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from __future__ import division
import re
from collections import OrderedDict
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
from mmdet import datasets
from mmdet.core import (CocoDistEvalmAPHook, CocoDistEvalRecallHo... | mmdet/apis/train.py | Apache-2.0 | 8,771 | 2997bf3daa455424363a21e8817ae61e4f8758b3 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.models.discriminator import Discriminator
from core.loss.diffaug import get_default_transforms
class GANLoss(nn.Module):
def __init__(
self,
image_size,
channels_in=3,
perceptual=False):
... | core/loss/gan_loss.py | Apache-2.0 | 1,793 | 7cde7174215fa76aebdba2443ae421e91b308f42 | Python | py | |
from functools import partial
import torch.nn as nn
from catalyst.dl.utils import criterion
class DiceLoss(nn.Module):
def __init__(
self,
eps: float = 1e-7,
threshold: float = None,
activation: str = "Sigmoid"
):
super().__init__()
self.loss_fn = partial(
... | catalyst/contrib/criterion/dice.py | Apache-2.0 | 1,085 | fcf9fa6c91a1320a0651daf957446dcdc8714ef2 | 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.
from typing import List
from dataclasses import dataclass
import io
import torch
import polymetis
import polymetis_pb2
from polymetis.utils.da... | polymetis/polymetis/python/polymetis/robot_client/metadata.py | MIT | 4,285 | 299805614913400c1d10c24d46721c5c55060f75 | Python | py | |
#
from __future__ import division
import os
import numpy as np
import torch
import torch.nn as nn
from torch.nn.functional import interpolate
from ...nn import ConcurrentModule, SyncBatchNorm
from .base import BaseNet
__all__ = ['FCN', 'get_fcn', 'get_fcn_resnet50_pcontext', 'get_fcn_resnet50_ade',
'get_fc... | experiments/segmentation/encoding/models/sseg/fcn.py | MIT | 7,774 | b29e66cc02ef0f83a7f511ee9b184e18e82084f5 | Python | py | |
import logging
from typing import Callable, List
import torch.nn as nn
from .predict import predict
from chemprop.data import MoleculeDataLoader, StandardScaler
def evaluate_predictions(preds: List[List[float]],
targets: List[List[float]],
num_tasks: int,
... | chemprop/train/evaluate.py | MIT | 3,843 | 42198ee902aae45a3aa8fa373912b7760d2bcbaa | Python | py | |
import argparse
import functools
import json
import os
import random
import re
from typing import Dict
import numpy as np
import torch
SEPARATOR = {"stars": "".join(["*"] * 100), "dashes": "".join(["-"] * 100), "dots": "".join(["."] * 100)}
def get_device(device_type: str) -> torch.device:
if device_type == "cp... | auxiliary/utils.py | MIT | 3,399 | 29982d98e4ec4d0ddd39d12438506be48be976a9 | Python | py | |
"""NTM Read and Write Heads."""
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
def _split_cols(mat, lengths):
"""Split a 2D matrix to variable length columns."""
assert mat.size()[1] == sum(lengths), "Lengths must be summed to num columns"
l = np.cumsum([0] + lengths)... | ntm/head.py | BSD-3-Clause | 4,105 | 29982fee0d8f47caac19a07df273f9b1e783bd93 | Python | py | |
from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def create_var(tensor, requires_grad=None):
if requires_grad is None:
return Variable(tensor).cuda()
else:
return Variable(tensor, requires_grad=requires... | fast_jtnn/nnutils.py | MIT | 2,081 | f9a417c0af7e4280f48bde5dc48e5f1e6cce8e6a | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 2020/9/15
# @Author : Shanlei Mu
# @Email : slmu@ruc.edu.cn
r"""
KGAT
##################################################
Reference:
Xiang Wang et al. "KGAT: Knowledge Graph Attention Network for Recommendation." in SIGKDD 2019.
Reference code:
https://github.com/xiangwang1... | recbole/model/knowledge_aware_recommender/kgat.py | MIT | 12,523 | 29987a80a09ecde09ab5a2e09f5e4c1d7286af88 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
def _sum_tensor_scalar(tensor, scalar, expand_size):
if scalar is not None:
scalar = scalar.expand(expand_size).contiguous()
else:
return tensor
if tensor is None:
return scalar
return tensor + scalar
... | seq2seq/models/modules/linear.py | MIT | 2,189 | 5eccf1defb40a750b88843bd6b078c46ce760207 | Python | py | |
import numpy as np
import random
import copy
from collections import namedtuple, deque
from model import Actor, Critic
import torch
import torch.nn.functional as F
import torch.optim as optim
BUFFER_SIZE = int(1e6) # replay buffer size
BATCH_SIZE = 128 # minibatch size
GAMMA = 0.99 # discount fact... | ddpg_agent.py | MIT | 8,254 | 29988b39e4b45057a1848d573c7fe48e94ea6706 | Python | py | |
import torch
import torch.nn as nn
from layers import Pooling, Concat, GCN, EventLSTM, MLP, TimeAttention
###########################################
# TEMPORAL GCN ARCHITECTURES ##############
###########################################
class DyGED_CT(nn.Module):
def __init__(self, adj_size, n_feature, n_hidde... | source/models.py | MIT | 4,084 | 29989a7ad5c8166dd9bed4b38c0a1542ee42e66e | Python | py | |
"""
Copyright (c) Microsoft Corporation
Licensed under the MIT license.
