_mark_with_existence_check, base=ttorch.Size)
@pytest.mark.unittest
class TestTorchModule:
def test_float32(s... | test/torch/test_module.py | Apache-2.0 | 873 | fec4e08c04ab51ec906abf2ff224c8725e2b2a01 | Python | py | |
# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import numpy as np
# PyTorch-backed implementations
def qmul(q, r):
"""
Multiply quaternion(s) q with q... | learning3d/pnlk_utils/quaternion.py | MIT | 6,793 | fec4e61e3cae3f98ac390a95fec88214999ddea1 | Python | py | |
import argparse
import logging
import torch
import data
import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.infer.mcmc import NUTS
from pyro.infer.mcmc.api import MCMC
logging.basicConfig(format='%(message)s', level=logging.INFO)
pyro.enable_validation(__debug__)
pyro.set_rng_seed(... | examples/eight_schools/mcmc.py | MIT | 1,764 | 45f708b1fc9bff0baecafbd2897a939129f47f60 | Python | py | |
import argparse
import os
from typing import Dict, Optional, Sequence, Union
import pytorch_lightning as pl
import torch.nn as nn
from loguru import logger
from ray.tune.integration.pytorch_lightning import TuneReportCallback
from slp.plbind.helpers import EarlyStoppingWithLogs, FixedWandbLogger
from slp.util.system i... | slp/plbind/trainer.py | MIT | 20,525 | fec4f453409ec1a48ff3f5038166770a10445e0b | Python | py | |
#!/usr/bin/env python3
# Copyright 2020 The Johns Hopkins University (author: Jiatong Shi)
import torch
import numpy as np
import copy
import time
import librosa
from scipy import signal
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def create_src_key_padding_mask(src_len, max_len):
bs... | model/utils.py | Apache-2.0 | 5,357 | fec52c2604350d8367a9d6afb7a2aba2523069d6 | Python | py | |
#!/usr/bin/env python3
# encoding: utf-8
import re
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
import torch
import torch.nn as nn
import math
import numpy as np
import time
import os
import sys
import torchvision
#__all__ = ['DenseNet121','DenseNet169... | model/model.py | MIT | 15,651 | fec53b83ec7fd5f6934299927d9b62a05930c8f5 | Python | py | |
import random
import torch
from torch import nn
import torch.nn.functional as F
import pytorch_lightning as pl
import spacetimeformer as stf
class LSTM_Encoder(nn.Module):
def __init__(
self,
input_dim: int = 1,
hidden_dim: int = 256,
n_layers: int = 2,
dr... | spacetimeformer/lstm_model/lstm_model.py | MIT | 5,393 | 4b2a4b125ef141e49012a85e6b364a173b4bb4ed | Python | py | |
import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from mmcv.ops import RoIAlign, RoIPool
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets.pipelines import Compose
from mmdet.models import... | mmdet/apis/inference.py | Apache-2.0 | 7,353 | fec570ddab96f961c0cf9c14f4b9dab986ddd7cf | Python | py | |
from __future__ import absolute_import, division, print_function, unicode_literals
import unittest
import torch
from tests.utils import jitVsGlow
class TestExp(unittest.TestCase):
def test_exp_basic(self):
"""Test of the PyTorch exp Node on Glow."""
def test_f(a):
b = torch.exp(a)
... | torch_glow/tests/nodes/exp_test.py | Apache-2.0 | 443 | fec57ac086e686810625260719f25e4651ca759c | Python | py | |
import torch
import math
import numpy as np
def get_rotated_data(raw_data, n_rotations):
'''
Computes n-way rotation on the data.
