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import cv2
import os
import numpy as np
import torch
from torch.utils.data import Dataset
class CULane(Dataset):
def __init__(self, path, image_set, transforms=None):
super(CULane, self).__init__()
assert image_set in ('train', 'val', 'test'), "image_set is not valid!"
self.data_dir_path ... | src/dataset/CULane.py | MIT | 3,021 | fef9cb8843b79e121daec739ce9d3a7046158005 | Python | py | |
import torch
from torch.utils.data import Dataset,DataLoader
from torch.nn import Module
import torch.nn as nn
from torch.optim import SGD
from torch.optim.lr_scheduler import StepLR
"""
配置:
a=1层conv,b个feature,c个class
"""
class FakeData(Dataset):
def __init__(self, klass_num, dup=100):
super(FakeData,self... | gd_and_cnn1.py | MIT | 2,435 | fef9d9b295708ff00cdedeeeb31504de6ab1ab8d | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from skimage.measure import compare_ssim
import torch
from torch.autograd import Variable
from lpips import dist_model
class PerceptualLoss(torch.nn.Module):
def __init__(self, model='... | lpips/__init__.py | BSD-2-Clause | 5,760 | fef9e161bd6a8b983306edbc57607883174ee671 | Python | py | |
"""
Adapted from
https://github.com/Jackson-Kang/Pytorch-VAE-tutorial/blob/master/01_Variational_AutoEncoder.ipynb
A simple implementation of Gaussian MLP Encoder and Decoder trained on MNIST
"""
import logging
import os
import hydra
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from ... | 08_reproduceability/vae_solution/vae_mnist.py | Apache-2.0 | 3,561 | 70a6d70c7afa73a8f74234f9b6c5dfc210ec0ed8 | Python | py | |
import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import torch.nn.functional as F
class PairedRevGANModel(BaseModel):
''' Paired 2D-RevGAN model '''
def name(self):
return 'PairedRevGANModel'
@staticmethod
def modify_c... | models/paired_revgan_model.py | BSD-3-Clause | 9,327 | 4bbbd6e73f052620f81685bd0b87c71ee29d8ead | Python | py | |
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
from io import open
import hashlib
import argparse
from layers import RNNModel, AWDLSTMEncoder, DropoutLinearDecoder
from utils import count_parameters,... | generate.py | MIT | 4,652 | 4b3a62b833c7abe2b6830d36b277ed91d552be27 | Python | py | |
from typing import Dict
from torch.utils.data import DataLoader
import albumentations as A
from albumentations.pytorch import ToTensorV2
from .coco import COCODataset
from .voc import VOCDataset
from .crowdhuman import CrowdHumanDataset
from .mot import MOTTrackingDataset
from .kitti import KITTITrackingDataset
from ... | centernet_lightning/datasets/builder.py | MIT | 2,238 | 12ba314fc8642299bee8d90db2430085f9351486 | Python | py | |
"""A collection of fully-specified model interfaces."""
import abc
import logging
import torch as th
from . import ModelInterface
from .utils import get_logger, plot_grad_flow
LOG = get_logger(__name__)
# HAS_AMP = False
# if th.cuda.is_available():
# try:
# HAS_AMP = True
# from apex import ... | ttools/interfaces.py | MIT | 15,822 | a941ab91c0d4fb46e3948d0b6f8910d224ffad5d | Python | py | |
import torch
import torch.nn.functional as F
from torch_geometric.transforms import SIGN
from torch.utils.data import DataLoader
import torch
from torch_sparse import SparseTensor
from Precomputing.base import PrecomputingBase
class SGC(PrecomputingBase):
def __init__(self, args, data, train_idx):
super(... | Precomputing/SGC.py | MIT | 562 | fefa212e51763157dca79377e40d005274c53ad6 | Python | py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from DetectionHub.structures.bounding_box import BoxList
from .roi_mask_feature_extractors import make_roi_mask_feature_extractor
from .roi_mask_predictors import make_roi_mask_predictor
from .inference import make_roi_mask_post_proc... | DetectionHub/modeling/roi_heads/mask_head/mask_head.py | MIT | 3,101 | b232d305a6e90c5dde9f01b05d72ac4b97e19c66 | Python | py | |
from typing import Dict, List, Any
import torch
import torch.nn.functional as F
import numpy as np
from allennlp.data import Vocabulary
from allennlp.data.dataset_readers.dataset_utils import bio_tags_to_spans
from allennlp.models import Model
from allennlp.modules import TextFieldEmbedder, Seq2SeqEncoder, TimeDistrib... | eventx/models/eventx_model.py | MIT | 14,110 | 10f7f587bd83a342c5419976a2619cff75ec5ffe | Python | py | |
'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import shake
class BasicBlock(nn.Module):
... | cifar10/models/resnet_flymake.py | MIT | 4,030 | b232a6dca2d8e906f37b01ff5c94119b36a0067b | Python | py | |
import torch
from catalyst.registry import Model
@Model
class Model(torch.nn.Module):
"""
Your model
"""
def __init__(self, **kwargs):
"""
Model init.
