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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dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/ins_test.py | import torch
import torch.nn as nn
import torchvision.models as models
from torch.nn import functional as F
class ReNet(nn.Module):
def __init__(self, n_input, n_units, patch_size=(1, 1), usegpu=True):
super(ReNet, self).__init__()
self.patch_size_height = int(patch_size[0])
self.patch_s... | 7,940 | 33.376623 | 77 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/dense.py | from collections import OrderedDict
import torch
import torch.nn as nn
from .bn import ABN
class DenseModule(nn.Module):
def __init__(self, in_channels, growth, layers, bottleneck_factor=4, norm_act=ABN, dilation=1):
super(DenseModule, self).__init__()
self.in_channels = in_channels
self... | 1,414 | 32.690476 | 113 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/residual.py | from collections import OrderedDict
import torch.nn as nn
from .bn import ABN
class IdentityResidualBlock(nn.Module):
def __init__(self,
in_channels,
channels,
stride=1,
dilation=1,
groups=1,
norm_act=ABN,
... | 3,522 | 38.58427 | 118 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/functions.py | import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from . import _ext
# Activation names
ACT_LEAKY_RELU = "leaky_relu"
ACT_ELU = "elu"
ACT_NONE = "none"
def _check(fn, *args, **kwargs):
success = fn(*args, **kwargs)
if not success:
... | 10,357 | 31.778481 | 102 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/misc.py | import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
return inputs.view((in_size[0], in_size[1], -... | 336 | 27.083333 | 72 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/bn.py | from collections import OrderedDict, Iterable
from itertools import repeat
try:
# python 3
from queue import Queue
except ImportError:
# python 2
from Queue import Queue
import torch
import torch.nn as nn
import torch.autograd as autograd
try:
from .functions import inplace_abn, inplace_abn_sync
... | 7,785 | 34.552511 | 115 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/build.py | import os
from torch.utils.ffi import create_extension
sources = ['src/lib_cffi.cpp']
headers = ['src/lib_cffi.h']
extra_objects = ['src/bn.o']
with_cuda = True
this_file = os.path.dirname(os.path.realpath(__file__))
extra_objects = [os.path.join(this_file, fname) for fname in extra_objects]
ffi = create_extension(... | 543 | 20.76 | 75 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/modules/_ext/__init__.py |
from torch.utils.ffi import _wrap_function
from .__ext import lib as _lib, ffi as _ffi
__all__ = []
def _import_symbols(locals):
for symbol in dir(_lib):
fn = getattr(_lib, symbol)
if callable(fn):
locals[symbol] = _wrap_function(fn, _ffi)
else:
locals[symbol] = fn
... | 378 | 22.6875 | 53 | py |
dsb2018_topcoders | dsb2018_topcoders-master/albu/src/pytorch_zoo/inplace_abn/models/wider_resnet.py | import sys
from collections import OrderedDict
from functools import partial
import torch.nn as nn
from ..modules import IdentityResidualBlock, ABN, GlobalAvgPool2d
class WiderResNet(nn.Module):
def __init__(self,
structure,
in_channels,
norm_act=ABN,
... | 3,084 | 31.473684 | 120 | py |
DeepSDF | DeepSDF-main/generate_training_meshes.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import argparse
import json
import numpy as np
import os
import torch
import deep_sdf
import deep_sdf.workspace as ws
def code_to_mesh(experiment_directory, checkpoint, keep_normalized=False):
specs_filename = os.path.join(experimen... | 3,974 | 27.392857 | 91 | py |
DeepSDF | DeepSDF-main/plot_log.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import deep_sdf
import deep_sdf.workspace as ws
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-... | 2,974 | 27.333333 | 87 | py |
DeepSDF | DeepSDF-main/train_deep_sdf.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import torch
import torch.utils.data as data_utils
import signal
import sys
import os
import logging
import math
import json
import time
import deep_sdf
import deep_sdf.workspace as ws
class LearningRateSchedule:
def get_learning_rat... | 17,127 | 27.932432 | 88 | py |
DeepSDF | DeepSDF-main/reconstruct.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import argparse
import json
import logging
import os