'''
x_avg = torch.mean(raw_data[:, 0].float())
y_avg = torch.mean(raw_data[:, 1].float())
x = raw_data[:, 0] - x_avg
y = raw_data[:, 1] - y_avg
ss = torch... | transforms.py | MIT | 2,619 | 1ed1289d8ca07f544da28b26b0792a2e0b0f11a9 | Python | py | |
import os
from PIL import Image
import numpy as np
import torch
from torchvision.io import read_video, write_jpeg
from torch.utils.data import Dataset
from torchvision import transforms as T
__all__ = ('MVTecDataset', 'StcDataset')
# URL = 'ftp://guest:GU.205dldo@ftp.softronics.ch/mvtec_anomaly_detection/mvtec_anom... | custom_datasets/loader.py | BSD-3-Clause | 9,370 | fdef25b8975f0938ac06adebc05e484329e417ff | Python | py | |
import torch
import torch.nn as nn
# model
class net_Softmax(nn.Module):
def __init__(self):
super().__init__()
self.op = nn.Softmax()
def forward(self, input):
return self.op(input)
_model_ = net_Softmax()
# dummy input for onnx generation
_dummy_ = torch.randn(1, 2, 3, 3)
| res/PyTorchExamples/examples/Softmax/__init__.py | Apache-2.0 | 313 | fec589d468d1d05028198223dc2c8a566c5963ab | Python | py | |
import torch
import torch.utils.data
import numpy as np
import cv2
import math
import os
from abc import ABC, abstractmethod
try:
import face_alignment
from face_alignment.detection.sfd import FaceDetector
except ImportError:
raise ImportError(
"Please install face alignment package from "
... | utils/crop_as_in_dataset.py | Apache-2.0 | 24,725 | fec59c4668f5e7dba0139c043c336d94aebedca4 | Python | py | |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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 re... | megatron/model/bert_model.py | MIT | 7,614 | fec5a436624b10765d8ddcad793f353c4f82032f | Python | py | |
from .reduced_basis import SVDBasis
import h5py
import numpy as np
from pathlib import Path
import json
import functools
from tqdm import tqdm
from pycbc.waveform import (get_td_waveform, get_fd_waveform,
get_waveform_filter_length_in_time)
from pycbc.types.frequencyseries import FrequencyS... | lfigw/waveform_generator.py | MIT | 78,308 | b25b65355f72a51bba600cf488e07c50895adc2e | Python | py | |
import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_retinanet_postprocessor
from .loss import make_retinanet_loss_evaluator
from ..anchor_generator import make_anchor_generator_retinanet
from fcos_core.modeling.box_coder import BoxCoder
class Retina... | fcos_core/modeling/rpn/retinanet/retinanet.py | BSD-2-Clause | 5,444 | fec5b4ca980f996076ca08cb77a12efff1be44b0 | Python | py | |
"""Common training loop parts."""
import copy
import logging
import pathlib
from typing import Any, Generic, Iterable, List, Mapping, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torch import nn
from torch.optim import Optimizer
from kgm.utils.common import NonFiniteLossError, get_value_from_nest... | src/kgm/training/base.py | MIT | 14,748 | b2b94e1d4590fb9768de861f449189293d8b5513 | Python | py | |
import argparse
import pandas as pd
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader, Dataset, ConcatDataset
import os
import json
import resource
import sys
import pickle
sys.path.insert(1, 'src')
from model import Model
from utils import *
from data import *
from time import ... | train_baseline.py | Apache-2.0 | 8,338 | 4213bbb75e53a62bed9cb002658323a70df1b32a | Python | py | |
import math
from torch import nn
from quati.modules.positional_encoding import PositionalEncoding
class PositionalEmbedding(nn.Module):
"""Implements an Embedding Layer with absolute Positional Encoding.
Args:
vocab_size (int): vocabulary size
size (int): embeddings size
max_seq_len... | quati/modules/positional_embedding.py | MIT | 1,002 | 5e46203b204796b678388b1d884fd9ddd7041b9f | Python | py | |