Args:
**kwargs: model params
"""
super().__init__()
def forward(self, x):
"""
... | examples/_empty/src/model.py | Apache-2.0 | 770 | fefa640bbae989353318604493bc289d7ad79fe1 | Python | py | |
"""
Evaluation with AutoAttack.
"""
import json
import time
import argparse
import shutil
import os
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from autoattack import AutoAttack
from core.data import get_data_info
from core.data import load_data
from core.models import create_mode... | eval-aa.py | MIT | 2,737 | 10bf8a50daf873e1ff217c3557127c8c2f7821c4 | Python | py | |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2018 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Utility functions for training."""
import codecs
import functools
import logging
import numpy as np
import os
import time
import torch
import yaml
logge... | neural_sp/bin/train_utils.py | Apache-2.0 | 5,383 | fefa8446008472acece0dd6dba0070e897adc214 | Python | py | |
import unittest
import json
import os
import random
import responses
import torchvision
from lightly.api import routes
class TestGetPresignedURL(unittest.TestCase):
def setup(self, n_data=1000, psuccess=1.):
self.filename = 'filename'
self.dataset_id = 'XYZ'
self.sample_id = 'ABC'
... | tests/api/test_GetPresignedURL.py | MIT | 3,296 | fefa8aa6bbcecbb3773d2851abd0220b0d3a382d | Python | py | |
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in wri... | tests/test_vote_ensembled.py | Apache-2.0 | 3,094 | e94213733f184533b4fdac25007e063b4ed83a6d | Python | py | |
# example of calculating the frechet inception distance
import os
import json
import argparse
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch
from torchvision import models, transforms
import torch.nn.functional as F
from skimage.measure import compare_ssim as ssim
import torch.nn... | eval_scripts/eval_disc_dataset.py | MIT | 7,275 | 28c8694408befca3dcf717bf0f9336d30b1a6ec2 | Python | py | |
"""Dilated ResNet"""
import math
import torch
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
from ..models.model_store import get_model_file
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution... | gluoncvth/models/resnet.py | MIT | 9,902 | fefad7f15174ec3fa49ea49f9fb2612fd721da19 | Python | py | |
import argparse
import logging
import random
import numpy as np
import pytorch_lightning as pl
import torch
import wandb
from envs import network_flow_env_builder
from pytorch_lightning.loggers import WandbLogger
# Default big-ish
from agents.pytorch_agents import CustomerOnehotDQN
from experiments_v2.base_ptl_agent_... | python/src/experiments_seminar_2/agents/one_hot_dqn_v2.py | MIT | 4,990 | 6528adb11ba38db85a29640f4e1e2039aa1c9650 | Python | py | |
import json
import logging
import numpy as np
import torch
def xavier(net):
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 and hasattr(m, 'weight'):
torch.nn.init.xavier_uniform_(m.weight.data, gain=1.)
if m.bias is not None:
m.bias.data.zero_()
elif classname.... | network/initializer.py | MIT | 7,889 | e97a53cd1982e93eaa38c068806cb7b26cf1501a | Python | py | |
import numpy as np
import torch
class ReplayBuffer:
"""
Implement replay buffer.
"""
def __init__(self, max_size):
"""
:param max_size: the maximum number of experiences stored in the replay buffer. int.
