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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deficient-efficient | deficient-efficient-master/count.py | '''Count parameters or mult-adds in models.'''
from __future__ import print_function
import math
import torch
import argparse
from torch.autograd import Variable
from models.wide_resnet import WideResNet, WRN_50_2
from models.darts import DARTS
from models.MobileNetV2 import MobileNetV2
from funcs import what_conv_blo... | 12,725 | 38.156923 | 138 | py |
deficient-efficient | deficient-efficient-master/funcs.py | import torch
import torch.nn.functional as F
from models import *
from models.wide_resnet import parse_options
def distillation(y, teacher_scores, labels, T, alpha):
return F.kl_div(F.log_softmax(y/T, dim=1), F.softmax(teacher_scores/T, dim=1)) * (T*T * 2. * alpha)\
+ F.cross_entropy(y, labels) * (1. - ... | 2,477 | 26.533333 | 112 | py |
deficient-efficient | deficient-efficient-master/load_wrn50_2.py | import re
import torch
import torch.nn.functional as F
from torch.utils import model_zoo
from models.blocks import Conv
from models.wide_resnet import WRN_50_2
from collections import OrderedDict
def all_equal(iterable_1, iterable_2):
return all([x == y for x,y in zip(iterable_1, iterable_2)])
# functional model... | 4,000 | 36.046296 | 138 | py |
deficient-efficient | deficient-efficient-master/collate_results.py | # open schedule json, then search for which machines the longest progressed job
# has run on
import json
import sys
import os
import torch
import subprocess
from subprocess import PIPE
from collections import OrderedDict
from funcs import what_conv_block
from models.wide_resnet import WideResNet, WRN_50_2
from models.... | 5,460 | 37.730496 | 125 | py |
deficient-efficient | deficient-efficient-master/history.py | # opens checkpoints and prints the commands used to run each
import torch
import os
import argparse
parser = argparse.ArgumentParser(description='Inspect saved checkpoints')
parser.add_argument('--match', type=str, default=None, help='Filter checkpoints by keyword.')
if __name__ == '__main__':
args = parser.parse... | 766 | 33.863636 | 113 | py |
deficient-efficient | deficient-efficient-master/models/resnet.py | '''This is a rewriting of the native resnet definition that comes with Pytorch, to allow it to use our blocks and
convolutions for imagenet experiments. Annoyingly, the pre-trained models don't use pre-activation blocks.'''
import torch
import torch.nn as nn
import math
import torchvision.models.resnet
import torch.u... | 6,623 | 34.047619 | 120 | py |
deficient-efficient | deficient-efficient-master/models/hashed.py | # HashedNet Convolutional Layer: https://arxiv.org/abs/1504.04788
from functools import reduce
import torch
import torch.nn as nn
import torch.nn.functional as F
class HashedConv2d(nn.Conv2d):
"""Conv2d with the weights of the convolutional filters parameterised using
a budgeted subset of parameters and rand... | 5,827 | 48.811966 | 176 | py |
deficient-efficient | deficient-efficient-master/models/darts.py | # DARTS network definition
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.checkpoint import checkpoint
from collections import namedtuple
from .blocks import DepthwiseSep
from .wide_resnet import group_lowrank, compres... | 11,450 | 32.979228 | 429 | py |
deficient-efficient | deficient-efficient-master/models/wide_resnet.py | # network definition
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
# wildcard import for legacy reasons
if __name__ == '__main__':
from blocks import *
else:
from .blocks import *
def parse_options(convty... | 10,717 | 36.872792 | 106 | py |
deficient-efficient | deficient-efficient-master/models/decomposed.py | # Substitute layer explicitly decomposing the tensors in convolutional layers
# All implemented using tntorch: https://github.com/rballester/tntorch
# All also use a separable design: the low-rank approximate pointwise
# convolution is preceded by a grouped convolution
import math
import torch
import torch.nn as nn
imp... | 8,252 | 39.856436 | 99 | py |
deficient-efficient | deficient-efficient-master/models/MobileNetV2.py | import torch
import torch.nn as nn
import math
# wildcard import for legacy reasons
if __name__ == '__main__':
import sys
sys.path.append("..")
