repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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DCN-T | DCN-T-main/models/network_local_global.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
from models.backbone import build_backbone
from einops import rearrange
from utils.gensp.ssn_sp import ssn_iter
def find_surrounding(input, l, h_shift_unit=1, w_shift_unit=1... | 21,738 | 30.597384 | 178 | py |
DCN-T | DCN-T-main/models/backbone/hrnet.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by RainbowSecret (yhyuan@pku.edu.cn)
# ------------------------------------------------------------------------------
import os
import logging
import torch.nn as nn
imp... | 23,865 | 38.776667 | 281 | py |
DCN-T | DCN-T-main/models/backbone/swin.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu, Yutong Lin, Yixuan Wei
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.... | 27,190 | 37.243319 | 186 | py |
DCN-T | DCN-T-main/models/backbone/resnet.py | import math
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from models.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
import os
import torchvision
torchvision.models.resnext50_32x4d()
__model_file = {
18: 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
34: '... | 10,918 | 36.651724 | 106 | py |
DCN-T | DCN-T-main/models/backbone/mobilenetv2.py | """
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch
import torch.nn as ... | 6,265 | 31.466321 | 120 | py |
DCN-T | DCN-T-main/models/backbone/vgg.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
import torch
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://downloa... | 4,738 | 31.909722 | 113 | py |
DCN-T | DCN-T-main/models/backbone/__init__.py | import torch.nn as nn
from models.backbone import resnet
from models.backbone.hrnet import hrnet18
from models.backbone.vgg import vgg16_bn
from models.backbone.mobilenetv2 import mobilenetv2
from models.backbone.swin import swin_tiny
def build_backbone(args, backbone, in_channels):
if backbone == 'hrnet18':
... | 1,223 | 41.206897 | 76 | py |
DCN-T | DCN-T-main/models/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.dat... | 3,218 | 35.579545 | 115 | py |
DCN-T | DCN-T-main/models/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import numpy as np
from torc... | 834 | 26.833333 | 157 | py |
DCN-T | DCN-T-main/models/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import torc... | 12,932 | 44.861702 | 116 | py |
DCN-T | DCN-T-main/dataloaders/custom_transforms.py | import torch
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter,ImageEnhance
import torchvision.transforms.functional as F
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple):... | 4,141 | 27.565517 | 87 | py |
DCN-T | DCN-T-main/dataloaders/utils.py | import matplotlib.pyplot as plt
import numpy as np
import torch
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks)... | 3,438 | 31.140187 | 84 | py |
DCN-T | DCN-T-main/dataloaders/datasets/WHU_Hi.py | import os
import torch
import scipy.io as scio
import numpy as np
from PIL import Image
from torch.utils import data
from utils.path_utils import Path
from torchvision import transforms
from torch.utils.data import DataLoader
from dataloaders import custom_transforms as tr
from glob import glob
class TrainDataset(data... | 5,932 | 28.517413 | 193 | py |
DCN-T | DCN-T-main/dataloaders/datasets/WHU_Hi_whole.py | import os
import torch
import scipy.io as scio
import numpy as np
from PIL import Image
from torch.utils import data
from utils.path_utils import Path
from torchvision import transforms
from torch.utils.data import DataLoader
from dataloaders import custom_transforms as tr
from glob import glob
class TrainDataset(data... | 6,218 | 28.614286 | 209 | py |
DCN-T | DCN-T-main/dataloaders/datasets/WHU_Hi_trn_val_split.py | import os
import torch
import scipy.io as scio
import numpy as np
from PIL import Image
from torch.utils import data
from utils.path_utils import Path
from torchvision import transforms
from torch.utils.data import DataLoader
from dataloaders import custom_transforms as tr
from glob import glob
class TrainDataset(data... | 7,326 | 27.733333 | 197 | py |
DCN-T | DCN-T-main/utils/saver.py | import os
import shutil
import torch
from collections import OrderedDict
import glob
import numpy as np
import scipy.io as scio
class Saver(object):
def __init__(self, args):
self.args = args
self.directory = os.path.join('./run', args.dataset, args.backbone+'_'+str(args.groups))
self.runs... | 3,034 | 43.632353 | 138 | py |
DCN-T | DCN-T-main/utils/summaries.py | import os
import torch
from torchvision.utils import make_grid
from tensorboardX import SummaryWriter
from dataloaders.utils import decode_seg_map_sequence
class TensorboardSummary(object):
def __init__(self, directory):
self.directory = directory
def create_summary(self):
writer = SummaryWrit... | 1,194 | 48.791667 | 108 | py |
DCN-T | DCN-T-main/utils/gensp/pair_wise_distance.py | import torch
from torch.utils.cpp_extension import load_inline
#from .pair_wise_distance_cuda_source import source
print("compile cuda source of 'pair_wise_distance' function...")
