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# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.2.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %reload_ext autoreload
# %aut... | notebook/GCN-LSTM-dataset.py | Apache-2.0 | 6,050 | 45d29443860492df6500428f8b3fc824f6ef69f9 | Python | py | |
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class LossComputer:
def __init__(self, criterion, is_robust, dataset, alpha=None, gamma=0.1, adj=None, min_var_weight=0, step_size=0.01, normalize_loss=False, btl=False, sp=0.0, mode='', ratio=0.0):
self.criteri... | Figure_4_and_table_3/loss.py | MIT | 9,836 | 5fc71d46f7a3238d2864420ad9bd18048817dd9f | Python | py | |
import argparse
import gc
import os
import shutil
import numpy as np
import scipy.io as io
import torch
import torch.optim as optim
from tqdm import tqdm
import losses
import networks
import utils
from input_prepocessing import input_preparation, resize_images
from sensor import Sensor
from show_results import show
f... | main.py | BSD-4-Clause-UC | 12,496 | 12f3cf62c764408592d3538195b8feaf3220129e | Python | py | |
import numpy as np
import os
import pytest
import random
import utils
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
utils.fix_random_seeds()
@pytest.mark.parametrize("arg, expected", [
[
np.array([0.0, 0.25, 0.75]),
np.array([0.22721977, 0.29175596, 0.48102426])... | test/test_utils.py | Apache-2.0 | 3,497 | e631f57b359f8a211f0c6d2f49450e509956ce32 | 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/core/test_data.py | Apache-2.0 | 1,729 | 12d163a50be7c544b20cbaefa07d380ad8885ffb | Python | py | |
from pathlib import Path
import numpy as np
import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.utils import dense_to_sparse
from Utils import compute_KNN_graph
class ConnectivityData(InMemoryDataset):
""" Dataset for the connectivity data."""
def __init__(self,
... | Dataset.py | MIT | 1,479 | 298a9e1385053c3565a6b5c8065568e5b212ab7f | Python | py | |
from typing import Tuple
import torch
from allennlp.common import Registrable
class Retriever(Registrable):
def _post_process_scores(self, scores: torch.Tensor):
raise NotImplementedError
def __call__(self, scores: torch.Tensor) -> Tuple[torch.LongTensor, torch.LongTensor]:
"""
Parame... | examples_allennlp/utils/retrieval/retrievers/retriever.py | Apache-2.0 | 553 | f9cf83dbc1544849a72e2777c25209a26403e558 | Python | py | |
import torch
from torch import nn
from torch import Tensor
from torch.nn import functional as F
from torch.nn.utils.weight_norm import weight_norm
from torch.nn.utils.spectral_norm import spectral_norm
from typing import Callable, Any
LayerFunc = Callable[[nn.Module], None]
def sigmoid_range(x, low, high):
"Si... | mmedit/models/colorizers/blocks.py | Apache-2.0 | 6,933 | 298ae27788750084080197acf6d4c4bb1c9d91f6 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.inits import glorot
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, softmax, degree
import random
"""Reproducibility"""
seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed(seed... | partial_algorithm/GNN.py | MIT | 10,759 | 347ed91aaf5044281d018202fb7ae56b86f674f4 | Python | py | |
import math
import os
import sys
import time
import os.path
import torch
import torch.nn.functional as F
import torch.optim as optim
from envs import create_atari_env
from model import ActorCritic, load_model
from torch.autograd import Variable
from test import test
import pdb
def setup(args):
# logging
log_... | train.py | MIT | 7,991 | 298b0d4f9287573c24f17228af57ef42d79af567 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from .. import _C
class _ROIAlign(Function):
@staticmethod
def forward... | fcos/core/layers/roi_align.py | BSD-3-Clause | 2,208 | 298b1ac043b949d8a322ea61de9d54b13f6deb8b | Python | py | |
import glob
import os
import random
import numpy as np
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from PIL import Image
class ImageDataset(Dataset):
def __init__(self, root, transforms_=None, mode="train"):
self.transform = transforms.Compose(transforms_)
s... | GAN/Pix2Pix/dataset.py | MIT | 861 | 298b1d66f1f88fb39a764ea41166e17a474564bf | Python | py | |
# Helper functions of for now unspecified class
import NeuralNet
import torch
import torch.optim as optim
from torch.optim import Adam, SGD
class Helper():
def __init__():
pass
#Loads a pytorch NN given a path (String) Returns a Neural Net torch object
