repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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pytorch-playground | pytorch-playground-master/svhn/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'svhn-data'))
num_workers = kwargs.setdefault('num_wor... | 1,565 | 34.590909 | 93 | py |
pytorch-playground | pytorch-playground-master/svhn/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/svhn/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch SVHN Example')
parser.add_argument('--ch... | 5,590 | 43.023622 | 125 | py |
pytorch-playground | pytorch-playground-master/stl10/model.py | import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
from utee import misc
from collections import OrderedDict
print = misc.logger.info
model_urls = {
'stl10': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/stl10-866321e9.pth',
}
class SVHN(nn.Module):
def __init__(self... | 2,071 | 30.876923 | 89 | py |
pytorch-playground | pytorch-playground-master/stl10/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from IPython import embed
import os
def get(batch_size, data_root='/mnt/local0/public_dataset/pytorch/', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'stl10-data'))
num_w... | 1,678 | 36.311111 | 101 | py |
pytorch-playground | pytorch-playground-master/stl10/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/stl10/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch SVHN Example')
parser.add_argument('--ch... | 5,473 | 42.102362 | 124 | py |
pytorch-playground | pytorch-playground-master/imagenet/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utee import misc
from collections import OrderedDict
__all__ = ['Inception3', 'inception_v3']
model_urls = {
'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}
def inception_v3(pretrained=Fals... | 11,908 | 34.549254 | 98 | py |
pytorch-playground | pytorch-playground-master/imagenet/resnet.py | import torch.nn as nn
import math
from utee import misc
from collections import OrderedDict
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytor... | 5,916 | 32.055866 | 109 | py |
pytorch-playground | pytorch-playground-master/imagenet/squeezenet.py | import math
import torch
import torch.nn as nn
from utee import misc
from collections import OrderedDict
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
model_urls = {
'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
'squeezenet1_1': 'https://download.pytorch.org... | 5,022 | 35.398551 | 101 | py |
pytorch-playground | pytorch-playground-master/imagenet/vgg.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
__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://download.pytorch.or... | 4,505 | 32.132353 | 113 | py |
pytorch-playground | pytorch-playground-master/imagenet/dataset.py | from utee import misc
import os
import os.path
import numpy as np
import joblib
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=False, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'imagenet-data'))
print("Building IMAGENET data loader, 50000 for train, 50000 f... | 1,927 | 29.603175 | 113 | py |
pytorch-playground | pytorch-playground-master/imagenet/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/imagenet/alexnet.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self... | 1,637 | 29.333333 | 84 | py |
pytorch-playground | pytorch-playground-master/mnist/model.py | import torch.nn as nn
from collections import OrderedDict
import torch.utils.model_zoo as model_zoo
from utee import misc
print = misc.logger.info
model_urls = {
'mnist': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/mnist-b07bb66b.pth'
}
class MLP(nn.Module):
def __init__(self, input_dims, n_hiddens, ... | 1,660 | 34.340426 | 85 | py |
pytorch-playground | pytorch-playground-master/mnist/dataset.py | from torch.utils.data import DataLoader
import torch
from torchvision import datasets, transforms
import os
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'mnist-data'))
kwargs.pop('input_size', None)
num_... | 1,398 | 41.393939 | 93 | py |