import random
import time
import torch
import deep_sdf
import deep_sdf.workspace as ws
def reconstruct(
decoder,
num_iterations,
latent_size,
test_sdf,
stat,
cl... | 8,410 | 28.204861 | 88 | py |
DeepSDF | DeepSDF-main/networks/deep_sdf_decoder.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import torch.nn as nn
import torch
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(
self,
latent_size,
dims,
dropout=None,
dropout_prob=0.0,
norm_layers=(),
... | 3,353 | 29.490909 | 82 | py |
DeepSDF | DeepSDF-main/deep_sdf/workspace.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import json
import os
import torch
model_params_subdir = "ModelParameters"
optimizer_params_subdir = "OptimizerParameters"
latent_codes_subdir = "LatentCodes"
logs_filename = "Logs.pth"
reconstructions_subdir = "Reconstructions"
reconstruc... | 5,048 | 24.371859 | 82 | py |
DeepSDF | DeepSDF-main/deep_sdf/utils.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import logging
import torch
def add_common_args(arg_parser):
arg_parser.add_argument(
"--debug",
dest="debug",
default=False,
action="store_true",
help="If set, debugging messages will be printe... | 1,643 | 25.095238 | 75 | py |
DeepSDF | DeepSDF-main/deep_sdf/data.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import glob
import logging
import numpy as np
import os
import random
import torch
import torch.utils.data
import deep_sdf.workspace as ws
def get_instance_filenames(data_source, split):
npzfiles = []
for dataset in split:
... | 4,980 | 27.959302 | 87 | py |
DeepSDF | DeepSDF-main/deep_sdf/mesh.py | #!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import logging
import numpy as np
import plyfile
import skimage.measure
import time
import torch
import deep_sdf.utils
def create_mesh(
decoder, latent_vec, filename, N=256, max_batch=32 ** 3, offset=None, scale=None
):
start = t... | 4,089 | 28.214286 | 95 | py |
sRGB-TIR | sRGB-TIR-main/inference_batch.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
from utils import get_config, get_data_loader_folder, pytorch03_to_pytorch04, load_inception
from trainer i... | 7,300 | 44.347826 | 132 | py |
sRGB-TIR | sRGB-TIR-main/LoG_loss.py | from __future__ import print_function
from __future__ import division
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim as optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.... | 3,467 | 28.389831 | 104 | py |
sRGB-TIR | sRGB-TIR-main/utils.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
#from torch.utils.serialization import load_lua
import torchfile
from torch.utils.data import DataLoader
from networks import Vgg16
from torch.au... | 19,790 | 49.746154 | 141 | py |
sRGB-TIR | sRGB-TIR-main/data.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.utils.data as data
import os.path
def default_loader(path):
return Image.open(path).convert('RGB')
def default_flist_reader(f... | 3,942 | 29.099237 | 105 | py |
sRGB-TIR | sRGB-TIR-main/networks.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from torch import nn
from torch.autograd import Variable
import torch
import torch.nn.functional as F
try:
from itertools import izip as zip
... | 23,069 | 39.052083 | 157 | py |
sRGB-TIR | sRGB-TIR-main/log_visualize.py | from __future__ import print_function
from __future__ import division
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim as optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.... | 3,811 | 26.228571 | 104 | py |
sRGB-TIR | sRGB-TIR-main/train.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from utils import get_all_data_loaders, prepare_sub_folder, write_html, write_loss, get_config, write_2images, Timer
import argparse
from torch.a... | 4,383 | 43.734694 | 120 | py |
sRGB-TIR | sRGB-TIR-main/trainer.py | """
Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from networks import AdaINGen, MsImageDis, VAEGen
from utils import weights_init, get_model_list, vgg_preprocess, load_vgg16, get_scheduler
from ... | 39,378 | 50.141558 | 117 | py |
SIFRank | SIFRank-master/embeddings/sent_emb_sif.py | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "Sponge"
# Date: 2019/6/19
import numpy
import torch
import nltk
from nltk.corpus import stopwords
english_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
stop_words = set(stopwords.words("english"))
wnl=nltk.W... | 12,578 | 38.432602 | 158 | py |