from models.blocks import *
from models.wide_resnet import compression, group_lowrank
# only used in the first convolution, which we do not substitute by convention
... | 8,316 | 33.086066 | 118 | py |
deficient-efficient | deficient-efficient-master/models/blocks.py | # blocks and convolution definitions
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.checkpoint import checkpoint, checkpoint_sequential
if __name__ == 'blocks' or __name__ == '__main__':
from hashed import HashedConv2d, HalfHashe... | 18,941 | 43.992874 | 120 | py |
multimodal-vae-public | multimodal-vae-public-master/vision/sample.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from torchvision.utils import save_image
from train... | 5,676 | 40.437956 | 96 | py |
multimodal-vae-public | multimodal-vae-public-master/vision/model.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
class MVAE(nn.Module):
def __init__(self, n_latents=250, use_cuda=False):
sup... | 8,131 | 36.13242 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/vision/datasets.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import random
import numpy as np
from copy import deepcopy
from PIL import Image
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
N_MODALITIES = 6
VALID_P... | 4,896 | 36.669231 | 78 | py |
multimodal-vae-public | multimodal-vae-public-master/vision/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.utils import save_image
f... | 19,025 | 47.659847 | 107 | py |
multimodal-vae-public | multimodal-vae-public-master/mnist/sample.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import sys
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms
from torchvision.utils import save_image
from train ... | 4,692 | 37.154472 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/mnist/model.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torch.nn.parameter import Parameter
class MVAE(nn.Module):
"""Multimoda... | 5,973 | 31.11828 | 73 | py |
multimodal-vae-public | multimodal-vae-public-master/mnist/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from tor... | 10,817 | 39.215613 | 105 | py |
multimodal-vae-public | multimodal-vae-public-master/fashionmnist/sample.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import sys
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from torchvision.utils import save_image
from train import loa... | 4,827 | 37.624 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/fashionmnist/model.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
# MAP from index to the interpretable label
LABEL_IX_TO_STRING = {0: 'T-shirt/top... | 6,482 | 30.779412 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/fashionmnist/datasets.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from torchvision.datasets import MNIST
class FashionMNIST(MNIST):
"""`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset.
Args:
root (string): Root directory of da... | 1,428 | 46.633333 | 96 | py |
multimodal-vae-public | multimodal-vae-public-master/fashionmnist/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from mo... | 10,820 | 39.226766 | 105 | py |
multimodal-vae-public | multimodal-vae-public-master/multimnist/sample.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import sys
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from torchvision.utils import save_image
from datasets import ... | 5,196 | 36.121429 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/multimnist/utils.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import string
import random
import time
import math
import torch
from torch.autograd import Variable
max_length = 4 # max of 4 characters in an image
all_characters = '0123456789'
n_characters = len(all_chara... | 1,417 | 23.877193 | 60 | py |
multimodal-vae-public | multimodal-vae-public-master/multimnist/model.py | """This model will be quite similar to mnist/model.py
except we will need to be slightly fancier in the
encoder/decoders for each modality. Likely, we will need
convolutions/deconvolutions and RNNs.
"""
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
imp... | 9,790 | 34.219424 | 83 | py |
multimodal-vae-public | multimodal-vae-public-master/multimnist/datasets.py | """
This script generates a dataset similar to the MultiMNIST dataset
described in [1]. However, we remove any translation.
[1] Eslami, SM Ali, et al. "Attend, infer, repeat: Fast scene
understanding with generative models." Advances in Neural Information
Processing Systems. 2016.
"""
from __future__ import division
... | 13,354 | 37.93586 | 113 | py |
multimodal-vae-public | multimodal-vae-public-master/multimnist/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision impo... | 11,314 | 39.555556 | 105 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba/sample.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from torchvision.utils import save_image
from train import load_checkpoin... | 5,535 | 38.542857 | 82 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba/model.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from datasets import N_ATTRS
class MVAE(nn.Module):
"""Multimodal Variational Autoencoder.