pytorch-playground | pytorch-playground-master/mnist/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/mnist/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--wd', type=float, default=0.... | 5,502 | 41.992188 | 125 | py |
pytorch-playground | pytorch-playground-master/cifar/model.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from IPython import embed
from collections import OrderedDict
from utee import misc
print = misc.logger.info
model_urls = {
'cifar10': 'http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/cifar10-d875770b.pth',
'cifar100': 'http://ml.cs.tsinghua.... | 2,809 | 36.972973 | 122 | py |
pytorch-playground | pytorch-playground-master/cifar/dataset.py | import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
def get10(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs):
data_root = os.path.expanduser(os.path.join(data_root, 'cifar10-data'))
num_workers = kwargs.setdefault('nu... | 2,937 | 40.380282 | 96 | py |
pytorch-playground | pytorch-playground-master/cifar/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/cifar/train.py | import argparse
import os
import time
from utee import misc
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import dataset
import model
from IPython import embed
parser = argparse.ArgumentParser(description='PyTorch CIFAR-X Example')
parser.add_argument('... | 5,777 | 42.119403 | 125 | py |
pytorch-playground | pytorch-playground-master/utee/quant.py | from torch.autograd import Variable
import torch
from torch import nn
from collections import OrderedDict
import math
from IPython import embed
def compute_integral_part(input, overflow_rate):
abs_value = input.abs().view(-1)
sorted_value = abs_value.sort(dim=0, descending=True)[0]
split_idx = int(overflow... | 6,302 | 32.705882 | 124 | py |
pytorch-playground | pytorch-playground-master/utee/misc.py | import cv2
import os
import shutil
import pickle as pkl
import time
import numpy as np
import hashlib
from IPython import embed
class Logger(object):
def __init__(self):
self._logger = None
def init(self, logdir, name='log'):
if self._logger is None:
import logging
if ... | 7,772 | 32.943231 | 114 | py |
pytorch-playground | pytorch-playground-master/utee/__init__.py | 0 | 0 | 0 | py | |
pytorch-playground | pytorch-playground-master/utee/selector.py | from utee import misc
import os
from imagenet import dataset
print = misc.logger.info
from IPython import embed
known_models = [
'mnist', 'svhn', # 28x28
'cifar10', 'cifar100', # 32x32
'stl10', # 96x96
'alexnet', # 224x224
'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', # 224x224
'resnet18', 'resnet3... | 5,245 | 29.5 | 80 | py |
pytorch-playground | pytorch-playground-master/script/convert.py | import os
import numpy as np
import tqdm
from utee import misc
import argparse
import cv2
import joblib
parser = argparse.ArgumentParser(description='Extract the ILSVRC2012 val dataset')
parser.add_argument('--in_file', default='val224_compressed.pkl', help='input file path')
parser.add_argument('--out_root', default=... | 1,337 | 25.76 | 113 | py |
coling2018-xling_argument_mining | coling2018-xling_argument_mining-master/code/annotationProjection/readDocs.py | import sys
def readDoc(fn,index0=1):
hh=[]
hd = {}
h=[]
for line in open(fn):
line = line.strip()
if line=="":
if h!=[]:
hh.append(h)
str=" ".join([x[0] for x in h])
hd[str] = h
h=[]
else:
x = line.split("\t")
word,label = x[index0],x[-1]
h.app... | 434 | 17.125 | 39 | py |
coling2018-xling_argument_mining | coling2018-xling_argument_mining-master/code/annotationProjection/projectArguments.py | import sys
from readDocs import readDoc as rd
# project argument spans from source to target document
# Steffen Eger
# 03/2018
# SAMPLE USAGE:
# python2 projectArguments.py train_full.dat test_full.dat dev_full.dat essays.aligned essays.aligned.bidirectional
#
# Inputs:
# $x_full.dat: train, test, dev annota... | 5,486 | 27.878947 | 132 | py |
checklist | checklist-master/setup.py | from setuptools import setup, find_packages
from setuptools.command.develop import develop
from setuptools.command.install import install
from setuptools.command.bdist_egg import bdist_egg
from setuptools.command.egg_info import egg_info