print("NOTE: if you avoid this process, you make .cu file and compile it following https://pytorch.org/tutorials/advanced/cpp_extension.ht... | 2,010 | 42.717391 | 142 | py |
DCN-T | DCN-T-main/utils/gensp/ssn_sp.py | import math
import torch
from .pair_wise_distance import PairwiseDistFunction
from .sparse_utils import naive_sparse_bmm
def calc_init_centroid(images, num_spixels_width, num_spixels_height):
"""
calculate initial superpixels
Args:
images: torch.Tensor
A Tensor of shape (B, C, H, W)
... | 6,715 | 39.457831 | 122 | py |
DCN-T | DCN-T-main/utils/gensp/sparse_utils.py | import torch
def naive_sparse_bmm(sparse_mat, dense_mat, transpose=False):
if transpose:
return torch.stack([torch.sparse.mm(s_mat, d_mat.t()) for s_mat, d_mat in zip(sparse_mat, dense_mat)], 0)
else:
return torch.stack([torch.sparse.mm(s_mat, d_mat) for s_mat, d_mat in zip(sparse_mat, dense_m... | 583 | 35.5 | 113 | py |
DCN-T | DCN-T-main/utils/gensp/src/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='pair_wise_distance',
ext_modules=[
CUDAExtension('pair_wise_distance_cuda', [
'pair_wise_distance_cuda_source.cu',
])
],
cmdclass={
'build_ext': BuildExtensi... | 329 | 24.384615 | 67 | py |
ips | ips-main/main.py | #!/usr/bin/env python
import os
import yaml
from pprint import pprint
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader
from utils.utils import Logger, Struct
from data.megapixel_mnist.mnist_dataset import MegapixelMNIST
from data.traffic.traffic_dataset import TrafficSigns... | 2,606 | 32.423077 | 100 | py |
ips | ips-main/training/iterative.py | import sys
import numpy as np
import torch
from utils.utils import adjust_learning_rate
def init_batch(device, conf):
"""
Initialize the memory buffer for the batch consisting of M patches
"""
if conf.is_image:
mem_patch = torch.zeros((conf.B, conf.M, conf.n_chan_in, *conf.patch_size)).to(devi... | 8,473 | 35.683983 | 111 | py |
ips | ips-main/architecture/ips_net.py | import sys
import math
import torch
import torch.nn as nn
from torchvision.models import resnet18, resnet50, ResNet18_Weights, ResNet50_Weights
from utils.utils import shuffle_batch, shuffle_instance
from architecture.transformer import Transformer, pos_enc_1d
class IPSNet(nn.Module):
"""
Net that runs all t... | 9,636 | 33.053004 | 103 | py |
ips | ips-main/architecture/transformer.py | import math
import torch
from torch import nn
def pos_enc_1d(D, len_seq):
if D % 2 != 0:
raise ValueError("Cannot use sin/cos positional encoding with "
"odd dim (got dim={:d})".format(D))
pe = torch.zeros(len_seq, D)
position = torch.arange(0, len_seq).unsqueeze(1)
... | 4,554 | 28.967105 | 116 | py |
ips | ips-main/utils/utils.py | import sys
import math
import numpy as np
from sklearn.metrics import accuracy_score, roc_auc_score
from collections import defaultdict
import torch
from torch import nn
class Struct:
def __init__(self, **entries):
self.__dict__.update(entries)
def adjust_learning_rate(n_epoch_warmup, n_epoch, max_lr, op... | 4,670 | 31.894366 | 110 | py |
ips | ips-main/data/camelyon/extract_feat.py | #!/usr/bin/env python
import os
import h5py
from pathlib import Path
import argparse
import yaml
import pandas as pd
import torch
from torch.utils.data import DataLoader
from pretraining.model.byol_model import BYOLModel
from data.camelyon.camelyon_dataset import CamelyonImages, PatchSampler
os.environ["CUDA_VISIBLE_... | 5,204 | 28.742857 | 101 | py |
ips | ips-main/data/camelyon/camelyon_dataset.py | import os
import random
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset, Sampler
from torchvision import transforms
from .datamodel import SlideManager
from .cam_methods import remove_alpha_channel
class PatchSampler(Sampler):
FILL_TOKEN = -1
SLIDE_END_TOKEN = -2
def __i... | 4,191 | 28.111111 | 89 | py |
ips | ips-main/data/traffic/traffic_dataset.py | import os
