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"""
3D scattering quantum chemistry regression
==========================================
Description:
This example trains a classifier combined with a scattering transform to
regress molecular atomization energies on the QM7 dataset. Here, we use full
charges, valence charges and core charges. A linear regression is ... | examples/3d/scattering3d_qm7_torch.py | BSD-3-Clause | 12,151 | b2879021de266c5187da107df991a793f2b865be | Python | py | |
import sys
import random
import numpy as np
import torch
import torch.nn.functional as F
# digit_text = ['null', 'eins', 'zwei', 'drei', 'vier', 'fuenf', 'sechs', 'sieben', 'acht', 'neun'];
digit_text_german = ['null', 'eins', 'zwei', 'drei', 'vier', 'fuenf', 'sechs', 'sieben', 'acht', 'neun'];
digit_text_english = [... | utils/text.py | MIT | 1,557 | 3a706d6823444c0558c5336ef05fde606a670b5b | Python | py | |
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from config import *
from torch.nn.utils.weight_norm import weight_norm
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions.categorical import Categorical
class QuestionParser(nn.Module... | model.py | MIT | 17,079 | 429d429a31efbb9cdd79f7c244e7164044add530 | Python | py | |
# coding=utf-8
# Copyright 2021 Google AI The HuggingFace Inc. team. 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
#
... | src/transformers/models/canine/modeling_canine.py | Apache-2.0 | 70,017 | 3a707ff05a2e4796303a97699e0c921a1791fc62 | Python | py | |
import json
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn._functions.packing import PackPadded
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, PackedSequence
from utils import to_gpu
class ML... | yelp/models.py | BSD-3-Clause | 21,906 | 3a7085a70b48508ecffcf14a6e25282455a9bb36 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
import gc
import unittest
import tests.test_utils as test_utils
import torch
from mmf.common.sample import SampleList
from mmf.models.mmf_transformer import MMFTransformer, MMFTransformerModalityConfig
from mmf.modules.encoders import (
EncoderFactory,
Identi... | tests/models/test_mmf_transformer.py | BSD-3-Clause | 16,774 | f94b8b8001291db8c0cfa61f6a01e7df90776b1b | Python | py | |
import torch
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from mmdet.models.builder import NECKS
from torch import nn
class UpBlock(nn.Module):
"""Upsample block for DRRG and TextSnake."""
def __init__(self, in_channels, out_channels):
super().__init__()
assert isinstance... | mmocr/models/textdet/necks/fpn_unet.py | Apache-2.0 | 2,839 | 3a709be9225c1a1cd982991d1d7d5906f86c79f4 | Python | py | |
import argparse
import time
import numpy as np
import networkx as nx
import torch
import torch.nn as nn
import torch.nn.functional as F
# self-defined
from datasets import load_mouse_mammary_gland, load_tissue
from models import GraphSAGE
class Trainer:
def __init__(self, params):
self.params = params
... | out/production/cci/train.py | MIT | 3,996 | 3a709f03bade296a28c44639e3f7b66fb2e5dec8 | Python | py | |
import torch.utils.data as data
import os
import sys
import random
import numpy as np
import cv2
from PIL import Image
import random
from .utils import RandomResampler, SymmetricalResampler, SymmetricalSequentialResampler
cv2_loader = True
cv2.setNumThreads(0) #fix the opencv resize stuck issue
def pil_loader(ro... | BERT/datasets/covid.py | MIT | 19,405 | 7cfbfebd732da5cade9a380feac28d96aeaf4928 | Python | py | |
import torch
import random
import numpy as np
from cogdl.tasks import build_task
from cogdl.datasets import build_dataset
from cogdl.models import build_model
from cogdl.utils import build_args_from_dict
def get_default_args():
cuda_available = torch.cuda.is_available()
args = {
"dataset": "cora",
... | tests/models/ssl/test_generative_models.py | MIT | 4,971 | 3a70c16df35b26166b918ef2219e3d695dfe37dd | Python | py | |
#!/usr/bin/env python
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
from tensorboardX import SummaryWriter
import argparse, os, sys, subprocess
import setproctitle, colorama
import numpy as np
from tqdm import tqdm
from glob import glob
from os.path imp... | zmf_main_py37.py | Apache-2.0 | 23,398 | 1079ab8a2062dbcbba9d5cd05eabbdfdd77dde04 | Python | py | |
from torch.nn import Module, ModuleList
from torch.nn.functional import relu