... | 7,415 | 31.243478 | 74 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba/datasets.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import copy
import random
import numpy as np
import numpy.random as npr
from PIL import Image
from random import shuffle
from scipy.misc import imresize
import torch
from torch.utils.data.... | 6,170 | 39.333333 | 111 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision impo... | 11,037 | 40.340824 | 105 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba19/model.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
sys.path.append('../celeba')
from datasets import N_ATTRS
class MVAE(nn.Module):
"""... | 8,328 | 32.316 | 91 | py |
multimodal-vae-public | multimodal-vae-public-master/celeba19/train.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import shutil
import numpy as np
from tqdm import tqdm
from itertools import combinations
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
fro... | 14,718 | 40.345506 | 129 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/main.py | from pytorch_lightning.callbacks import ModelCheckpoint
import pytorch_lightning as pl
import yaml
import argparse
import utilities
import os
import torch
import shutil
def datasetFactory(config, do, args=None):
c_data =config["data"]
if args is None:
gl = utilities.GettingLists(data_for_training=c_da... | 10,447 | 45.435556 | 153 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/reconstruction_data.py | from main import choosing_model
import yaml
import argparse
import utilities
import os
import torch
import pytorch_lightning as pl
import numpy as np
import matplotlib.pyplot as plt
from utilities import to_numpy
def saving_files(x, y, out, database, name):
PATH = "make_graph/data"+'/'+database+'/'+name
x = ... | 2,493 | 32.253333 | 94 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/reconstruction_plot.py | from main import choosing_model
import yaml
import argparse
import utilities
import os
import torch
import pytorch_lightning as pl
import numpy as np
import matplotlib.pyplot as plt
from utilities import to_numpy
def plotting(in_, NN_out, out, name, database,
k_list =[1,2,3,4], save=False, vmin=-0.5, vma... | 4,950 | 35.138686 | 94 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/OOD.py | import yaml
from evaluation import saving_files
import argparse
import utilities
from utilities import to_numpy
import os
import torch
import pytorch_lightning as pl
import numpy as np
import matplotlib.pyplot as plt
def load_ood(arg, size = 64, dir_skeleton= None):
if dir_skeleton is None:
dir_skeleton... | 5,428 | 42.087302 | 100 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/evaluation.py | import yaml
import argparse
import utilities
import os
import torch
import numpy as np
from main import datasetFactory
import pytorch_lightning as pl
def saving_files(data, database, name, dir_= "make_graph"):
if len(data) != 1:
PATH = os.path.join(dir_, "test_loss", database)
if not os.path.exi... | 3,492 | 36.159574 | 98 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/sFNO_epsilon_v2.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .FNO import fourier_conv_2d
from .basics_model import LayerNorm, get_grid2D, FC_nn
from timm.models.layers import DropPath, trunc_normal_
import torch.nn.functional as F
from utilities import LpLoss
from .sFNO import IO_layer
################... | 10,276 | 35.967626 | 114 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/FNO_residual.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .FNO import fourier_conv_2d
from .basics_model import LayerNorm, get_grid2D, FC_nn, set_activ
import torch.nn.functional as F
from utilities import LpLoss
from timm.models.layers import DropPath
#######################################
# Integ... | 5,887 | 34.46988 | 109 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/basics_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
##########################################
# Fully connected Layer
##########################################
class FCLayer(nn.Module):
"""Fully connected layer """
def __init__(self, in_feature, out_feature,
... | 6,354 | 38.228395 | 94 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/FNO.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .basics_model import get_grid2D, set_activ, FC_nn
from utilities import LpLoss
#######################################