def load(path):
model = ConNet()
model.load_state_dic... | modules/helper.py | MIT | 957 | 298b2660a3f6b7af72e0daaee8f71e2cb0bea916 | Python | py | |
import click
import json
import pandas as pd
import numpy as np
from scipy import stats
import torch
from dataset import Triage
from torch.utils.data import DataLoader
from models import load_tokenizer
MAX_LEN = 512
VALID_BATCH_SIZE = 16
def predict(model, data_loader, device):
model.eval()
id_list = []
... | predict.py | MIT | 2,971 | 298b374802f02134c5094db65e3ebe7a141e7bf9 | Python | py | |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Acknowledgement: Part of the codes are adapted from Zuxuan Wu
import os
import numpy as np
import... | models/audioPreview_model.py | CC-BY-4.0 | 11,338 | 298b3d5273bc0c4cb95053e95aa522457f8fe7d6 | Python | py | |
"""
# Code borrowded from:
# https://github.com/zijundeng/pytorch-semantic-segmentation/blob/master/utils/transforms.py
#
#
# MIT License
#
# Copyright (c) 2017 ZijunDeng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software... | transforms/transforms.py | BSD-3-Clause | 12,026 | e638fe550acbcf618662ee27fa3b7f0c056d49db | Python | py | |
# -*- coding: future_fstrings -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import json
import random
import numpy as np
from pathlib import Path
from . import io
from . import misc
from . import logging
PARAM_FNAME = "params.json"
logger... | utils/train.py | MIT | 3,648 | 298b4670d23674950920f1b3f5b9c83c9151eaa3 | Python | py | |
from typing import Dict, List
import numpy as np
import sklearn.metrics as skmetrics
import torch
from sklearn.neighbors import KNeighborsClassifier
from src import utils
def evaluate_knn_on_pretrained_model(
model: torch.nn.Module,
device: torch.device,
loaders: Dict[str, torch.utils.data.DataLoader],
... | src/training/knn.py | MIT | 1,468 | 298b4ac630940fed76534469f0bd83779c951aa4 | Python | py | |
##########################
# Utility functions #
# THIS FILE IS OBSOLETE #
##########################
import torch
import time, sys
from transformers import RobertaTokenizer, BertTokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
tokenizer_bert = BertTokenizer.from_pretrained('bert-base-case... | scripts/roberta/utils.py | Apache-2.0 | 4,505 | 7c2a506691a9520d4abfdd10d2b8bdf2d34fe4c7 | Python | py | |
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
# Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file e... | egs/librispeech/ASR/conformer_ctc/train.py | Apache-2.0 | 23,336 | 298b74112420a34797f6b4ab01fa8942b0e85a23 | Python | py | |
import torch
# region new attention
class Attention(torch.nn.Module):
""" New attention base"""
def __init__(self, **kw):
super(Attention, self).__init__(**kw)
def normalize(self, attention_scores, ctx_mask=None):
raise NotImplemented("use subclass")
def compute_scores(self, qry, ct... | qelos_core/att.py | MIT | 4,765 | 298b765ef8d3eb82f3126aaa84d449df77a56475 | Python | py | |
import copy
import cv2
import os
import torch.utils.data as data
import scipy.io as io
import numpy as np
from PIL import Image
from util.config import config as cfg
from skimage.draw import polygon as drawpoly
from util.misc import find_bottom, find_long_edges, split_edge_seqence, \
norm2, vector_cos, vector_sin
... | dataset/dataload.py | MIT | 7,195 | 298b9b3dac44eba82d50356e8206fb07e14c6b11 | Python | py | |
# 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 required by appli... | nemo/collections/asr/data/audio_to_text_dataset.py | Apache-2.0 | 14,670 | 4239355530f234f951d10985b7838db952524eed | Python | py | |
import torch
import torch.nn as nn
from torchsummary import summary
from config import device, im_size
import migrate_model
class conv2DBatchNormRelu(nn.Module):
def __init__(
self,
in_channels,
n_filters,
k_size,
stride,
padding,
... | models_v16_3.py | MIT | 8,541 | 45b75f2fc075afe87277716c10265e4f3b3b2315 | Python | py | |
import torch
import torch.distributed as dist
from torch.nn import Parameter
from torch.nn import Module
from apex.parallel import DistributedDataParallel as DDP
import argparse
import os
parser = argparse.ArgumentParser(description='allreduce hook example')
parser.add_argument("--local_rank", default=0, type=int)
ar... | furnace/apex/tests/distributed/ddp_race_condition_test.py | MIT | 2,326 | 298baeb0571f7381ae880761d03b7573af7fb69a | Python | py | |
import argparse
import logging
import time