from os import path
import sys
import hashlib
from functools import partial
from collections import namedtuple
import urllib.request
import zipfile
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import ssl
ssl._create_default_https_context = ssl._create_unverif... | 10,490 | 29.060172 | 111 | py |
ips | ips-main/data/megapixel_mnist/mnist_dataset.py | import os
import json
import numpy as np
import torch
class MegapixelMNIST(torch.utils.data.Dataset):
""" Loads the Megapixel MNIST dataset """
def __init__(self, conf, train=True):
with open(os.path.join(conf.data_dir, "parameters.json")) as f:
self.parameters = json.load(f)
self... | 1,705 | 27.915254 | 86 | py |
ips | ips-main/data/megapixel_mnist/make_mnist.py | # Adapted from https://github.com/idiap/attention-sampling
import os
import argparse
import json
import numpy as np
from keras.datasets import mnist
class MegapixelMNIST:
"""
Class to create an artificial megapixel mnist dataset
"""
class Sample(object):
def __init__(self, dataset, idxs, pos... | 11,506 | 30.787293 | 96 | py |
lenspyx | lenspyx-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,842 | 28.510101 | 79 | py |
MNTDP | MNTDP-master/src/models/ssn_wrapper.py | import logging
from collections import OrderedDict
from operator import itemgetter
import networkx as nx
import torch
from torch import nn
from torch.utils.data import DataLoader
from src.models.samplers.arch_sampler import ArchSampler
from src.models.samplers.conditional_softmax_sampler import \
CondiSoftmaxArch... | 10,577 | 36.378092 | 109 | py |
MNTDP | MNTDP-master/src/models/base.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
def LinearNet(sizes, dropout_p):
layers = []
last_size = sizes[0]
if isinstance(l... | 2,279 | 30.232877 | 75 | py |
MNTDP | MNTDP-master/src/models/change_layer_llmodel.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from torch import nn
from src.models.ll_model import LifelongLearningModel
from src.models.modular_model import ModularModel
... | 8,891 | 36.361345 | 101 | py |
MNTDP | MNTDP-master/src/models/ExhaustiveSearch.py | import logging
from collections import OrderedDict
from functools import partial
from operator import itemgetter
from pathlib import Path
import networkx as nx
import torch
import torch.nn as nn
from src.models.change_layer_llmodel import FrozenSequential
from src.models.utils import is_dummy_block, execute_step, gra... | 7,909 | 38.55 | 107 | py |
MNTDP | MNTDP-master/src/models/_utils.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 functools import partial
import torch.nn as nn
from torch.nn import init
# from lileb.utils.misc import pretty_wrap
def load_state_... | 3,497 | 34.693878 | 115 | py |
MNTDP | MNTDP-master/src/models/resnet.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 torch import nn
from torchvision.models.resnet import conv3x3, conv1x1
from src.models.utils import Flatten
class Contiguousize(nn.Mod... | 5,577 | 31.811765 | 77 | py |
MNTDP | MNTDP-master/src/models/ll_model.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import abc
import logging
import os
import time
from functools import partial
import numpy as np
import ray
import torch
from ray import tun... | 12,984 | 41.713816 | 84 | py |
MNTDP | MNTDP-master/src/models/experience_replay_llmodel.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import operator
from functools import reduce
import numpy as np
import torch
from torch import nn
from src.models.change_layer... | 13,211 | 37.973451 | 120 | py |
MNTDP | MNTDP-master/src/models/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import operator
import re
from concurrent.futures.process import ProcessPoolExecutor
from functools import reduce
import networkx as nx
impor... | 4,923 | 30.564103 | 102 | py |
MNTDP | MNTDP-master/src/models/hat_llmodel.py | import logging
import torch
import numpy as np