from modeling.modules import PretrainedEmbeddingLayer, MLP, CellLayer, SequentialModel, AttendedState
import torch
from modeling.activation_factory import ActivationFactory
class MultiAttentionRNN(Module):
def __init__(self, embeddings,
... | emotions/semeval2018/modeling/multi_attention_rnn.py | Apache-2.0 | 2,225 | d7dd67875053219268cad4d91b33afcc6a64f8f5 | Python | py | |
# -*- coding: utf-8 -*-
"""
@author: Abderrahmen Amich
@email: aamich@umich.edu
"""
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
from easydict import EasyDict
def get_transform():
'''
@return: torch transforms to use in CI... | cifar.py | MIT | 4,135 | 7cc760cdd6adeeedd9b625a3ffdad77ff129ff5f | Python | py | |
import pytest
import kornia as kornia
import kornia.testing as utils # test utils
import torch
from torch.testing import assert_allclose
from torch.autograd import gradcheck
class TestPyrUp:
def test_shape(self):
inp = torch.zeros(1, 2, 4, 4)
pyr = kornia.geometry.PyrUp()
assert pyr(inp... | test/geometry/transform/test_pyramid.py | Apache-2.0 | 4,471 | 3a71083bad5646637dcb327197164217fc196eda | Python | py | |
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
import sys
import torch
import torch.nn as nn
def parse_skipgram(fname):
with open(fname) as f:
toks = list(f.read().split())
nb_nodes = int(toks[0])
nb_feature... | utils/process.py | MIT | 7,425 | cc07ad2835fab5fd8ca41185f13e09175cf772ea | Python | py | |
# Copyright (c) 2019-2020, 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 required by a... | kaolin/ops/gcn.py | ECL-2.0,Apache-2.0 | 7,593 | d7bfcca3638f7e1a93bb9710d02bf722b107654a | Python | py | |
import torch
from build_model import build_fairmot, load_model
with torch.no_grad():
input_data = torch.randn(1, 3, 608, 1088, dtype=torch.float32, device='cuda')
net = build_fairmot()
model = load_model(net, "../../weights/fairmot_dla34.pth")
model = model.to(torch.device('cuda'))
model.eval()
... | Software/trt_impl/build_onnx_engine.py | MIT | 914 | 5e8507091cef65f5af7208c160c6fc2beeeacc24 | Python | py | |
import csv
from logging import Logger
import os
import sys
from typing import List
import numpy as np
from tensorboardX import SummaryWriter
import torch
from tqdm import trange
import pickle
from torch.optim.lr_scheduler import ExponentialLR
from .evaluate import evaluate, evaluate_predictions
from .predict import p... | chemprop/train/run_training.py | MIT | 10,907 | 7cc04c9d71e66c51e05eac1884a3f1eef9134108 | Python | py | |
import re
import numpy as np
import os
import torch
import openpyxl
from dpsniper.classifiers.feature_transformer import FlagsFeatureTransformer
from dpsniper.attack.ml_attack import MlAttack
from dpsniper.classifiers.torch_optimizer_factory import AdamOptimizerFactory, SGDOptimizerFactory
from dpsniper.classifiers.c... | dpsniperOutSample/run.py | MIT | 7,173 | 3a71274b6cd1b5793963671146cfccb5c1412873 | Python | py | |
from _collections import OrderedDict
from pytracking.tracker.transt.config import cfg
import cv2
import numpy as np
import torch
class BaseTracker:
"""Base class for all trackers."""
def __init__(self, params):
self.params = params
self.visdom = None
def predicts_segmentation_mask(self):... | TransT-fusion/pytracking/tracker/base/basetracker.py | MIT | 3,574 | 10dd28332a721025ce5e05a8323eafd31e878c02 | Python | py | |
#!/usr/bin/env python3
"""This is an example to train a task with DDPG algorithm written in PyTorch.
Here it creates a gym environment InvertedDoublePendulum. And uses a DDPG with
1M steps.
"""
import gym
import torch
from torch.nn import functional as F
from garage import wrap_experiment
from garage.envs import Gar... | examples/torch/ddpg_pendulum.py | MIT | 2,533 | 3a71479bf64fb1f8593dc4ca22ab502275d06993 | Python | py | |
# Copyright 2020 The OATomobile Authors. 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 applica... | oatomobile/datasets/carla.py | Apache-2.0 | 29,235 | 3a714810a73ad152628ad891988a5d5d656e1eb1 | Python | py | |
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from pytorch_lightning.profiler import SimpleProfiler
from pytorch_lightning.loggers import WandbLogger
import pytorch_lightning as pl
from typing import List
import utils.callbacks
import importlib
import os
def init_lit_model(hparams: dict) -> p... | project/utils/init_utils.py | MIT | 6,494 | d74ec8c7f5c6a127f52677632b5fe34c4cf591d3 | Python | py | |