"""
self._max_size = max_size
self._data = {
"... | utils.py | MIT | 7,156 | fefb1524e5fb69dce2d79dccdc29be336b48a91d | Python | py | |
import os
import torch
def save_checkpoint(epoch, step, model, optimizer, save_path):
"""
Save checkpoint pickle file with model weights and other experimental settings
Args:
epoch (Int): Current epoch when model is being saved
step (Int): Mini-batch iteration count when mod... | checkpoint.py | MIT | 1,187 | 455ef69ce183f13aa8381fd9e2e1d465124d8da8 | Python | py | |
import logging
from typing import Any, Dict, Iterable, List, Tuple
import difflib
import sqlparse
from overrides import overrides
import torch
from allennlp.common.util import pad_sequence_to_length
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import Attention, Se... | allennlp_semparse/models/atis/atis_semantic_parser.py | Apache-2.0 | 27,962 | fefb4d2fe8dcbe62d61b0f34433b9d2c52bc0b5c | Python | py | |
import logging
from abc import abstractmethod
from typing import List, Dict, Iterable, Tuple, Union
import networkx as nx
from Bio import SeqIO
from openomics.database.annotation import *
from openomics.database.base import Database
from openomics.database.sequence import SequenceDatabase
class Interactions(Database... | openomics/database/interaction.py | MIT | 45,498 | 5e3009be81b5d1a4598e509280ba3ad502ad1e87 | Python | py | |
import torch.utils.data as data
import torch
import torchvision.transforms as transforms
import numpy as np
from PIL import Image
import os
class frame_dataset(data.Dataset):
def __init__(self, args):
self.frame_dir = args.frame_dir
self.frame_addr = np.asarray([os.path.join(self.frame_dir... | data/frame_dataset.py | MIT | 1,000 | 1e4355c8017218d7a81df500df6c043f56677053 | Python | py | |
import json
import torch
from PIL import Image
from torchvision import transforms
import io
import numpy as np
# defines the model
def model_fn(model_dir):
model_path = f'{model_dir}/infer-pytorch-ic-resnet50.pt'
model = torch.load(model_path)
model.eval()
return model
def input_fn(request_body, req... | right_size_your_sagemaker_endpoints/inference.py | MIT-0 | 1,364 | 34083b76ffe518086da3700e03b5b4168a6bf12e | Python | py | |
import torch
import torch.nn as nn
import torch.nn.functional as F
class L2Norm(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weight = nn.Parameter(torch.Tensor(sel... | SFD/net_s3fd.py | MIT | 5,845 | fefb980d843a5fe2169bd3152da7f373c1666af8 | Python | py | |
import math, torch
import torch.nn as nn
from ..initialization import initialize_resnet
from ..SharedUtils import additive_func
from .SoftSelect import select2withP, ChannelWiseInter
from .SoftSelect import linear_forward
from .SoftSelect import get_width_choices as get_choices
def conv_forward(inp... | zero_cost_utils/models/shape_searchs/SearchSimResNet_width.py | MIT | 14,164 | fefba16a121d446dda25378b08485eb17f5e1a83 | Python | py | |
from pathlib import Path
import json
import scipy.sparse as sp
import torch
from ogb.nodeproppred import PygNodePropPredDataset
import collections
from copy import deepcopy
import logging
import warnings
from typing import Any, Dict, Sequence, Union
from rgnn_at_scale.data import split
import numpy as np
from sacred im... | examples/Graph_Adversarial_Learning/Targeted/Poisoning/PyTorch/sga_experiment_rgnn_scale_models.py | MIT | 16,036 | dfbe679941c6491ba1d582746bd856db775159fa | Python | py | |
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in wri... | tests/test_handler_stats.py | Apache-2.0 | 5,803 | 248be9f3290cbcdd14ab5d59581a975b744b2a2d | Python | py | |
#!/usr/bin/python
# _____________________________________________________________________________
# ----------------
# import libraries
# ----------------
# standard libraries
# -----
import os
import math
import torch
# configuration module
# -----
import config
# custom functions
# -----
def update_target_net(n... | utils/general.py | MIT | 1,746 | fefbce8074a394b8a3973f0f26165706eb49720e | Python | py | |
"""
This file contains the definition of the SMPL model
It is adapted from opensource project GraphCMR (https://github.com/nkolot/GraphCMR/)
"""
from __future__ import division
import torch
import torch.nn as nn
import numpy as np
import scipy.sparse
try:
import cPickle as pickle
except ImportError:
import pi... | metro/modeling/_smpl.py | MIT | 10,823 | e98bd4ade27f3d1c07b85307caf59d16f8044ce4 | Python | py | |
from GNN.globals import *
import math
from GNN.utils import *
from GNN.graph_samplers import *
from GNN.norm_aggr import *
import torch
import scipy.sparse as sp
import scipy
import numpy as np
import time
import hashlib
def _coo_scipy2torch(adj, coalesce=True, use_cuda=False):
"""
convert a sc... | GNN/pytorch_version/minibatch.py | MIT | 10,957 | fefc0aa9029ace0b060e909cd6f4adfb0a555d42 | Python | py | |
import argparse
import json
import time
import sys
import torch
# import test
import test_mapgiou
# from models_diou_arcface import *
# from models_diou import *
from models_slim import *
# from models import *
from utils.datasets import JointDataset, collate_fn
from utils.utils import *
from utils.lo... | train_exp_sparsity.py | MIT | 13,361 | 4b4f16d2e3802d77d2f8d6f6b25c544506d74fef | Python | py | |
from pathlib import Path
from typing import List, Union
import torch.nn.utils.rnn as rnn
class Editor:
@staticmethod
def get_lines(
text_corpus_paths: List[Path],
):
lines = list()
for text_corpus_path in text_corpus_paths:
with open(text_corpus_path) as f:
... | trext/utils/editor.py | MIT | 2,667 | 34814bd976f8a8a41954cc7ec11a5957da2d20c6 | Python | py | |
import argparse
import yaml
import cv2
import torch
from torch.autograd import Variable
from models.yolov3 import *
from utils.utils import *
from utils.parse_yolo_weights import parse_yolo_weights
def main():
"""
Visualize the detection result for the given image and the pre-trained model.