from src.models.ll_model import LifelongLearningModel
from src.models.utils import get_conv_out_size
from src.utils.misc import count_params
logger = logging.getLogger(__name__)
class MLPHAT(torch.nn.Module):
def __init__(self,inputsize, clipgrad, thres_cosh, thr... | 15,715 | 34.316854 | 101 | py |
MNTDP | MNTDP-master/src/models/ewc_llmodel.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 https://github.com/kuc2477/pytorch-ewc
# Credits to Ha Junsoo - [kuc2477](https://github.com/kuc2477)
###
import itertools
from co... | 8,187 | 33.842553 | 101 | py |
MNTDP | MNTDP-master/src/models/PSSN_llmodel.py | import logging
import os
from collections import defaultdict
from operator import itemgetter
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
from sklearn.neighbors import KNeighborsClassifier
from src.models.ExhaustiveSearch import ExhaustiveSearch
from src.models.SPNN import SPNN
from src... | 36,518 | 41.316338 | 82 | py |
MNTDP | MNTDP-master/src/models/modular_model.py | """
Abstract class representing Modular approaches. Contains all methods allowing
to interact with a model as a combination of blocks.
/!\ Only works with Vision models in the current implementation, supporting
other modalities would require splitting this class to remove all of the CV
specific stuff.
"""
import abc
fr... | 4,445 | 33.734375 | 79 | py |
MNTDP | MNTDP-master/src/models/PNN_llmodel.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from src.models.ll_model import LifelongLearningModel
class PNNLinearBlock(nn.Module):
def __init__(self, in_sizes, out_size, scalar_mult=1.0, split_v=False):
super(PNNLinearBlock, self).__init__()
assert isinstance(in_sizes, (lis... | 4,276 | 36.191304 | 103 | py |
MNTDP | MNTDP-master/src/models/SPNN.py | import copy
import torch
import torch.nn as nn
from src.models.change_layer_llmodel import FrozenSequential
from src.models.utils import is_dummy_block, _get_used_nodes
from supernets.networks.StochasticSuperNetwork import StochasticSuperNetwork
class SPNN(StochasticSuperNetwork):
# IN_NODE = 'IN'
# OUT_NOD... | 2,977 | 34.452381 | 76 | py |
MNTDP | MNTDP-master/src/models/zoo/ImageNetResnet.py |
__all__ = ['ImageNetResNet', 'resnet18', 'resnet34', 'resnet50']
import torch
from torch import nn
from torchvision.models.resnet import Bottleneck, conv1x1, BasicBlock
class ImageNetResNet(nn.Module):
def __init__(self, block, layers, in_planes, num_classes=1000,
zero_init_residual=False, grou... | 4,638 | 40.053097 | 106 | py |
MNTDP | MNTDP-master/src/models/zoo/CifarResnet.py | '''
From https://github.com/akamaster/pytorch_resnet_cifar10/blob/master/resnet.py
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of t... | 4,926 | 32.067114 | 78 | py |
MNTDP | MNTDP-master/src/models/samplers/constant_sampler.py | import torch
from src.modules.samplers.arch_sampler import ArchSampler
class ConstantArchGenerator(ArchSampler):
def __init__(self, initial_p, *args, **kwargs):
super().__init__(*args, **kwargs)
self.initial_p = initial_p
def forward(self, z=None):
if self.frozen:
raise R... | 474 | 28.6875 | 76 | py |
MNTDP | MNTDP-master/src/models/samplers/arch_sampler.py | import torch
from torch import nn
class ArchSampler(nn.Module):
def __init__(self, distrib_dim, all_same, deter_eval, var_names=None,
*args, **kwargs):
super().__init__()
self.distrib_dim = distrib_dim
self.all_same = all_same
self.deter_eval = deter_eval
... | 3,307 | 32.755102 | 79 | py |
MNTDP | MNTDP-master/src/models/samplers/static_sampler.py | import numpy as np
import torch
import torch.nn.init as weight_init
from torch import nn
from torch.nn import Parameter
from src.models.samplers.arch_sampler import ArchSampler
class StaticArchGenerator(ArchSampler):
def __init__(self, initial_p, *args, **kwargs):
super().__init__(*args, **kwargs)
... | 1,341 | 31.731707 | 79 | py |