SIFRank | SIFRank-master/model/method.py | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "Sponge"
# Date: 2019/6/19
import numpy as np
import nltk
from nltk.corpus import stopwords
from model import input_representation
import torch
wnl=nltk.WordNetLemmatizer()
stop_words = set(stopwords.words("english"))
def cos_sim_gpu(x,y):
assert x.sh... | 6,850 | 30.283105 | 117 | py |
ivs-demo | ivs-demo-master/interaction_net.py | from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import cv2
import matplotlib.pyplot a... | 11,028 | 36.13468 | 130 | py |
ivs-demo | ivs-demo-master/propagation_net.py | from __future__ import division
import torch
from torch.autograd import Variable
from torch.nn import Parameter
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import... | 11,258 | 35.674267 | 130 | py |
ivs-demo | ivs-demo-master/utils.py | from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import matplotlib.pyplot as plt
from ... | 6,241 | 31.852632 | 131 | py |
ivs-demo | ivs-demo-master/model.py | from __future__ import division
import torch
from torch.autograd import Variable
from torch.utils import data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
# general libs
import cv2
import matplotlib.pyplot a... | 5,056 | 37.9 | 180 | py |
BNNAS | BNNAS-main/thop/count_hooks.py | import argparse
import torch
import torch.nn as nn
import numpy as np
multiply_adds = 1
def count_ABN(m, x, y):
x = x[0]
# bn
nelements = x.numel()
total_ops = 4 * nelements
m.total_ops = torch.Tensor([int(total_ops)])
for p in m.parameters():
m.total_params += torch.Tensor([p.numel()... | 4,575 | 26.733333 | 96 | py |
BNNAS | BNNAS-main/thop/profile.py | import torch
import torch.nn as nn
from .count_hooks import *
register_hooks = {
nn.Conv1d: count_convNd,
nn.Conv2d: count_convNd,
nn.Conv3d: count_convNd,
nn.ConvTranspose2d: count_convtranspose2d,
# nn.BatchNorm1d: count_bn,
# nn.BatchNorm2d: count_bn,
# nn.BatchNorm3d: count_bn,
# n... | 2,185 | 25.02381 | 71 | py |
BNNAS | BNNAS-main/SPOS/retrain/train_from_scratch.py | import os
import sys
import numpy as np
import time
import torch
import glob
import random
import logging
import argparse
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from model import Network
import pickle
from config import confi... | 8,985 | 37.900433 | 130 | py |
BNNAS | BNNAS-main/SPOS/retrain/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResidua... | 2,349 | 36.301587 | 129 | py |
BNNAS | BNNAS-main/SPOS/retrain/model.py | import torch.nn as nn
import math
from torch_blocks import *
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, ou... | 3,353 | 31.882353 | 93 | py |
BNNAS | BNNAS-main/SPOS/retrain/eval-SPOS/scripts/train_from_scratch.py | import os
import sys
import numpy as np
import time
import torch
import glob
import random
import logging
import argparse
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from model import Network
import pickle
from config import confi... | 8,986 | 37.904762 | 130 | py |
BNNAS | BNNAS-main/SPOS/retrain/eval-SPOS/scripts/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResidua... | 2,349 | 36.301587 | 129 | py |
BNNAS | BNNAS-main/SPOS/retrain/eval-SPOS/scripts/model.py | import torch.nn as nn
import math
from torch_blocks import *
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, ou... | 3,353 | 31.882353 | 93 | py |
BNNAS | BNNAS-main/SPOS/supernet/main.py | import os
import sys
import time
import numpy as np
import torch
import argparse
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from super_model import SuperNetwork
from train import train
from config import config
import functools
print=functools.partial(print,flush=Tru... | 5,086 | 40.357724 | 131 | py |
BNNAS | BNNAS-main/SPOS/supernet/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResi... | 2,309 | 36.868852 | 129 | py |
BNNAS | BNNAS-main/SPOS/supernet/super_model.py | import torch.nn as nn
import math
from torch_blocks import *
import copy
import pdb
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
... | 3,509 | 31.803738 | 93 | py |
BNNAS | BNNAS-main/SPOS/supernet/train.py | import os
import torch
from torch import nn
from torch.autograd import Variable
import time
import numpy as np
from config import config
import copy
import functools
print=functools.partial(print,flush=True)
from pdb import set_trace
import sys
sys.path.append("../..")