"""
import os
from typing import Union
import torch
import json
import copy
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
... | deepspeed/runtime/config.py | MIT | 42,713 | 4b7e99ae99b7a05066a28555096bce938ea412a5 | Python | py | |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import mmcv
import torch
from mmedit.apis import init_model, restoration_face_inference
from mmedit.utils import modify_args
def parse_args():
modify_args()
parser = argparse.ArgumentParser(description='Restoration demo')
parser.a... | demo/restoration_face_demo.py | Apache-2.0 | 1,876 | 45f053045f780ff9acb87c4fb3915d02511d0468 | Python | py | |
# Imports
import numpy as np
import torch
from phimal_utilities.data import Dataset
from phimal_utilities.data.burgers import BurgersDelta
from DeePyMoD_SBL.deepymod_torch.library_functions import library_1D_in
from DeePyMoD_SBL.deepymod_torch.DeepMod import DeepModDynamic
from DeePyMoD_SBL.deepymod_torch.training im... | paper/figures/Burgers_noise/cluster/run_cluster.py | MIT | 1,782 | 2998ab8c3e29680ca63d48f313e78fcb2d0845b3 | Python | py | |
import torch
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
class Meter:
"""
This class is used to keep track of the metrics in the train and dev loops.
"""
def __init__(self, target_classes):
"""
:param target_classes: The classes for whom the met... | evaluation/tools/utils.py | MIT | 3,624 | 2998bd0cca8971d44fcba2eed2ab87f4396853e4 | Python | py | |
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Function
from collections import OrderedDict
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout=0.1)... | models/vit.py | MIT | 5,063 | e95a9589e279b5f6e2426eff0be7743d049d739c | Python | py | |
from typing import Dict, Tuple, Callable
import numpy as np
import torch
from torch import nn
class CNN(nn.Module):
def __init__(self, network_params: Dict, activation: bool = True):
super().__init__()
input_size = network_params["input_size"]
output_size = network_params["output_size"]... | classifier/classes/modules/base/CNN.py | MIT | 8,081 | 249bf0c3253546a18b4dd0b2400700dc028e6b4a | Python | py | |
#
# transform_a.py
#
# Author(s):
# Matteo Spallanzani <spmatteo@iis.ee.ethz.ch>
#
# Copyright (c) 2020-2021 ETH Zurich.
#
# 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.ap... | systems/CIFAR10/VGG/preprocess/transform_a.py | Apache-2.0 | 1,496 | d7366cae1b60899fea74c8094b835c7c3cb3cdbd | Python | py | |
from __future__ import print_function, division
import warnings
warnings.filterwarnings("ignore")
import os.path
import pandas as pd
import torch
import torch.nn as nn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import dlib
import os
import argparse
def rect_to_bb(rect):
... | predict.py | CC-BY-4.0 | 9,260 | 5e0ac5086d6f08e93f258b6f00758345fb0c6bd0 | Python | py | |
# Copyright 2020 Nagoya University (Tomoki Hayashi)
# Copyright 2021 Carnegie Mellon University (Jiatong Shi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Muskit singing voice synthesis model."""
from contextlib import contextmanager
from distutils.version import LooseVersion
import logging
from typ... | muskit/svs/muskit_model.py | Apache-2.0 | 30,756 | ff64c3cb8586fa697cd00ca44db44e3556c5617b | Python | py | |
from torch.nn import init
### initalize the module
def init_weights(net, init_type='normal'):
#print('initialization method [%s]' % init_type)
if init_type == 'kaiming':
net.apply(weights_init_kaiming)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_t... | nnunet/unet_nested/utils.py | Apache-2.0 | 1,099 | 29991169ab2b0ae789624e18ff1c5994a9638fa9 | Python | py | |
# --------------------------------------------------------
# Visual Detection: State-of-the-Art
# Copyright: Hanbo Zhang
# Licensed under The MIT License [see LICENSE for details]
# Written by Hanbo Zhang
# --------------------------------------------------------
import torch
import torch.nn as nn
from torch.autograd... | model/op2l/rois_pair_expanding_layer.py | MIT | 2,362 | b2afa6f16dccf8ffcd603cc5fd69c26dcea93165 | Python | py | |
"""
Copyright (c) 2019-present NAVER Corp.