from argparse import ArgumentParser
from pathlib import Path
import tensorrt as trt
import torch
from mmdet2trt.models.builder import build_wraper
from mmdet2trt.models.detectors import TwoStageDetectorWraper
from mmdet.apis import init_detector
from mmdet.apis.inference impo... | mmdet2trt/mmdet2trt.py | Apache-2.0 | 11,826 | 298bbec70564219b4cf834f8aad22c172fcbb370 | Python | py | |
"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
import cv2
import argparse
import matplotlib.pyplot as plt
import util.io
from torchvision.transforms import Compose
from dpt.models import DPTDepthModel
from dpt.midas_net import MidasNet_large
from dpt.transforms import Re... | run_monodepth.py | MIT | 8,295 | d74fe2932a15fd844b3096b4bb2f89f128a817d5 | Python | py | |
# Copyright (c) 2019, 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 applic... | kaolin/models/PointNet2.py | ECL-2.0,Apache-2.0 | 39,025 | 4256c667775f5c171e18f7bc99cb14f2b5001c2c | Python | py | |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. 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... | src/transformers/pipelines.py | Apache-2.0 | 60,848 | fd7d7b0886f5acf0ab0918ce73eb8645e7701823 | Python | py | |
# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, device='cuda'):
super(Attention, self).__init__()
self.attention_... | ocr/layers/attn.py | Apache-2.0 | 4,125 | 298be3d18a52deb60a481afa58945d1fa2cba866 | Python | py | |
import torch
import torch.nn as nn
class JointsOHKMMSELoss(nn.Module):
def __init__(self, use_target_weight, topk=8):
super(JointsOHKMMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='none')
self.use_target_weight = use_target_weight
self.topk = topk
de... | mmskeleton/models/loss/JointsOHKMMSELoss.py | Apache-2.0 | 1,732 | 5e635e7d085eaccbe46b29102fadc414d84ed3d4 | Python | py | |
import copy
import mmcv
import torch
from mmcv.parallel import DataContainer as DC
from mmcv.runner import force_fp32
from os import path as osp
from torch import nn as nn
from torch.nn import functional as F
from mmdet3d.core import (Box3DMode, bbox3d2result, merge_aug_bboxes_3d,
show_result... | mmdet3d/models/detectors/mvx_two_stage.py | Apache-2.0 | 21,871 | 298c1d5b6a5d135dac106187da42c43b2a956cac | Python | py | |
import logging
import os.path as osp
import torch
from torch import nn
from tensorboardX import SummaryWriter
from lib.test import test
from lib.utils import checkpoint, precision_at_one, \
Timer, AverageMeter, get_prediction, get_torch_device
from lib.solvers import initialize_optimizer, initialize_scheduler
f... | lib/train.py | MIT | 6,322 | 6b28b71cfbc31707fcfd48e309b3faef3d23e3c5 | Python | py | |
"""mobilenetv2 in pytorch
[1] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
MobileNetV2: Inverted Residuals and Linear Bottlenecks
https://arxiv.org/abs/1801.04381
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearBottleNeck(nn.Module):
... | models/mobilenetv2.py | MIT | 2,845 | 298c290eca334967d5a30d0b40b040a93a515bbb | Python | py | |
import gpytorch
import torch
from . import priors
class ExactGP_RBF(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood, prior):
super(ExactGP_RBF, self).__init__(train_x, train_y, likelihood)
# prior
if prior == "Lineal":
#self.mean_module = priors.Lineal... | excursion/models/__init__.py | Apache-2.0 | 1,978 | 7c806bf6328b5483226748d12a6d3eec359d3f88 | Python | py | |
import argparse
import logging
import random
import os
import torch
import pickle
import yaml
import json
from tqdm import tqdm
import numpy as np
from tensorboardX import SummaryWriter
import torch.optim as optim
from data import SQuADDataset
from model import RNet
from math import log2
from metrics import *
def eva... | source/train.py | MIT | 8,652 | d71527df84a9b48ecc053109343de63565163ed0 | Python | py | |
import os
import argparse
import logging
import time
import numpy as np
import numpy.random as npr
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
parser = argparse.ArgumentParser()
parser.add_argume... | latent_ode_tinfocnf_randtheta_augparam.py | MIT | 21,022 | 298c67b3fe637590e699f7c1fdd4099f61f6f39a | Python | py | |
from typing import Optional
from overrides import overrides
import torch
from allennlp.training.metrics.metric import Metric
@Metric.register("boolean_accuracy")
class BooleanAccuracy(Metric):
"""
Just checks batch-equality of two tensors and computes an accuracy metric based on that.