from utils import *
def train(train_dataprovider,... | 2,229 | 34.396825 | 129 | py |
BNNAS | BNNAS-main/SPOS/search/main.py | import os
import sys
import time
import numpy as np
import torch
import argparse
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from super_model import SuperNetwork
from train import *
from search import *
from config import config
import ... | 4,246 | 39.836538 | 134 | py |
BNNAS | BNNAS-main/SPOS/search/tester.py | import torch
import sys
from imagenet_dataset import get_train_dataprovider, get_val_dataprovider
# sys.path.append("../..")
# from utils import *
import tqdm
from pdb import set_trace
assert torch.cuda.is_available()
train_dataprovider, val_dataprovider = None, None
def accuracy(output, target, topk=(1,)):
maxk... | 3,356 | 27.449153 | 84 | py |
BNNAS | BNNAS-main/SPOS/search/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResi... | 2,309 | 36.868852 | 129 | py |
BNNAS | BNNAS-main/SPOS/search/ea.py | import os
import sys
import time
import glob
import numpy as np
import pickle
import torch
import logging
import argparse
import torch
import random
from pdb import set_trace
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
np.random.seed(0)
random.seed(0)
torch.backends.cudnn.deterministic = True
from super_model ... | 10,468 | 32.99026 | 168 | py |
BNNAS | BNNAS-main/SPOS/search/super_model.py | import torch.nn as nn
import math
from torch_blocks import *
import copy
import pdb
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
... | 2,896 | 31.920455 | 93 | py |
BNNAS | BNNAS-main/SPOS/search/imagenet_dataset.py | import os
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.utils.data as data
import cv2
import tarfile
import PIL
from PIL import Image
import tqdm
class OpencvResize(object):
def __init__(self, size=256):
... | 3,666 | 26.992366 | 115 | py |
BNNAS | BNNAS-main/SPOS/search/eval.py | import os
import torch
import pickle
def main():
info = torch.load('log/ea_results.pth.tar')['vis_dict']
cands = sorted([cand for cand in info if 'err' in info[cand]],
key=lambda cand: info[cand]['err'])[:10]
for cand in cands:
print(cand, info[cand]['err'])
if __name__ == '_... | 340 | 20.3125 | 66 | py |
BNNAS | BNNAS-main/SPOS/search/train.py | import os
import torch
from torch import nn
import torch.nn.functional as F
from datetime import datetime
from torch.autograd import Variable
import time
import numpy as np
from config import config
import random
import functools
print=functools.partial(print,flush=True)
import sys
sys.path.append("../..")