# Copyright (c) 2020-2021 impersonator.org authors (Wen Liu and Zhixin Piao). All rights reserved.
import unittest
import torch
from easydict import EasyDict
from iPERCore.services.options.options_setup import setup
from iPERCore.tools.trainers.lwg_trainer import LWGTrainer
class TrainerTestCase(unittest.TestCase):... | tests/test_trainers/test_lwg_trainer.py | Apache-2.0 | 927 | 3a7153436ae67662dec67ed19869b8b6f3935f7f | Python | py | |
# author: Vinit Sarode (vinitsarode5@gmail.com) 03/23/2020
import open3d as o3d
import argparse
import os
import sys
import logging
import numpy
import numpy as np
import torch
import torch.utils.data
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqd... | examples/test_pcn.py | MIT | 4,342 | 3a7154c9317954917387be6e20044d09cddfe9e7 | Python | py | |
from __future__ import print_function, division
import os
from PIL import Image
import numpy as np
from torch.utils.data import Dataset
from myinfo import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class VOCSegmentation(Dataset):
"""
PascalVoc dataset
"""
NU... | dataloaders/datasets/pascal.py | MIT | 4,492 | fcb0af646f5cda293a4102fda553e3708e5379b5 | Python | py | |
"""
Copyright (c) 2019-2020 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... | examples/classification/main.py | Apache-2.0 | 21,464 | fd8790a82bd5bfd7b5db5c2e44ee46d62cd28b33 | Python | py | |
# Owner(s): ["oncall: distributed"]
import itertools
import math
import sys
from copy import deepcopy
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.distributed.fsdp import CPUOffload
from torch.distributed.fsdp import FlatParameter
from torch.distributed.fsdp import FullyShardedDa... | test/distributed/fsdp/test_fsdp_summon_full_params.py | Intel | 17,330 | b288c567947aa5bf70287cfabd991ab2829604cf | Python | py | |
# camera-ready
from datasets import ToyDatasetEvalKL # (this needs to be imported before torch, because cv2 needs to be imported before torch for some reason)
from model import ToyNet
import torch
import torch.utils.data
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy a... | 1dregression/1/mlmcmcL16_eval.py | MIT | 3,199 | 802c80b8dfd74f269e82679f5e631fdad3df0037 | Python | py | |
import torch
import torch.fx
from torch import nn, Tensor
from .utils import _log_api_usage_once
def stochastic_depth(input: Tensor, p: float, mode: str, training: bool = True) -> Tensor:
"""
Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
<https://arxiv.org/abs/1603.09382>`_ ... | network/stochastic_depth.py | BSD-3-Clause | 2,232 | e9e00c6908773d9f6dbd07111ec1c1e5e72b4863 | Python | py | |
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | test/torch/distribution/test_torch_isqf.py | Apache-2.0 | 8,781 | 3a71894072e203ec378232b8b7d84b9cb96f741a | Python | py | |
# Uncomment to run tests locally
# import sys
# import os
# sys.path = [os.getcwd()] + sys.path
import torch
import numpy as np
from sklearn.ensemble import ExtraTreesRegressor
import itertools
import mushroom_rl
from mushroom_rl.environments.environment import MDPInfo
from mushroom_rl.policy.td_policy import TDPolic... | tests/algorithms/helper/utils.py | MIT | 15,243 | 10538f1d9e5664c5041844b332b40c7b8641ef7f | Python | py | |
import tqdm
from torchvision.utils import save_image
from datasets import inverse_data_transform
from evaluation.fid_score import get_fid, get_fid_stats_path
from .abc_runner import *
from .sample_runner import *
__all__ = ['FidRunner']
class FidRunner(SampleRunner):
@torch.no_grad()
def run(self):
... | runners/fid_runner.py | MIT | 3,210 | 1023a07e2660f251ba03f050cafb2560fd0a912d | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class HMN(nn.Module):
def __init__(self,pooling_size,dim):
super(HMN,self).__init__()
self.dim=dim
self.pooling_size=pooling_size
self.maxout2=nn... | maxout_and_experts.py | MIT | 2,477 | 7074b483b2804fc2f04b87cdf5fe18428d3bf800 | Python | py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018-2021, 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 ... | src/transformers/models/megatron_bert/modeling_megatron_bert.py | Apache-2.0 | 79,206 | 3a71c0b956ef958781097d56bae227d126674b5a | Python | py | |
# Copyright 2019 The Texar Authors. 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 applicable ... | texar/torch/modules/pretrained/xlnet_utils.py | Apache-2.0 | 14,306 | 7cb071088b5238e6f63e60e59361597b309212b7 | Python | py | |
import functools
import torch.nn as nn
from csrank.modules.object_mapping import DenseNeuralNetwork
from csrank.modules.scoring.feta import FETAScoring
from csrank.objectranking.object_ranker import SkorchObjectRanker
from csrank.rank_losses import HingedRankLoss
class FETAObjectRanker(SkorchObjectRanker):
"""A... | csrank/objectranking/feta_object_ranker.py | Apache-2.0 | 2,767 | 10de5469b79e38acacdb28fa68e5653456f57384 | Python | py | |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import torch