That is, if your p... | allennlp/training/metrics/boolean_accuracy.py | Apache-2.0 | 4,318 | 298c70ff80ea61c46fe4f8959d92a478a8c5379c | Python | py | |
import torch
import torch.nn as nn
import torchvision
# VGG architecter, used for the perceptual loss using a pretrained VGG network
class VGG19(torch.nn.Module):
def __init__(self, requires_grad=False):
super().__init__()
vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features... | model/vgg.py | MIT | 2,423 | 298c77fc7e13d265039568822588b239d45d87d2 | Python | py | |
import torch.nn as nn
from hierarchical_clustering.relaxed.models.model import HypHCModel, index_to_one_hot
class HypHCLinear(HypHCModel):
""" Hyperbolic linear model for hierarchical clustering. """
def __init__(self, rank=2, temperature=0.05, init_size=1e-3, max_scale=1. - 1e-3, alphabet_size=4,
... | hierarchical_clustering/relaxed/models/linear.py | MIT | 898 | 1e54d8d46c526d26fd4897c84d68f03449ff8bd5 | Python | py | |
import torch
import torch.nn as nn
from omegaconf import OmegaConf
import sys
sys.path.append("..")
# sys.path.append("../image_synthesis")
from image_synthesis.utils.misc import instantiate_from_config
from image_synthesis.taming.models.vqgan import GumbelVQ, VQModel
from image_synthesis.taming.models.cond_transformer... | image_synthesis/modeling/codecs/image_codec/taming_gumbel_vqvae.py | MIT | 10,011 | fda46eb941b0f28589834e0a6a79c5c609d13ef3 | Python | py | |
# coding=utf-8
# Copyright 2020-present, the HuggingFace Inc. 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 a... | examples/movement-pruning/emmental/modules/masked_nn.py | Apache-2.0 | 4,532 | 298c7e5e51de02b3538ff62f2580d2b9b4836bf6 | Python | py | |
#!/usr/bin/env python 3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 PanXu, Inc. All Rights Reserved
#
"""
acsa model outputs
Authors: PanXu
Date: 2020/07/18 16:00:00
"""
import torch
from easytext.model import ModelOutputs
class ACSAModelOutputs(ModelOutputs):
"""
ACSA Model Outputs
"""
def... | acsa/models/acsa_model_outputs.py | MIT | 403 | 298c93adbad7fdc9939dc1c6e1517dadcff66399 | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
from im2mesh.layers import (
ResnetBlockFC, CResnetBlockConv1d,
CBatchNorm1d, CBatchNorm1d_legacy,
ResnetBlockConv1d
)
class Decoder(nn.Module):
''' Decoder class.
It does not perform any form of normalization.
Args:
dim (int): i... | im2mesh/onet_m/models/decoder.py | MIT | 9,393 | 5e28f0c1eef729cf4485b43508a7463e2125596e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
import math
import copy
from load_data import EXTRA_CHARS
class OneHotEmbedding(nn.Module):
def __init__(self, alphabet_size):
super().__init__()
self.alphabet_size = alphabet_... | transformer.py | MIT | 10,910 | 7cbeb0bf1522df09678022958807c43dce703d71 | Python | py | |
"""
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer_m... | examples/evaluate_STSb_datasets/sbert_embbeding/training_NLI.py | Apache-2.0 | 4,865 | b2742fcba1361e2cac8cb98bb83e6effa96053d7 | 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.
"""
RoBERTa: A Robustly Optimized BERT Pretraining Approach.
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional... | fairseq/models/roberta/model.py | MIT | 26,608 | d7ced9190ca60a922e289958b9f636875f41b125 | Python | py | |
# import torch
import torch.nn as nn
import torch.nn.functional as F
# from torch.autograd import Variable
# from functools import partial
__all__ = [
'C2D'
]
class C2D(nn.Module):
def __init__(self,
sample_size,
sample_duration,
num_classes=400):
s... | models/c2d.py | MIT | 3,342 | fda389e57d50d82c5a313bdfb4298a9e52d5cb86 | Python | py | |
import torch
import copy
from tqdm import tqdm
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
from typing import Optional, List, Union
from torch_geometric.typing import OptPairTensor, Adj, Size, OptTensor
from torch.utils.checkpoint import checkpoint
from torch import Te... | src/ogbn-proteins/utils.py | Apache-2.0 | 13,312 | 1076d0ee82a8abf22151bea90e5a107bad7d64cc | Python | py | |
import time
from itertools import product
from loggers import CSVLogger, PrintLogger, FileLogger, multi_logger
import torch
from torch.optim import Adam
import os
import networkx as nx
import pickle
import numpy as np
import logging
from model_runner import ModelRunner,execute_runners
import cProfile
Data... | GCN_II/GCN_temporal_communities_main.py | MIT | 7,714 | 0398ed48c9dce16fb5c8e3fab54247ea60dcc07f | Python | py | |
import copy
from PIL import Image
from PIL import ImageEnhance
from torch.utils.data import DataLoader, Dataset
import torch
import numpy as np
import glob
import torchvision
import matplotlib.pyplot as plt
import random
import cv2
np.seterr(divide='ignore', invalid='ignore')
class heatmap_dataset(Dataset):
def _... | hourglass_four/dataloader/dataset_four.py | Apache-2.0 | 18,573 | 298d0dd919439dded9ae57dfdb84da76f675fa75 | Python | py | |
import os
import sys
import gym
import numpy as np
import torch
from gym.spaces.box import Box