from setuptools.command.build_py import build_py
from subprocess import check_cal... | 3,011 | 33.62069 | 131 | py |
checklist | checklist-master/checklist/perturb.py | import numpy as np
import collections
import re
import os
import json
import pattern
from pattern.en import tenses
from .editor import recursive_apply, MunchWithAdd
def load_data():
cur_folder = os.path.dirname(__file__)
basic = json.load(open(os.path.join(cur_folder, 'data', 'lexicons', 'basic.json')))
na... | 21,734 | 36.217466 | 113 | py |
checklist | checklist-master/checklist/test_suite.py | import collections
from collections import defaultdict, OrderedDict
import dill
import json
from .abstract_test import load_test, read_pred_file
from .test_types import MFT, INV, DIR
from .viewer.suite_summarizer import SuiteSummarizer
class TestSuite:
def __init__(self, format_example_fn=None, print_fn=None):
... | 16,352 | 39.477723 | 136 | py |
checklist | checklist-master/checklist/test_types.py | from .abstract_test import AbstractTest
from .expect import Expect
class MFT(AbstractTest):
def __init__(self, data, expect=None, labels=None, meta=None, agg_fn='all',
templates=None, name=None, capability=None, description=None):
"""Minimum Functionality Test
Parameters
-... | 4,974 | 44.227273 | 92 | py |
checklist | checklist-master/checklist/editor.py | import collections
import itertools
import string
import numpy as np
import re
import copy
import os
import json
import munch
import pickle
import csv
from .viewer.template_editor import TemplateEditor
from .multilingual import multilingual_params, get_language_code
class MunchWithAdd(munch.Munch):
def __add__(se... | 29,139 | 37.041775 | 150 | py |
checklist | checklist-master/checklist/expect.py | import numpy as np
import itertools
def iter_with_optional(data, preds, confs, labels, meta, idxs=None):
# If this is a single example
if type(data) not in [list, np.array, np.ndarray]:
return [(data, preds, confs, labels, meta)]
if type(meta) not in [list, np.array, np.ndarray]:
meta = ite... | 18,549 | 35.089494 | 140 | py |
checklist | checklist-master/checklist/abstract_test.py | from abc import ABC, abstractmethod
import dill
from munch import Munch
import numpy as np
import inspect
from .expect import iter_with_optional, Expect
from .viewer.test_summarizer import TestSummarizer
def load_test(file):
dill._dill._reverse_typemap['ClassType'] = type
with open(file, 'rb') as infile:
... | 21,832 | 37.848754 | 155 | py |
checklist | checklist-master/checklist/multilingual.py | import collections
from iso639 import languages
def get_language_code(language):
to_try = [languages.name, languages.inverted, languages.part1]
l_to_try = [language.capitalize(), language.lower()]
for l in l_to_try:
for t in to_try:
if l in t:
if not t[l].part1:
... | 5,646 | 48.104348 | 83 | py |
checklist | checklist-master/checklist/pred_wrapper.py | import numpy as np
class PredictorWrapper:
@staticmethod
def wrap_softmax(softmax_fn):
"""Wraps softmax such that it outputs predictions and confidences
Parameters
----------
softmax_fn : fn
Takes lists of inputs, outputs softmax probabilities (2d np.array)
... | 1,310 | 27.5 | 99 | py |
checklist | checklist-master/checklist/__init__.py | 0 | 0 | 0 | py | |
checklist | checklist-master/checklist/text_generation.py | from transformers import AutoTokenizer, AutoModelForMaskedLM
import collections
import itertools
import numpy as np
import re
from transformers import GPT2Config
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from pattern.en import wordnet... | 15,163 | 44.951515 | 175 | py |
checklist | checklist-master/checklist/viewer/viewer.py | from .template_editor import TemplateEditor
from .test_summarizer import TestSummarizer
from .suite_summarizer import SuiteSummarizer | 133 | 43.666667 | 45 | py |
checklist | checklist-master/checklist/viewer/template_editor.py | import ipywidgets as widgets
from traitlets import Unicode, List, Dict
import os
import typing
import itertools
try:
from IPython.core.display import display, Javascript
except:
raise Exception("This module must be run in IPython.")