from torch.distributions import Transform, constraints
from pyro.distributions.conditional import ConditionalTransformModule
from pyro.distributions.torch_transform import TransformModule
from pyro.distributions.util impo... | pyro/distributions/transforms/generalized_channel_permute.py | Apache-2.0 | 11,422 | a91eaaa78390c6435d0bda83257de98ce6241b7c | Python | py | |
import os
import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from commons.utils import run_lstm, seq_conditional_weighted_num, SIZE_CHECK
from models.syntaxsql.net_utils import to_batch_seq
class KeyWordPredictor(nn.Module):
'''Predict if the next token is (SQL key words):
... | models/syntaxsql/modules/keyword_predictor.py | MIT | 6,987 | 5fa92e3419c68c003d746659e91c8105815d7ca6 | Python | py | |
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
def generate_training_data(T=24):
"""
:param T: what is the T ? @Thomas
:return:
"""
netDemandFull = 1e3 * np.loadtxt('netDemandFull.csv') # convert to KW
netDemand = netDemandFull[76, :] # Pick a node with... | CaseStudy_Synthetic/processData.py | MIT | 2,725 | 7c2da594ce216928447c9cb6fb37de4c2a27337b | Python | py | |
import os.path as osp
from pytorch_lightning import LightningModule
import torch
import torch.nn as nn
import torch_optimizer as optim
import torchmetrics.functional as F
from typing import List, Tuple, Union
from kosmoss import CONFIG, DATA_PATH
class ThreeDCorrectionModule(LightningModule):
def __init__(se... | src/kosmoss/parallel/mlp.py | MIT | 4,931 | 3a720bc0c6c0d5da9d4043630cfbcec7c93643d1 | Python | py | |
import copy
import glob
import os
import time
import pdb
from collections import deque
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from a2c_ppo_acktr import algo
from a2c_ppo_acktr.arguments import get_args
from a2c_ppo_acktr.envs import... | main.py | MIT | 12,409 | ffb2457ff8eb24f6b1931bb8ece1f7b212c21db7 | Python | py | |
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import pretrainedmodels
import config
from models import *
class ModelInfo:
def __init__(self,
factory,
pretrained_settings,
args,
batch_s... | wienerschnitzelgemeinschaft/src/Dmytro/src/experiments.py | MIT | 24,920 | 3a7224f2948a91d84eef29676d17cacf09d7171c | Python | py | |
from typing import Dict, Optional
import torch
from torch import Tensor
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.color import rgb_to_grayscale
class RandomGrayscale(IntensityAugmentationBase2D):
r"""Apply random transformation to Grayscale according to a probabi... | kornia/augmentation/_2d/intensity/grayscale.py | ECL-2.0,Apache-2.0 | 2,416 | b22bae86b2a172039a1ba2e5e91e90a703e4bd98 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 ... | torchbeast/monobeast.py | Apache-2.0 | 22,325 | 4250a5af4c397dbde8c4fd45b5e780cf09b9f21d | Python | py | |
import logging
import math
import os
import time
import datetime
import traceback
from typing import Dict, Optional, List, Tuple, Union, Iterable, Any
import torch
import torch.optim.lr_scheduler
from allennlp.common import Params
from allennlp.common.checks import ConfigurationError, parse_cuda_device
from allennlp... | allennlp/training/trainer.py | Apache-2.0 | 40,270 | 3a725ee054e17986c9b4a21cf3f79c5ea3fb9f20 | Python | py | |
# coding: utf-8
from __future__ import print_function
import time
start = time.time()
from collections import Counter, defaultdict
import random
import sys
import argparse
import numpy as np
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torch.nn import functional as... | msda_src/tagging/tagging_moe.py | MIT | 15,258 | 4b7d5f8f2eb6292406688c69236bd0a3d205ab8c | Python | py | |
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
from torch.autograd import Variable
from torch.nn import CrossEntropyLoss
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from typing import Optional
import time
from pdb import set_trace as st
class Attacker(... | attacks.py | MIT | 24,531 | 3a7279c035b01432df73c180dce542d600cab1a8 | Python | py | |
#!/usr/bin/env python3
import numpy as np
import torch
from itypes import TorchStruct
device = torch.device('cuda:0')
print()
s = TorchStruct(dims="hwc")
s.x = torch.tensor([[[1, 2, 3], [4, np.nan, np.inf]]], device=device)
s.y = TorchStruct(dims="hwc")
s.y.z = torch.tensor([[7, 8, 9], [10, np.nan, np.inf]], device=... | examples/struct/torch_struct.py | MIT | 1,533 | 3a7284d6039a72e165fd350f3e5413a6beadb76f | Python | py | |
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torch._utils_internal import get_file_path_2
import unittest
import math
import random
import numpy as np
from PIL import Image
try:
import accimage
except ImportError:
accimage = None
try:
from scip... | test/test_transforms.py | BSD-3-Clause | 52,229 | 3a72a6150e642a296c9b0f7dcdcdb2555f2abe8c | Python | py | |
"""Test configurations for `backpack.core.derivatives` for CONVOLUTIONal layers
Required entries:
"module_fn" (torch.nn.Module): Module class of layer.