from utils ... | 3,286 | 36.781609 | 128 | py |
BNNAS | BNNAS-main/utils/nas_utils.py | import os
import shutil
import numpy as np
import torch
import torch.nn as nn
import math
import joblib
from torch.autograd import Variable
from collections import defaultdict
import torch.distributed as dist
import copy
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon):
super(C... | 10,709 | 29.864553 | 126 | py |
BNNAS | BNNAS-main/utils/flops_test_blocks_gpu.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
op_keys = [
'PreProcessing',
'mobilenet_3x3_ratio_3',
'mobilenet_3x3_ratio_6',
'mobilenet_5x5_ratio_3',
'mobilenet_5x5_rat... | 3,921 | 35.314815 | 129 | py |
BNNAS | BNNAS-main/utils/flops_test_blocks_cpu.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
op_keys = [
'PreProcessing',
'mobilenet_3x3_ratio_3',
'mobilenet_3x3_ratio_6',
'mobilenet_5x5_ratio_3',
'mobilenet_5x5_rat... | 3,539 | 35.122449 | 125 | py |
BNNAS | BNNAS-main/utils/imagenet.py | import os
import numpy as np
import torch
import torch.nn as nn
import cv2
import random
import PIL
from PIL import Image
from torch.utils.data import Sampler
import torchvision.transforms as transforms
import math
import torchvision.datasets as datasets
from pdb import set_trace
## data augmentation functions
class Op... | 7,060 | 33.276699 | 115 | py |
BNNAS | BNNAS-main/BNNAS/retrain/train_from_scratch.py | import os
import sys
import numpy as np
import time
import torch
import glob
import random
import logging
import argparse
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from model import Network
import pickle
from config import confi... | 8,985 | 37.900433 | 130 | py |
BNNAS | BNNAS-main/BNNAS/retrain/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResidua... | 2,349 | 36.301587 | 129 | py |
BNNAS | BNNAS-main/BNNAS/retrain/model.py | import torch.nn as nn
import math
from torch_blocks import *
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, ou... | 3,353 | 31.882353 | 93 | py |
BNNAS | BNNAS-main/BNNAS/retrain/eval-SPOS/scripts/train_from_scratch.py | import os
import sys
import numpy as np
import time
import torch
import glob
import random
import logging
import argparse
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from model import Network
import pickle
from config import confi... | 8,985 | 37.900433 | 130 | py |
BNNAS | BNNAS-main/BNNAS/retrain/eval-SPOS/scripts/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResidua... | 2,349 | 36.301587 | 129 | py |
BNNAS | BNNAS-main/BNNAS/retrain/eval-SPOS/scripts/model.py | import torch.nn as nn
import math
from torch_blocks import *
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, ou... | 3,353 | 31.882353 | 93 | py |
BNNAS | BNNAS-main/BNNAS/supernet/main.py | import os
import sys
import time
import numpy as np
import torch
import argparse
import torch.backends.cudnn as cudnn
from super_model import SuperNetwork
from train import train
from config import config
import functools
print=functools.partial(print,flush=True)
import apex
sys.path.append("../..")
from utils imp... | 5,154 | 38.96124 | 131 | py |
BNNAS | BNNAS-main/BNNAS/supernet/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResi... | 2,309 | 36.868852 | 129 | py |
BNNAS | BNNAS-main/BNNAS/supernet/super_model.py | import torch.nn as nn
import math
from torch_blocks import *
import copy
import pdb
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
... | 3,509 | 31.803738 | 93 | py |
BNNAS | BNNAS-main/BNNAS/supernet/train.py | import os
import torch
from torch import nn
from torch.autograd import Variable
import time
import numpy as np
from config import config
import copy
import functools
print=functools.partial(print,flush=True)
from pdb import set_trace
import sys
sys.path.append("../..")
from utils import *
def train(train_dataprovider,... | 2,230 | 33.859375 | 129 | py |
BNNAS | BNNAS-main/BNNAS/search/torch_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math
from config import config
blocks_keys = config.blocks_keys
blocks_dict = {
'mobilenet_3x3_ratio_3':lambda inp, oup, stride : InvertedResi... | 2,309 | 36.868852 | 129 | py |
BNNAS | BNNAS-main/BNNAS/search/ea.py | import os
import sys
import time
import numpy as np
import pickle
import torch
import random
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
np.random.seed(0)
random.seed(0)
torch.backends.cudnn.deterministic = True
from super_model import SuperNetwork
from config import config
import sys
sys.setrecursionlimit(1... | 10,921 | 33.024922 | 168 | py |
BNNAS | BNNAS-main/BNNAS/search/super_model.py | import torch.nn as nn
import math
from torch_blocks import *
import copy
import pdb
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
... | 2,896 | 31.920455 | 93 | py |
BNNAS | BNNAS-main/BNNAS/search/eval.py | import os
import torch
import pickle
def main():
info = torch.load('log/ea_results.pth.tar')['vis_dict']
cands = sorted([cand for cand in info if 'err' in info[cand]],
key=lambda cand: info[cand]['err'])[:10]
for cand in cands:
print(cand, info[cand]['err'])
if __name__ == '_... | 340 | 20.3125 | 66 | py |
fnn-release | fnn-release/src/keras_to_fnn.py | """! @brief Interface module between keras and FNN.