from baselines import bench
from baselines.common.atari_wrappers import make_atari, wrap_deepmind
from baselines.common.vec_env.vec_env import \
VecEnvWrapper, VecEnv, CloudpickleWrapper, clear_mpi_env_vars
from baselin... | a2c_ppo_acktr/envs.py | BSD-3-Clause | 11,462 | 298d0fd8893196bcaf20f272be5a4953c979529d | Python | py | |
from transformers import GPT2Tokenizer,GPT2LMHeadModel
from pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTModel, OpenAIGPTLMHeadModel
import torch
import numpy as np
import math
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
model.to('cuda')
model.eval()... | metric/lm_score.py | MIT | 1,277 | 298d52651d28ccf235caf7c4a53d3a59a396ed33 | Python | py | |
import torch
import torch.nn as nn
from .py_utils import TopPool, BottomPool, LeftPool, RightPool
from .py_utils.utils import convolution, residual, corner_pool
from .py_utils.losses import CornerNet_Loss
from .py_utils.modules import hg_module, hg, hg_net
def make_pool_layer(dim):
return nn.Sequential()
def ma... | 6_cornernet_lite/lib/core/models/CornerNet.py | Apache-2.0 | 2,786 | 298d7372f740aac217ff41c1871e6e373fa8e9b3 | Python | py | |
# MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... | art/preprocessing/standardisation_mean_std/utils.py | MIT | 2,403 | b2a5d64931a3b87af644c652ed1e21b59359013f | Python | py | |
import random
import math
import torch
from torch import nn
import torchvision
from torchvision.ops import misc as misc_nn_ops
from .image_list import ImageList
from .roi_heads import paste_masks_in_image
class GeneralizedRCNNTransform(nn.Module):
"""
Performs input / target transformation before feeding the... | torchvision/models/detection/transform.py | BSD-3-Clause | 7,369 | 45fe037a86eb64d33c1ebe762a3a0736cd0a42ce | Python | py | |
import numpy as np
import torch
import torch.nn.functional as F
from dnnv.nn import Operation, OperationVisitor, operations
def convert(operations):
converter = PytorchConverter()
for op in operations:
_ = converter.visit(op)
return PytorchModel(converter.operations, converter.inputs, operations)... | dnnf/pytorch.py | MIT | 14,374 | ff4351cb2c42f2961e274a3bf4f3df71a4de1feb | Python | py | |
import torch
import torchvision.models as models
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
def encoder(base_model):
model,n_out=None,None
if base_model == "resnet18":
model = models.resnet18(pretrained=True)
n_out = 512
elif base_model == "res... | modules.py | MIT | 3,342 | 298d94d2d882d48e825d13aa2c0a4e335e9e7966 | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 20-6-3 下午5:14
# @Author : zhuying
# @Company : Minivision
# @File : MultiFTNet.py
# @Software : PyCharm
from torch import nn
import torch.nn.functional as F
from src.model_lib.MiniFASNet import MiniFASNetV1,MiniFASNetV2,MiniFASNetV1SE,MiniFASNetV2SE
class FTGenerator(nn.Module):
... | anti_spoofing/model_lib/MultiFTNet.py | Apache-2.0 | 2,773 | f934df4d2575a8a9193401f122b383aef21e358f | Python | py | |
# Copyright 2021 The HuggingFace 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
#
# Unless required by applicabl... | src/accelerate/launchers.py | Apache-2.0 | 7,641 | 298db439b048c3da62f89c192c3d26aafdf0e48d | Python | py | |
# -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:52
# @Author : zhoujun
from torch.utils.data import DataLoader
from torchvision import transforms
import random
import pathlib
from . import dataset
def get_datalist(train_data_path, val_data_path, validation_split=0.1):
"""
获取训练和验证的数据list
:param train_d... | data_loader/__init__.py | MIT | 5,145 | 4b59eda6bbbf174eedce46c30244f7a2bb06694f | Python | py | |
import os
from abc import abstractmethod, ABC
from typing import Union, Tuple
import h5py
import numpy as np
import scipy.io as scio
import scipy.sparse
from scipy.sparse import load_npz
from sklearn.datasets import load_svmlight_file
from .benchmark_utils import Dataset
__all__ = (
"get_load_fn", "get_cv_fn",
... | falkon/benchmarks/common/datasets.py | MIT | 37,537 | 03a18daaa37d4c4745b46c69c9232fbbc6d30d8a | Python | py | |
import torch
import numpy as np
torch.manual_seed(120)
from tqdm import tqdm
from pytorch3d.loss import chamfer_distance
from torch_nurbs_eval.surf_eval import SurfEval
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import offset_eval as off
import sys
sys.path.insert(... | examples/surface_offsetting.py | BSD-3-Clause | 10,184 | 298dd3525444e199b19a147a9520939607b4a9ba | Python | py | |
import os.path as osp
import numpy as np
import math
import torch
import json
import copy
import transforms3d
import scipy.sparse
import cv2
from pycocotools.coco import COCO
from core.config import cfg
from graph_utils import build_coarse_graphs
from noise_utils import synthesize_pose
from smpl import SMPL
from coo... | data/Human36M/dataset.py | MIT | 29,938 | 6b16b61dfe2e9e6dde41895f94ad2216048d7363 | Python | py | |
#!/usr/bin/env
# -*- coding: utf-8 -*-
"""
main.py:
"""
import sys
sys.path.append("..")