MNTDP | MNTDP-master/src/models/samplers/conditional_softmax_sampler.py | import networkx as nx
import torch
import torch.nn.functional as f
from torch.distributions import Categorical
from src.models.samplers.softmax_sampler import SoftmaxArchGenerator
class CondiSoftmaxArchGenerator(SoftmaxArchGenerator):
def sample_archs(self, batch_size, probas, force_deterministic=False):
... | 4,644 | 41.614679 | 106 | py |
MNTDP | MNTDP-master/src/models/samplers/softmax_sampler.py | from collections import defaultdict
import torch
import torch.nn.functional as f
from torch import nn
from torch.distributions import Categorical
from src.models.samplers.arch_sampler import ArchSampler
class SoftmaxArchGenerator(ArchSampler):
def __init__(self, groups, graph, *args, **kwargs):
super().... | 5,636 | 36.085526 | 78 | py |
MNTDP | MNTDP-master/src/datasets/TensorDataset.py | from torch.utils.data import Dataset
class MyTensorDataset(Dataset):
def __init__(self, *tensors, transforms=None):
if transforms:
assert tensors[0][0].dim() == 3 # Only Images for now
self.transforms = transforms
assert all(tensors[0].size(0) == tensor.size(0) for tensor in ... | 614 | 28.285714 | 78 | py |
MNTDP | MNTDP-master/src/train/training.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import logging
from collections import defaultdict
import torch
from ignite.engine import Events
from ignite.handlers import Tim... | 14,880 | 44.368902 | 116 | py |
MNTDP | MNTDP-master/src/train/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import time
from collections import defaultdict
import torch
import torchvision
import torchvision.transforms.functional as t... | 9,588 | 32.883392 | 88 | py |
MNTDP | MNTDP-master/src/train/ray_training.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import abc
import logging
import numbers
import os
from collections import defaultdict, OrderedDict
import numpy as np
import torch
from ign... | 17,617 | 39.223744 | 107 | py |
MNTDP | MNTDP-master/src/train/ignite_utils.py | import torch
from ignite.engine import Engine
from ignite.utils import convert_tensor
def _prepare_batch(batch, device=None, non_blocking=False):
"""Prepare batch for training: pass to a device with options.
"""
x, y, *z = batch
return (convert_tensor(x, device=device, non_blocking=non_blocking),
... | 6,597 | 38.987879 | 120 | py |
MNTDP | MNTDP-master/src/experiments/base_experiment.py | import logging
import os
import shutil
import tempfile
import threading
from collections import defaultdict
import numpy as np
import torch
import visdom
from ctrl.strategies.mixed_strategy import MixedStrategy
from src.models import HATLLModel
from src.models.utils import normalize_params_names
from src.utils import... | 16,672 | 43.461333 | 99 | py |
MNTDP | MNTDP-master/src/experiments/stream_tuning.py | import logging
import os
import random
import shutil
import sys
import time
from collections import defaultdict
from copy import deepcopy
from functools import partial
from os import path
import numpy as np
import pandas
import ray
import torch
import visdom
from ray import tune
from ray.tune import CLIReporter
from r... | 30,055 | 40.802503 | 114 | py |
MNTDP | MNTDP-master/src/optimizers/__init__.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 functools import partial
import torch.optim as optim
def get_optim_by_name(name):
if name == 'sgd':
return optim.SGD
... | 2,144 | 29.642857 | 113 | py |
MNTDP | MNTDP-master/src/utils/plotting.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from collections import defaultdict
from functools import partial
from math import pi
from numbers import Number
from operator i... | 39,672 | 39.815844 | 84 | py |
HyperLISTA | HyperLISTA-main/main.py | """
file: main.py
author: Xiaohan Chen
last modified: 2021.10.06
Main script to perform the backpropagation based training for sparse coding task.