"""
pars... | demo.py | MIT | 3,320 | fefc56b99380b84ccd0afa37cd7e9cd565664014 | Python | py | |
from torch import nn
from jcopdl.layers import conv_block, linear_block
class CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Sequential(
conv_block(3, 8),
conv_block(8, 16),
conv_block(16, 32),
conv_block(32, 64),
... | 14 - Convolutional Neural Network/src/model.py | MIT | 584 | 4266200b61a803c4588cdb929cc58af38ea06a21 | Python | py | |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 5 19:13:50 2021
@author: behnood
"""
#from __future__ import print_function
import matplotlib.pyplot as plt
#%matplotlib inline
# from numpy import linalg as LA
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import numpy as np
#from models import *
... | MiSiCNet_SimGit.py | Apache-2.0 | 8,883 | ff5b7eb9f9059b16c3a4acaee26e2b66eed3dfb3 | Python | py | |
import torch
from .BaseAttacker import BaseAttacker
class PGDAttacker(BaseAttacker):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# hardcode
def _build(self, eta, iteration):
self.eta = eta
self.iteration = 100
@torch.no_grad()
def... | attacker/PGD.py | MIT | 1,620 | fefc75b16015bc9fe6f7f2bc7085d308e83a6c42 | Python | py | |
import torch
from torch import nn as nn
from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import Upsample, make_layer
# from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM
torch.backends.cudnn.benchmark = True
class UDown(nn.Module):
"""Downscaling with maxpool then double conv... | basicsr/archs/tm_arch.py | Apache-2.0 | 9,369 | 1e4addeade6b93321a683206e2bca4ad03703d75 | Python | py | |
# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, s... | 3d_segmentation/ignite/unet_evaluation_array.py | Apache-2.0 | 4,252 | 5e5b4745b6a96010af5553111c306f933e566136 | Python | py | |
import argparse
from tqdm import tqdm
from typing import Any, Dict
import json
import pickle
import numpy as np
import torch
import sys
sys.path.append("../../")
from visdialch.data.get_bert_embeddings import BertEmbedder
class EmbeddingExtractor:
def __init__(self, config):
super().__init__()
self... | tests/extract_visdial_bert_emb.py | BSD-3-Clause | 3,478 | 45d07ffadd65d4863f6bd48f5a9f07a04c175b7a | Python | py | |
"""
An :class:`Activation` is just a function
that takes some parameters and returns an element-wise activation function.
For the most part we just use
`PyTorch activations <http://pytorch.org/docs/master/nn.html#non-linear-activations>`_.
Here we provide a thin wrapper to allow registering them and instantiating them ... | allennlp/nn/activations.py | Apache-2.0 | 3,235 | 10c2cccf0c9f5f39e6df33e220e2ffb273933ee9 | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ucb_rl2_meta.distributions import Categorical
from ucb_rl2_meta.utils import init
init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.
constant_(x, 0))
init_relu_ = lambda m: init(m, nn.in... | ucb_rl2_meta/model.py | MIT | 10,706 | 5ebcee875fd6670359cb512483fe83fd46abe528 | Python | py | |
import torch
from torchvision import datasets, transforms
import torchvision.models as models
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
import logging
import argparse
from tqdm import tqdm
import numpy as np
import time
logging.basicConfig(
format="%(asctime)s - %(l... | collaborative_system/collaboration_cv.py | Apache-2.0 | 4,560 | 70a747565374c6784c27a91e795ce5f49ae3da4f | Python | py | |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1)... | models/our_model.py | MIT | 10,666 | fefcdf99acdba3f9395e0330a560f984bfe1c788 | Python | py | |
import sys
import os
import subprocess
import numpy as np
import torch
def do_tensorflow_routine(path_name_file, grad_needed_for_embeddings = True):
output_file = path_name_file.replace(".txt","") + "_emb_sgsc.txt"
os.chdir(os.path.join("/data/megh98/projects/graph-prompt-project/baselines/syn/bne_resources"))... | baselines/bne/bne_resources/run_bne_for_pt.py | MIT | 1,662 | 70696a34e5e43cf05846a6b82d9624fb593ca6a6 | Python | py | |
from functools import reduce
import operator
import torch
import torch.nn as nn
from torch.autograd import Variable
count_ops = 0
conv_ops = 0
count_params = 0
def get_num_gen(gen):
return sum(1 for x in gen)
def is_leaf(model):
return get_num_gen(model.children()) == 0
def get_layer_info(layer):
la... | utils/measure.py | MIT | 4,514 | fefd2fc43732bd68f7d19acda52e702d301fc5fc | Python | py | |
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class ResNetSimCLR(nn.Module):
def __init__(self, base_model, out_dim):
super(ResNetSimCLR, self).__init__()