DIRECTORY = os.path.abspath(os.path.dirname(__file__))
# import logging
# lo... | 4,125 | 39.058252 | 123 | py |
checklist | checklist-master/checklist/viewer/fake_data.py | tag_dict = {'pos_adj': 'good', 'air_noun': 'flight', 'intens': 'very'}
raw_templates = [
['It', 'is', ['good', 'a:pos_adj'], ['flight', 'air_noun'], '.'],
['It', ['', 'a:mask'], ['very', 'a:intens'], ['good', 'pos_adj'], ['', 'mask'],'.']
]
suggests = [
['was', 'day'],
['been', 'day'],
['been', 'we... | 3,770 | 29.658537 | 96 | py |
checklist | checklist-master/checklist/viewer/suite_summarizer.py | import ipywidgets as widgets
from traitlets import Unicode, List, Dict
import os
import typing
from spacy.lang.en import English
from copy import deepcopy
try:
from IPython.core.display import display, Javascript
except:
raise Exception("This module must be run in IPython.")
DIRECTORY = os.path.abspath(os.path.... | 2,105 | 36.607143 | 87 | py |
checklist | checklist-master/checklist/viewer/__init__.py | def _jupyter_nbextension_paths():
return [{
'section': 'notebook',
'src': 'static',
'dest': 'viewer',
'require': 'viewer/extension'
}]
| 175 | 21 | 37 | py |
checklist | checklist-master/checklist/viewer/test_summarizer.py | import ipywidgets as widgets
from traitlets import Unicode, List, Dict
import os
import typing
from spacy.lang.en import English
from copy import deepcopy
try:
from IPython.core.display import display, Javascript
except:
raise Exception("This module must be run in IPython.")
DIRECTORY = os.path.abspath(os.path.... | 5,039 | 38.375 | 106 | py |
checklist | checklist-master/checklist/viewer/static/__init__.py | 0 | 0 | 0 | py | |
checklist | checklist-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 4,326 | 30.355072 | 102 | py |
CQMaxwell | CQMaxwell-main/RKRefErrorDatadelta10.py | import bempp.api
import numpy as np
import math
from RKconv_op import *
print("Bempp version used : " + bempp.api.__version__)
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def... | 11,402 | 34.194444 | 133 | py |
CQMaxwell | CQMaxwell-main/libVersions.py | import bempp.api
import scipy
import numpy
import matplotlib
print("Bempp version :", bempp.api.__version__)
print("Scipy version :", scipy.__version__)
print("Numpy version :", numpy.__version__)
print("Matplotlib version :", matplotlib.__version__)
| 251 | 27 | 53 | py |
CQMaxwell | CQMaxwell-main/RKconv_op.py |
class Conv_Operator:
import numpy as np
tol=10**-16
def __init__(self,apply_elliptic_operator,order=2):
self.order=order
self.delta=lambda zeta : self.char_functions(zeta,order)
self.apply_elliptic_operator=apply_elliptic_operator
def get_integration_parameters(self,N,T):
tol=self.tol
dt=(T*1.0)/N
L... | 8,340 | 27.467577 | 248 | py |
CQMaxwell | CQMaxwell-main/FramesAndConditions.py | import numpy as np
import bempp.api
import math
from RKconv_op import *
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def create_rhs(grid,N,T,m):
#grid=bempp.api.shapes.spher... | 10,911 | 33.1 | 113 | py |
CQMaxwell | CQMaxwell-main/RKRefErrorDatadelta01.py | import bempp.api
import numpy as np
import math
from RKconv_op import *
print("Bempp version used : " + bempp.api.__version__)
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def... | 11,463 | 34.058104 | 133 | py |
CQMaxwell | CQMaxwell-main/d10RKRefErrorData.py | import bempp.api
import numpy as np
import math
from RKconv_op import *
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def create_rhs(grid,dx,N,T,m):