"""
import os
import numpy as np
import configargparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import utils
import... | 16,873 | 44.728997 | 108 | py |
HyperLISTA | HyperLISTA-main/main_grid_search.py | import os
import numpy as np
import configargparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import utils
import models
from data.sc import create_sc_data
# Argument Parsing
parser = configargparse.get_arg_parser(description='Configurations for ALISTA experiement'... | 15,256 | 41.736695 | 100 | py |
HyperLISTA | HyperLISTA-main/models/utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def shrink(x, theta):
return x.sign() * F.relu(x.abs() - theta)
def shrink_ss(x, theta, p):
x_abs = x.abs()
threshold = torch.quantile(x_abs, 1-p, dim=1, keepdims=True)
if isinstance(p, torch.Tensor) and p.numel() > 1:
thresh... | 548 | 25.142857 | 90 | py |
HyperLISTA | HyperLISTA-main/models/fista.py | """
file: models/lista.py
author: Xiaohan Chen
last modified: 2021.05.28
Implementation LISTA with support selection.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from .utils import shrink, shrink_ss... | 4,269 | 31.105263 | 83 | py |
HyperLISTA | HyperLISTA-main/models/na_alista.py | """
file: models/na_alista.py
author: Xiaohan Chen
last modified: 2021.05.28
Implementation NA_ALISTA with support selection, transplanted from the official
GitHub repo.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.f... | 8,522 | 31.284091 | 112 | py |
HyperLISTA | HyperLISTA-main/models/ada_lista.py | """
file: models/ada_lista.py
author: Xiaohan Chen
last modified: 2021.08.10
Implementation Ada-LISTA.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from .utils import shrink, shrink_ss
class AdaLIS... | 6,545 | 30.776699 | 98 | py |
HyperLISTA | HyperLISTA-main/models/lista.py | """
file: models/lista.py
author: Xiaohan Chen
last modified: 2021.10.06
Implementation the basic LISTA model.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from .utils import shrink
class LISTA(nn.... | 6,185 | 30.085427 | 103 | py |
HyperLISTA | HyperLISTA-main/models/alista.py | """
file: models/alista.py
author: Xiaohan Chen
last modified: 2021.04.05
Implementation ALISTA with support selection.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from .utils import shrink, shrink_... | 7,029 | 30.666667 | 85 | py |
HyperLISTA | HyperLISTA-main/models/adaptive_mm_alista.py | """
file: models/adaptive_mm_alista.py
author: Xiaohan Chen
last modified: 2021.05.02
Implementation ALISTA with single parameter and support selection, with momentum.
"""
import math
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.fun... | 10,448 | 33.599338 | 130 | py |
HyperLISTA | HyperLISTA-main/data/sc.py | """
File: sc.py
Created: September 9, 2019
Revised: March 11, 2020
Author: Howard Heaton, Xiaohan Chen
Purpose: Define a function for generating the data used in training
and/or testing of the LSKM Model for the LASSO Problem.