self.resnet_dict = {"resnet18": models.resnet18(pretrained=False, norm_layer=nn.InstanceNorm2d),
... | simclr/models/resnet_simclr.py | MIT | 1,175 | fefd46a6fc2cc69b9e6c5e91a8653cde35cd6138 | Python | py | |
import torch
from ..builder import HEADS, build_head
from .standard_roi_head import StandardRoIHead
from mmdet.core import bbox2roi
@HEADS.register_module()
class MaskTextspotterHead(StandardRoIHead):
def __init__(self, mask_textspotter_head, **kwargs):
assert mask_textspotter_head is not None
s... | mmdet/models/roi_heads/mask_textspotter_roi_head.py | Apache-2.0 | 3,509 | 24eedee7df16091c5bc74d9d9029a1d15c3d9a4f | Python | py | |
import os
import shutil
import gc
import torch
from ..datasets.dataset import DreemDataset
from ..models.modulo_net import ModuloNet
from ..models.modulo_net.normalization import initialize_standardization_parameters
from .descriptions import three_groups_record_description
from .utils import generate_memmaps
from ..... | dreem_learning_open/test/test_models_features_only.py | MIT | 4,554 | 52b60533f2a22ff18d0c86ad7ea3be62acfe6161 | Python | py | |
import numpy as np
import torch
from tensorboardX import SummaryWriter
from torch import nn
from config import device, im_size, grad_clip, print_freq
from data_gen_6 import DIMDataset
from models_v16 import DIMModel
from utils import parse_args, save_checkpoint, AverageMeter, clip_gradient, get_logger, get_learning_ra... | train_v16_6.py | MIT | 7,275 | 1092c0a43d0cc7485597fb2a9b9021eabf7a950b | Python | py | |
#!/usr/bin/env python
# coding: utf-8
import os
import pickle
import subprocess
import torch
import torch.nn as nn
from torchvision import models
MODELS = {
'supervised': ['', ''],
'moco-v1': ['https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v1_200ep/moco_v1_200ep_pretrain.pth.tar', 'pth'],
'm... | download_and_prepare_models.py | MIT | 8,120 | fefd9ccfd5b45cd70bd8601761a76787d87c9a07 | Python | py | |
import bisect
import operator
import numpy as np
import torch
from torch.utils import data
from multilayer_perceptron import *
from utils import *
def preprocess_weights(weights):
w_later = np.abs(weights[-1])
w_input = np.abs(weights[0])
for i in range(len(weights) - 2, 0, -1):
w_later = np.matm... | neural_interaction_detection.py | Apache-2.0 | 3,905 | fefdcc4b39abbd36a5e2708a7747476f4a6c3a8b | Python | py | |
from typing import Union
from itertools import zip_longest
import torch
import torch.nn as nn
from ctt.models.transformers.ctt1 import _DiurnalContactTracingTransformer
import ctt.models.modules as mods
import ctt.models.attn as attn
class _DiurnalContactTracingTransformerV2(_DiurnalContactTracingTransformer):
... | ctt/models/transformers/ctt2.py | Apache-2.0 | 13,293 | 12ef53376411c614f34cb3d52b576bdde562ca48 | Python | py | |
import numpy as np
import torch
import torch.nn.functional as F
from pretrainedmodels.models.dpn import DPN
from pretrainedmodels.models.dpn import pretrained_settings
class DPNEncorder(DPN):
def __init__(self, feature_blocks, *args, **kwargs):
super().__init__(*args, **kwargs)
self.feature_blocks = np.c... | models/encoders/dpn.py | MIT | 4,155 | fefe11f498a812838d7e908ed45666f6d7f31fca | Python | py | |
from __future__ import absolute_import, division
import os
import torch.nn as nn
import scipy.io as scio
import cv2
import numpy as np
import torch
def loadmat(datasetPath, dataPath):
'''load .mat as ndarray'''
dataFullPath = os.path.join(datasetPath,dataPath)
dataDict = scio.loadmat(dataFullPath)
name... | PF/ops.py | MIT | 1,717 | 1e031c048ee70da639829363d216ea501e52da83 | Python | py | |
#encoding:utf-8
#
#created by xiongzihua
#
'''
txt描述文件 image_name.jpg x y w h c x y w h c 这样就是说一张图片中有两个目标
'''
import os
import sys
import os.path
import random
import numpy as np
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import cv2
import matplotlib.pyplot as plt
class... | dataset.py | MIT | 10,622 | b2540f5be29eb07b137084c9f64a2c28ad234a57 | Python | py | |
"""
Copyright (c) 2019 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 writin... | pytorch_toolkit/nncf/nncf/dynamic_graph/patch_pytorch.py | Apache-2.0 | 6,329 | fefe5763cf944f63816a7e452ef1d79bd7016687 | Python | py | |
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNMapEncoder(nn.Module):
def __init__(self, map_channels, hidden_channels, output_size, masks, strides, patch_size):
super(CNNMapEncoder, self).__init__()
self.convs = nn.ModuleList()
patch_size_x = ... | trajectron/model/components/map_encoder.py | MIT | 6,818 | fefe72a5d7e8ec8028dd38c885d5a30c2247446a | Python | py | |