MIT License
"""
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from basenet.vgg16_bn import vgg16_bn, init_weights
class double_conv(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch):
super(double_conv, self).__i... | craft.py | MIT | 2,759 | a97446f5509818d09ca6f25d1828866393ed3cc0 | Python | py | |
import numpy as np
import torch
from torchvision import datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from skimage.measure import compare_psnr, compare_ssim
import skimage.io as sio
from glow.glow import Glow
from dcgan.dcgan import Generator
import json
import os
import warnings
... | solvers/inpainter.py | MIT | 21,413 | e9cfe90b66d9caf0ea7a960c41dc70171ad91e5b | Python | py | |
import torch
from . import samplers
def make_data_sampler(dataset, shuffle, distributed, is_train=True):
# Only do weighted sampling for training
if distributed:
# if is_train:
# return samplers.DistributedWeightedSampler(dataset, shuffle=shuffle)
# else:
return samplers.Dis... | datasets/build_samplers.py | Apache-2.0 | 1,624 | 2999783cf059803651e101f86706b68bbf9ffd85 | Python | py | |
import time
import os
import numpy as np
import cv2
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms
import datasets.dataset
from core.utils import *
from cfg.cfg import parse... | app/libs/action_localization/spatio-temporal-libs/test_images.py | MIT | 11,297 | 10a201d1a7da6f638bec5fbfcefdc2981b0fbbae | Python | py | |
"""
# Author : GS Oh
# Experiment : The gaussian experiments presented in the HCNAF paper
# Note : Define the gaussian experiments
"""
# GS: if condition == 1, then generate 8 gaussians, else 4 gaussians
import torch
import sklearn
import numpy as np
def sample_dataset(num_samples, args, flag_sample_targ... | HCNAF_Gaussians/exp_hcnaf_gaussians.py | Apache-2.0 | 2,326 | 2999d1111ac470322bfb52b6065edf3e6aa75d98 | Python | py | |
import math
import torch
from .module import Module
from ..parameter import Parameter
from ..utils.rnn import PackedSequence
class RNNBase(Module):
def __init__(self, mode, input_size, hidden_size,
num_layers=1, bias=True, batch_first=False,
dropout=0, bidirectional=False):
... | torch/nn/modules/rnn.py | MIT | 24,197 | 5e4b785971bcbe364b3d84b0a730833a8d40bab8 | Python | py | |
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
from . import _utils, _io, _logger
from ._graph_execution_manager import... | orttraining/orttraining/python/training/ortmodule/_training_manager.py | MIT | 16,130 | d7606eee404e10ff8d71d1a24d250e9880f46fbe | Python | py | |
from collections import Counter, OrderedDict
from functools import wraps
import numpy as np
from transformers import BertTokenizer
import torch
def _check_build_bert_vocab(func):
@wraps(func)
def _wrapper(self, *args, **kwargs):
if self.bert_tokenizer is None:
self.build_vocab()
re... | utils/vocab.py | Apache-2.0 | 11,960 | 299a0959db3c993461200773dd9b4e74edb15a46 | 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 | 35,374 | f94fb22098d54cd865808a4365327a0de142b459 | Python | py | |
import torch
import torch.nn as nn
from typing import Union, Dict, Optional
from schnetpack import units as spk_units
import schnetpack.nn as snn
from schnetpack import properties
import numpy as np
__all__ = ["CoulombPotential", "DampedCoulombPotential", "EnergyCoulomb", "EnergyEwald"]
class CoulombPotential(nn.M... | src/schnetpack/atomistic/electrostatic.py | MIT | 12,651 | b215714960c369d455f8a54940b0ab7171ffa37e | Python | py | |
#!/usr/bin/env python3
import time
from functools import partial
import numpy as np
import torch
from torch import roll, zeros # <1>
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # <2>
GRID_SHAPE = (2048, 2048)
def timer(fxn, max_time=5):
N = 0
total_time = 0
fxn() # prime... | 07. Chapter_7/pytorch/diffusion_pytorch.py | MIT | 2,508 | fdf631152f85d367f82eea0450da0a5968dd11b1 | Python | py | |
"""Test `diameter_learning.nets.layers.diameter` code
"""
import shutil
from itertools import product
import math
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from diameter_learning.settings import TEST_OUTPUT_PATH
from diameter_learning.nets.layers import (
CenterOfMass2DEx... | test/nets/layers/test_diameter.py | MIT | 12,548 | a93982e289398a8723dd07ad40d7c57a4952f2ce | Python | py | |
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import copy
import torch
from torch import nn
import pdb
from .build import META_ARCH_REGISTRY
from ..backbones import build_backbone
from ..backbones.resnet import Bottleneck
from ..heads import build_reid_heads
from ..model_utils impor... | fastreid/modeling/meta_arch/HAA_BASELINE.py | MIT | 10,364 | 4b0eaacedd3a6279683517888035e30e65e3f864 | Python | py | |
# adapted from Trainer.py of huggingface transformers
import json
import logging
import os
import random
from typing import Callable, Dict, List, NamedTuple, Optional, Tuple
import numpy as np
import torch
from torch import nn
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Da... | orttraining/orttraining/test/python/orttraining_transformer_trainer.py | MIT | 16,408 | b2228bff28241b1490b797633ea4febaa8f9d319 | Python | py | |
# -*- coding: utf-8 -*-
from config import opt
import models
import datasets
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from utils import save_pr, now, eval_metric
def collate_fn(batch):
data, label = zip(*batch)
return data, label
def test(**kwargs):
pass
def train(**kwar... | Algorithm-code/RelationExtraction_CNN+ATT/main_att.py | MIT | 4,048 | 299a517bd6f7babf0441b6f38e04f2144c0ea5e3 | Python | py | |
"""
This module contains neural networks for predicting device performance,
based on tabular data and m2py labels
"""
import torch
import torch.nn as nn
class OPV_df_NN(nn.Module):
"""
expects tabular data predicting 4 OPV device metrics: PCE, Voc, Jsc, and FF
"""
def __init__(self, in_dims, out... | m2py/networks/morphology_networks.py | Apache-2.0 | 5,561 | 299a61cb72b97d1d4e986311af1b8fb2ccfb5dd4 | Python | py | |
"""Visualizes CNN activation maps to see where the CNN focuses on to extract features.