@details This package provides the \ref keras_file_to_txt and
\ref keras_to_txt functions to convert a keras model
into a txt file which can be read by FNN.
Programmatic usage
------------------
Use the \ref keras_file_to_txt function as follows to convert a `.h5` ... | 9,773 | 37.940239 | 109 | py |
fnn-release | fnn-release/test/pyfnn.py | #!/usr/bin/env python
from abc import ABC, abstractmethod
import numpy as np
#--------------------------------------------------
# activation functions
#--------------------------------------------------
def construct_activation(name, **kwargs):
activation_class = dict(
linear=LinearActivation,
... | 10,189 | 27.948864 | 131 | py |
fnn-release | fnn-release/test/test_1.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from pyfnn import fromfile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
def unit_test(Ne):
Nx = 5
Ni = 6
Ny = 4
alpha = np.random.randn(Nx)
beta = np.rand... | 2,181 | 27.337662 | 97 | py |
fnn-release | fnn-release/test/test_4.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,669 | 28.666667 | 98 | py |
fnn-release | fnn-release/test/test_9.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,948 | 27.631068 | 104 | py |
fnn-release | fnn-release/test/test_7.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,398 | 26.895349 | 98 | py |
fnn-release | fnn-release/test/test_6.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,420 | 26.511364 | 98 | py |
fnn-release | fnn-release/test/test_5.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,804 | 28.526316 | 112 | py |
fnn-release | fnn-release/test/test_10.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,953 | 27.679612 | 104 | py |
fnn-release | fnn-release/test/test_8.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from pyfnn import fromfile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
def unit_test(Ne):
Nx = 5
Ni = 6
Ny = 4
alpha = np.random.randn(Nx)
beta = np.rand... | 2,255 | 27.923077 | 99 | py |
fnn-release | fnn-release/test/keras_to_fnn.py | ../src/keras_to_fnn.py | 22 | 22 | 22 | py |
fnn-release | fnn-release/test/test_3.py |
import numpy as np
import tensorflow as tf
from keras_to_fnn import keras_file_to_txt
from subprocess import run as srun
from pyfnn import fromfile
from scipy.io import FortranFile
from tqdm import trange
# set double precision in tensorflow
tf.keras.backend.set_floatx('float64')
# use double format in fortan
fortra... | 2,353 | 26.694118 | 98 | py |
pyqubo | pyqubo-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 ------------------------------------------------------------... | 6,160 | 27.655814 | 79 | py |
ContextMonitor | ContextMonitor-master/dsn2020/evaluate_pipeline.py | import os, sys, glob, pickle, time
#from pylab import rcParams
import numpy as np
import pandas as pd
import keras as K
from keras.models import Model, load_model
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, precision_recall_curve, f1_score, roc_curve, auc, jaccard_scor... | 26,358 | 53.348454 | 343 | py |
ContextMonitor | ContextMonitor-master/dsn2020/evaluate_pipeline_all.py | import os, sys, glob, pickle, time
import numpy as np
import pandas as pd
import keras as K
from keras.models import Model, load_model
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, precision_recall_curve, f1_score, roc_curve, auc, jaccard_score
from experimental_setup im... | 17,041 | 50.023952 | 218 | py |
ContextMonitor | ContextMonitor-master/dsn2020/lstm_sequence_nonpadded.py | import os, glob, sys, _pickle
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing import sequence
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
class needlePassing:
def __init__(self, dataPath, task):
... | 12,525 | 41.033557 | 190 | py |
ContextMonitor | ContextMonitor-master/dsn2020/summarizeConfMatrix.py | import numpy as np
import pandas as pd
import glob, os, sys, math
from keras.models import load_model
class summarizeConf:
def __init__(self, path):
self.data_path = path
def iterategesturePaths(self, clf_mode, kinvars, model_num):
result_dict = dict()
file_keys = list()
coun... | 9,332 | 48.909091 | 228 | py |