# Fourier Convolution,
# \int_D k(x-y) v(y) dy
# = \mathcal{F}^{-1}(P \mathcal{F}(v))
###################################... | 6,612 | 39.078788 | 119 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/sFNO_epsilon_v1.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .FNO import fourier_conv_2d
from .basics_model import LayerNorm, get_grid2D, FC_nn, set_activ
import torch.nn.functional as F
from utilities import LpLoss
from timm.models.layers import DropPath
#######################################
# Integ... | 6,482 | 35.627119 | 110 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/sFNO.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .FNO import fourier_conv_2d
from .basics_model import LayerNorm, get_grid2D, FC_nn, set_activ
import torch.nn.functional as F
from utilities import LpLoss
#######################################
# Integral Operator Layer
#####################... | 5,862 | 35.64375 | 110 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/models/sFNO_epsilon_v2_updated.py | import pytorch_lightning as pl
import torch
from torch import optim, nn
from .FNO import fourier_conv_2d
from .basics_model import LayerNorm, get_grid2D, set_activ, GroupNorm
import torch.nn.functional as F
from utilities import LpLoss
from timm.models.layers import DropPath, trunc_normal_
import os
from .sFNO_epsilon_... | 10,270 | 38.35249 | 112 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/utilities/model_factory.py | from models import *
def choosing_model(config):
c_nn = config["model"]
c_train = config["train"]
# 7 Hz data only contains the real part of the field
if config["Project"]["database"]=='GRF_7Hz':
if config["Project"]["name"] == "FNO":
model =FNO(
wavenum... | 7,309 | 42.254438 | 84 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/utilities/loss.py | import torch
#loss function with rel/abs Lp loss
class LpLoss(object):
def __init__(self, d=2, p=2, size_average=True, reduction=True):
super(LpLoss, self).__init__()
#Dimension and Lp-norm type are postive
assert d > 0 and p > 0
self.d = d
self.p = p
self.reductio... | 1,326 | 27.234043 | 113 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/utilities/loading_data.py | import numpy as np
import torch
from bisect import bisect
import os
from torch.utils.data import Dataset, DataLoader
def to_numpy(x):
return x.detach().cpu().numpy()
#files Loader
def MyLoader(GL, do = "train", config = None, args=None):
if config is not None:
batch_size = config['train']['batchsize']
w... | 6,671 | 43.18543 | 158 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/visualization_code/projection.py | """
Project a model or multiple models to a plane spaned by given directions.
"""
import numpy as np
import torch
import os
import copy
import h5py
import sys
import random
from projection_helper import sizeof, shapeof
sys.path.append('/Users/xmt/code/github/loss-landscape')
import net_plotter
import h5_util
imp... | 18,597 | 31.742958 | 112 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/visualization_code/create_surface.py | """
Calculate the loss surface in parallel.
Code adapted from Tom Goldstein's implementation of the 2018 NeurIPS paper:
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer and Tom Goldstein.
Visualizing the Loss Landscape of Neural Nets. NIPS, 2018.
Github: https://github.com/tomgoldstein/loss-lands... | 16,827 | 42.25964 | 159 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/visualization_code/projection_helper.py | import torch
import h5py
import sys
import os
sys.path.append('../')
import utilities
def sizeof(t):
n = 0
if isinstance(t, list):
for w in t:
n += w.numel()
elif isinstance(t, torch.Tensor):
n = t.numel()
elif isinstance(t, h5py.Dataset):
n = t.size
else:
... | 1,144 | 23.361702 | 69 | py |
Fine-tuning-NOs | Fine-tuning-NOs-master/visualization_code/create_trajectory.py | import numpy as np
import torch
import copy
import math
import h5py
import os
import argparse
import sys
import json
import tqdm
'''
Code adapted from Tom Goldstein's implementation of the 2018 NeurIPS paper:
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer and Tom Goldstein.
Visualizing the Loss Landscape of Neural ... | 5,937 | 41.113475 | 170 | py |
XGBOD | XGBOD-master/xgbod_demo.py | '''
Demo codes for XGBOD.
Author: Yue Zhao
notes: the demo code simulates the use of XGBOD with some changes to expedite
the execution. Use the full code for the production.