Reference:
- Zagoruyko and Komodakis. Paying more attention to attention: Improving the
performance of convolutional neural networks via attention transfer. ICLR, 2017
- Zhou et al. Omni-Scale Feature Learning for Pers... | tools/visualize_actmap.py | MIT | 6,447 | ff8a71d98ccdfa98a86f81df5c4c9d4b1865e94a | Python | py | |
import argparse
import pickle
import torch
import torch.nn as nn
import torch.optim as optim
from pathlib import Path
from torch.utils.data import DataLoader
from transformers.modeling_bert import BertConfig
from pretrained.tokenization import BertTokenizer as ETRITokenizer
from gluonnlp.data import SentencepieceTokeni... | BERT_pairwise_text_classification/train.py | MIT | 5,914 | 299a77b4e0ec3d47b00621046760bba0b6d49ce2 | Python | py | |
import logging
import math
import numpy as np
import torch
import torch.distributed as dist
from . import default_hooks as default
def _orthogonalize(matrix, epsilon=0):
"""
Applies Gram-Schmidt procedure to orthogonalize a given 2D tensor.
If epsilon is 0, this is equivalent to `torch.qr(matrix, out=(m... | torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py | Intel | 35,365 | 6b0d747d460d2cfc2661319d5599b8fa44e70694 | Python | py | |
import torch
import torchvision
import torchvision.transforms as T
import foolbox as fb
from tqdm import tqdm
from .base import Adversary
from .dict2obj import Config
from .config import *
class ModelNotDefineError(Exception): pass
class LossNotDefineError(Exception): pass
class OptimNotIncludeError(Exception): pa... | src/loadopts.py | MIT | 12,698 | 299a94919523925ec8d24a6a734b860bf807bcb9 | Python | py | |
import torch
from torch import nn
from torch.nn import init
import torch.nn.functional as F
import math
from torch.autograd import Variable
import numpy as np
config_vgg = {'convert': [[128,256,512,512,512],[64,128,256,512,512]], 'deep_pool': [[512, 512, 256, 128], [512, 256, 128, 128], [True, True, True... | KRN_edge.py | MIT | 10,393 | 299ac5a5e4661d2adc9d19acb8af6c6cdd6fcb92 | Python | py | |
from torch.utils.data import Dataset
class AvazuDataset(Dataset):
def __init__(self, num_sample=NUM_SAMPLE):
pass
def __len__(self):
pass
def __getitem__(self):
pass
| ctr_data/avazu.py | MIT | 218 | 5f67fce84244aa0ddfdd38ddae2ee9ab125397f8 | Python | py | |
import torch
from tqdm import tqdm
from tqdm.notebook import tqdm as tqdm_notebook
from nfractal.utils.dev import isnotebook
"""
Base Sampler Class
"""
class Sampler():
def sample(self):
raise NotImplementedError("Sampler is an abstract class")
"""
Uniform Sampler Class
This class samples from... | nfractal/samplers.py | MIT | 4,679 | 299acf51ebdf5debe00380fe690f301df4821c4d | Python | py | |
# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
import torch
from torch.optim.optimizer import Optimizer
class AdagradRMSProp(Optimizer):
"""
Implements a mash-up of the Adagrad algorithm and RMSProp. For the precise
update equation see equations 10 and 11 in refer... | pyro/optim/adagrad_rmsprop.py | Apache-2.0 | 2,924 | 299ad49f18182f1fc39107d1776aa5b9917b4b87 | Python | py | |
from asyncio.constants import LOG_THRESHOLD_FOR_CONNLOST_WRITES
import time
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from tqdm import trange
from stable_baselines3.common import distributions
from stable_baselines3.common.base_class import BaseA... | stable_baselines3/common/on_policy_algorithm_multi_level.py | MIT | 29,709 | f92fc40f0360817892502705bedfadc116eca18c | Python | py | |
import torch
import torch.nn as nn
from fastai.layers import ConvLayer, PixelShuffle_ICNR, ResBlock
from torch import Tensor
arch_name = "cloudnet_plus"
class ContractionBlock(nn.Module):
def __init__(self, ni, residual=False):
super().__init__()
if residual:
self.conv1 = ResBlock(ni... | cloud/models/cloudnetplus.py | MIT | 7,561 | 6547b6037ca9d4ed3c6d00893cf5748f974f3070 | 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/trainer/connectors/test_signal_connector.py | Apache-2.0 | 2,101 | a957cac4df51814b152442df18828db1ac082a2a | Python | py | |
from pathlib import Path
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import skimage.io
import torch
from omegaconf import OmegaConf
from torch.utils.data import DataLoader, RandomSampler
from torch.utils.data.dataset import Dataset as TorchDataset
from ls_axon_segmentation.enums import Class... | ls_axon_segmentation/data_module/lm_data_module.py | MIT | 8,640 | 28fb394880d2e8625364c2319feb2eeddcd0f689 | Python | py | |
#! /usr/bin/env python