import torch
from torch import nn
import numpy as np
import dataset
import glob
import pandas as pd
import tqdm
import sys
import json
import os
import logging
# own modules
from GreedyInfoMax.stock.models import load_stock_model
from GreedyInfoMax.stock.models.loss_supervised_fn import (
Supervised_Loss,
targ... | convert.py | MIT | 7,444 | fefea6609ef9e3f0f5e634747eb742ef93459f8e | Python | py | |
import glob
import cv2
import random
import numpy as np
import pickle
import os
from torch.utils import data
class TrainDataset(data.Dataset):
def __init__(self, config):
super().__init__()
self.config = config
train_list_file = os.path.join(config.datasets_dir, config.train_list)
... | data_manager.py | MIT | 2,137 | 1e4d565c92da0ed169b497ff8090225c7d9f0831 | Python | py | |
"""
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... | segmenation_baseline/training_agb_avg_word_embeddings.py | Apache-2.0 | 5,358 | f98ed9b3e8b981a25366991ab41753a567a53087 | Python | py | |
# ==============================================================================
# Copyright 2019 Florent Mahoudeau. 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
#
# ... | model/micResNet.py | MIT | 17,410 | 4b7a2f1aa6dd1f2f993050e2956f0d1e89c4711e | Python | py | |
# System imports
import sys, os
# 3rd party imports
import pytorch_lightning as pl
from pytorch_lightning import LightningModule
import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch.utils.data import random_split
from torch_geometric.data import DataLoader
import numpy as np
... | examples/Pipelines/TrackML_Example/LightningModules/Filter/filter_base.py | MIT | 15,988 | feff25e72c08d167ef317a6e276f6f432b3cc231 | Python | py | |
import unittest as _unittest
import warnings as _warnings
from contextlib import redirect_stdout as _redirect_stdout
from io import StringIO as _StringIO
import torch as _torch
import torchvision as _torchvision
import torchutils as _tu
from ._networks import (_CoverageNetwork, _MultipleInputNetwork,
... | test/test_models.py | MIT | 4,820 | 5f70ea7dd22776f779db1f45160d27fea1fd2447 | Python | py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not in sys.path:
sys.path.insert(0, root_dir)
from models.base import Base
from models.... | romp/lib/models/modelv1.py | MIT | 5,420 | fd905f39e1ec2860380b05b487ee1b36d3025ef2 | Python | py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Provides utilities useful for multiprocessing.
"""
from multiprocessing import Lock, RawArray
from collections.abc i... | parlai/core/thread_utils.py | MIT | 5,976 | 7ceaa632fc56e4c72e102d8931c388a0291ad53c | Python | py | |
import os
import random
import numpy as np
from torch.utils import data
from PIL import Image
class TorchAdaptor(data.Dataset):
"""Dataset class for the CelebA dataset."""
def __init__(self, dataset, transform, for_training=True):
"""Initialize and preprocess the CelebA dataset."""
... | deeploader/plats/torch/torch_adaptor.py | Apache-2.0 | 1,042 | 7c86615c717c92ae4e69dca2a994590ab787b401 | Python | py | |
import time
import torch
from queue import deque
from collections import OrderedDict
from contextlib import contextmanager
class RecordManager:
def __init__(self, window_size=None, summary_mode=None, sync_cuda=False):
self.groups = OrderedDict()
self.records = OrderedDict()
self.ticks = d... | runner_master/runner/record.py | MIT | 6,392 | feff4e1b44824f543f3ad21133f74bce920401c9 | Python | py | |
# header files
import torch
import torch.nn as nn
import torchvision
from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm
from torch.nn.modules.utils import _pair
import numpy as np
import skimage
from skimage import io, transform
import glob
import csv
from PIL import Image
import time
im... | code/utils/utils.py | MIT | 7,022 | feff89cd5f0357bef9e33962a5bac36fbf060133 | Python | py | |
from dataclasses import dataclass
from typing import Any
from typing import Dict
from typing import List
import torch
import torch.nn as nn
import transformers
from transformers import BertConfig, BertModel
import warnings
import jiant.proj.main.components.container_setup as container_setup
import jiant.proj.main.mod... | src/probing-tasks/jiant/jiant/proj/main/modeling/model_setup.py | MIT | 13,867 | feff9045224d078bea40d4129f19510f5679fecb | Python | py | |
import torch.nn as nn
from collections import OrderedDict
import sys
print(__name__)
print(sys.path)
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
self.embedding = ... | recognize/crnn.py | MIT | 8,213 | fcccc5f5786c0c4aaca7dcbf0212730c5f55ecfa | Python | py | |