'''
import os
import random
import scipy.io as scio
import numpy as np
from sklearn.preprocessing import StandardScaler, normalize
from sklearn... | 8,726 | 39.21659 | 79 | py |
XGBOD | XGBOD-master/xgbod_full.py | import os
import pandas as pd
import numpy as np
import scipy.io as scio
from sklearn.preprocessing import StandardScaler, Normalizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import LocalOutlierFactor
from sklearn.linear_model import Logistic... | 12,621 | 35.479769 | 118 | py |
La-MAML | La-MAML-main/main.py | import importlib
import datetime
import argparse
import time
import os
import ipdb
from tqdm import tqdm
import torch
from torch.autograd import Variable
import parser as file_parser
from metrics.metrics import confusion_matrix
from utils import misc_utils
from main_multi_task import life_experience_iid, eval_iid_tas... | 6,437 | 32.185567 | 154 | py |
La-MAML | La-MAML-main/main_multi_task.py | import time
import os
from tqdm import tqdm
import torch
from torch.autograd import Variable
def eval_iid_tasks(model, tasks, args):
model.eval()
result = []
for t, task_loader in enumerate(tasks):
rt = 0
for (i, (x, y, super_y)) in enumerate(task_loader):
if args.cuda:
... | 2,818 | 30.674157 | 154 | py |
La-MAML | La-MAML-main/metrics/metrics.py | ### We directly copied the metrics.py model file from the GEM project https://github.com/facebookresearch/GradientEpisodicMemory
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from ... | 2,348 | 28 | 128 | py |
La-MAML | La-MAML-main/dataloaders/idataset.py |
import numpy as np
from PIL import Image
import torch
from torchvision import datasets, transforms
import os
from dataloaders import cifar_info
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, x, y, trsf, pretrsf = None, imgnet_like = False, super_y = None):
self.x, self.y = x, y
... | 4,465 | 29.8 | 105 | py |
La-MAML | La-MAML-main/dataloaders/cifar_info.py | from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from torchvision.datasets.vision import VisionDataset
from torchvision.datasets.utils import check_integrity, download_an... | 8,912 | 36.607595 | 100 | py |
La-MAML | La-MAML-main/dataloaders/task_sampler.py | # coding=utf-8
import numpy as np
import torch
import warnings
import ipdb
class MultiTaskSampler(object):
'''
MultiTaskSampler: yield a batch of indexes at each iteration.
Indexes are calculated by keeping in account 'classes_per_it' and 'num_samples',
In fact at every iteration the batch indexes will... | 3,363 | 39.047619 | 103 | py |
La-MAML | La-MAML-main/dataloaders/class_incremental_loader.py | import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from dataloaders.idataset import _get_datasets, DummyDataset
import random
import ipdb
# --------
# Datase... | 14,676 | 38.138667 | 119 | py |
La-MAML | La-MAML-main/dataloaders/multi_task_loader.py | import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import datasets, transforms
from dataloaders.idataset import _get_datasets, DummyDataset
from dataloaders.task_sampler import MultiTaskS... | 21,088 | 41.863821 | 164 | py |
La-MAML | La-MAML-main/dataloaders/task_incremental_loader.py | import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import datasets
from dataloaders.idataset import DummyArrayDataset
import os
class IncrementalLoader:
def __init__(
self,
opt,
shuffle=True,
seed=1,
):
self.... | 3,964 | 30.468254 | 120 | py |
La-MAML | La-MAML-main/utils/misc_utils.py | import datetime
import glob
import json
import os
import random
import ipdb
import numpy as np
import torch
from tqdm import tqdm
def to_onehot(targets, n_classes):
onehot = torch.zeros(targets.shape[0], n_classes).to(targets.device)
onehot.scatter_(dim=1, index=targets.long().view(-1, 1), value=1.)
retu... | 4,173 | 26.642384 | 103 | py |
La-MAML | La-MAML-main/model/lamaml.py | import random
import numpy as np
import ipdb
import math
import torch
import torch.nn as nn
from model.lamaml_base import *
class Net(BaseNet):
def __init__(self,
n_inputs,
n_outputs,
n_tasks,
args):
super(Net, self).__init__... | 3,695 | 31.421053 | 126 | py |
La-MAML | La-MAML-main/model/meta-bgd.py | import random
from random import shuffle
import numpy as np
import ipdb
import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import model.meta.learner as Learner
import model.meta.modelfactory as mf
from model.optimizers_lib import optimizers_lib
from ast import literal_eval
"""
This ba... | 11,558 | 34.897516 | 121 | py |
La-MAML | La-MAML-main/model/gem.py | ### This is a copy of GEM from https://github.com/facebookresearch/GradientEpisodicMemory.