# grid=bempp.api.shapes.cu... | 11,396 | 33.853211 | 133 | py |
CQMaxwell | CQMaxwell-main/Old Scripts/RKtemp.py | import bempp.api
import math
from RKconv_op import *
print("Bempp version used : " + bempp.api.__version__)
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def create_rhs(grid,dx... | 11,384 | 34.247678 | 133 | py |
CQMaxwell | CQMaxwell-main/Old Scripts/load-test.py | import scipy.io
import numpy as np
#v=1j*np.zeros((2,1))
#scipy.io.savemat('data/test.mat',dict(v=v))
mat_contents=scipy.io.loadmat('data/cond.mat')
freqCond=mat_contents['freqCond']
freqs = np.concatenate((freqCond[0,1:],np.conj(freqCond[0,1:])))
conds = np.concatenate((freqCond[1,1:],freqCond[1,1:]))
norms = np.conc... | 1,273 | 23.5 | 64 | py |
CQMaxwell | CQMaxwell-main/Old Scripts/test_RK.py |
import numpy as np
def freq_der(s,b):
return s**1*np.exp(-1*s)*b
import math
from RKconv_op import *
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def create_rhs(N,T,m):
i... | 2,333 | 24.933333 | 106 | py |
CQMaxwell | CQMaxwell-main/Old Scripts/MaxwellFrames.py | import bempp.api
import numpy as np
import math
from RKconv_op import *
def create_timepoints(c,N,T):
m=len(c)
time_points=np.zeros((1,m*N))
for j in range(m):
time_points[0,j:m*N:m]=c[j]*1.0/N*np.ones((1,N))+np.linspace(0,1-1.0/N,N)
return T*time_points
def create_rhs(grid,N,T,m):
#grid=bempp.api.shapes.spher... | 9,655 | 33 | 113 | py |
Semi-Online-KD | Semi-Online-KD-master/main.py | import argparse
import yaml
import os
import torch
from trainer import build_trainer
from utils.utils import save_code, save_opts
def main():
parser = argparse.ArgumentParser(description='KnowledgeDistillation')
parser.add_argument('--configs', '-c', dest='params', default='./configs/sokd.yaml')
parser.a... | 1,116 | 31.852941 | 88 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/base_trainer.py | from datetime import datetime
from tensorboardX import SummaryWriter
import os
import logging
from utils.utils import create_logger, output_process, fix_random
class BaseTrainer(object):
def __init__(self, experimental_name='debug', seed=None):
# BASE
self.current_time = datetime.now().strftime(... | 1,135 | 31.457143 | 93 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/vanilla.py | import torch.nn as nn
import torch
from tqdm import tqdm
from trainer.base_trainer import BaseTrainer
from models import model_dict
from utils.utils import count_parameters_in_MB, AverageMeter, accuracy, save_checkpoint
from dataset import get_dataloader
class Vanilla(BaseTrainer):
def __init__(self, params, exp... | 7,150 | 41.820359 | 131 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/__init__.py | from trainer.sokd import SemiOnlineKnowledgeDistillation
from trainer.vanilla import Vanilla
def build_trainer(**kwargs):
maps = dict(
sokd=SemiOnlineKnowledgeDistillation,
vanilla=Vanilla,
)
return maps[kwargs['distillation_type']](kwargs)
| 273 | 20.076923 | 56 | py |
Semi-Online-KD | Semi-Online-KD-master/trainer/sokd.py | import torch
from trainer.vanilla import Vanilla
from utils.utils import accuracy, AverageMeter, save_checkpoint
from kd_losses import SoftTarget
from models import model_dict
class SemiOnlineKnowledgeDistillation(Vanilla):
def __init__(self, params):
# Model
self.teacher_name = params.get('teach... | 8,214 | 46.212644 | 126 | py |
Semi-Online-KD | Semi-Online-KD-master/dataset/__init__.py | from torchvision import transforms
from torchvision import datasets
import torch
def get_dataset(data_name, data_path):
"""
Get dataset according to data name and data path.