# coding=utf-8
# Copyright (c) 2019 Uber 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
#
# Unles... | ludwig/encoders/sequence_encoders.py | Apache-2.0 | 98,152 | 1e9af5b6baea4ccb754036f6edf2aa549b47498c | Python | py | |
# coding=utf-8
# Copyright 2020 Google AI, Google Brain, the HuggingFace Inc. team and Microsoft 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/lic... | examples/research_projects/bert-loses-patience/pabee/modeling_pabee_albert.py | Apache-2.0 | 14,134 | 5e17352dc19b54c9f0008d7a9263c1b1ca1d4538 | Python | py | |
import os
import numpy as np
import h5py
import json
import torch
from imageio import imread
from PIL import Image
from tqdm import tqdm
from collections import Counter
from random import seed, choice, sample
import cv2
import torchvision.transforms
def create_input_files(dataset, json_path, image_folder, captions_per... | SAT_tutorial/utils.py | MIT | 10,219 | 129af4543b940947b707cdf6fcec12c449fc85d7 | Python | py | |
import sys
import datetime
import torch
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt
from model import VAE
from plot_utils import display_and_save_batch
from arguments ... | Implementations/Variational-Autoencoder/train.py | MIT | 8,464 | 299b3bd16befa94655ba670ad5c807d622fe123f | Python | py | |
import SinGAN.functions as functions
import SinGAN.models as models
import os
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import math
import matplotlib.pyplot as plt
from SinGAN.imresize import imresize
from tqdm import tqdm
import numpy as np
def train(opt,Gs,Zs,reals,NoiseAmp):
real... | SinGAN_pytorch/SinGAN/training.py | MIT | 14,878 | ff9944113bfebce8d4cf1808aaa1c3a5c4dfdbff | Python | py | |
###############################################################################
# Language Modeling on Wikitext-2
#
# This file generates new sentences sampled from the language model
#
###############################################################################
import argparse
import torch
import data
parser = ... | experiments/ptb_wikitext/model/generate.py | MIT | 3,111 | 299b7d88495edad6c98bdbf351d7de39a94e46aa | Python | py | |
import torch
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data import SubsetRandomSampler
from torch.utils.data import sampler
class UnNormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
... | L16/helper_data.py | MIT | 8,382 | 299b84794e248ff181c4edd8fede898930b48be2 | Python | py | |
#!/Users/fa/anaconda/bin/python
import sys
sys.path = ['../feedbackmodels'] + sys.path
from feedbackmodels import models as mod
sys.path = ['../utils', '../monolingual-word-aligner'] + sys.path
import utils
import FBSimilarityMeasures as fb
import numpy as np
import gensim
from gensim.models import Word2Vec
from sklea... | featuremodels/models.py | MIT | 11,455 | 299b8be17ec020b493e41e40a07bccc399a56d46 | Python | py | |
import numpy as np
from PDE_find import Diff, Diff2, FiniteDiff
import Data_generator as Data_generator
import scipy.io as scio
from requests import get
from inspect import isfunction
import math
import pdb
import random
import matplotlib.pyplot as plt
import torch.nn as nn
import torch
from torch.nn import... | codes/setup.py | MIT | 17,367 | 299ba674316d26eb2e16e2077beaba7a65639556 | Python | py | |
"""
Copyright (C) 2018 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 torch.utils.serialization import load_lua
from torch.utils.data import DataLoader
from networks import Vgg16
from torch.autograd import Vari... | MUNIT/utils.py | Apache-2.0 | 19,599 | 299bc48e2d2f913e98a2f3e0eeacb77ae6b0d121 | Python | py | |
from __future__ import absolute_import, division, print_function
import torch
from torch.distributions import constraints
from torch.nn import Parameter
import pyro
import pyro.distributions as dist
from pyro.contrib.gp.models.model import GPModel
from pyro.contrib.gp.util import conditional
from pyro.params import p... | pyro/contrib/gp/models/vgp.py | MIT | 6,828 | 299be17a889b1868ffb29ad1e5b6436e6828f4e8 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer, ConvLayer
from layers.SelfAttention_Family import FullAttention, AttentionLayer
from layers.Embed import DataEmbedding
import numpy as np
class Transformer(nn.Module):... | models/seq2seq/Transformer.py | Apache-2.0 | 2,945 | 10f053f042cda2444b9d78863f0c75527fa9321b | Python | py | |
"""
This example is largely adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
import os
import random
from argparse import ArgumentParser, Namespace
from collections import OrderedDict
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.nn.para... | pl_examples/domain_templates/imagenet.py | Apache-2.0 | 9,022 | 299c078d172119f6dd05c4948065096f758fbb7f | Python | py | |
#!/usr/bin/env python
#coding:utf-8
"""
Tencent is pleased to support the open source community by making NeuralClassifier available.
Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance
with the Licens... | dataset/dataset.py | Apache-2.0 | 15,424 | 299c35aed88f4416c0c665b9023b32c3dc07817b | 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/plugins/training_type/tpu_spawn.py | Apache-2.0 | 11,820 | 299c362e7181aba25d7084076afaa34c8f1e9717 | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:56
# @Author : THU
from torch import nn
from torchocr.networks.losses.DetBasicLoss import BalanceCrossEntropyLoss, MaskL1Loss, DiceLoss
class DBLoss(nn.Module):
def __init__(self, alpha=1.0, beta=10, ohem_ratio=3, reduction='mean', eps=1e-6):
"""
... | CAIL2020/cocr/torchocr/networks/losses/DBLoss.py | Apache-2.0 | 2,232 | 299c3eb18122e58cdbbb5ec4cd261afe0cc78ee4 | Python | py | |
# Owner(s): ["oncall: jit"]
import unittest
import os
import random
import enum
import copy
from functools import reduce
import operator
import torch
from torch.nn import functional
from torch.testing._internal.common_utils import run_tests, ProfilingMode, GRAPH_EXECUTOR # TEST_WITH_ROCM
from torch.testing._interna... | test/test_jit_cuda_fuser.py | Intel | 153,327 | 299c738c570ab0b57166ff8af8b1b347a6cbca15 | Python | py | |
# Higher order DGNet
# by Sølve Eidnes, 2021
# See "Order theory for discrete gradient methods" (https://arxiv.org/abs/2003.08267)
#
# This code is based on
# DGNet: Deep Energy-based Modeling of Discrete-Time Physics (2020)
# by Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi
# https://github.com/tksmatsubara/d... | experiment-spring/dgnet_compare_training.py | MIT | 13,540 | 7c8b0ddd1f5884ab26af8d3942c1cf06e02327a7 | Python | py | |
from agent.ddpg import *
from agent.replay_buffer import *
import torch
class MADDPG(object):
def __init__(self, **kwargs):
"""
Initializes multiple agents using DDPG algorithm.
Parameters
----------
kwargs :
"""
self.num_agents = kwargs.get("num_agents", 2)... | Collaboration-and-Competition-using-MADDPG/maddpgcollaboration/agent/maddpg.py | MIT | 3,153 | 299c93a76301a303c60bfe9ba5c1432ff610556f | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def one_hot(index, classes):
out_idx = torch.arange(classes, device=index.device)
out_idx = torch.unsqueeze(out_idx, 0)
index = torch.unsqueeze(index, -1)
return (index == out_idx).float()
class Fo... | ml3d/torch/modules/losses/focal_loss.py | MIT | 1,716 | 299c9af1de9114f6a5a90ccd0eeb05ca798e8ff3 | Python | py | |
from torch.autograd import Function
from .backend import _backend
__all__ = ['trilinear_devoxelize']
class TrilinearDevoxelization(Function):
@staticmethod
def forward(ctx, features, coords, resolution, is_training=True):
"""
:param ctx:
:param coords: the coordinates of points, Floa... | pvcnn/modules/functional/devoxelization.py | MIT | 1,467 | e9dd014e393a468b8b5b40b8654860fc7ab87eea | Python | py | |
# coding=utf-8
"""Test _utils module."""