### In order to ensure complete reproducability, we do not change the file and treat it as a baseline.
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in th... | 9,366 | 36.468 | 112 | py |
La-MAML | La-MAML-main/model/lamaml_base.py | import random
from random import shuffle
import numpy as np
import ipdb
import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import model.meta.learner as Learner
import model.meta.modelfactory as mf
from scipy.stats import pearsonr
import datetime
class BaseNet(torch.nn.Module):
def ... | 4,545 | 29.10596 | 112 | py |
La-MAML | La-MAML-main/model/lamaml_cifar.py | import random
import numpy as np
import ipdb
import math
import torch
import torch.nn as nn
from model.lamaml_base import *
class Net(BaseNet):
def __init__(self,
n_inputs,
n_outputs,
n_tasks,
args):
super(Net, self).__init__(n... | 5,732 | 35.987097 | 119 | py |
La-MAML | La-MAML-main/model/agem.py | ### This is a pytorch implementation of AGEM based on https://github.com/facebookresearch/agem.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
i... | 9,569 | 34.576208 | 112 | py |
La-MAML | La-MAML-main/model/meralg1.py | # An implementation of MER Algorithm 1 from https://openreview.net/pdf?id=B1gTShAct7
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.o... | 6,478 | 31.888325 | 159 | py |
La-MAML | La-MAML-main/model/iid2.py | import torch
import numpy as np
import random
import model.meta.learner as Learner
import model.meta.modelfactory as mf
import ipdb
import sys
if not sys.warnoptions:
import warnings
warnings.simplefilter("once")
"""
Multi task
big batch size, set increment 100 so that it is treated as 1 task with all c... | 2,878 | 30.637363 | 107 | py |
La-MAML | La-MAML-main/model/eralg4.py | # An implementation of Experience Replay (ER) with reservoir sampling and without using tasks from Algorithm 4 of https://openreview.net/pdf?id=B1gTShAct7
# Copyright 2019-present, IBM Research
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory o... | 9,335 | 32.342857 | 154 | py |
La-MAML | La-MAML-main/model/icarl.py | # Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
import random
import model.meta.learner as Learner
import model.meta.modelfactory as mf
import sys
... | 10,217 | 40.536585 | 116 | py |
La-MAML | La-MAML-main/model/meta/learner.py | import math
import os
import sys
import traceback
import numpy as np
import ipdb
import torch
from torch import nn
from torch.nn import functional as F
class Learner(nn.Module):
def __init__(self, config, args = None):
"""
:param config: network config file, type:list of (string, list)
:... | 10,679 | 34.364238 | 143 | py |
La-MAML | La-MAML-main/model/optimizers_lib/bgd_optimizer.py | import torch
from torch.optim.optimizer import Optimizer
class BGD(Optimizer):
"""Implements BGD.