# Copyright (c) 2020 Graphcore Ltd. All rights reserved.
import numpy as np
import operator
import popart
import pytest
import torch
import itertools
from op_tester import op_tester
# `import test_util` requires adding to sys.path
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parent.... | tests/integration/operators_test/shapeddropout_test.py | MIT | 10,560 | 7cfeee514266b3050c1f49f45e7fffc5f711a526 | Python | py | |
#!/usr/bin/env python3
import os
import models
import decoder
from train_test import train, validate
import create_dataloader
import tqdm
import argparse
import logging
from collections import OrderedDict
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import torch
import torch.nn as nn
import to... | shivam_machine/run.py | MIT | 6,465 | 10f2d1fd5417e39902539fc295165cfc05e899fa | Python | py | |
import torch
import torch.nn as nn
from torch.autograd import Variable
nllloss = nn.NLLLoss()
def losses_joint(out, labels: torch._C._TensorBase, time_steps: int):
"""
Defines loss
:param out: output from the network
:param labels: Ground truth labels
:param time_steps: Length of the program
:... | src/Models/loss.py | MIT | 505 | 24e10131d166ec426710b0ba789ab1bd63cd2477 | Python | py | |
import os
import pickle
import os
import pickle
import argparse
import time
import subprocess
import torch
from torch.autograd import Variable
import numpy as np
from utils import DataLoader
from helper import getCoef, sample_gaussian_2d, get_mean_error, get_final_error
from helper import *
from grid import getSeque... | gym_collision_avoidance/envs/policies/social_lstm/test.py | MIT | 26,093 | d7f2ded36aa4c48c07c9ce21389e4443831f359e | Python | py | |
# torch imports
import torch.nn.functional as F
import torch.nn as nn
class BinaryClassifier(nn.Module):
"""
Define a neural network that performs binary classification.
The network should accept your number of features as input, and produce
a single sigmoid value, that can be rounded to a label: 0 or ... | source_pytorch/model.py | MIT | 1,717 | f9a88371a3e05d98a9e027de30dbf57919d759e1 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import \
setup_environment # noqa F401 isort:skip
import argparse
import os
import ... | tools/test_net.py | MIT | 10,947 | 24c606a82ffc049b6c5adbbbbdb352264616a3ff | Python | py | |
from typing import Any, Union
import torch
from torch.nn import CrossEntropyLoss
from saifooler.attacks.attack import SaifoolerAttack
from saifooler.viewers.viewer import Viewer3D
class FGSMOptimizer(torch.optim.Optimizer):
def __init__(self, params, epsilon):
if epsilon < 0.:
raise ValueE... | saifooler/attacks/fgsm_attack.py | MIT | 1,364 | fec67152ff341364d70e2fb500a0a191c17964d7 | 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 argparse
import tempfile
import unittest
import tests.utils as test_utils
import torch
from fairseq import search
from fairseq.data.di... | tests/test_sequence_generator.py | MIT | 22,827 | 429d1801f3074be75d8fd86e91fe46d9d457fdae | Python | py | |
import os
from typing import List, Tuple
from tqdm import tqdm
import torch
from torch.autograd import Variable
from DA2Lite.trainer.common import TrainerBase
from DA2Lite.core.log import get_logger
logger = get_logger(__name__)
class Classification(TrainerBase):
def __init__(self,
cfg_util,
... | DA2Lite/trainer/classification.py | MIT | 4,809 | 1041e81d01df3c97d45d47b70e28502980fc8cca | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | examples/run_squad.py | Apache-2.0 | 24,672 | ff7ae1afa82f0e4322daf0280f24a59d657d1b06 | Python | py | |
from __future__ import print_function
import torch
import torch.utils.data as data
import torchvision
import torchnet as tnt
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# from Places205 import Places205
import numpy as np
import random
from torch.utils.data.dataloader import defa... | data/rotationloader.py | Apache-2.0 | 7,834 | fec6b42a2335555e53317d54972b37c18f5e6e6f | Python | py | |
# -*- coding:utf-8 -*-
import argparse
import string
import csv
import os
import pandas as pd
import cv2
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data
import torch.nn.functional as F
from utils import CTCLabelConverter, AttnLabelConverter
from dataset import RawDataset, AlignCo... | deep-text-recognition-benchmark/recognize.py | MIT | 18,671 | fec6dec7452412c289b4b4a2b54541b3b5e7a19e | Python | py |
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