ContextMonitor | ContextMonitor-master/dsn2020/vae_experimentalsetup.py | import os, sys, time , glob
from sys import argv
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from lstm_sequence_nonpadded import needlePassing
from experimental_setup import experimentalSetup
from lstm_vaesuturing import lstmVAE
from vae_keras import VAE
from keras import bac... | 8,263 | 39.70936 | 139 | py |
ContextMonitor | ContextMonitor-master/dsn2020/lstm_vaesuturing.py | import os, sys, glob, math, _pickle, time, gc
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pylab import rcParams
import seaborn as sns
import tensorflow as tf
from keras import optimizers, Sequential
from keras.models import Model, load_model
from keras.utils import plot_model
from keras... | 34,892 | 53.265941 | 225 | py |
ContextMonitor | ContextMonitor-master/dsn2020/losorelabelledSuboptimals.py | import os, sys, glob, pickle, math
import numpy as np
import pandas as pd
import keras as K
import scipy.stats as ss
from scipy.stats import multivariate_normal
from scipy.spatial import distance
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from sklearn.model_selecti... | 20,257 | 54.19891 | 225 | py |
ContextMonitor | ContextMonitor-master/dsn2020/lstm_experimentalsetup.py | import os, glob, sys, _pickle, time, math, gc
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.models import Model, load_model
from keras import optimizers, Sequential
from keras.utils import plot_model
from keras import layers #Dense, LSTM, RepeatVector, TimeDistributed, Dropout, Masking, Batc... | 10,185 | 45.940092 | 166 | py |
ContextMonitor | ContextMonitor-master/dsn2020/vae_keras.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf
from keras.layers import Lambda, Input, Dense, LSTM, Flatten, TimeDistributed, Dropout, RepeatVector
from keras.models import Model
from keras.datasets import mnist
fr... | 14,991 | 45.414861 | 171 | py |
ContextMonitor | ContextMonitor-master/dsn2020/visualizemaps.py | import os, sys, glob
import keras as K
from keras.models import Model, load_model
class visualizeMaps:
def __init__(self, path):
self.data_path = path
def get_model(self):
"""
Loading model to check activation
"""
model_path = "/home/student/Documents/samin/detection/... | 833 | 31.076923 | 165 | py |
lmc-atomi | lmc-atomi-main/jax/prox_lmc_jax.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
from fastprogress import progress_bar
import fire
import random
impo... | 17,453 | 33.838323 | 180 | py |
lmc-atomi | lmc-atomi-main/jax/prox_sgld.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
import itertools
from fastprogress import progress_bar
from typing im... | 21,334 | 34.79698 | 150 | py |
lmc-atomi | lmc-atomi-main/jax/lmc_jax.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
import itertools
from fastprogress import progress_bar
from typing im... | 13,901 | 36.072 | 170 | py |
lmc-atomi | lmc-atomi-main/jax/prox_jax.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
import jax.numpy as jnp
from jax.scipy.linalg import sqrtm
from jax.scipy.optimize import minimize
def prox_laplace(x, gamma):
return jnp.sign(x) * jnp.maximum(jnp.abs(x) - gamma, 0)
def prox_gaussian(x, gamma):
re... | 2,672 | 27.43617 | 149 | py |
lmc-atomi | lmc-atomi-main/jax/sgld.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
import itertools
from fastprogress import progress_bar
from typing im... | 17,599 | 34.845214 | 150 | py |
lmc-atomi | lmc-atomi-main/jax/sgld_opt.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
import itertools
from fastprogress import progress_bar
from typing im... | 17,324 | 35.018711 | 150 | py |
lmc-atomi | lmc-atomi-main/jax/lmc_laplace_jax.py | # Copyright 2023 by Tim Tsz-Kit Lau
# MIT License
# To install JAX, see its documentations
# Install libraries: pip install -U numpy matplotlib scipy seaborn fire fastprogress SciencePlots scikit-image pylops pyproximal jax blackjax optax
import os
import itertools
from fastprogress import progress_bar
from typing im... | 1,496 | 24.372881 | 147 | py |
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