A simple usage of BGD would be:
for samples, labels in batches:
for mc_iter in range(mc_iters):
optimizer.randomize_weights()
output = model.forward(samples)
loss = ... | 5,328 | 46.580357 | 119 | py |
La-MAML | La-MAML-main/model/optimizers_lib/optimizers_lib.py | import torch.optim as optim
from .bgd_optimizer import BGD
def bgd(model, **kwargs):
# logger = kwargs.get("logger", None)
# assert(logger is not None)
bgd_params = {
"mean_eta": kwargs.get("mean_eta", 1),
"std_init": kwargs.get("std_init", 0.02),
"mc_iters": kwargs.get("mc_iters",... | 2,099 | 37.181818 | 151 | py |
fiery | fiery-master/evaluate.py | from argparse import ArgumentParser
import torch
from tqdm import tqdm
from fiery.data import prepare_dataloaders
from fiery.trainer import TrainingModule
from fiery.metrics import IntersectionOverUnion, PanopticMetric
from fiery.utils.network import preprocess_batch
from fiery.utils.instance import predict_instance_... | 3,908 | 36.951456 | 106 | py |
fiery | fiery-master/visualise.py | import os
from argparse import ArgumentParser
from glob import glob
import cv2
import numpy as np
import torch
import torchvision
import matplotlib as mpl
import matplotlib.pyplot as plt
from PIL import Image
from fiery.trainer import TrainingModule
from fiery.utils.network import NormalizeInverse
from fiery.utils.in... | 5,095 | 36.470588 | 118 | py |
fiery | fiery-master/train.py | import os
import time
import socket
import torch
import pytorch_lightning as pl
from pytorch_lightning.plugins import DDPPlugin
from fiery.config import get_parser, get_cfg
from fiery.data import prepare_dataloaders
from fiery.trainer import TrainingModule
def main():
args = get_parser().parse_args()
cfg = g... | 1,540 | 29.215686 | 101 | py |
fiery | fiery-master/fiery/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialRegressionLoss(nn.Module):
def __init__(self, norm, ignore_index=255, future_discount=1.0):
super(SpatialRegressionLoss, self).__init__()
self.norm = norm
self.ignore_index = ignore_index
self.future_di... | 3,378 | 33.835052 | 111 | py |
fiery | fiery-master/fiery/data.py | import os
from PIL import Image
import numpy as np
import cv2
import torch
import torchvision
from pyquaternion import Quaternion
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.splits import create_splits_scenes
from nuscenes.utils.data_classes import Box
from lyft_dataset_sdk.lyftdataset import LyftDatas... | 19,735 | 41.62635 | 136 | py |
fiery | fiery-master/fiery/metrics.py | from typing import Optional
import torch
from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.metrics.functional.classification import stat_scores_multiple_classes
from pytorch_lightning.metrics.functional.reduction import reduce
class IntersectionOverUnion(Metric):
"""Computes intersection... | 11,415 | 43.59375 | 117 | py |
fiery | fiery-master/fiery/trainer.py | import torch
import torch.nn as nn
import pytorch_lightning as pl
from fiery.config import get_cfg
from fiery.models.fiery import Fiery
from fiery.losses import ProbabilisticLoss, SpatialRegressionLoss, SegmentationLoss
from fiery.metrics import IntersectionOverUnion, PanopticMetric
from fiery.utils.geometry import cu... | 11,419 | 42.754789 | 112 | py |
fiery | fiery-master/fiery/models/distributions.py | import torch
import torch.nn as nn
from fiery.layers.convolutions import Bottleneck
class DistributionModule(nn.Module):
"""
A convolutional net that parametrises a diagonal Gaussian distribution.
"""
def __init__(
self, in_channels, latent_dim, min_log_sigma, max_log_sigma):
super()... | 1,871 | 31.842105 | 114 | py |
fiery | fiery-master/fiery/models/future_prediction.py | import torch
from fiery.layers.convolutions import Bottleneck
from fiery.layers.temporal import SpatialGRU
class FuturePrediction(torch.nn.Module):
def __init__(self, in_channels, latent_dim, n_gru_blocks=3, n_res_layers=3):
super().__init__()
self.n_gru_blocks = n_gru_blocks
# Convoluti... | 1,488 | 39.243243 | 97 | py |
fiery | fiery-master/fiery/models/fiery.py | import torch
import torch.nn as nn
from fiery.models.encoder import Encoder
from fiery.models.temporal_model import TemporalModelIdentity, TemporalModel
from fiery.models.distributions import DistributionModule
from fiery.models.future_prediction import FuturePrediction
from fiery.models.decoder import Decoder
from fi... | 15,090 | 43.385294 | 118 | py |