"""
import os
import scipy.io
import numpy as np
import torch
from torch.uti... | 4,423 | 38.150442 | 104 | py |
iNLG | iNLG-main/scripts/extract_visual_features.py | import os
import torch
import clip
from PIL import Image
import numpy as np
import h5py
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(prog='ExtractVisualFeature', description='Extract visual features with CLIP')
parser.add_argument('--input_image_dir', type=str, default='./image/')
parser.add_... | 1,696 | 33.632653 | 110 | py |
iNLG | iNLG-main/code/main.py | import os
os.environ["DISABLE_TQDM"] = "1"
import os
from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
import wandb
import params
import utils
from trainer import CLIPCapTrainer, ContraClipCapTrainer, SelfTrainer
from evaluate import text_evaluate, text_evaluate_gpt2
def _main(args):
# I... | 6,680 | 37.843023 | 148 | py |
iNLG | iNLG-main/code/clipcap.py | """
Reference: https://github.com/rmokady/CLIP_prefix_caption
"""
from torch import nn
import numpy as np
import torch
import torch.nn.functional as nnf
from tqdm import tqdm
from typing import Tuple, List, Union, Optional
from transformers import GPT2LMHeadModel
from nlgeval import NLGEval
import params
import utils... | 9,960 | 39.657143 | 139 | py |
iNLG | iNLG-main/code/evaluate.py | import torch
from datasets import Dataset
import utils
def text_evaluate(args, trainer, dataset, tokenizer, metric_list, phase='val'):
num_beams = args.num_beams if args.num_beams > 0 else None
outputs = trainer.predict(
test_dataset=dataset,
max_length=args.max_output_length,
num_bea... | 3,344 | 42.441558 | 144 | py |
iNLG | iNLG-main/code/utils.py | import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["WANDB_DISABLED"] = "true"
import datasets
import json
import h5py
import clip
import numpy as np
import torch
from collections import defaultdict
import wandb
import copy
from datasets import disable_caching
disable_caching()
import text_evaluation
... | 18,052 | 36.454357 | 159 | py |
iNLG | iNLG-main/code/model.py | import pdb
import torch
import torch.nn as nn
from torch.nn import functional as nnf
from typing import Tuple, Optional
from transformers import AutoConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.models.t5.modeling_t5 import T5LayerNorm, T5Model, T5ForConditionalGeneration, T5EncoderMo... | 16,452 | 43.831063 | 165 | py |
iNLG | iNLG-main/code/trainer.py | from transformers import Seq2SeqTrainer
import clip
import torch
import torch.nn as nn
torch.autograd.set_detect_anomaly(True)
from transformers.trainer_pt_utils import get_parameter_names
from info_nce import InfoNCE
import wandb
import utils
class SelfTrainer(Seq2SeqTrainer):
"""Self-implemented Trainer."""
... | 11,178 | 46.978541 | 123 | py |
L-MCL | L-MCL-main/main_imagenet_baseline.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data.distributed
import os
import shutil
import argparse
import numpy as np
import models
import ... | 13,155 | 40.898089 | 141 | py |
L-MCL | L-MCL-main/utils.py | import torch
import os
import torch.nn as nn
import numpy as np
import math
import torch.nn.functional as F
from bisect import bisect_right
import logging
__all__ = ['cal_param_size', 'cal_multi_adds', 'get_data_folder', 'CrossEntropyLoss_label_smooth',
'adjust_lr', 'DistillKL', 'set_logger']
def cal_pa... | 4,645 | 26.820359 | 107 | py |
L-MCL | L-MCL-main/main_layer_mcl_imagenet_meta.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data.distributed
import os
import shutil
import argparse
import numpy as np
import models
import ... | 20,310 | 42.123142 | 139 | py |
L-MCL | L-MCL-main/main_layer_mcl_cifar_meta.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import os
import shutil
import argparse
import numpy as np
import models
import torchvision
import torchvision.transforms as transforms
from utils import cal_param_size, cal_multi_adds,... | 19,656 | 40.91258 | 171 | py |
L-MCL | L-MCL-main/main_cifar_baseline.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import os
import shutil
import argparse
import numpy as np
import models
import torchvision
import torchvision.transforms as transforms
from utils import cal_param_size, cal_multi_adds,... | 10,197 | 37.923664 | 137 | py |
L-MCL | L-MCL-main/dataset/common_functions.py | import collections
import glob
import logging
import os
import re
import numpy as np
import scipy.stats
import torch
LOGGER_NAME = "PML"
LOGGER = logging.getLogger(LOGGER_NAME)
NUMPY_RANDOM = np.random
COLLECT_STATS = True
def set_logger_name(name):
global LOGGER_NAME
global LOGGER
LOGGER_NAME = name
... | 14,474 | 27.720238 | 88 | py |
L-MCL | L-MCL-main/dataset/class_sampler.py | import torch
from torch.utils.data.sampler import Sampler
import sys
import os
from . import common_functions as c_f
# modified from
# https://raw.githubusercontent.com/bnulihaixia/Deep_metric/master/utils/sampler.py
class MPerClassSampler(Sampler):
"""
At every iteration, this will return m samples per clas... | 2,570 | 37.954545 | 86 | py |
L-MCL | L-MCL-main/dataset/imagenet.py | """
get data loaders
"""
from __future__ import print_function
import os
import torch
import numpy as np
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
import torch.utils.data.distributed
class ImageFolderSample(datasets.ImageFolder):
""": Folder datas... | 7,352 | 35.949749 | 110 | py |
L-MCL | L-MCL-main/models/resnet_imagenet.py | import torch
import torch.nn as nn
__all__ = [
'resnet18_imagenet',
'resnet34_imagenet',
'resnet50_imagenet',
]
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 7,660 | 34.142202 | 97 | py |
L-MCL | L-MCL-main/models/hcgnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
import math
__all__ = ['hcgnet_A1_cifar', 'hcgnet_A2_cifar']
'''
Yang et al. Gated Convolutional Networks with Hybrid Connectivity for Image Classification. AAAI-2020.