"""
transform_train, transform_test = data_transform(data_name)
if data_name.lower() == 'cifar100':
train_dataset = ... | 1,750 | 38.795455 | 113 | py |
Semi-Online-KD | Semi-Online-KD-master/models/__init__.py | from .wrn import wrn_40_1, wrn_40_2
model_dict = {
'wrn_40_1': wrn_40_1,
'wrn_40_2': wrn_40_2
}
| 106 | 12.375 | 35 | py |
Semi-Online-KD | Semi-Online-KD-master/models/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from copy import deepcopy
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
... | 5,436 | 33.411392 | 116 | py |
Semi-Online-KD | Semi-Online-KD-master/kd_losses/st.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftTarget(nn.Module):
'''
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
'''
def __init__(self,... | 563 | 23.521739 | 53 | py |
Semi-Online-KD | Semi-Online-KD-master/kd_losses/__init__.py | from .st import SoftTarget
| 27 | 13 | 26 | py |
Semi-Online-KD | Semi-Online-KD-master/utils/utils.py | import logging
import colorlog
import os
import time
import shutil
import torch
import random
import numpy as np
from shutil import copyfile
def create_logger():
"""
Setup the logging environment
"""
log = logging.getLogger() # root logger
log.setLevel(logging.DEBUG)
format_str = '%(ascti... | 4,987 | 27.340909 | 85 | py |
Semi-Online-KD | Semi-Online-KD-master/utils/__init__.py | 0 | 0 | 0 | py | |
Simplified_DMC | Simplified_DMC-master/location_dmc.py | import argparse
import os
import torch
from torch.utils.data import DataLoader
from torch import optim
import numpy as np
from data.MUSIC_dataset import MUSIC_Dataset, MUSIC_AV_Classify
from model.base_model import resnet18
from model.dmc_model import DMC_NET
from sklearn import cluster, metrics
import numpy as np
f... | 8,957 | 41.254717 | 138 | py |
Simplified_DMC | Simplified_DMC-master/data/pair_video_audio.py | import os
import pdb
audio_dir = './MUSIC/solo/audio'
video_dir = './MUSIC/solo/video'
all_audios = os.listdir(audio_dir)
audios = [ audio for audio in all_audios if audio.endswith('.flac')]
all_videos = os.listdir(video_dir)
videos = [video for video in all_videos if video.endswith('.mp4')]
fid = open('solo_pair... | 469 | 22.5 | 69 | py |
Simplified_DMC | Simplified_DMC-master/data/MUSIC_dataset.py | import numpy as np
import librosa
from PIL import Image, ImageEnhance
import pickle
import random
import os
import torchvision.transforms as transforms
import json
import torch
def augment_image(image):
if(random.random() < 0.5):
image = image.transpose(Image.FLIP_LEFT_RIGHT)
enhancer = ImageEnhance.Br... | 9,784 | 42.29646 | 117 | py |
Simplified_DMC | Simplified_DMC-master/data/base_sampler.py | import torch
from torch.utils.data.sampler import Sampler
Class BaseSampler(Sampler):
def __init__(self):
super(BaseSampler,self).__init__()
def __len__(self):
def __iter__(self):
| 203 | 17.545455 | 44 | py |
Simplified_DMC | Simplified_DMC-master/data/cut_audios.py | import numpy as np
import librosa
import pickle
import os
import pdb
with open('data_indicator/music/solo/solo_pairs.txt','r') as fid:
audios = [line.strip().split(' ')[0] for line in fid.readlines()]
audio_dir = './MUSIC/solo/audio'
save_dir = './MUSIC/solo/audio_frames'
#def audio_extract(wav_name, sr=22000):
... | 1,685 | 33.408163 | 93 | py |
Simplified_DMC | Simplified_DMC-master/data/data_split.py | import os
import json
solo_videos = './MUSIC_label/MUSIC_solo_videos.json'
solo_videos = json.load(open(solo_videos, 'r'))
solo_videos = solo_videos['videos']
trains = []
vals = []
for _, item in solo_videos.items():
for i, vid in enumerate(item):
if i < 5:
vals.append(vid)
else:
... | 825 | 24.030303 | 71 | py |