import torch
from .base_reducer import BaseReducer
class DivisorReducer(BaseReducer):
def unpack_loss_info(self, loss_info):
losses, loss_indices, reduction_type, kwargs = super().unpack_loss_info(
loss_info
)
if reduction_type != "already_reduced":
kwargs = {"divi... | src/pytorch_metric_learning/reducers/divisor_reducer.py | MIT | 1,174 | 7ce5661c7148f7afe8e51b6bb007e1596fe2018a | 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... | torchmetrics/setup_tools.py | Apache-2.0 | 3,278 | dfde2bc6e42b8975e410e21dc0b6d3c617324ca1 | Python | py | |
import argparse
import os
import re
import time
import torch
import pandas as pd
from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video_set
from training.zoo.classifiers import DeepFakeClassifier
if __name__ == '__main__':
parser = argparse.ArgumentParser("Predict test videos")
... | predict_folder.py | MIT | 2,225 | f9cce00bae16ad927185a4a96c1e02be79d2b11d | Python | py | |
import os
import sys
import inspect
import argparse
import yaml
import numpy as np
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
import torch
import torch.optim as optim
import torch.nn as... | src/classification_train.py | MIT | 8,359 | 4b77c5dd7d6342e536f6cc1ca2aa8999b5d16838 | Python | py | |
'''
retrain models on sst5
'''
import numpy as np
import os
import yaml
import argparse
from tqdm import tqdm
import torch
import torch.optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from model.supernet import TextClassifier
from task.utils import logger, set_seed
from .search_supernet... | task/text_classification/retrain.py | Apache-2.0 | 9,645 | 45dbcce91f4e2cb4084ff76d0924148d5f8e1df2 | Python | py | |
import unittest
import numpy as np
import pytest
import torch
from numpy.testing import assert_almost_equal
from torch_audiomentations.augmentations.gain import Gain
from torch_audiomentations.utils.dsp import (
convert_decibels_to_amplitude_ratio,
convert_amplitude_ratio_to_decibels,
)
class TestGain(unitt... | tests/test_gain.py | MIT | 25,747 | feffd05602d841617f55fee7f0da8cd475a7f88d | Python | py | |
from hythe.libs.dispatch.dispatcher import Dispatcher
from torch.multiprocessing import Process, set_start_method
class ParallelDispatcher(Dispatcher):
def __init__(self, dispatch_list=None):
super(ParallelDispatcher, self).__init__(dispatch_list)
def dispatch(self):
try:
set_sta... | libs/dispatch/parallel_dispatcher.py | MIT | 766 | feffea1affedafdb3991320b38e1b269011021f6 | Python | py | |
import os
import torch
from data.args import get_args, print_args, init_args
from models.graph_models.model import RuleBasedModel
from scripts.train import build_test_loader, check_model_iou
torch.backends.cudnn.benchmark = True
def main(args):
args.vocab, val_loader = build_test_loader(args)
init_args(args... | scripts/test_iou.py | MIT | 1,039 | e98ee0aff1253a2e0d125c2f3ded8f44af778da9 | Python | py | |
import torch
import torch.nn as nn
import sys
sys.path.append("../../")
from general.train import TrainTemplate, get_default_train_arguments, start_training
from general.mutils import get_param_val, general_args_to_params
from general.parameter_scheduler import add_scheduler_parameters, scheduler_args_to_params
from ... | experiments/molecule_generation/train.py | MIT | 4,297 | 1e0c1c49a6b84401c3087a73d22568386766d7d8 | Python | py | |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : peike.li@yahoo.com
@File : dataset.py
@Time : 8/30/19 9:12 PM
@Desc : Dataset Definition
@License : This source code is licensed under the license found in the
LICENSE file in the root directory of this... | datasets/extractor_dataset.py | MIT | 2,284 | 3a00204779cba946d035e20a3bea9521f0577686 | Python | py | |
import argparse
import torch
import time
import imageio
import numpy as np
from pathlib import Path
from torch.autograd import Variable
from utils.make_env import make_env
from algorithms.maddpg import MADDPG
def run(config):
model_path = (Path('./models') / config.env_id / config.model_name /
(... | evaluate.py | MIT | 4,800 | 42837a13c9d5eb988082c06bf22fe1ba03086bb0 | Python | py | |
import os
import datetime as dt
import torch
import torch.distributed as dist
import math
import time
import pickle
import statistics
from collections import defaultdict
class DistEnv:
def __init__(self, local_rank, world_size, backend='nccl'):
assert(local_rank >= 0)
assert(world_size > 0)
... | 1D_CAGNET/utils.py | MIT | 5,354 | 7c3ead53c5dcafe3c37975074f02d5cb5a05745e | Python | py | |