from __future__ import absolute_import, division, print_function
import os
import shutil
from future.standard_library import install_aliases
import numpy as np
import pytest
import torch
from iftorch._utils import dataloader, reverse_mapping
from iftorch.save_utils import (
... | tests/test_utils.py | Apache-2.0 | 3,930 | 2480220ed8f92016852f7f33be4ef6a40522f934 | Python | py | |
import argparse
import os
from os import path
import copy
import numpy as np
import torch
from torch import nn
from ganstab.gan_training import utils
from ganstab.gan_training.checkpoints import CheckpointIO
from ganstab.gan_training.distributions import get_ydist, get_zdist, interpolate_sphere
from ganstab.gan_trainin... | interpolate_class.py | MIT | 3,080 | d7cc03de3103802b9000d803e45b842f92851d3f | Python | py | |
import torch
from torch import nn
from torch.autograd import Variable
from torch.autograd import Function
import torchtext
from torchtext import data
import io
import os
import numpy as np
from data.indep_bernoulli.load_data import load_data
# Data processing
# ------------------------------------------------------... | utils.py | MIT | 7,162 | 299cf0a30fdc262b45f8619846e2c7a4efa342cc | Python | py | |
import os
import torchvision
from matplotlib import pyplot as plt
import torch
import glob
import os
from shutil import copyfile
from io import BytesIO
from PIL import Image
import time
import heapq
from heapq import heappush
from heapq import heappop
from torchvision import datasets
import sys
import gc
gc.collect()... | AJCAI2021/predict_age.py | MIT | 2,747 | b2fea4c5f43afc0275bc0660327fe50b81100f35 | Python | py | |
import copy
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
from . import transforms as T
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
self.cate... | src/notebooks/coco_utils.py | MIT | 9,320 | 299d5189880b068d20a3e49ea7fbfa8ce0d2cfc7 | Python | py | |
import numpy as np
import torch
import kornia.geometry as tgm
import pickle
import os
from PIL import Image
from torchvision import transforms
from torch.nn import functional as F
import cv2
from pathlib import Path
from tqdm import tqdm
import argparse
import pandas as pd
def alignmentError(wB, hB, wA, hA, XA, YA, ... | evaluation/evalCorr/getResults.py | MIT | 11,688 | 299d55f7ac04b9ef653d4f4d4d2251a531a84c51 | Python | py | |
import argparse
import json
import os
import random
import time
import numpy as np
import torch.distributed as dist
import torch.utils.data.distributed
# from apex.fp16_utils import FP16_Optimizer
# from apex.parallel import DistributedDataParallel
from warpctc_pytorch import CTCLoss
from data.data_loader import Audi... | train.py | MIT | 19,036 | 10f8b77ff14c51e0a67c45667bf0715b78e57685 | Python | py | |
"""
Implementation of "Attention is All You Need"
"""
import torch
import torch.nn as nn
import numpy as np
import onmt
from onmt.modules.position_ffn import PositionwiseFeedForward
MAX_SIZE = 5000
class TransformerDecoderLayer(nn.Module):
"""
Args:
d_model (int): the dimension of keys/values/queries... | onmt/decoders/transformer.py | MIT | 11,272 | 24d39b4c1fc3524ad4cebe3d11276d5e817f30b6 | Python | py | |
import os
import logging
import torch
from corrupter import BernCorrupter, BernCorrupterMulti
from read_data import index_ent_rel, graph_size, read_data
from config import config, overwrite_config_with_args
from logger_init import logger_init
from data_utils import inplace_shuffle, heads_tails
from select_gpu import se... | pretrain.py | MIT | 2,134 | 299da192c30bc4582ebc3556b2730d4e69f9ecd2 | Python | py | |
#!/usr/bin/env python
import torch
from torch.utils.data import DataLoader
import pickle
from rdkit import Chem
from tqdm import tqdm
import data_struct as ds
from data_struct import MolData, Vocabulary
from data_struct import Variable, decrease_learning_rate
from model import RNN
import sys
def Transfer(restore_from ... | transfer_learning.py | MIT | 2,648 | 299da65890836a8b2e93d6dbf149bbb3dbdd43c4 | Python | py | |
from random import choice
from loguru import logger
import torch
import pandas as pd
from torch.utils.data import Dataset
class CustomDataset(Dataset):
"""Custom dataset that reads one bitext chunk at a time.
Parameters
----------
bitext_file : str or list of str
Path to the bitext file (*.c... | seq2seq/data.py | MIT | 4,085 | 299dcbd7d7ab93aa767cb646fa7a28927d46a98a | Python | py | |
# 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, software
# distributed under th... | tests/helpers/boring_model.py | Apache-2.0 | 6,311 | 128ed292aec430c17ec9df1106e50802c6e5125d | Python | py | |
import os
import logging
import argparse
import numpy as np
from tqdm import tqdm
import torch
from torch.serialization import default_restore_location
from seq2seq import models, utils
from seq2seq.data.dictionary import Dictionary
from seq2seq.data.dataset import Seq2SeqDataset, BatchSampler
from seq2seq.beam impor... | translate_beam.py | MIT | 11,405 | 299e060e10af4eb8d6b3c1b973210da6c5d28261 | Python | py | |
'''
This file is a part of the official implementation of
1) "DISCO: accurate Discrete Scale Convolutions"
by Ivan Sosnovik, Artem Moskalev, Arnold Smeulders, BMVC 2021
arxiv: https://arxiv.org/abs/2106.02733
2) "How to Transform Kernels for Scale-Convolutions"
by Ivan Sosnovik, Artem Moskalev, Arnold Smeu... | models/basis/base.py | MIT | 1,950 | 299e44527a4213867f38e0a0c524d1ee3fe30722 | Python | py | |
#!/usr/bin/env python3 -u
# 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 logging
import os
from fairseq.dataclass.initialize import add_defaults, hydra_init
from fairseq_cli.train i... | fairseq_cli/hydra_train.py | MIT | 2,664 | 299e562ddece1d8050680565f1ddccf46fc77a83 | Python | py | |
# coding=utf-8
# Copyright 2019 HuggingFace 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 applicable law or ag... | tests/test_modeling_common.py | Apache-2.0 | 34,946 | b284ee6ec2e9c8a56ed385c90c55d7aeb6662cb9 | Python | py | |
"""
algorithm:
"""
import torch
import torch.nn as nn
from utils import v_wrap, set_init, push_and_pull, record,push
import torch.nn.functional as F
import torch.multiprocessing as mp
from shared_adam import SharedAdam
import gym
import os
import random
import numpy as np
import time
from a3c.NN import DiscreteNet
os... | ReDRL/Red_a3c.py | MIT | 6,985 | 299ec32f708eb3df417e9cf256720d509bbe157c | Python | py | |
import time
import torch
from util import MovingAverage
def aggreg_multi_gpu(model, dataloader, hc, dim, TYPE=torch.float64, model_gpus=1):
""""Accumulate activations and save them on multiple GPUs
* this function assumes the model is on the first `model_gpus` GPUs
so that it can write the activa... | multigpu.py | MIT | 4,968 | 299ed197e32cbc4f93569ee37eddcbe5784b3c07 | Python | py | |
import torch
import numpy as np
def point_form(boxes):
""" Convert prior_boxes to (xmin, ymin, xmax, ymax)
representation for comparison to point form ground truth data.