Simplified_DMC | Simplified_DMC-master/data/__init__.py | 2 | 0 | 0 | py | |
Simplified_DMC | Simplified_DMC-master/data/cut_videos.py | import os
import cv2
import pdb
def video2frame(video_path, frame_save_path, frame_interval=1):
vid = cv2.VideoCapture(video_path)
fps = vid.get(cv2.CAP_PROP_FPS)
#pdb.set_trace()
success, image = vid.read()
count = 0
while success:
count +=1
if count % frame_interval == 0:
... | 2,377 | 32.492958 | 99 | py |
Simplified_DMC | Simplified_DMC-master/model/base_model.py | import torch
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'r... | 9,147 | 38.261803 | 106 | py |
Simplified_DMC | Simplified_DMC-master/model/audio_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Unet(nn.Module):
def __init__(self, fc_dim=64, num_downs=5, ngf=64, use_dropout=False):
super(Unet, self).__init__()
# construct unet structure
unet_block = UnetBlock(
ngf * 8, ngf * 8, input_nc=None,
... | 3,744 | 33.675926 | 74 | py |
Simplified_DMC | Simplified_DMC-master/model/vision_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Resnet(nn.Module):
def __init__(self, original_resnet):
super(Resnet, self).__init__()
self.features = nn.Sequential(
*list(original_resnet.children())[:-1])
# for param in self.features.parameters():
... | 4,152 | 27.445205 | 69 | py |
Simplified_DMC | Simplified_DMC-master/model/base_model_v1.py | import torch
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet101_2']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'r... | 9,008 | 38.169565 | 106 | py |
Simplified_DMC | Simplified_DMC-master/model/dmc_model.py | import torch
import torch.nn as nn
import random
class Cluster_layer(nn.Module):
def __init__(self, input_dim = 512, num_cluster=2, iters=4, beta=-30, **kwargs):
super(Cluster_layer, self).__init__()
self.input_dim = input_dim
self.num_cluster = num_cluster
self.iters = iters
... | 3,338 | 35.293478 | 152 | py |
Simplified_DMC | Simplified_DMC-master/model/__init__.py | 1 | 0 | 0 | py | |
synfeal | synfeal-main/utils.py | import numpy as np
import os
import cv2
import torch
import torch
import math
import yaml
from sklearn.metrics import mean_squared_error
from torchsummary import summary
from yaml.loader import SafeLoader
from colorama import Fore
from scipy.spatial.transform import Rotation as R
from models.loss_functions import Bet... | 7,993 | 29.51145 | 146 | py |
synfeal | synfeal-main/dataset.py | import cv2
import torch.utils.data as data
import numpy as np
import torch
import os
import yaml
from PIL import Image
from yaml.loader import SafeLoader
from utils import read_pcd, matrixToXYZ, matrixToQuaternion, normalize_quat
# pytorch datasets: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
c... | 4,547 | 34.53125 | 137 | py |
synfeal | synfeal-main/utils_ros.py | import copy
import math
import tf
import rospy
import os
from geometry_msgs.msg import Pose, Point
from visualization_msgs.msg import *
from std_msgs.msg import Header, ColorRGBA
from synfeal_collection.src.pypcd import PointCloud
def write_pcd(filename, msg, mode='binary'):
pc = PointCloud.from_msg(msg)
... | 7,402 | 30.105042 | 122 | py |
synfeal | synfeal-main/deprecated/raycast_example.py | #!/usr/bin/env python3
# stdlib
import sys
import argparse
# 3rd-party
import trimesh
import numpy as np
import time
def main():
# parser = argparse.ArgumentParser(description='Data Collector')
# parser.add_argument('-m', '--mode', type=str, default='interactive',
# help='interactive/... | 1,910 | 24.144737 | 109 | py |
synfeal | synfeal-main/deprecated/rotation_to_direction.py | #!/usr/bin/env python3