import argparse
import logging
import importlib
from dl import logger, args
from dl.train import Trainer
from dl.info import *
import shutil
from dl.dataloader import Dataset, MLPDataset, collate_fn
from torch.utils.data import ... | dl/main.py | MIT | 4,984 | 1062621fcb47460dc5be602d93ff022492ef2d6e | Python | py | |
import os
import logging
import torch
import torch.nn.functional as F
from utils import metrics
logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO)
logger = logging.getLogger(__name__)
def train(model, optimizer, train_dataloader, valid_dataloader, args):
be... | trainer.py | MIT | 2,170 | dfe064bcbd0f084c2a27d3eeb9eccd02a6906842 | Python | py | |
# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# -------------------------------------------------------------------... | InnerEye/ML/models/losses/ece.py | MIT | 2,163 | 298e0d247b8760eb3a885c992b48181f9951d5db | Python | py | |
import functools
import importlib
import logging
import pkgutil
import sys
from collections import OrderedDict
from inspect import isfunction, getmembers
import torch
import torchvision
import models.discriminator_vgg_arch as SRGAN_arch
import models.feature_arch as feature_arch
import models.fixup_resnet.Discriminat... | codes/trainer/networks.py | Apache-2.0 | 9,475 | 422d6f913ce3bff3b2acef4f9562d13b37699147 | Python | py | |
import backproppers
import calculators
import fp_selectors
import loggers
import selectors
import time
import torch
import torch.nn as nn
import trainer as trainer
start_time_seconds = time.time()
class SelectiveBackpropper:
def __init__(self,
model,
optimizer,
... | lib/SelectiveBackpropper.py | MIT | 9,438 | 298e175a8fb9c89a23e53cb69c5f60247293527a | Python | py | |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Uniform(Distribution):
r"""
Generates uniformly distributed random samples from the half-open interval
``... | torch/distributions/uniform.py | Intel,X11 | 3,066 | b212c52695c24c52766d9cf317ee2ce52271cc52 | Python | py | |
import torch
import numpy as np
import RLPython.RLHelper.libRLHelper as lib
# Replay Buffer do:
# store (state, action, reward, done) information (append)
# cap path (path_finish)
# get state, action list (get_tensor)
# calculate returns (get_returns)
# calculate gae function (get_gae)
class ReplayBuffer():
def _... | RLPython/replaybuffer.py | MIT | 2,331 | cc189ef777a44259e714adbc7d2a84b9cdccbfa7 | Python | py | |
#
# Lightnet related postprocessing
# These are functions to transform the output of the network to brambox detection dataframes
# Copyright EAVISE
#
import logging
import numpy as np
import torch
from .util import BaseTransform
from distutils.version import LooseVersion
try:
import pandas as pd
except Modu... | lightnet/data/transform/_postprocess.py | MIT | 14,269 | 298e21c8f3229dc04c8465c62bf4e5019201129d | Python | py | |
# Copyright (c) 2017-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. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import numpy as np
import torch
im... | fairseq/data/language_tuple_noise_dataset.py | MIT | 11,900 | b2358956eb7f7558dd0fbbc4e0bd1795e49a0581 | Python | py | |
import os
import torch.utils.data as data
import numpy as np
from torchvision import transforms as T
from data_objects.transforms import Normalize, generate_test_sequence
import pandas as pd
from pathlib import Path
def get_eval_paths():
data_dir = Path("/media/mdt/WD/zalo/speech/data/Train-Test-Data/feature/publ... | data_objects/VoxcelebTestset.py | MIT | 4,026 | 105b983988b085e027c06fd8547b32f91b76438d | Python | py | |
import numpy as np
import os,json,sys
from typing import Dict, Sequence, Type, Callable, List, Optional
import torch
from torch import nn