# Copyright (c) 2018, Curious AI Ltd. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View... | mean_teacher/utils.py | MIT | 4,162 | 8010735163ae64d98b9b4ee8e81cd9cffe789a4e | Python | py | |
import importlib
import inspect
from typing import List
import torch.nn as nn
import torch.optim as opt
from utils.constants import *
from utils.data_loaders import *
models = {x: {} for x in types}
"""
handles loading in model references as well as saving and loading
"""
def _read_all_class_names():
"""
p... | utils/model_utils.py | MIT | 3,191 | 3a005840da9979f1d06a9a4f9e8bbe8d42c42594 | Python | py | |
# https://github.com/pyg-team/pytorch_geometric/blob/master/examples/graph_saint.py
import os.path as osp
import argparse
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Flickr
from torch_geometric.loader import GraphSAINTRandomWalkSampler
from torch_geometric.nn import GraphConv
... | examples/graph_saint.py | MIT | 3,642 | 3a0066c3e0a93821c55cf57098a88ebb1322a462 | Python | py | |
import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
######################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal(m... | model.py | MIT | 1,896 | 28fd21f962ba562db7ee1d78c68f2445a193b8bb | Python | py | |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
from ... | tools/train.py | MIT | 7,267 | 4b98acda90e30bc6e91f43f81559af4753836f43 | Python | py | |
from sciwing.data.contextual_lines import LineWithContext
from sciwing.data.seq_label import SeqLabel
from sciwing.datasets.seq_labeling.base_seq_labeling import BaseSeqLabelingDataset
from sciwing.numericalizers.numericalizer import Numericalizer
from torch.utils.data import Dataset
from typing import Dict, List, Any
... | sciwing/datasets/seq_labeling/conll_yago_dataset.py | MIT | 6,241 | 3a00ca0ae4ae36beee13ac88e1281a283c71e525 | Python | py | |
import math
import torch
from torch.optim import Optimizer
import numpy as np
class Adam(Optimizer):
r"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
... | optimizers/adam_mnk.py | Apache-2.0 | 9,276 | 5e87f2b6af5dfd02112460b5ab45dcbef8777cd3 | Python | py | |
import torch
import torch.nn.functional as F
import torch.nn as nn
drop = 0.25
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True)
class BasicBlock(nn.Module):
ex... | models/LadderNet.py | Apache-2.0 | 7,757 | 1e907d4b0935acc1cbdbb4fdc275f177f83107fc | Python | py | |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import argparse
import os
import time
from loguru import logger
import cv2
import torch
import sys
sys.path.append("D:\mynetwork\YOLOX-main")
from yolox.data.data_augment import ValTransform
# from yolox.data.datasets impo... | .history/tools/demo_20211008153203.py | Apache-2.0 | 14,802 | 3a00fb3111d9394f6c21a01b2e6024ffce8c775b | Python | py | |
import torch
import torch.nn as nn
from torch.nn import functional as F
from tensorboardX import SummaryWriter
from satflow.models.layers.GResBlock import GResBlock
from satflow.models.layers.Normalization import SpectralNorm
from satflow.models.layers.ConvGRU import ConvGRU
from satflow.models.layers.Attention impor... | satflow/models/layers/Generator.py | MIT | 5,654 | 3a010294780d31e6718af2226d3c8170e21e4497 | Python | py | |
import csv
import glob
import os
import re
import cv2
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import torch.utils.data
import imgaug as ia
from imgaug import augmenters as iaa
from misc.utils import cropping_center
from .augs import (
add_to_brightness,
add_to_contrast,
a... | dataloader/train_loader.py | MIT | 6,761 | fcb7902f048c79ebef6ad1a4804122b6fa5b0684 | 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/profiler/advanced.py | Apache-2.0 | 3,800 | 3a017d72ff5e07c6a147a25ad744b675a70c21dd | Python | py | |
import io
import itertools
import unittest
from parameterized import parameterized
from torchaudio._internal.module_utils import is_sox_available
from torchaudio.backend import sox_io_backend
from torchaudio.utils import sox_utils
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,... | test/torchaudio_unittest/backend/sox_io/smoke_test.py | BSD-2-Clause | 6,372 | cc0f7b5f26b640edb208363d9e1eadb37ff311b0 | Python | py | |
import torch
from torch import nn
from torch.nn import functional as F
from math import log, pi, exp
import numpy as np
from scipy import linalg as la
logabs = lambda x: torch.log(torch.abs(x))
class ActNorm(nn.Module):
def __init__(self, in_channel, logdet=True):
super().__init__()
... | model.py | MIT | 11,193 | 34126e34742c5e69ff5ff487cbaeace995815d05 | Python | py |
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