"module_kwargs" (dict): Dictionary used for layer initialization.
"input_kwargs" (dict): Parameters required by torch.nn.Module class
"size": Requ... | test/core/derivatives/convolution_settings.py | MIT | 4,114 | 5f83f3a029392715198a744196ac12ca42dbc44c | Python | py | |
# 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 logging
import math
from functools import lru_cache
from typing import Callable, Dict, Iterable, Optional, Sequence, Tuple
import to... | src/beanmachine/ppl/experimental/inference_compilation/ic_infer.py | MIT | 22,001 | 28a4583c181233fe4b48ea333798cbeae22b7f3a | Python | py | |
# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# 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 applicabl... | pennylane/transforms/classical_jacobian.py | Apache-2.0 | 7,509 | a96a1dcceb96735b40e704177c57512756eb8038 | Python | py | |
import time
import numpy as np
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from models import *
from utilw import data_generator
class Model:
def __init__(self,config):
self.model = None
self.config = config
... | train.py | MIT | 4,611 | 3a72e678b3cd858beb2abd553299c2ca82bac96f | Python | py | |
# -*- encoding: utf-8 -*-
# ----------------------------------------------
# filename :nommer.py
# description :NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition
# date :2021/12/28 17:45:26
# author :clark
# version number :1.0
# --------------------------... | models/nommer.py | Apache-2.0 | 20,770 | 345848d8a13a91c362e9d1b834f9d240e231a7e7 | Python | py | |
import torch
from torch import nn, optim
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
from utils.OT import SinkhornDistance
import numpy as np
from models.resnet import ResNet18
import random
def mixup_process(out, y, lam):
indices = np.random.permutation(out.size(0))
... | cifar10_100/models/resnet18_classifier.py | MIT | 2,702 | f9376e9b6e7afb602acf0c499f2e005845a1e29e | Python | py | |
import numpy as np
import json
import torch
import torch.optim as optim
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch.utils.data as data
import argparse
import logging
import os
import copy
from math import *
import datetime
from m... | experiments.py | MIT | 41,917 | 4ba11051174bd2d19a5b9861c067fe9c09ba5263 | Python | py | |
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
# Writer will output to ./runs/ directory by default
writer = SummaryWriter('./GeometricVisualization/runs')
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((... | mytensorboard.py | MIT | 888 | 24e3e6bb65d9486d3dbf23ef6ca82405fa373da8 | Python | py | |
#!/usr/bin/env python3
from ruamel.yaml import YAML, dump, RoundTripDumper
#
import os
import math
import argparse
import numpy as np
import time
import sys
import torch
from rpg_baselines.envs import vec_env_wrapper as wrapper
from scipy.spatial.transform import Rotation
#
from flightgym import QuadrotorEnv_v1
def... | flightrl/examples/test_vec_env.py | MIT | 2,912 | 3a7326e19540fd74c4437499d4a8d9bea2fd1e5b | Python | py | |
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch... | implementations/lsgan/lsgan.py | MIT | 6,581 | 3a73299aeb31aa85c7796a548f18dd27d0193e0f | Python | py | |
import abc
import copy
from collections import defaultdict
from typing import Dict, Optional, Tuple
import torch
from torch import nn
from torch.nn.utils import parametrize
from .utils import FakeSparsity, module_to_fqn, fqn_to_module
SUPPORTED_MODULES = {
nn.Linear
}
class BaseSparsifier(abc.ABC):
r"""Bas... | torch/ao/sparsity/sparsifier/base_sparsifier.py | Intel | 10,866 | 7c482b0f93dabc6e8a2fd002c84904aa07457188 | Python | py | |
"""
Proof-of-concept for porting mmcv DataContainer concept to netharn. Depending
on how well this works these features might be useful as a standalone module or
to contribute to torch proper.