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from modules.transformers import BertModel,BertConfig,BertTokenizer
from modules.MLP import MLP
from lo... | nlp_basictasks/heads/attack_for_cls.py | MIT | 5,397 | 298e3a5263896b495aff4452be821c2a5b0777bc | Python | py | |
# Copyright (C) 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 applicable ... | nemo/collections/asr/parts/jasper.py | Apache-2.0 | 17,345 | f98ababb562ac5bd0708770184f93c172dae1d59 | Python | py | |
import torch
from dp_layer import DPLayer
def make_data():
a_range=torch.arange(1,3,dtype=torch.float,requires_grad=True).view((1,-1))
a_range=torch.cat([a_range,a_range+2],dim=0).unsqueeze(0)
a_range=a_range ** 2
return a_range
def test_dp_forward():
image=make_data()
layer=DPLayer('diff_sq... | dp_layer/tests/test_dp_layer_forward.py | MIT | 731 | 298e61f2a6f5552427fe2dfcecd278ae775962bf | Python | py | |
import cv2
import glob
import imageio
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import torch
import torchvision.datasets as dset
from torchvision import transforms
def make_folder(path):
if not os.path.exists(path):
os.makedirs(path... | utils.py | MIT | 11,368 | 70a66d1b1b1dafc08485d5032c5b19afb20e379f | Python | py | |
# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, ... | MinkowskiEngine/MinkowskiChannelwiseConvolution.py | MIT | 8,259 | 4bbf04f89703158ff47b211910563b37e5159b2e | Python | py | |
import logging
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
import torch.nn as nn
from mmdet.core import auto_fp16, get_classes, tensor2imgs
class BaseDetector(nn.Module):
"""Base class for detectors"""
__metaclass__ = ABCMeta
... | mmdet/models/detectors/base.py | Apache-2.0 | 4,894 | 6b260aa839e367691156486ebec8cf12a205695f | Python | py | |
'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
parser =... | main.py | MIT | 4,713 | 10fbc1fec615b6f5322b64380e4cf1c3107948f9 | Python | py | |
import argparse
import os
import numpy as np
import math
import itertools
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
from mnistm import MNISTM
import torch.nn as nn
... | .history/implementations/pixelda/pixelda_try_20190106200721.py | MIT | 18,474 | 5e23606bda0a34f467a341faa242c224e16f2041 | Python | py | |
from typing import Dict, Tuple
import torch
import torch.distributed.rpc as rpc
import torch.testing._internal.dist_utils as dist_utils
from torch import Tensor
from torch.testing._internal.dist_utils import (
dist_init,
get_timeout_error_regex,
worker_name,
wait_until_pending_futures_and_users_flushed... | torch/testing/_internal/distributed/rpc/jit/rpc_test_faulty.py | Intel,X11 | 8,445 | 347580a9c317c48273f60634865758bd3c82b9c6 | Python | py | |
import torch
class get_dataset(torch.utils.data.Dataset):
def __init__(self, dataset, feature_len=1):
super(get_dataset, self)
self.dataset= dataset
self.feature_len= feature_len
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
data= self.dataset[idx]
features= ... | EyeBlinkDetection/modules/data.py | MIT | 808 | 298ef737ad27dd8de1d7eed84aeaeae02607024e | Python | py | |
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilin... | layers/discriminator.py | MIT | 891 | 298f12e51e02b25700f830cd045a6fd97a29b87d | Python | py | |
import mmcv
import numpy as np
import random
import torch
from PIL import Image
from pathlib import Path
from torch.utils import data as data
from basicsr.data.transforms import augment, paired_random_crop, totensor
from basicsr.utils import FileClient, get_root_logger
class Vimeo90KDataset(data.Dataset)... | basicsr/data/vimeo90k_dataset-40.py | MIT | 5,782 | 298f14517728bd67ae6767569e5e60cfc3d6ebc1 | Python | py | |
"""
File that involves dataloaders for the Visual Genome dataset.