fiery | fiery-master/fiery/models/temporal_model.py | import torch.nn as nn
from fiery.layers.temporal import Bottleneck3D, TemporalBlock
class TemporalModel(nn.Module):
def __init__(
self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0,
n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=Tr... | 2,120 | 32.666667 | 104 | py |
fiery | fiery-master/fiery/models/encoder.py | import torch.nn as nn
from efficientnet_pytorch import EfficientNet
from fiery.layers.convolutions import UpsamplingConcat
class Encoder(nn.Module):
def __init__(self, cfg, D):
super().__init__()
self.D = D
self.C = cfg.OUT_CHANNELS
self.use_depth_distribution = cfg.USE_DEPTH_DIST... | 3,910 | 36.247619 | 119 | py |
fiery | fiery-master/fiery/models/decoder.py | import torch.nn as nn
from torchvision.models.resnet import resnet18
from fiery.layers.convolutions import UpsamplingAdd
class Decoder(nn.Module):
def __init__(self, in_channels, n_classes, predict_future_flow):
super().__init__()
backbone = resnet18(pretrained=False, zero_init_residual=True)
... | 3,676 | 38.967391 | 106 | py |
fiery | fiery-master/fiery/layers/convolutions.py | from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
"""2D convolution followed by
- an optional normalisation (batch norm or instance norm)
- an optional activation (ReLU, LeakyReLU, or ... | 7,593 | 34.32093 | 114 | py |
fiery | fiery-master/fiery/layers/temporal.py | from collections import OrderedDict
import torch
import torch.nn as nn
from fiery.layers.convolutions import ConvBlock
from fiery.utils.geometry import warp_features
class SpatialGRU(nn.Module):
"""A GRU cell that takes an input tensor [BxTxCxHxW] and an optional previous state and passes a
convolutional ga... | 11,152 | 38.549645 | 120 | py |
fiery | fiery-master/fiery/utils/visualisation.py | import numpy as np
import torch
import matplotlib.pylab
from fiery.utils.instance import predict_instance_segmentation_and_trajectories
DEFAULT_COLORMAP = matplotlib.pylab.cm.jet
def flow_to_image(flow: np.ndarray, autoscale: bool = False) -> np.ndarray:
"""
Applies colour map to flow which should be a 2 ch... | 12,488 | 32.572581 | 121 | py |
fiery | fiery-master/fiery/utils/network.py | import torch
import torch.nn as nn
import torchvision
def pack_sequence_dim(x):
b, s = x.shape[:2]
return x.view(b * s, *x.shape[2:])
def unpack_sequence_dim(x, b, s):
return x.view(b, s, *x.shape[1:])
def preprocess_batch(batch, device, unsqueeze=False):
for key, value in batch.items():
if... | 1,236 | 27.113636 | 89 | py |
fiery | fiery-master/fiery/utils/geometry.py | import PIL
import numpy as np
import torch
from pyquaternion import Quaternion
def resize_and_crop_image(img, resize_dims, crop):
# Bilinear resizing followed by cropping
img = img.resize(resize_dims, resample=PIL.Image.BILINEAR)
img = img.crop(crop)
return img
def update_intrinsics(intrinsics, top... | 10,875 | 33.526984 | 117 | py |
fiery | fiery-master/fiery/utils/instance.py | from typing import Tuple
import torch
import torch.nn.functional as F
import numpy as np
from scipy.optimize import linear_sum_assignment
from fiery.utils.geometry import mat2pose_vec, pose_vec2mat, warp_features
# set ignore index to 0 for vis
def convert_instance_mask_to_center_and_offset_label(instance_img, futu... | 13,871 | 40.657658 | 119 | py |
LiDAR2INS | LiDAR2INS-master/ceres/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Ceres Solver documentation build configuration file, created by
# sphinx-quickstart on Sun Jan 20 20:34:07 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
... | 7,957 | 31.748971 | 94 | py |
xgboost | xgboost-master/tests/ci_build/test_r_package.py | """Utilities for packaging R code and running tests."""
import argparse
import os
import shutil
import subprocess
from pathlib import Path
from platform import system
from test_utils import R_PACKAGE, ROOT, DirectoryExcursion, cd, print_time, record_time
def get_mingw_bin() -> str:
return os.path.join("c:/rtools... | 10,217 | 31.438095 | 88 | py |
xgboost | xgboost-master/tests/ci_build/tidy.py | #!/usr/bin/env python
import argparse
import json
import os
import re
import shutil
import subprocess
import sys
from multiprocessing import Pool, cpu_count
from time import time
import yaml
def call(args):
'''Subprocess run wrapper.'''
completed = subprocess.run(args,
stdout=s... | 10,858 | 34.486928 | 82 | py |
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