References:
https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py
https://github.com/open-mml... | netharn/data/data_containers.py | Apache-2.0 | 34,643 | 3a733a7ac7fc93b89ab73b91f7d4fb1def2026e7 | Python | py | |
from loguru import logger
import cv2
import torch
from yolox.data.data_augment import preproc
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
from yolox.utils.visualize import plot_tracking
from yolox.tracker.byte_tracker import BYTETracker
from yolox.tracking_utils... | tools/demo_track.py | MIT | 13,475 | 3a7352a40e9a353fed807437ad283e3e012d0b1e | Python | py | |
import os
import torch
import pickle
from progress.bar import Bar
from utils import AverageMeter, adjust_learning_rate
class Trainer(object):
"""docstring for Trainer"""
def __init__(self, Model, Optimizer, Loss, Metrics, File, vis, opts):
super(Trainer, self).__init__()
... | trainer.py | MIT | 5,041 | 3a73575d0df51eba6b00d6431f48f95099a0f2a9 | Python | py | |
################################################################################
# Copyright (c) 2021 ContinualAI. #
# Copyrights licensed under the MIT License. #
# See the accompanying LICENSE file for terms. ... | avalanche/evaluation/metrics/topk_acc.py | MIT | 13,096 | a9676bf1bc14421428bd122ff2f5f98f043e9a07 | Python | py | |
import torch
sigmoid = torch.nn.Sigmoid()
x1 = torch.tensor(1.0, requires_grad=True)
w1 = torch.tensor(0.5, requires_grad=True)
w2 = torch.tensor(1.0, requires_grad=True)
w3 = torch.tensor(1.0, requires_grad=True)
w4 = torch.tensor(0.5, requires_grad=True)
w5 = torch.tensor(0.5, requires_grad=True)
y1 = x1*w1
y1.r... | ML_Lecture5_SelfStudy5/ex3_lecture.py | MIT | 1,406 | 5fc5c313d53b8a6957eda61209ca090d4bb2f062 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
from torchvision.models.resnet import resnet18, resnet34, resnet50
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import os
import... | deepcca/conv_dcca_mnist.py | MIT | 22,407 | 346a87f2c15035f316eaf807c1e2145427162ddd | Python | py | |
import os
from unittest.mock import patch
import pytest
from pytorch_lightning import Trainer
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.base import EvalModelTemplate
import tests.base.develop_pipelines as tpipes
try:
import torch_xla
# TODO: The tests are aborted... | tests/models/test_tpu.py | Apache-2.0 | 5,088 | 5ec2e7e7d9492b22f7e4c5dc9842804cecd3807e | Python | py | |
import numpy as np
import torch
import torch.nn as nn
from .. import UnsupervisedModelWrapper
from cogdl.wrappers.tools.wrapper_utils import evaluate_node_embeddings_using_logreg
class DGIModelWrapper(UnsupervisedModelWrapper):
@staticmethod
def add_args(parser):
# fmt: off
parser.add_argume... | cogdl/wrappers/model_wrapper/node_classification/dgi_mw.py | MIT | 3,262 | 3a7377c6ec11ee484adcd525118b48f3ecf55a06 | Python | py | |
# Copyright (c) 2020 Horizon Robotics and ALF Contributors. 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... | alf/environments/random_alf_environment.py | Apache-2.0 | 8,440 | a94a203c1e608a027a29e7e95461506e6b4ed76e | Python | py | |
# Copyright 2018-2019 Open-MMLab. All rights reserved.
# Licensed under the Apache License, Version 2.0.
import logging
import os
import sys
import os.path as osp
import time
import torch
import torch.optim
from . import hooks
from .log_buffer import LogBuffer
from .priority import get_priority
from .hooks import (... | siam_tracker/runner/runner.py | MIT | 13,270 | 45c18a871d5cc5cbf91aa6837c67eb392898da41 | Python | py | |
# -*- coding: utf-8 -*-
"""
Adversarial robustness objective, and corresponding weights_manager.
Copyright (c) 2019 Xuefei Ning, Junbo Zhao
"""
# pylint: disable=missing-docstring,invalid-name,no-self-use
import sys
import weakref
import functools
import contextlib
from collections import OrderedDict
import os
import ... | examples/plugins/robustness/1_robustness.py | MIT | 29,491 | e9c3767fd5ee5f23e8ee8c956a0a606823485033 | 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/training.py | Apache-2.0 | 26,785 | 4b6135916371757f3086aab8214a4653e0903496 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
from pathlib import Path
from typing import Callable, Optional, Union
import numpy as ... | tests/common_testing.py | BSD-3-Clause | 6,445 | 3a73bfeb186f312a890a01a7e15d8f549ee007ae | Python | py | |
import numpy as np
import pytest
import torch
from rfvision.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
return np.random.randint(0, 2, size, dtyp... | tests/test_utils/test_masks.py | Apache-2.0 | 25,988 | 3a73c9f5ec19fe1797f218b3128976db9f971e62 | Python | py | |
import torch
import numpy as np
import os
from datetime import datetime
import data
import metrics
import plots
from pytorch_spa_nn import SpaNn
torch.set_printoptions(linewidth=180, precision=8, edgeitems=12)
np.set_printoptions(linewidth=180)