from scipy.spatial.transform import Rotation as R
#rotate_y90 = R.from_euler('y', 90, degrees=True).as_matrix()
rotate_y90 = R.from_euler('x', 40, degrees=True).as_quat()
matrix = R.from_quat(rotate_y90).as_matrix()
print(matrix)
print(matrix[:,0]) | 274 | 26.5 | 61 | py |
synfeal | synfeal-main/synfeal_collection/src/automatic_data_collection.py | #!/usr/bin/env python3
# stdlib
import random
import os
from xml.parsers.expat import model
# 3rd-party
import rospy
import tf
import numpy as np
import trimesh
from geometry_msgs.msg import Pose
#from interactive_markers.interactive_marker_server import *
#from interactive_markers.menu_handler import *
from visualiz... | 17,567 | 39.018223 | 125 | py |
synfeal | synfeal-main/synfeal_collection/src/interactive_data_collection.py | #!/usr/bin/env python3
import copy
import rospy
from std_msgs.msg import Header, ColorRGBA
from geometry_msgs.msg import Point, Pose, Vector3, Quaternion
from interactive_markers.interactive_marker_server import *
from interactive_markers.menu_handler import *
from visualization_msgs.msg import *
from gazebo_msgs.srv ... | 9,016 | 42.350962 | 108 | py |
synfeal | synfeal-main/synfeal_collection/src/save_dataset.py | #!/usr/bin/env python3
import rospy
import os
from visualization_msgs.msg import *
from cv_bridge import CvBridge
from tf.listener import TransformListener
from utils import write_intrinsic, write_img, write_transformation
from utils_ros import read_pcd, write_pcd
from sensor_msgs.msg import PointCloud2, Image, PointF... | 6,373 | 37.630303 | 127 | py |
synfeal | synfeal-main/synfeal_collection/src/pypcd_no_ros.py | """
The MIT License (MIT)
Copyright (c) 2015 Daniel Maturana, Carnegie Mellon University
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the righ... | 18,853 | 36.859438 | 114 | py |
synfeal | synfeal-main/synfeal_collection/src/__init__.py | 0 | 0 | 0 | py | |
synfeal | synfeal-main/synfeal_collection/src/pypcd.py | """
The MIT License (MIT)
Copyright (c) 2015 Daniel Maturana, Carnegie Mellon University
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the righ... | 18,804 | 36.837022 | 114 | py |
synfeal | synfeal-main/models/pointnet.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
# this is a regularization to avoid overfitting! It adds another term to the cost function to penalize the complexity of the models.
def feature_t... | 5,796 | 34.564417 | 143 | py |
synfeal | synfeal-main/models/pointnet_classification.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
class STN3d(nn.Module):
def __init__(self):
super(STN3d, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.co... | 4,884 | 32.006757 | 128 | py |
synfeal | synfeal-main/models/loss_functions.py | import torch
from torch import nn
class BetaLoss(nn.Module):
def __init__(self, beta= 512):
super(BetaLoss, self).__init__()
self.beta = beta
#self.loss_fn = torch.nn.L1Loss() # PoseNet said that L1 was the best
self.loss_fn = torch.nn.MSELoss()
def forward(self, pred, targ):
... | 1,656 | 39.414634 | 261 | py |
synfeal | synfeal-main/models/poselstm.py |
from turtle import forward
from unicodedata import bidirectional
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from torchvision import transforms, models
# based on: https://github.com/hazirbas... | 6,396 | 33.766304 | 120 | py |
synfeal | synfeal-main/models/posenet.py |
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from torchvision import transforms, models
#https://github.com/youngguncho/PoseNet-Pytorch/blob/6c583a345a20ba17f67b76e54a26cf78e2811604/posenet_si... | 7,521 | 34.314554 | 116 | py |
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