Args:
boxes: (tensor) center-size default boxes from priorbox layers.
Return:
boxes: (tensor) Converted xmin, ymin, ... | utils/box_utils.py | MIT | 13,257 | 299eecea612eef1ab8c36bcbf82b533fe88d7ca1 | Python | py | |
# Copyright (c) 2015-present, Facebook, Inc. Modified By Wenhao Wang, Weipu Zhang.
# All rights reserved.
import warnings
warnings.filterwarnings('ignore')
from models_balance.gem import GeneralizedMeanPoolingP
import sys
import os
import pdb
import argparse
import time
from collections import OrderedDict, defaultdict
... | Test/final/V5_baseline_CC_ref/extract_feature_r.py | MIT | 10,057 | fceb5376a5936d9d9c146f0424dd0981a466c797 | Python | py | |
import math
import pytest
import torch
from torch.nn.functional import pad
from pyro.ops.gaussian import Gaussian, gaussian_tensordot
from tests.common import assert_close
def random_gaussian(batch_shape, dim, rank):
"""
Generate a random Gaussian for testing.
"""
log_normalizer = torch.randn(batch_... | tests/ops/test_gaussian.py | MIT | 3,614 | 1eb2a2f0b1ffa46becf20243eb07e86d02752ac7 | Python | py | |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Functions for training and evalu... | federated/train.py | BSD-3-Clause-Attribution | 14,037 | e9a8cea4db971d2f645a0ebf49cf10ba7a9a9097 | Python | py | |
import torch
from collections import OrderedDict
from os import path as osp
from tqdm import tqdm
from basicsr.archs import build_network
from basicsr.losses import build_loss
from basicsr.metrics import calculate_metric
from basicsr.utils import imwrite, tensor2img
from basicsr.utils.registry import MODEL_REGISTRY
fr... | basicsr/models/hifacegan_model.py | Apache-2.0,MIT | 11,782 | 1ec6f673cd2d1dcb24a2b6a9ad9b6eef472f3505 | Python | py | |
import logging
from os import listdir
from os.path import splitext
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
class BasicDataset(Dataset):
def __init__(self, images_dir: str, masks_dir: str, scale: float = 1.0, mask_suffix: str = ''):
... | train/data_loading.py | MIT | 3,100 | ff28a21b9985b1618d5ecffb5884442b7344df3c | Python | py | |
import torch.nn as nn
class InnerConvBlock(nn.Module):
def __init__(
self,
input_dim,
filters,
kernel_size,
strides=1,
relu_activation=True,
batch_normalization=True,
):
super(InnerConvBlock, self).__init__()
self.relu_activation = relu_a... | src/model/pytorch/base_layers.py | MIT | 2,646 | e95f19c4b470654cc2b1b9e10449cc4184156f4f | Python | py | |
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.nn import Parameter
import math
def myphi(x,m):
x = x * m
return 1-x**2/math.factorial(2)+x**4/math.factorial(4)-x**6/math.factorial(6) + \
x**8/math.factorial(8) - x**9/math.factorial(... | net_sphere.py | MIT | 6,209 | 1e6b31e2ab67feb061c0165f015e51ac4fbea5ea | Python | py | |
""" train.py
Main interface to train the GNNs that will be later explained.
"""
import argparse
import os
import pickle
import random
import shutil
import time
import matplotlib
import matplotlib.colors as colors
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as Figure... | train.py | Apache-2.0 | 37,322 | 52e49b83dcac3c965fc00f7d03032917386a9080 | Python | py |
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