def continue_training_from_checkpoint(checkpoint, max_epochs, new_check... | pytorch_train_network.py | MIT | 17,511 | e94d774c397e31cf1c9a159a36f319984b8ac998 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import logging
import gym.spaces as spaces
from rlberry.agents import AgentWithSimplePolicy
from rlberry.agents.utils.memories import Memory
from rlberry.agents.torch.utils.training import optimizer_factory
from rlberry.agents.torch.utils.models import default_poli... | rlberry/agents/torch/ppo/ppo.py | MIT | 15,226 | 3a73e2e45fb47b58d1b43f4ce327e17ac3853016 | Python | py | |
import math
import torch
import torch.nn as nn
from src.org.campagnelab.dl.funnel.transformer.EncoderDecoder import clones
import torch.nn.functional as F
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, k... | src/org/campagnelab/dl/funnel/transformer/Attention.py | MIT | 2,810 | 3a73e7363bd851973e958f9b8b115711f2bbdf9e | Python | py | |
import torch
import numpy as np
from collections import namedtuple
def prepare_data(x_particles, t_particles, x_sample, t_sample):
t_grid, x_grid = np.meshgrid(t_sample, x_sample, indexing='ij')
rand_idx = np.random.permutation(t_grid.size)
grid_data = torch.tensor(np.concatenate((t_grid.reshape(-1, 1), x... | src/temporal_normalizing_flows/preprocessing.py | MIT | 961 | 3a74107160edb4eb30c0078bcd05d1835e48d85e | Python | py | |
import argparse
from torch.utils.data import DataLoader
from bert_pytorch import parse_args
from .model import BERT
from .trainer import BERTTrainer
from .dataset import BERTDataset, WordVocab
import random
import torch
import numpy as np
def train():
# Make all randomness deterministic
random.seed(1337)
... | torchbenchmark/models/BERT_pytorch/bert_pytorch/__main__.py | BSD-3-Clause | 2,191 | 1e91d38e24368168d6d3ac1b1a4a1a6dd976ed63 | Python | py | |
import torch
from torch.autograd import Function
from torch.nn.modules.module import Module
from torch.autograd import Variable
import os
from torch.autograd.function import once_differentiable
torch_ver = torch.__version__[:3]
if torch_ver=="0.4":
from torch.utils.cpp_extension import load
build_path = o... | lib/model/roi_align.py | Apache-2.0 | 7,818 | 3a7425f02d417e49533a06517c4ca67e26f4f254 | Python | py | |
# -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
#
# pylint: disable= no-member, arguments-differ, invalid-name
#
# Pre-trained models on MUV
import torch.nn.functional as F
from ...model_zoo import GCNPredictor, GATPredictor, WeavePr... | python/dgllife/model/pretrain/moleculenet/muv.py | Apache-2.0 | 8,227 | 3a743740107a4c4b7070fe0d3040242984d1830b | Python | py | |
import inspect
import torch
def batch_callable(f):
if not callable(f):
return False
return 'batch' in inspect.signature(f).parameters.keys()
def padshape(x, target_dim, dim=-1):
"""
Insert singleton dimensions in position `dim` until the tensor as
dimensionality `target_dim`.
"""
... | nitorch/nn/generators/utils.py | MIT | 882 | 3a7443f43b768237c23e34864d64da7cc47a4027 | Python | py | |
import torch.nn as nn
class ClassificationHead(nn.Module):
def __init__(self, embedding_size, output_size):
super().__init__()
self.embedding_size = embedding_size
# self.head = nn.Sequential(
# nn.Linear(embedding_size, 256),
# nn.ReLU(inplace=True),
# ... | models/ClassificationHead.py | Apache-2.0 | 631 | ff0c3d99185fc7c1079cff96ab58a1bbd2aaa03a | Python | py | |
from torch.nn import Module, Linear, GRUCell, LayerNorm
import torch as T
class GruAttDecoder(Module):
"""
Attention based GRU decoder that attends a custom sequence. Another
difference from the classical gru decoder is that here I manually loop over
steps instead of relying on the internals provided ... | mlmo/layers/decoders/gru_att_decoder.py | MIT | 6,766 | 457b21790fffa79342106afd49082903fb56a82e | Python | py | |
import torch
from torch.nn.functional import cross_entropy, interpolate
__all__ = [
"cross_entropy2d",
"multi_scale_cross_entropy2d",
"bootstrapped_cross_entropy2d",
]
def cross_entropy2d(input, target, weight=None, size_average=True):
n, c, h, w = input.size()
nt, ht, wt = target.size()
# H... | neodroidvision/segmentation/evaluation/experimental/cross_entropy_2d.py | Apache-2.0 | 2,492 | fcaba251404a7313c3cc2298a433ef9308d505a3 | Python | py | |
import torch
import torch.nn as nn
import pytorch_lightning as pl
from .set_transformer import ISAB, PMA
class SetEncoder(pl.LightningModule):
def __init__(self,cfg):
super(SetEncoder, self).__init__()
self.linear = cfg.linear
self.bit16 = cfg.bit16
self.norm = cfg.norm
asse... | src/nesymres/architectures/set_encoder.py | MIT | 3,933 | 7c2bdfb2f270cc4897caaf42e49883b40c4977f6 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class _conv(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias):
super(_conv, self).__init__(in_channels = in_channels, out_channels = out_channels,