https://github.com/winycg/HC... | 11,234 | 45.8125 | 115 | py |
L-MCL | L-MCL-main/models/lmcl_hcgnet_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import numpy as np
import math
__all__ = ['lmcl_hcgnet_A1_cifar', 'lmcl_hcgnet_A2_cifar']
'''
Yang et al. Gated Convolutional Networks with Hybrid Connectivity for Image Classification. AAAI-2020.
https://github.com... | 15,237 | 44.08284 | 117 | py |
L-MCL | L-MCL-main/models/wrn_cifar.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn_16_2_cifar', 'wrn_40_2_cifar', 'wrn_28_4_cifar']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm... | 4,567 | 36.752066 | 116 | py |
L-MCL | L-MCL-main/models/lmcl_hetero_imagenet.py | import torch.nn as nn
import torch.nn.functional as F
import math
import sys
sys.path.append('..')
from .lmcl_resnet_imagenet import lmcl_resnet18_imagenet, lmcl_resnet50_imagenet
from .lmcl_shufflenetv2_imagenet import lmcl_ShuffleNetV2_1x_imagenet
__all__ = ['lmcl_res18_res50_imagenet', 'lmcl_res18_shufflenetv2_1x... | 1,444 | 31.111111 | 100 | py |
L-MCL | L-MCL-main/models/lmcl_resnet_cifar.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['lmcl_resnet56_cifa... | 11,155 | 34.415873 | 139 | py |
L-MCL | L-MCL-main/models/lmcl_shufflenetv2_imagenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['lmcl_ShuffleNetV2_05x_imagenet', 'lmcl_ShuffleNetV2_1x_imagenet']
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
... | 10,240 | 37.939163 | 99 | py |
L-MCL | L-MCL-main/models/lmcl_hetero_cifar.py | import torch.nn as nn
import torch.nn.functional as F
import math
import sys
sys.path.append('..')
from .lmcl_resnet_cifar import lmcl_resnet56_cifar, lmcl_resnet110_cifar, lmcl_resnet32_cifar
from .lmcl_wrn_cifar import lmcl_wrn_16_2_cifar, lmcl_wrn_40_2_cifar, lmcl_wrn_28_4_cifar
from .lmcl_shufflenetv2_cifar import... | 2,667 | 39.424242 | 95 | py |
L-MCL | L-MCL-main/models/utils.py |
import os
import sys
import time
import math
import operator
from functools import reduce
import torch.nn as nn
import torch
import torch.nn.init as init
def cal_param_size(model):
return sum([i.numel() for i in model.parameters()])
count_ops = 0
def measure_layer(layer, x, multi_add=1):
delta_ops = 0
... | 2,210 | 23.842697 | 86 | py |
L-MCL | L-MCL-main/models/shufflenetv2_cifar.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['ShuffleNetV2_05x_cifar', 'ShuffleNetV2_1x_cifar']
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
'''Channel shu... | 5,893 | 33.670588 | 99 | py |
L-MCL | L-MCL-main/models/aggregator.py | import torch.nn as nn
import torch.nn.functional as F
import math
import torch
class Aggregator(nn.Module):
def __init__(self, dim_in, number_stage, number_net):
super(Aggregator, self).__init__()
self.number_stage = number_stage
self.number_net = number_net
for i in range(self.num... | 1,073 | 36.034483 | 76 | py |
L-MCL | L-MCL-main/models/shufflenetv2_imagenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['ShuffleNetV2_05x_imagenet', 'ShuffleNetV2_1x_imagenet']
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
'''Chan... | 5,927 | 34.497006 | 99 | py |
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