"""
import json
import os
import h5py
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import Resize, Compose, ToTensor, Normalize
from dataloaders.blob import Blob
from lib.fpn.box_... | dataloaders/visual_genome.py | MIT | 14,181 | a9186854447ba83a6ef2335a119ecdb7e59c518a | Python | py | |
import torch
import torchvision
from omegaconf import DictConfig
class Model(torchvision.models.ResNet):
def __init__(self, config: DictConfig):
block_name = config.model.backbone.resnet_block
if block_name == 'basic':
block = torchvision.models.resnet.BasicBlock
elif block_nam... | ptgaze/models/mpiifacegaze/backbones/resnet_simple.py | MIT | 1,748 | a937e97a3dcef845d275de91dbec90e9fc11ff46 | Python | py | |
from tqdm import tqdm
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import argparse
import os
import torch
import logging
logging.basicConfig(format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
datefmt='%d-%m-%Y:%H:%M:%S')
logging.getLogger().s... | DPLM-Transformer/eval_ppl_by_gpt2.py | Apache-2.0 | 2,863 | 298f61846954751e944acc229390b05ad0f4b107 | Python | py | |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import time
import random
import copy
import gym
import cv2
import os
from PIL import Image
from gym import spaces
from gym.utils import seeding
import numpy as np
import torch
from torch.autograd import grad
from .capac_controller import CapacController
# from... | utils/gym_wrapper_e2e_thro.py | MIT | 24,438 | 7cde5a1d6287cd65c85f14a9cdeacd2406a7abc6 | Python | py | |
"""
@author: Yifei Ji
@contact: jiyf990330@163.com
"""
from typing import Tuple, Optional, List, Dict
import os
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import tqdm
from .lwf import Classifier as ClassifierBase
__all__ = ['Classifier', 'CoTuningLoss', 'Relationship']
clas... | talib/finetune/co_tuning.py | MIT | 6,067 | 4208c503464170dca8319c1584cb43e0b1c6b7d6 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.weight_norm import weight_norm
from torch.autograd import Variable
from utils.config import *
from model.modules import *
class Generator(nn.Module):
def __init__(self, d_model, vocab, W=None):
supe... | model/generators.py | MIT | 2,698 | 52b335f0b27b55092edf0def72453f0aa56c3b0f | Python | py | |
#
# Copyright (c) 2020, NVIDIA 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 ... | nvtabular/torch_dataloader.py | Apache-2.0 | 9,026 | 34738a7f807376461206ab60cbae9d4284769f7b | Python | py | |
# encoding: utf-8
import copy
import itertools
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import random
from scipy.spatial.distance import cdist
from sklearn.preprocessing import normalize
from torch import nn, optim
from torch.utils.data import dataloader... | models/bdb_with_one_branch.py | MIT | 9,078 | 24ab78e7c6cc53007ad4431e50997c1cdd4486c4 | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype
class MixedOp(nn.Module):
def __init__(self, C, stride):
super(MixedOp, self).__init__()
self._ops = nn.ModuleList(... | Analysis/darts/cnn/model_search_cost.py | MIT | 12,185 | 298fbe086d95ed84b25aad3b5cada33676ed2a5e | Python | py | |
from abc import abstractmethod
import torchvision.transforms as transforms
class TransformsConfig(object):
def __init__(self, opts):
self.opts = opts
@abstractmethod
def get_transforms(self):
pass
class AgingTransforms(TransformsConfig):
def __init__(self, opts):
super(AgingTransforms, self).__init__(o... | configs/transforms_config.py | Apache-2.0,BSD-2-Clause,MIT | 2,634 | 298fc72a190d8eef7a75cec58501a872f0782516 | Python | py | |
import os
import torch
import numpy as np
from tqdm import tqdm
#import imageio
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
# io utils
from pytorch3d.io import load_obj
# datastructures
from pytorch3d.structures import Meshes, join_meshes_as_batch, join_meshes_as_scene, Point... | projects/sonar_mapping/fit_real_mesh_from_sss_optimize_loop.py | BSD-3-Clause | 14,664 | 5f0f7ee08b57abf33832b3d467d4cf0593441b8a | Python | py | |
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from core.algorithms.onpolicy_sync.losses import PPO
from core.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from projects.objectnav_baselines.experiments.objectnav_base import ObjectNavBaseConfig
from utils.experiment_utils import Builde... | projects/objectnav_baselines/experiments/objectnav_mixin_ddppo.py | MIT | 1,592 | f94eb6295e44975ab44fce8bf2427026bd899fa5 | Python | py | |
# coding:utf-8
import torch
import numpy as np
import json
import opennre
from opennre import encoder, model, framework
import sys
import os
import argparse
import logging
print(torch.cuda.is_available())
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt', default='',
help='Checkpoint... | train_supervised_cnn.py | MIT | 4,728 | 1e532d36b255f80944965f9f0248d490df5db171 | Python | py | |
import torch as th
import contextlib
from .cg_controller import DeepCoordinationGraphMAC
from modules.agents import REGISTRY as agent_REGISTRY
import itertools
import numpy as np
class DCGnoshareMAC (DeepCoordinationGraphMAC):
""" Implements DCG without any parameter sharing between agents (Boehmer et al., 2020).... | src/controllers/dcg_noshare_controller.py | Apache-2.0 | 6,580 | b27c2944976e39bdd16d5138081db91b43d3f42b | Python | py |
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