kernel_size = ke... | ops.py | MIT | 3,429 | 24ac7d9cc953362751e6fad4a29ca54150db4407 | Python | py | |
import random
import warnings
from collections import OrderedDict
from functools import wraps
from typing import Any, Callable, Generator, Iterator, List, Optional, cast
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import BatchSampler
from ignite.engine.engine import Engine
from ... | ignite/engine/deterministic.py | BSD-3-Clause | 11,440 | 4286b5acc662fd2660f8982bfdc20c9afc1ce64e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.output_layer import *
from models.utils import *
from models import base
class Model(base.SRNNBase):
def __init__(self, d_data, d_emb, d_mlp, d_rnn, d_lat, n_layer, dropout=0.,
**kwargs):
super(Model, self).__init__(d_data, ... | speech/models/srnn.py | MIT | 1,506 | ff9f96568344d5689396640c6f895fc6921a014d | Python | py | |
import os
import torch
import torch.nn as nn
import tree
import senreps
import numpy as np
from init import create_data, parse_corpus
from DC import DC
from senreps import fetch_fake_pos_tags
from torch import Tensor
from transformers import *
from collections import defaultdict
device = torch.device("cuda:0" if torch... | Assignment1/train_pos.py | MIT | 6,973 | 7cbf143420692fa28783604b744b15bf8a21afb1 | Python | py | |
'''
Author: Shuailin Chen
Created Date: 2020-11-27
Last Modified: 2021-06-20
'''
# 2020-10-16 将softmax层移除，因为ptorch里面的计算交叉熵的函数内部已经集成了softmax
# 统一设置所有的 drop out 概率
# Rodrigo Caye Daudt
# https://rcdaudt.github.io/
# Daudt, R. C., Le Saux, B., & Boulch, A. "Fully convolutional siamese networks for change detection". In 2... | mmseg/models/siambackbones/siamunet_diff.py | Apache-2.0 | 7,758 | 3a74a0cc447e624218300f2640f44735a4c06170 | Python | py | |
"""Training GCMC model on the MovieLens data set by mini-batch sampling.
The script loads the full graph in CPU and samples subgraphs for computing
gradients on the training device. The script also supports multi-GPU for
further acceleration.
"""
import os, time
import argparse
import logging
import random
import stri... | examples/pytorch/gcmc/train_sampling.py | Apache-2.0 | 21,160 | 3a74ab72b18631b095a93d5a45673e9d25288787 | Python | py | |
# -*- coding: utf-8 -*-
"""Main module."""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, kl_divergence as kl
from scvi.models.distributions import (
ZeroInflatedNegativeBinomial,
NegativeBinomial,
Poisson,
)
from scvi.models.m... | scvi/models/vae.py | MIT | 17,064 | 3a74aeb83c84d25408ec4fc7154a7ce108337413 | Python | py | |
#! /home/jan/anaconda3/bin/python3
import functions
import csv
import os
import itertools
import functions
from classes import MLP, MLP_w_torchlrp, AlexNet
import subprocess
import os
import sys
import time
import torch
import random
import pathlib
import argparse
import torchvision
import matplotlib.pyplot as plt
#f... | src/main.py | MIT | 2,584 | 1ed626b396df69b65a0d5b4fe26a0998e39a89bd | Python | py | |
import math
import cmath
import warnings
import torch
import torch.backends.cudnn as cudnn
from torch._six import PY37
from ..nn.modules.utils import _single, _pair, _triple, _quadruple, _list_with_default
from collections import OrderedDict
from typing import Dict, Optional
_builtin_table: Optional[Dict[int, str]]... | torch/jit/_builtins.py | Intel | 6,064 | 10a0b159dbee3c50c2b2878b76a3c5ae7e6249a3 | Python | py | |
#! /usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
class OhemSphereLoss(nn.Module):
def __init__(self, in_feats, n_classes, thresh=0.7, scale=14, *args, **kwargs):
super(OhemSphereLoss, self).__init__(*args, **kwargs)
self.thresh = thresh
self.scale = scale
... | sphere_loss.py | MIT | 2,314 | fdcbd1e92e12f505926fe595b15d2e228e410dbe | Python | py | |
# encoding: utf-8
import math
import torch as th
import torch
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=... | models/resnet.py | MIT | 6,370 | b277335647ce2db81c9e28b6a5f2c8455c875169 | Python | py | |
import math
import numpy as np
import torch
from torch import optim
from torch import nn
import torch.utils.data
from torch.nn import BatchNorm1d, Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, Sigmoid
from torch.autograd import Variable
import warnings
from .data_sampler import DataSampler
from ctgan.data_tran... | snsynth/pytorch/nn/patectgan.py | MIT | 19,882 | 03cbbc26a826d8fe71bcfcc68b4974bfdcdd3450 | Python | py | |
import os
import shutil
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, ConcatDataset, Sampler
from deepSM import SMData
from deepSM import utils
import deepSM.beat_time_converter as BTC
from deepSM import wavutils
from deepSM import StepPlacement
from deepSM import SMDataset
from d... | deepSM/SMDUtils.py | MIT | 8,848 | d7f84bef3d6e6c969b3f189f264a14c84051297b | Python | py |
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